diff --git a/.vscode/launch.json b/.vscode/launch.json
index e582557..87ed9da 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -12,9 +12,9 @@
             "console": "integratedTerminal",
             "justMyCode": true,
             "args": [
-                "--exp", "test",
+                "--exp", "with_exp",
                 "--reset_db", "True",
-                "--job", "1"
+                "--job", "24"
             ]
         }
     ]
diff --git a/SQL_analysis_experiment.sql b/SQL_analysis_experiment.sql
new file mode 100644
index 0000000..7894992
--- /dev/null
+++ b/SQL_analysis_experiment.sql
@@ -0,0 +1,85 @@
+select distinct experiment.idx_scenario, 
+n_max_trial, prf_size, prf_conn, cap_limit_prob_type, cap_limit_level, diff_new_conn, remove_t, netw_prf_n,
+mean_count_firm_prod, mean_count_firm, mean_count_prod,
+mean_max_ts_firm_prod, mean_max_ts_firm, mean_max_ts_prod,
+mean_n_remove_firm_prod, mean_n_all_prod_remove_firm, mean_end_ts
+from iiabmdb.with_exp_experiment as experiment
+left join 
+(
+select 
+idx_scenario,
+sum(count_firm_prod) / count(*) as mean_count_firm_prod, # Note to use count(*), to include NULL
+sum(count_firm) / count(*) as mean_count_firm,
+sum(count_prod) / count(*) as mean_count_prod,
+sum(max_ts_firm_prod) / count(*) as mean_max_ts_firm_prod,
+sum(max_ts_firm) / count(*) as mean_max_ts_firm,
+sum(max_ts_prod) / count(*) as mean_max_ts_prod,
+sum(n_remove_firm_prod) / count(*) as mean_n_remove_firm_prod,
+sum(n_all_prod_remove_firm) / count(*) as mean_n_all_prod_remove_firm,
+sum(end_ts) / count(*) as mean_end_ts
+from (
+select sample.id, idx_scenario,
+count_firm_prod, count_firm, count_prod,
+max_ts_firm_prod, max_ts_firm, max_ts_prod,
+n_remove_firm_prod, n_all_prod_remove_firm, end_ts
+from iiabmdb.with_exp_sample as sample
+# 1 2 3 + 9
+left join iiabmdb.with_exp_experiment as experiment
+on sample.e_id = experiment.id
+left join (select s_id, 
+count(distinct id_firm, id_product) as count_firm_prod,
+count(distinct id_firm) as count_firm,
+count(distinct id_product) as count_prod,
+max(ts) as end_ts
+from iiabmdb.with_exp_result group by s_id) as s_count
+on sample.id = s_count.s_id
+# 4
+left join # firm prod
+(select s_id, max(ts) as max_ts_firm_prod from
+(select s_id, id_firm, id_product, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_firm, id_product) as ts
+group by s_id) as s_max_ts_firm_prod
+on sample.id = s_max_ts_firm_prod.s_id
+# 5
+left join # firm
+(select s_id, max(ts) as max_ts_firm from
+(select s_id, id_firm, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_firm) as ts
+group by s_id) as s_max_ts_firm
+on sample.id = s_max_ts_firm.s_id
+# 6
+left join # prod
+(select s_id, max(ts) as max_ts_prod from
+(select s_id, id_product, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_product) as ts
+group by s_id) as s_max_ts_prod
+on sample.id = s_max_ts_prod.s_id
+# 7 
+left join
+(select s_id, count(distinct id_firm, id_product) as n_remove_firm_prod
+from iiabmdb.with_exp_result
+where `status` = "R"
+group by s_id) as s_n_remove_firm_prod
+on sample.id = s_n_remove_firm_prod.s_id
+# 8
+left join
+(select s_id, count(distinct id_firm) as n_all_prod_remove_firm from
+(select s_id, id_firm, count(distinct id_product) as n_remove_prod
+from iiabmdb.with_exp_result
+where `status` = "R"
+group by s_id, id_firm) as s_n_remove_prod
+left join iiabmdb_basic_info.firm_n_prod as firm_n_prod
+on s_n_remove_prod.id_firm = firm_n_prod.code
+where n_remove_prod = n_prod
+group by s_id) as s_n_all_prod_remove_firm
+on sample.id = s_n_all_prod_remove_firm.s_id
+) as secnario_count
+group by idx_scenario
+) as secnario_mean
+on experiment.idx_scenario = secnario_mean.idx_scenario;
\ No newline at end of file
diff --git a/SQL_analysis_experiment_test.sql b/SQL_analysis_experiment_test.sql
new file mode 100644
index 0000000..8099a32
--- /dev/null
+++ b/SQL_analysis_experiment_test.sql
@@ -0,0 +1,94 @@
+select * from iiabmdb.with_exp_result limit 0, 20;
+select count(distinct s_id) from iiabmdb.with_exp_result;
+select count(*) from iiabmdb.with_exp_sample;
+
+select distinct s_id, id_firm, id_product from iiabmdb.with_exp_result order by s_id, id_firm, id_product;
+
+select distinct s_id, count(distinct id_firm, id_product) as count_firm_prod from iiabmdb.with_exp_result group by s_id;
+
+select distinct s_id, count(distinct id_firm, id_product) as count_firm_prod 
+from iiabmdb.with_exp_result group by s_id;
+
+select distinct s_id, 
+count(distinct id_firm, id_product) as count_firm_prod,
+count(distinct id_firm) as count_firm,
+count(distinct id_product) as count_prod
+from iiabmdb.with_exp_result group by s_id;
+
+# 控制问题需要处理，否则最后 experiment avg出来的东西不对
+# 1 2 3
+select 
+idx_scenario,
+sum(count_firm_prod) / count(*) as mean_count_firm_prod, # Note to use count(*), to include NULL
+sum(count_firm) / count(*) as mean_count_firm,
+sum(count_prod) / count(*) as mean_count_prod
+from (
+select sample.id, idx_scenario, count_firm_prod, count_firm, count_prod 
+from iiabmdb.with_exp_sample as sample
+left join iiabmdb.with_exp_experiment as experiment
+on sample.e_id = experiment.id
+left join (select s_id, 
+count(distinct id_firm, id_product) as count_firm_prod,
+count(distinct id_firm) as count_firm,
+count(distinct id_product) as count_prod
+from iiabmdb.with_exp_result group by s_id) as s_count
+on sample.id = s_count.s_id) as secnario_count
+group by idx_scenario;
+
+# 4 5 6
+select sample.id, idx_scenario, max_ts_firm_prod, max_ts_firm, max_ts_prod
+from iiabmdb.with_exp_sample as sample
+left join iiabmdb.with_exp_experiment as experiment
+on sample.e_id = experiment.id
+left join # firm prod
+(select s_id, max(ts) as max_ts_firm_prod from
+(select s_id, id_firm, id_product, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_firm, id_product) as ts
+group by s_id) as s_max_ts_firm_prod
+on sample.id = s_max_ts_firm_prod.s_id
+left join # firm
+(select s_id, max(ts) as max_ts_firm from
+(select s_id, id_firm, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_firm) as ts
+group by s_id) as s_max_ts_firm
+on sample.id = s_max_ts_firm.s_id
+left join # prod
+(select s_id, max(ts) as max_ts_prod from
+(select s_id, id_product, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_product) as ts
+group by s_id) as s_max_ts_prod
+on sample.id = s_max_ts_prod.s_id;
+
+# 7 8 9 
+select sample.id, idx_scenario, n_remove_firm_prod, n_all_prod_remove_firm, end_ts
+from iiabmdb.with_exp_sample as sample
+left join iiabmdb.with_exp_experiment as experiment
+on sample.e_id = experiment.id
+left join
+(select s_id, count(distinct id_firm, id_product) as n_remove_firm_prod
+from iiabmdb.with_exp_result
+where `status` = "R"
+group by s_id) as s_n_remove_firm_prod
+on sample.id = s_n_remove_firm_prod.s_id
+left join
+(select s_id, count(distinct id_firm) as n_all_prod_remove_firm from
+(select s_id, id_firm, count(distinct id_product) as n_remove_prod
+from iiabmdb.with_exp_result
+where `status` = "R"
+group by s_id, id_firm) as s_n_remove_prod
+left join iiabmdb_basic_info.firm_n_prod as firm_n_prod
+on s_n_remove_prod.id_firm = firm_n_prod.code
+where n_remove_prod = n_prod
+group by s_id) as s_n_all_prod_remove_firm
+on sample.id = s_n_all_prod_remove_firm.s_id
+left join
+(select s_id, max(ts) as end_ts 
+from iiabmdb.with_exp_result
+group by s_id) as s_end_ts
+on sample.id = s_end_ts.s_id;
\ No newline at end of file
diff --git a/SQL_analysis_risk.sql b/SQL_analysis_risk.sql
new file mode 100644
index 0000000..621186e
--- /dev/null
+++ b/SQL_analysis_risk.sql
@@ -0,0 +1,12 @@
+select * from
+(select s_id, id_firm, id_product, min(ts) as ts from iiabmdb.without_exp_result
+where `status` = 'D'
+group by s_id, id_firm, id_product) as s_disrupt
+where s_id in 
+(select s_id from
+(select s_id, id_firm, id_product, min(ts) as ts from iiabmdb.without_exp_result
+where `status` = 'D'
+group by s_id, id_firm, id_product) as t
+group by s_id
+having count(*) > 1)
+order by s_id;
\ No newline at end of file
diff --git a/SQL_export_high_risk_setting.sql b/SQL_export_high_risk_setting.sql
index abf784a..cfa3a17 100644
--- a/SQL_export_high_risk_setting.sql
+++ b/SQL_export_high_risk_setting.sql
@@ -1,19 +1,15 @@
-select count(*) from iiabmdb.without_exp_sample;
-
-select distinct s_id from iiabmdb.without_exp_result where ts > 0;
-select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id order by max_ts;
-select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a 
-inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b 
-on a.id = b.s_id
+select e_id, n_disrupt_sample, total_n_disrupt_firm_prod_experiment, dct_lst_init_disrupt_firm_prod from iiabmdb.without_exp_experiment as experiment
+inner join (
+select e_id, count(id) as n_disrupt_sample, sum(n_disrupt_firm_prod_sample) as total_n_disrupt_firm_prod_experiment from iiabmdb.without_exp_sample as sample 
+inner join (
+select * from 
+(select s_id, COUNT(DISTINCT id_firm, id_product) as n_disrupt_firm_prod_sample from iiabmdb.without_exp_result group by s_id
+) as count_disrupt_firm_prod_sample 
+where n_disrupt_firm_prod_sample > 1
+) as  disrupt_sample
+on sample.id = disrupt_sample.s_id
 group by e_id
-order by count desc;
-
-select e_id, count, max_max_ts, dct_lst_init_remove_firm_prod from iiabmdb.without_exp_experiment as a
-inner join 
-(select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a 
-inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b 
-on a.id = b.s_id
-group by e_id) as b
-on a.id = b.e_id
-where count > 10
-order by count desc;
+) as disrupt_experiment
+on experiment.id = disrupt_experiment.e_id
+order by n_disrupt_sample desc, total_n_disrupt_firm_prod_experiment desc
+limit 0, 95;
\ No newline at end of file
diff --git a/SQL_find_high_risk_setting.sql b/SQL_find_high_risk_setting.sql
new file mode 100644
index 0000000..21e7d21
--- /dev/null
+++ b/SQL_find_high_risk_setting.sql
@@ -0,0 +1,44 @@
+select max(ts_done) from iiabmdb.without_exp_sample;
+select min(ts_done) from iiabmdb.without_exp_sample;
+select count(*) from iiabmdb.without_exp_sample;
+
+select distinct s_id from iiabmdb.without_exp_result where ts > 0;
+select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id order by max_ts;
+select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a 
+inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b 
+on a.id = b.s_id
+group by e_id
+order by count desc;
+
+select e_id, count, max_max_ts, dct_lst_init_remove_firm_prod from iiabmdb.without_exp_experiment as a
+inner join 
+(select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a 
+inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b 
+on a.id = b.s_id
+group by e_id) as b
+on a.id = b.e_id
+where count > 10
+order by count desc;
+
+select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id;
+select * from iiabmdb.without_exp_result order by s_id limit 0,50;
+select s_id, COUNT(DISTINCT id_firm, id_product) as n_disrupt_firm_prod from iiabmdb.without_exp_result group by s_id;
+select * from 
+(select s_id, COUNT(DISTINCT id_firm, id_product) as n_disrupt_firm_prod_sample from iiabmdb.without_exp_result group by s_id) as count_disrupt_firm_prod_sample 
+where n_disrupt_firm_prod_sample > 1;
+
+select e_id, n_disrupt_sample, total_n_disrupt_firm_prod_experiment, dct_lst_init_disrupt_firm_prod from iiabmdb.without_exp_experiment as experiment
+inner join (
+select e_id, count(id) as n_disrupt_sample, sum(n_disrupt_firm_prod_sample) as total_n_disrupt_firm_prod_experiment from iiabmdb.without_exp_sample as sample 
+inner join (
+select * from 
+(select s_id, COUNT(DISTINCT id_firm, id_product) as n_disrupt_firm_prod_sample from iiabmdb.without_exp_result group by s_id
+) as count_disrupt_firm_prod_sample 
+where n_disrupt_firm_prod_sample > 1
+) as  disrupt_sample
+on sample.id = disrupt_sample.s_id
+group by e_id
+) as disrupt_experiment
+on experiment.id = disrupt_experiment.e_id
+order by n_disrupt_sample desc, total_n_disrupt_firm_prod_experiment desc
+limit 0, 95; # 20% of 475 experiment
\ No newline at end of file
diff --git a/SQL_migrate_db.sql b/SQL_migrate_db.sql
index 57fe3c6..0d1cc6e 100644
--- a/SQL_migrate_db.sql
+++ b/SQL_migrate_db.sql
@@ -1,7 +1,13 @@
-CREATE DATABASE iiabmdb_dissertation;
-RENAME TABLE iiabmdb.not_test_experiment TO iiabmdb_dissertation.not_test_experiment,
-iiabmdb.not_test_result TO iiabmdb_dissertation.not_test_result,
-iiabmdb.not_test_sample TO iiabmdb_dissertation.not_test_sample,
-iiabmdb.test_experiment TO iiabmdb_dissertation.test_experiment,
-iiabmdb.test_result TO iiabmdb_dissertation.test_result,
-iiabmdb.test_sample TO iiabmdb_dissertation.test_sample;
\ No newline at end of file
+CREATE DATABASE iiabmdb20230818;
+RENAME TABLE iiabmdb.not_test_experiment TO iiabmdb20230818.not_test_experiment,
+iiabmdb.not_test_result TO iiabmdb20230818.not_test_result,
+iiabmdb.not_test_sample TO iiabmdb20230818.not_test_sample,
+iiabmdb.test_experiment TO iiabmdb20230818.test_experiment,
+iiabmdb.test_result TO iiabmdb20230818.test_result,
+iiabmdb.test_sample TO iiabmdb20230818.test_sample;
+RENAME TABLE iiabmdb.with_exp_experiment TO iiabmdb20230818.with_exp_experiment,
+iiabmdb.with_exp_result TO iiabmdb20230818.with_exp_result,
+iiabmdb.with_exp_sample TO iiabmdb20230818.with_exp_sample,
+iiabmdb.without_exp_experiment TO iiabmdb20230818.without_exp_experiment,
+iiabmdb.without_exp_result TO iiabmdb20230818.without_exp_result,
+iiabmdb.without_exp_sample TO iiabmdb20230818.without_exp_sample;
\ No newline at end of file
diff --git a/__pycache__/controller_db.cpython-38.pyc b/__pycache__/controller_db.cpython-38.pyc
index d75a0e9..fc15b58 100644
Binary files a/__pycache__/controller_db.cpython-38.pyc and b/__pycache__/controller_db.cpython-38.pyc differ
diff --git a/__pycache__/firm.cpython-38.pyc b/__pycache__/firm.cpython-38.pyc
index f35eab2..5fc7d84 100644
Binary files a/__pycache__/firm.cpython-38.pyc and b/__pycache__/firm.cpython-38.pyc differ
diff --git a/__pycache__/model.cpython-38.pyc b/__pycache__/model.cpython-38.pyc
index b7c8463..d206e5a 100644
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diff --git a/__pycache__/orm.cpython-38.pyc b/__pycache__/orm.cpython-38.pyc
index 03a1404..318ee56 100644
Binary files a/__pycache__/orm.cpython-38.pyc and b/__pycache__/orm.cpython-38.pyc differ
diff --git a/analysis/20230818anova.mpx b/analysis/20230818anova.mpx
new file mode 100644
index 0000000..2122333
Binary files /dev/null and b/analysis/20230818anova.mpx differ
diff --git a/analysis/20230818anova_l36.mpx b/analysis/20230818anova_l36.mpx
new file mode 100644
index 0000000..11f5f35
Binary files /dev/null and b/analysis/20230818anova_l36.mpx differ
diff --git a/analysis/20230818anova_l36.mpx.bak b/analysis/20230818anova_l36.mpx.bak
new file mode 100644
index 0000000..11f5f35
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diff --git a/analysis/20230818anova_l36_clean.mpx b/analysis/20230818anova_l36_clean.mpx
new file mode 100644
index 0000000..960a6d2
Binary files /dev/null and b/analysis/20230818anova_l36_clean.mpx differ
diff --git a/analysis/20230818anova_visualization.csv b/analysis/20230818anova_visualization.csv
new file mode 100644
index 0000000..ebae0aa
--- /dev/null
+++ b/analysis/20230818anova_visualization.csv
@@ -0,0 +1,22 @@
+自变量,level,系统恢复用时R1,产业-企业边累计扰乱次数R2,产业-企业边最大传导深度R3,产业-企业边断裂总数R4
+采购策略P1,三供应商,2.144,2.826,1.156,0.7541
+采购策略P1,双供应商,2.146,2.65,1.133,0.7615
+采购策略P1,单供应商,2.261,2.519,1.121,0.7919
+是否规模偏好P2,倾向,2.196,2.661,1.137,0.7657
+是否规模偏好P2,不倾向,2.171,2.669,1.137,0.7726
+最大尝试次数P3,高,2.141,2.652,1.13,0.739
+最大尝试次数P3,中,2.124,2.652,1.127,0.7431
+最大尝试次数P3,低,2.286,2.691,1.154,0.8254
+是否已有连接偏好P4,倾向,2.191,2.663,1.133,0.7579
+是否已有连接偏好P4,不倾向,2.177,2.668,1.141,0.7804
+额外产能分布P5,均匀分布,2.316,2.681,1.158,0.8403
+额外产能分布P5,正态分布,2.052,2.65,1.115,0.698
+额外产能分布参数P6,高,1.914,2.624,1.098,0.6299
+额外产能分布参数P6,中,2.202,2.666,1.142,0.7655
+额外产能分布参数P6,低,2.436,2.705,1.171,0.9121
+新供应关系构成概率P7,低,2.24,2.672,1.143,0.764
+新供应关系构成概率P7,中,2.132,2.674,1.143,0.7859
+新供应关系构成概率P7,高,2.179,2.649,1.124,0.7575
+最大尝试时间步P8,低,1.726,2.646,1.123,0.7782
+最大尝试时间步P8,中,2.186,2.682,1.144,0.7599
+最大尝试时间步P8,高,2.64,2.667,1.143,0.7694
diff --git a/analysis/20230818experiment_result.csv b/analysis/20230818experiment_result.csv
new file mode 100644
index 0000000..15d7480
--- /dev/null
+++ b/analysis/20230818experiment_result.csv
@@ -0,0 +1,37 @@
+idx_scenario,n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n,mean_count_firm_prod,mean_count_firm,mean_count_prod,mean_max_ts_firm_prod,mean_max_ts_firm,mean_max_ts_prod,mean_n_remove_firm_prod,mean_n_all_prod_remove_firm,mean_end_ts
+0,7,1,1,uniform,5.0000,0.3000,3,3,2.7598,2.7598,2.1107,1.1107,1.1107,1.1107,0.6027,0.2120,1.5501
+1,5,1,1,uniform,10.0000,0.5000,5,2,2.6596,2.6566,2.1535,1.1535,1.1535,1.1535,0.8261,0.2897,2.2541
+2,3,1,1,uniform,15.0000,0.7000,7,1,2.5573,2.5528,2.1501,1.1501,1.1501,1.1501,0.9518,0.3141,3.1143
+3,7,1,1,uniform,5.0000,0.3000,3,2,2.5783,2.5783,2.0834,1.0834,1.0834,1.0834,0.6046,0.2135,1.5524
+4,5,1,1,uniform,10.0000,0.5000,5,1,2.5453,2.5423,2.1400,1.1400,1.1400,1.1400,0.8240,0.2836,2.3499
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diff --git a/analysis/anova.csv b/analysis/anova.csv
new file mode 100644
index 0000000..9771bc9
--- /dev/null
+++ b/analysis/anova.csv
@@ -0,0 +1,11 @@
+,mean_count_firm_prod,mean_count_firm,mean_count_prod,mean_max_ts_firm_prod,mean_max_ts_firm,mean_max_ts_prod,mean_n_remove_firm_prod,mean_n_all_prod_remove_firm,mean_end_ts
+prf_size,0.004,0.004,0.004,0.004,0.004,0.004,0.973,0.953,0.018
+prf_conn,0.884,0.884,0.841,0.841,0.841,0.841,0.821,0.888,0.63
+cap_limit_prob_type,0.708,0.723,0.517,0.517,0.517,0.517,0.002,0.001,0.002
+n_max_trial,0.611,0.613,0.724,0.724,0.724,0.724,0.898,0.869,0.796
+cap_limit_level,0.243,0.254,0.118,0.118,0.118,0.118,0,0,0
+diff_new_conn,0.216,0.229,0.058,0.058,0.058,0.058,0.002,0.002,0
+crit_supplier,0,0,0,0,0,0,0,0,0
+proactive_ratio,0.66,0.651,0.572,0.572,0.572,0.572,0.258,0.399,0.367
+remove_t,0.464,0.465,0.546,0.546,0.546,0.546,0.026,0.186,0
+netw_prf_n,0,0,0,0,0,0,0.019,0.069,0.003
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diff --git a/analysis/anova.xlsx b/analysis/anova.xlsx
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index 0000000..55eb306
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index 0000000..11f5f35
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diff --git a/analysis/anova_l36_clean.mpx.bak b/analysis/anova_l36_clean.mpx.bak
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index 0000000..960a6d2
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diff --git a/analysis/anova_p_value.png b/analysis/anova_p_value.png
new file mode 100644
index 0000000..7e940e8
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diff --git a/analysis/count.csv b/analysis/count.csv
index 9b72392..7ed69c7 100644
--- a/analysis/count.csv
+++ b/analysis/count.csv
@@ -1,3539 +1,31922 @@
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+23643,84,2.3,1
+23644,84,2.3,1
+23644,168,2.3.3,0
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+23648,95,2.3,1
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diff --git a/analysis/count_dcp.csv b/analysis/count_dcp.csv
index ca63932..d4160fe 100644
--- a/analysis/count_dcp.csv
+++ b/analysis/count_dcp.csv
@@ -1,700 +1,1908 @@
 up_id_firm,up_name_firm,up_id_product,up_name_product,down_id_firm,down_name_firm,down_id_product,down_name_product,count
-126,华为,1.4,工业互联网安全,170,Pseudo1,1,供给,118
-142,深信服,1.4.3,网络安全,126,华为,1.4,工业互联网安全,96
-41,启明星辰,1.4.5,数据安全,126,华为,1.4,工业互联网安全,92
-142,深信服,1.4.2,控制安全,126,华为,1.4,工业互联网安全,92
-53,天融信,1.4.3.6,沙箱类设备,142,深信服,1.4.3,网络安全,50
-23,和利时,1.4.2.7,工控原生安全,142,深信服,1.4.2,控制安全,50
-157,新华三,1.4.1,设备安全,126,华为,1.4,工业互联网安全,50
+126,华为,1.4,工业互联网安全,170,Pseudo1,1,供给,926
+41,启明星辰,1.4.5,数据安全,170,Pseudo1,1,供给,290
+41,启明星辰,1.4.5,数据安全,126,华为,1.4,工业互联网安全,290
+106,阿里巴巴,1.3,工业软件,170,Pseudo1,1,供给,212
+142,深信服,1.4.3,网络安全,126,华为,1.4,工业互联网安全,211
+142,深信服,1.4.3,网络安全,170,Pseudo1,1,供给,211
+29,京东工业品,1.3,工业软件,170,Pseudo1,1,供给,210
+142,深信服,1.4.2,控制安全,126,华为,1.4,工业互联网安全,205
+142,深信服,1.4.2,控制安全,170,Pseudo1,1,供给,204
+40,奇安信,1.4.4,平台安全,126,华为,1.4,工业互联网安全,152
+0,360科技,1.4.4,平台安全,126,华为,1.4,工业互联网安全,149
+98,Microsoft Azure,2,工业互联网平台,170,Pseudo1,1,供给,119
+102,Amazon AWS,2,工业互联网平台,170,Pseudo1,1,供给,118
+0,360科技,1.4.4,平台安全,170,Pseudo1,1,供给,100
+40,奇安信,1.4.4,平台安全,170,Pseudo1,1,供给,99
+99,Siemens,2.1,PaaS,102,Amazon AWS,2,工业互联网平台,79
+99,Siemens,2.1,PaaS,98,Microsoft Azure,2,工业互联网平台,79
+130,金蝶,1.3.2,采购供应,29,京东工业品,1.3,工业软件,77
+58,用友,1.3.2,采购供应,29,京东工业品,1.3,工业软件,77
+99,Siemens,2.1,PaaS,170,Pseudo1,1,供给,77
+130,金蝶,1.3.5,仓储物流,29,京东工业品,1.3,工业软件,75
+130,金蝶,1.3.5,仓储物流,106,阿里巴巴,1.3,工业软件,75
+130,金蝶,1.3.5,仓储物流,170,Pseudo1,1,供给,75
+130,金蝶,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,73
+58,用友,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,73
+157,新华三,1.4.1,设备安全,126,华为,1.4,工业互联网安全,63
+157,新华三,1.4.1,设备安全,170,Pseudo1,1,供给,63
+130,金蝶,1.3.2,采购供应,170,Pseudo1,1,供给,59
+58,用友,1.3.2,采购供应,170,Pseudo1,1,供给,58
+63,长扬科技,1.4.4.5,安全态势感知,40,奇安信,1.4.4,平台安全,50
+53,天融信,1.4.3.6,沙箱类设备,126,华为,1.4,工业互联网安全,50
 53,天融信,1.4.5.8,数据加密,41,启明星辰,1.4.5,数据安全,50
-41,启明星辰,1.4.3.2,流量检测,142,深信服,1.4.3,网络安全,50
+53,天融信,1.4.5.8,数据加密,170,Pseudo1,1,供给,50
+53,天融信,1.4.5.8,数据加密,126,华为,1.4,工业互联网安全,50
+63,长扬科技,1.4.4.5,安全态势感知,170,Pseudo1,1,供给,50
+53,天融信,1.4.3.6,沙箱类设备,170,Pseudo1,1,供给,50
+53,天融信,1.4.3.6,沙箱类设备,142,深信服,1.4.3,网络安全,50
+135,浪潮,1.3.2.1,供应链管理SCM,106,阿里巴巴,1.3,工业软件,50
+135,浪潮,1.3.2.1,供应链管理SCM,130,金蝶,1.3.2,采购供应,50
+135,浪潮,1.3.2.1,供应链管理SCM,170,Pseudo1,1,供给,50
+135,浪潮,1.3.2.1,供应链管理SCM,29,京东工业品,1.3,工业软件,50
+135,浪潮,1.3.2.1,供应链管理SCM,58,用友,1.3.2,采购供应,50
 53,天融信,1.4.2.3,工控漏洞扫描,142,深信服,1.4.2,控制安全,50
+53,天融信,1.4.2.3,工控漏洞扫描,170,Pseudo1,1,供给,50
+23,和利时,1.4.2.7,工控原生安全,126,华为,1.4,工业互联网安全,50
+53,天融信,1.4.2.3,工控漏洞扫描,126,华为,1.4,工业互联网安全,50
+63,长扬科技,1.4.4.5,安全态势感知,126,华为,1.4,工业互联网安全,50
+23,和利时,1.4.2.7,工控原生安全,170,Pseudo1,1,供给,50
+41,启明星辰,1.4.3.2,流量检测,126,华为,1.4,工业互联网安全,50
+140,山石网科,1.4.5.1,恶意代码检测系统,170,Pseudo1,1,供给,50
 140,山石网科,1.4.5.1,恶意代码检测系统,41,启明星辰,1.4.5,数据安全,50
-99,Siemens,2.1,PaaS,102,Amazon AWS,2,工业互联网平台,41
-135,浪潮,1.3.2.1,供应链管理SCM,130,金蝶,1.3.2,采购供应,40
-130,金蝶,1.3.5,仓储物流,106,阿里巴巴,1.3,工业软件,39
-63,长扬科技,1.4.4.5,安全态势感知,0,360科技,1.4.4,平台安全,38
-63,长扬科技,1.4.4.5,安全态势感知,40,奇安信,1.4.4,平台安全,38
-135,浪潮,1.3.2.1,供应链管理SCM,58,用友,1.3.2,采购供应,36
-99,Siemens,2.1,PaaS,98,Microsoft Azure,2,工业互联网平台,36
-130,金蝶,1.3.5,仓储物流,29,京东工业品,1.3,工业软件,33
-130,金蝶,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,23
-135,浪潮,1.3.2.1,供应链管理SCM,106,阿里巴巴,1.3,工业软件,23
-53,天融信,1.4.3.6,沙箱类设备,126,华为,1.4,工业互联网安全,23
-41,启明星辰,1.4.3.2,流量检测,126,华为,1.4,工业互联网安全,23
-58,用友,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,23
-53,天融信,1.4.5.8,数据加密,126,华为,1.4,工业互联网安全,21
-140,山石网科,1.4.5.1,恶意代码检测系统,126,华为,1.4,工业互联网安全,21
-142,深信服,1.4.3,网络安全,170,Pseudo1,1,供给,21
-53,天融信,1.4.2.3,工控漏洞扫描,126,华为,1.4,工业互联网安全,21
-23,和利时,1.4.2.7,工控原生安全,126,华为,1.4,工业互联网安全,21
-41,启明星辰,1.4.5,数据安全,170,Pseudo1,1,供给,19
-142,深信服,1.4.2,控制安全,170,Pseudo1,1,供给,19
-130,金蝶,1.3.2,采购供应,29,京东工业品,1.3,工业软件,14
-58,用友,1.3.2,采购供应,29,京东工业品,1.3,工业软件,14
-135,浪潮,1.3.2.1,供应链管理SCM,29,京东工业品,1.3,工业软件,14
-157,新华三,1.4.1,设备安全,170,Pseudo1,1,供给,9
-41,启明星辰,1.4.3.2,流量检测,170,Pseudo1,1,供给,6
-53,天融信,1.4.3.6,沙箱类设备,170,Pseudo1,1,供给,6
-99,Siemens,2.1,PaaS,170,Pseudo1,1,供给,5
-53,天融信,1.4.2.3,工控漏洞扫描,170,Pseudo1,1,供给,5
-40,奇安信,1.4.4,平台安全,126,华为,1.4,工业互联网安全,5
-98,Microsoft Azure,2,工业互联网平台,170,Pseudo1,1,供给,5
-63,长扬科技,1.4.4.5,安全态势感知,126,华为,1.4,工业互联网安全,5
-140,山石网科,1.4.5.1,恶意代码检测系统,170,Pseudo1,1,供给,5
-23,和利时,1.4.2.7,工控原生安全,170,Pseudo1,1,供给,5
-53,天融信,1.4.5.8,数据加密,170,Pseudo1,1,供给,5
-0,360科技,1.4.4,平台安全,126,华为,1.4,工业互联网安全,5
-102,Amazon AWS,2,工业互联网平台,170,Pseudo1,1,供给,5
-131,九物互联,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,4
-42,山大华天,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,4
-13,东方国信,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,4
-79,PTC,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,4
-78,OutSystems,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,3
-68,中望软件,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,3
-13,东方国信,2.1.3.4,应用管理服务,74,HoneyWell,2.1.3,工业物联网,3
-13,东方国信,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,3
-13,东方国信,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,3
-13,东方国信,2.1.3.7,制造类API,108,百度,2.1.3,工业物联网,3
-79,PTC,2.3.3,协议转换,126,华为,2.3,边缘层,3
-69,紫光集团,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,3
-79,PTC,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,3
-43,神舟软件,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,3
-16,东土科技,2.3.3,协议转换,126,华为,2.3,边缘层,3
-79,PTC,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,3
+41,启明星辰,1.4.3.2,流量检测,142,深信服,1.4.3,网络安全,50
+63,长扬科技,1.4.4.5,安全态势感知,0,360科技,1.4.4,平台安全,50
+41,启明星辰,1.4.3.2,流量检测,170,Pseudo1,1,供给,50
+23,和利时,1.4.2.7,工控原生安全,142,深信服,1.4.2,控制安全,50
+140,山石网科,1.4.5.1,恶意代码检测系统,126,华为,1.4,工业互联网安全,50
+102,Amazon AWS,2.1.4,工业大数据,99,Siemens,2.1,PaaS,49
+115,富士康,2.1.4,工业大数据,99,Siemens,2.1,PaaS,47
+84,Bosch,2.1.4,工业大数据,99,Siemens,2.1,PaaS,43
+53,天融信,1.4.5.7,数据恢复,41,启明星辰,1.4.5,数据安全,35
+53,天融信,1.4.5.6,数据容灾备份,41,启明星辰,1.4.5,数据安全,35
+52,天空卫士,1.4.5.5,敏感数据发现与监控,41,启明星辰,1.4.5,数据安全,35
+5,安华金和,1.4.5.9,数据防火墙,41,启明星辰,1.4.5,数据安全,35
+5,安华金和,1.4.5.5,敏感数据发现与监控,41,启明星辰,1.4.5,数据安全,35
+114,富勒科技,1.3.5.1,仓储物流管理WMS,130,金蝶,1.3.5,仓储物流,35
+140,山石网科,1.4.5.4,数据脱敏,41,启明星辰,1.4.5,数据安全,35
+140,山石网科,1.4.5.9,数据防火墙,41,启明星辰,1.4.5,数据安全,35
+37,绿盟,1.4.5.2,数据防泄漏系统,41,启明星辰,1.4.5,数据安全,35
+151,唯智信息,1.3.5.1,仓储物流管理WMS,130,金蝶,1.3.5,仓储物流,35
+162,壹进制,1.4.5.6,数据容灾备份,41,启明星辰,1.4.5,数据安全,35
+77,Oracle,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,35
+162,壹进制,1.4.5.7,数据恢复,41,启明星辰,1.4.5,数据安全,35
+5,安华金和,1.4.5.4,数据脱敏,41,启明星辰,1.4.5,数据安全,35
+53,天融信,1.4.5.2,数据防泄漏系统,41,启明星辰,1.4.5,数据安全,35
+37,绿盟,1.4.4.3,接入认证,40,奇安信,1.4.4,平台安全,34
+37,绿盟,1.4.2.2,工控主机卫士,142,深信服,1.4.2,控制安全,34
+27,江南天安,1.4.4.2,密钥管理,40,奇安信,1.4.4,平台安全,34
+27,江南天安,1.4.4.2,密钥管理,0,360科技,1.4.4,平台安全,34
+53,天融信,1.4.4.4,工业应用行为监控,40,奇安信,1.4.4,平台安全,34
+37,绿盟,1.4.3.1,网络漏洞扫描和补丁管理,142,深信服,1.4.3,网络安全,34
+37,绿盟,1.4.4.3,接入认证,0,360科技,1.4.4,平台安全,34
+11,北信源,1.4.4.2,密钥管理,0,360科技,1.4.4,平台安全,34
+30,可信华泰,1.4.2.6,隐私计算,142,深信服,1.4.2,控制安全,34
+53,天融信,1.4.3.4,攻击溯源,142,深信服,1.4.3,网络安全,34
+11,北信源,1.4.4.2,密钥管理,40,奇安信,1.4.4,平台安全,34
+54,网御星云,1.4.4.3,接入认证,0,360科技,1.4.4,平台安全,34
+40,奇安信,1.4.2.5,安全日志与审计,142,深信服,1.4.2,控制安全,34
+41,启明星辰,1.4.3.1,网络漏洞扫描和补丁管理,142,深信服,1.4.3,网络安全,34
+122,国民技术,1.4.2.6,隐私计算,142,深信服,1.4.2,控制安全,34
+54,网御星云,1.4.4.3,接入认证,40,奇安信,1.4.4,平台安全,34
+55,威努特,1.4.2.2,工控主机卫士,142,深信服,1.4.2,控制安全,34
+55,威努特,1.4.4.4,工业应用行为监控,0,360科技,1.4.4,平台安全,34
+55,威努特,1.4.4.4,工业应用行为监控,40,奇安信,1.4.4,平台安全,34
+53,天融信,1.4.3.5,负载均衡,142,深信服,1.4.3,网络安全,34
+99,Siemens,1.3.3,生产制造,29,京东工业品,1.3,工业软件,34
+59,优特捷,1.4.2.5,安全日志与审计,142,深信服,1.4.2,控制安全,34
+41,启明星辰,1.4.3.4,攻击溯源,142,深信服,1.4.3,网络安全,34
+41,启明星辰,1.4.3.5,负载均衡,142,深信服,1.4.3,网络安全,34
+53,天融信,1.4.4.4,工业应用行为监控,0,360科技,1.4.4,平台安全,34
+160,亚信科技,1.4.1.3,防毒墙,157,新华三,1.4.1,设备安全,33
+107,安恒信息,1.4.3.3,APT检测,142,深信服,1.4.3,网络安全,33
+75,IBM,1.3.3,生产制造,106,阿里巴巴,1.3,工业软件,33
+152,卫士通,1.4.4.1,身份鉴别与访问控制,40,奇安信,1.4.4,平台安全,31
+93,Cadence,1.3.1,设计研发,29,京东工业品,1.3,工业软件,31
+81,SAP,1.3.4,企业运营管理,106,阿里巴巴,1.3,工业软件,31
+81,SAP,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,31
+85,Dassault,1.3.1,设计研发,29,京东工业品,1.3,工业软件,31
+8,梆梆安全,1.4.1.1,工业防火墙,157,新华三,1.4.1,设备安全,30
+97,General Electric,1.3.3,生产制造,29,京东工业品,1.3,工业软件,30
+81,SAP,2.1.4.1,工业大数据存储,84,Bosch,2.1.4,工业大数据,29
+79,PTC,2.1.4.1,工业大数据存储,84,Bosch,2.1.4,工业大数据,29
+79,PTC,2.1.4.1,工业大数据存储,115,富士康,2.1.4,工业大数据,29
+51,天地和兴,1.4.2.1,工控安全监测与审计,142,深信服,1.4.2,控制安全,28
+121,广州智臣,1.4.2.4,安全隔离与信息交换系统,142,深信服,1.4.2,控制安全,28
+79,PTC,2.1.4.2,工业大数据管理,115,富士康,2.1.4,工业大数据,28
+100,Synopsys,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,28
+81,SAP,2.1.4.1,工业大数据存储,115,富士康,2.1.4,工业大数据,28
+17,国保金泰,1.4.2.4,安全隔离与信息交换系统,142,深信服,1.4.2,控制安全,28
+19,国泰网信,1.4.2.1,工控安全监测与审计,142,深信服,1.4.2,控制安全,28
+99,Siemens,1.3.3,生产制造,106,阿里巴巴,1.3,工业软件,28
+79,PTC,2.1.4.2,工业大数据管理,84,Bosch,2.1.4,工业大数据,28
+81,SAP,2.1.4.2,工业大数据管理,115,富士康,2.1.4,工业大数据,28
+81,SAP,2.1.4.2,工业大数据管理,84,Bosch,2.1.4,工业大数据,28
+99,Siemens,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,27
+53,天融信,1.4.1.5,统一威胁管理系统,157,新华三,1.4.1,设备安全,26
+158,信大捷安,1.4.4.1,身份鉴别与访问控制,0,360科技,1.4.4,平台安全,26
+97,General Electric,1.3.3,生产制造,106,阿里巴巴,1.3,工业软件,26
+58,用友,1.3.4.2,客户关系管理CRM,81,SAP,1.3.4,企业运营管理,26
+41,启明星辰,1.4.1.5,统一威胁管理系统,157,新华三,1.4.1,设备安全,25
+41,启明星辰,1.4.1.2,下一代防火墙,157,新华三,1.4.1,设备安全,25
+140,山石网科,1.4.1.4,入侵检测系统,157,新华三,1.4.1,设备安全,25
+39,Autodesk,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,25
+67,中国移动,1.2,工业互联网网络,170,Pseudo1,1,供给,25
+53,天融信,1.4.5.3,数据审计系统,41,启明星辰,1.4.5,数据安全,25
+53,天融信,1.4.1.4,入侵检测系统,157,新华三,1.4.1,设备安全,24
+133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,40,奇安信,1.4.4,平台安全,24
+80,Salesforce,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,24
+152,卫士通,1.4.4.1,身份鉴别与访问控制,0,360科技,1.4.4,平台安全,24
+5,安华金和,1.4.5.3,数据审计系统,41,启明星辰,1.4.5,数据安全,23
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,99,Siemens,1.3.3,生产制造,23
+106,阿里巴巴,1.2,工业互联网网络,170,Pseudo1,1,供给,23
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,39,Autodesk,1.3.1,设计研发,23
+77,Oracle,1.3.4,企业运营管理,106,阿里巴巴,1.3,工业软件,23
+45,石化盈科,1.3.4.2,客户关系管理CRM,80,Salesforce,1.3.4,企业运营管理,23
+75,IBM,1.3.3,生产制造,29,京东工业品,1.3,工业软件,23
+112,东华测试,1.3.3.7,故障预测与健康管理PHM,75,IBM,1.3.3,生产制造,23
+80,Salesforce,1.3.4,企业运营管理,106,阿里巴巴,1.3,工业软件,23
+79,PTC,2.1.4.1,工业大数据存储,102,Amazon AWS,2.1.4,工业大数据,23
+81,SAP,2.1.4.1,工业大数据存储,102,Amazon AWS,2.1.4,工业大数据,23
+53,天融信,1.4.3.3,APT检测,142,深信服,1.4.3,网络安全,22
+126,华为,1.1,工业自动化,170,Pseudo1,1,供给,22
+106,阿里巴巴,2.1.1,开发工具,99,Siemens,2.1,PaaS,22
+81,SAP,2.1.4.2,工业大数据管理,102,Amazon AWS,2.1.4,工业大数据,22
+55,威努特,1.4.1.2,下一代防火墙,157,新华三,1.4.1,设备安全,22
+130,金蝶,1.3.4.2,客户关系管理CRM,81,SAP,1.3.4,企业运营管理,22
+100,Synopsys,1.3.1,设计研发,29,京东工业品,1.3,工业软件,22
+14,东华软件,1.3.4.3,人力资源管理HRM,81,SAP,1.3.4,企业运营管理,22
+79,PTC,2.1.4.2,工业大数据管理,102,Amazon AWS,2.1.4,工业大数据,22
+140,山石网科,1.4.5.3,数据审计系统,41,启明星辰,1.4.5,数据安全,22
+93,Cadence,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,22
+40,奇安信,1.4.3.3,APT检测,142,深信服,1.4.3,网络安全,21
+118,工邦邦,1.3.3.6,运维保障系统MRO,97,General Electric,1.3.3,生产制造,21
+105,Intel,1.1,工业自动化,170,Pseudo1,1,供给,21
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,97,General Electric,1.3.3,生产制造,21
+37,绿盟,1.4.1.2,下一代防火墙,157,新华三,1.4.1,设备安全,21
+58,用友,1.3.4.3,人力资源管理HRM,81,SAP,1.3.4,企业运营管理,21
+158,信大捷安,1.4.4.1,身份鉴别与访问控制,40,奇安信,1.4.4,平台安全,21
+97,General Electric,1.2,工业互联网网络,170,Pseudo1,1,供给,21
+126,华为,1.2,工业互联网网络,170,Pseudo1,1,供给,21
+133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,0,360科技,1.4.4,平台安全,21
+50,索为系统,1.3.1.5,产品数据管理PDM,100,Synopsys,1.3.1,设计研发,21
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,75,IBM,1.3.3,生产制造,20
+11,北信源,1.4.4.2,密钥管理,126,华为,1.4,工业互联网安全,20
+45,石化盈科,1.3.4.2,客户关系管理CRM,77,Oracle,1.3.4,企业运营管理,20
+55,威努特,1.4.2.1,工控安全监测与审计,142,深信服,1.4.2,控制安全,20
+130,金蝶,1.3.4.3,人力资源管理HRM,77,Oracle,1.3.4,企业运营管理,20
+86,Dell EMC,1.1,工业自动化,170,Pseudo1,1,供给,20
+13,东方国信,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,20
+85,Dassault,2.1.1,开发工具,99,Siemens,2.1,PaaS,20
+85,Dassault,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,20
+130,金蝶,1.3.4.3,人力资源管理HRM,81,SAP,1.3.4,企业运营管理,20
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,39,Autodesk,1.3.1,设计研发,20
+50,索为系统,1.3.1.5,产品数据管理PDM,93,Cadence,1.3.1,设计研发,20
+79,PTC,2.1.3.5,容器服务,73,FANUC,2.1.3,工业物联网,20
+14,东华软件,1.3.4.3,人力资源管理HRM,77,Oracle,1.3.4,企业运营管理,20
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,19
+43,神舟软件,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,19
+138,启明信息,1.3.1.5,产品数据管理PDM,93,Cadence,1.3.1,设计研发,19
+37,绿盟,1.4.1.4,入侵检测系统,157,新华三,1.4.1,设备安全,19
+22,航天云网,2.1.3.1,物联网服务,108,百度,2.1.3,工业物联网,19
+112,东华测试,1.3.3.7,故障预测与健康管理PHM,97,General Electric,1.3.3,生产制造,19
+22,航天云网,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,19
+79,PTC,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,19
+58,用友,1.3.4.3,人力资源管理HRM,80,Salesforce,1.3.4,企业运营管理,19
+79,PTC,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,19
+130,金蝶,1.3.4.2,客户关系管理CRM,77,Oracle,1.3.4,企业运营管理,19
+58,用友,1.2.2,标识解析,126,华为,1.2,工业互联网网络,19
+63,长扬科技,1.4.1.1,工业防火墙,157,新华三,1.4.1,设备安全,18
+14,东华软件,1.3.4.3,人力资源管理HRM,80,Salesforce,1.3.4,企业运营管理,18
+13,东方国信,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,18
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,18
+22,航天云网,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,18
+22,航天云网,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,18
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,75,IBM,1.3.3,生产制造,18
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,39,Autodesk,1.3.1,设计研发,18
+4,爱创科技,1.2.2,标识解析,97,General Electric,1.2,工业互联网网络,18
+79,PTC,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,18
+68,中望软件,1.3.1.3,计算机辅助制造CAM,93,Cadence,1.3.1,设计研发,18
+25,华大九天,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,18
+39,Autodesk,1.3.1,设计研发,29,京东工业品,1.3,工业软件,18
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,100,Synopsys,1.3.1,设计研发,18
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,99,Siemens,1.3.1,设计研发,18
+15,东软集团,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,18
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,85,Dassault,1.3.1,设计研发,17
+13,东方国信,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,17
+13,东方国信,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,17
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,100,Synopsys,1.3.1,设计研发,17
+42,山大华天,1.3.1.3,计算机辅助制造CAM,39,Autodesk,1.3.1,设计研发,17
+13,东方国信,2.1.3.7,制造类API,108,百度,2.1.3,工业物联网,17
+58,用友,1.3.4.3,人力资源管理HRM,77,Oracle,1.3.4,企业运营管理,17
+135,浪潮,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,17
+55,威努特,1.4.4.4,工业应用行为监控,126,华为,1.4,工业互联网安全,17
+58,用友,1.3.4.2,客户关系管理CRM,77,Oracle,1.3.4,企业运营管理,17
+140,山石网科,1.4.1.5,统一威胁管理系统,157,新华三,1.4.1,设备安全,17
+130,金蝶,1.3.4.2,客户关系管理CRM,80,Salesforce,1.3.4,企业运营管理,17
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,75,IBM,1.3.3,生产制造,17
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,17
+138,启明信息,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,17
+55,威努特,1.4.1.1,工业防火墙,157,新华三,1.4.1,设备安全,17
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,17
+50,索为系统,1.3.1.5,产品数据管理PDM,85,Dassault,1.3.1,设计研发,17
+99,Siemens,1.3.1,设计研发,29,京东工业品,1.3,工业软件,17
+52,天空卫士,1.4.5.5,敏感数据发现与监控,170,Pseudo1,1,供给,17
+3,艾克斯特,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,17
+15,东软集团,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,17
+106,阿里巴巴,1.1,工业自动化,170,Pseudo1,1,供给,17
+52,天空卫士,1.4.5.5,敏感数据发现与监控,126,华为,1.4,工业互联网安全,17
+112,东华测试,1.3.3.7,故障预测与健康管理PHM,99,Siemens,1.3.3,生产制造,17
+13,东方国信,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,17
+79,PTC,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,17
+7,百望,2.2,IaaS,98,Microsoft Azure,2,工业互联网平台,17
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,85,Dassault,1.3.1,设计研发,17
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,17
+70,ABB,1.3.3.4,可编程逻揖控制系统PLC,99,Siemens,1.3.3,生产制造,17
+50,索为系统,1.3.1.5,产品数据管理PDM,99,Siemens,1.3.1,设计研发,16
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,93,Cadence,1.3.1,设计研发,16
+95,Schneider,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,16
+48,曙光信息,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,16
+135,浪潮,2.1.3.7,制造类API,108,百度,2.1.3,工业物联网,16
+130,金蝶,1.3.4.3,人力资源管理HRM,80,Salesforce,1.3.4,企业运营管理,16
+13,东方国信,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,16
+26,寄云科技,2.1.3.7,制造类API,108,百度,2.1.3,工业物联网,16
+73,FANUC,2.1.3,工业物联网,99,Siemens,2.1,PaaS,16
+79,PTC,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,16
+13,东方国信,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,16
+22,航天云网,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,16
+49,数码大方,1.3.1.6,产品生命周期管理PLM,93,Cadence,1.3.1,设计研发,16
+135,浪潮,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,16
+79,PTC,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,16
+164,震坤行,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,16
+116,概伦电子,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,16
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+138,启明信息,1.3.1.5,产品数据管理PDM,99,Siemens,1.3.1,设计研发,15
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+58,用友,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,15
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+115,富士康,2.1.4,工业大数据,102,Amazon AWS,2,工业互联网平台,15
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+102,Amazon AWS,2.1.4,工业大数据,102,Amazon AWS,2,工业互联网平台,15
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+55,威努特,1.4.1.3,防毒墙,157,新华三,1.4.1,设备安全,15
+135,浪潮,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,15
+140,山石网科,1.4.5.9,数据防火墙,170,Pseudo1,1,供给,15
+135,浪潮,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,15
+45,石化盈科,1.3.4.2,客户关系管理CRM,81,SAP,1.3.4,企业运营管理,15
+135,浪潮,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,15
+77,Oracle,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,15
+140,山石网科,1.4.5.9,数据防火墙,126,华为,1.4,工业互联网安全,15
+111,鼎捷软件,1.3.4.1,企业资源计划ERP,80,Salesforce,1.3.4,企业运营管理,15
+79,PTC,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,15
+22,航天云网,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,15
+26,寄云科技,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,15
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,15
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,15
+13,东方国信,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,15
+84,Bosch,2.1.4,工业大数据,98,Microsoft Azure,2,工业互联网平台,14
+25,华大九天,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,14
+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,14
+126,华为,2.3,边缘层,102,Amazon AWS,2,工业互联网平台,14
+135,浪潮,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,14
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,14
+151,唯智信息,1.3.5.1,仓储物流管理WMS,106,阿里巴巴,1.3,工业软件,14
+151,唯智信息,1.3.5.1,仓储物流管理WMS,170,Pseudo1,1,供给,14
+135,浪潮,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,14
+151,唯智信息,1.3.5.1,仓储物流管理WMS,29,京东工业品,1.3,工业软件,14
+9,北京航天测控,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,14
+79,PTC,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,14
+133,蓝盾股份,1.4.1.3,防毒墙,157,新华三,1.4.1,设备安全,14
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,14
+40,奇安信,1.4.2.1,工控安全监测与审计,142,深信服,1.4.2,控制安全,14
+109,宝信软件,1.3.3.1,制造执行系统MES,75,IBM,1.3.3,生产制造,14
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,93,Cadence,1.3.1,设计研发,14
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,14
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,14
+135,浪潮,2.1.3.5,容器服务,73,FANUC,2.1.3,工业物联网,14
+13,东方国信,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,14
+115,富士康,2.1.4,工业大数据,170,Pseudo1,1,供给,14
+68,中望软件,1.3.1.3,计算机辅助制造CAM,99,Siemens,1.3.1,设计研发,14
+13,东方国信,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,14
+84,Bosch,2.1.4,工业大数据,170,Pseudo1,1,供给,14
+13,东方国信,2.1.3.1,物联网服务,108,百度,2.1.3,工业物联网,14
+13,东方国信,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,14
+79,PTC,2.1.2,工业模型库,99,Siemens,2.1,PaaS,14
+79,PTC,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,14
+13,东方国信,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,14
+47,首自信,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,14
+22,航天云网,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,14
+13,东方国信,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,14
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,39,Autodesk,1.3.1,设计研发,14
+45,石化盈科,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,14
+46,适创科技,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,14
+84,Bosch,2.1.4,工业大数据,102,Amazon AWS,2,工业互联网平台,14
+68,中望软件,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,14
+68,中望软件,1.3.1.3,计算机辅助制造CAM,100,Synopsys,1.3.1,设计研发,14
+13,东方国信,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,14
+135,浪潮,2.1.3.4,应用管理服务,106,阿里巴巴,2.1.3,工业物联网,14
+144,树根互联,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,14
+138,启明信息,1.3.1.5,产品数据管理PDM,100,Synopsys,1.3.1,设计研发,14
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,14
+42,山大华天,1.3.1.3,计算机辅助制造CAM,100,Synopsys,1.3.1,设计研发,14
+43,神舟软件,1.3.1.5,产品数据管理PDM,85,Dassault,1.3.1,设计研发,14
+88,HPE,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,14
+56,芯愿景,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,14
+42,山大华天,1.3.1.3,计算机辅助制造CAM,99,Siemens,1.3.1,设计研发,14
+56,芯愿景,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,14
+79,PTC,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,14
+22,航天云网,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,14
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,14
+79,PTC,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,13
+135,浪潮,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,13
+79,PTC,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,13
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,13
+79,PTC,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,13
+135,浪潮,2.1.3.4,应用管理服务,74,HoneyWell,2.1.3,工业物联网,13
+148,腾讯,2.1.3,工业物联网,99,Siemens,2.1,PaaS,13
+135,浪潮,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,13
+79,PTC,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,13
+140,山石网科,1.4.5.4,数据脱敏,126,华为,1.4,工业互联网安全,13
+43,神舟软件,1.3.1.5,产品数据管理PDM,93,Cadence,1.3.1,设计研发,13
+126,华为,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,13
+79,PTC,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,13
+79,PTC,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,13
+50,索为系统,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,13
+79,PTC,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,13
+47,首自信,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,13
+47,首自信,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,13
+45,石化盈科,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,13
+45,石化盈科,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,13
+43,神舟软件,1.3.1.5,产品数据管理PDM,100,Synopsys,1.3.1,设计研发,13
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,75,IBM,1.3.3,生产制造,13
+79,PTC,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,13
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,99,Siemens,1.3.3,生产制造,13
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,99,Siemens,1.3.3,生产制造,13
+109,宝信软件,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,13
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,13
+13,东方国信,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,13
+56,芯愿景,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,13
+135,浪潮,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,13
+23,和利时,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,13
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,13
+22,航天云网,2.3.2,边缘数据处理,155,小米,2.3,边缘层,13
+22,航天云网,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,13
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,13
+22,航天云网,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,13
+88,HPE,1.1.3,工业服务器,105,Intel,1.1,工业自动化,13
+22,航天云网,2.1.3.5,容器服务,73,FANUC,2.1.3,工业物联网,13
+22,航天云网,2.1.3.4,应用管理服务,74,HoneyWell,2.1.3,工业物联网,13
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,13
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,13
+22,航天云网,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,13
+22,航天云网,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,13
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,13
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,13
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,13
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,99,Siemens,1.3.1,设计研发,13
+13,东方国信,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,13
+13,东方国信,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,13
+13,东方国信,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,13
+168,中控技术,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,13
+13,东方国信,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,13
+13,东方国信,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,13
+55,威努特,1.4.4.4,工业应用行为监控,170,Pseudo1,1,供给,13
+13,东方国信,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,13
+140,山石网科,1.4.5.4,数据脱敏,170,Pseudo1,1,供给,13
+71,Altair,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,13
+74,HoneyWell,2.1.3,工业物联网,99,Siemens,2.1,PaaS,13
+135,浪潮,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,13
+79,PTC,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,13
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,97,General Electric,1.3.3,生产制造,13
+108,百度,2.1.3,工业物联网,99,Siemens,2.1,PaaS,13
+26,寄云科技,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,13
+54,网御星云,1.4.2.4,安全隔离与信息交换系统,142,深信服,1.4.2,控制安全,13
+3,艾克斯特,1.3.1.5,产品数据管理PDM,85,Dassault,1.3.1,设计研发,13
+3,艾克斯特,1.3.1.5,产品数据管理PDM,93,Cadence,1.3.1,设计研发,13
+81,SAP,2.1.2,工业模型库,99,Siemens,2.1,PaaS,13
+15,东软集团,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,13
+79,PTC,2.1.3.7,制造类API,148,腾讯,2.1.3,工业物联网,13
+77,Oracle,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,13
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+116,概伦电子,1.3.1.7,电子设计自动化EDA,99,Siemens,1.3.1,设计研发,13
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+162,壹进制,1.4.5.6,数据容灾备份,126,华为,1.4,工业互联网安全,12
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+22,航天云网,1.2.2,标识解析,67,中国移动,1.2,工业互联网网络,12
+95,Schneider,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,12
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+45,石化盈科,2.1.4.1.2,分布式数据库,81,SAP,2.1.4.1,工业大数据存储,12
+79,PTC,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,12
+20,海基科技,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,12
+163,优也科技,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,12
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+41,启明星辰,1.4.3.1,网络漏洞扫描和补丁管理,126,华为,1.4,工业互联网安全,12
+22,航天云网,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,12
+42,山大华天,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,12
+41,启明星辰,1.4.3.1,网络漏洞扫描和补丁管理,170,Pseudo1,1,供给,12
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,12
+79,PTC,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,12
+81,SAP,2.1.4.2,工业大数据管理,99,Siemens,2.1,PaaS,12
+79,PTC,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,12
+96,Cisco,1.2.3,数据互通,126,华为,1.2,工业互联网网络,12
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,12
+42,山大华天,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,12
+23,和利时,1.3.3.4,可编程逻揖控制系统PLC,99,Siemens,1.3.3,生产制造,12
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,93,Cadence,1.3.1,设计研发,12
+26,寄云科技,2.1.3.2,平台基础服务,74,HoneyWell,2.1.3,工业物联网,12
+42,山大华天,1.3.1.3,计算机辅助制造CAM,93,Cadence,1.3.1,设计研发,12
+26,寄云科技,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,12
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+146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,12
+22,航天云网,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,12
+22,航天云网,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,12
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,12
+22,航天云网,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,12
+79,PTC,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,12
+46,适创科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,12
+109,宝信软件,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,12
+70,ABB,1.3.3.4,可编程逻揖控制系统PLC,75,IBM,1.3.3,生产制造,12
+58,用友,1.3.4.2,客户关系管理CRM,80,Salesforce,1.3.4,企业运营管理,12
+13,东方国信,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,12
+48,曙光信息,1.2.2,标识解析,126,华为,1.2,工业互联网网络,12
+13,东方国信,2.1.3.3,工业引擎服务,106,阿里巴巴,2.1.3,工业物联网,12
+49,数码大方,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,12
+13,东方国信,2.3.1,工业数据接入,124,海尔,2.3,边缘层,12
+135,浪潮,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,12
+59,优特捷,1.4.2.5,安全日志与审计,126,华为,1.4,工业互联网安全,12
+49,数码大方,1.3.3.1,制造执行系统MES,75,IBM,1.3.3,生产制造,12
+59,优特捷,1.4.2.5,安全日志与审计,170,Pseudo1,1,供给,12
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+136,美的,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,12
+49,数码大方,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,12
+64,中电智科,1.1.2,工业控制器,126,华为,1.1,工业自动化,12
+111,鼎捷软件,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,12
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+132,科远智慧,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,12
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,12
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,93,Cadence,1.3.1,设计研发,12
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+135,浪潮,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,12
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+88,HPE,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,12
+58,用友,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,12
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,12
+135,浪潮,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,12
+47,首自信,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,12
+79,PTC,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,12
+58,用友,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,12
+13,东方国信,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,12
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,12
+135,浪潮,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,12
+47,首自信,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,12
+47,首自信,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,12
+47,首自信,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,12
+135,浪潮,2.1.3.3,工业引擎服务,106,阿里巴巴,2.1.3,工业物联网,11
+135,浪潮,2.1.3.1,物联网服务,108,百度,2.1.3,工业物联网,11
+79,PTC,2.1.3.7,制造类API,126,华为,2.1.3,工业物联网,11
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,11
+78,OutSystems,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,11
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,39,Autodesk,1.3.1,设计研发,11
+114,富勒科技,1.3.5.1,仓储物流管理WMS,106,阿里巴巴,1.3,工业软件,11
+126,华为,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,11
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,11
+135,浪潮,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,11
+79,PTC,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,11
+77,Oracle,1.3.3.6,运维保障系统MRO,97,General Electric,1.3.3,生产制造,11
+28,金山云,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,11
+135,浪潮,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,11
+161,研华科技,2.3.3,协议转换,126,华为,2.3,边缘层,11
+161,研华科技,2.3.2,边缘数据处理,155,小米,2.3,边缘层,11
+6,安世亚太,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,11
+22,航天云网,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,11
+97,General Electric,2.1.3,工业物联网,99,Siemens,2.1,PaaS,11
+22,航天云网,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,11
+135,浪潮,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,11
+75,IBM,1.3.3,生产制造,170,Pseudo1,1,供给,11
+114,富勒科技,1.3.5.1,仓储物流管理WMS,170,Pseudo1,1,供给,11
+74,HoneyWell,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,11
+154,西格数据,2.1.4.1.1,关系型数据库,79,PTC,2.1.4.1,工业大数据存储,11
+22,航天云网,2.1.3.7,制造类API,148,腾讯,2.1.3,工业物联网,11
+22,航天云网,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,11
+22,航天云网,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,11
+13,东方国信,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,11
+22,航天云网,2.1.4.1.3,实时数据库,81,SAP,2.1.4.1,工业大数据存储,11
+58,用友,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,11
+58,用友,1.2.2,标识解析,67,中国移动,1.2,工业互联网网络,11
+13,东方国信,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,11
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,11
+57,亚控科技,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,11
+13,东方国信,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,11
+13,东方国信,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,11
+13,东方国信,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,11
+56,芯愿景,1.3.1.7,电子设计自动化EDA,99,Siemens,1.3.1,设计研发,11
+22,航天云网,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,11
+13,东方国信,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,11
+54,网御星云,1.4.4.3,接入认证,126,华为,1.4,工业互联网安全,11
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,39,Autodesk,1.3.1,设计研发,11
+114,富勒科技,1.3.5.1,仓储物流管理WMS,29,京东工业品,1.3,工业软件,11
+25,华大九天,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,11
+26,寄云科技,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,11
+26,寄云科技,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,11
+89,Rockwell,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,11
+9,北京航天测控,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,11
+149,天泽智云,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,11
+26,寄云科技,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,11
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,11
+53,天融信,1.4.1.3,防毒墙,157,新华三,1.4.1,设备安全,11
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,11
+20,海基科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,11
+13,东方国信,2.1.3.4,应用管理服务,106,阿里巴巴,2.1.3,工业物联网,11
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,11
+79,PTC,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,11
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,11
+13,东方国信,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,11
+41,启明星辰,1.4.3.5,负载均衡,126,华为,1.4,工业互联网安全,11
+41,启明星辰,1.4.3.5,负载均衡,170,Pseudo1,1,供给,11
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,11
+66,中国联通,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,11
+18,国能智深,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,11
+48,曙光信息,1.2.2,标识解析,67,中国移动,1.2,工业互联网网络,11
+168,中控技术,2.3.3,协议转换,99,Siemens,2.3,边缘层,11
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,11
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,93,Cadence,1.3.1,设计研发,11
+47,首自信,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,11
+124,海尔,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,11
+47,首自信,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,11
+47,首自信,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,11
+13,东方国信,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,11
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,11
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,11
+120,广州数控,1.2.3,数据互通,126,华为,1.2,工业互联网网络,11
+79,PTC,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,11
+45,石化盈科,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,11
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+78,OutSystems,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,10
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+166,中国电子科技网络信息安全,1.2.3,数据互通,106,阿里巴巴,1.2,工业互联网网络,10
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+125,华数机器人,1.2.3,数据互通,126,华为,1.2,工业互联网网络,10
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+135,浪潮,2.1.3.3,工业引擎服务,97,General Electric,2.1.3,工业物联网,10
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+135,浪潮,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,10
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+144,树根互联,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,9
+13,东方国信,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,9
+143,沈阳自动化研究所,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,9
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+168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,9
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+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,9
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+13,东方国信,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,9
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+122,国民技术,1.4.2.6,隐私计算,170,Pseudo1,1,供给,9
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+143,沈阳自动化研究所,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,7
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+13,东方国信,2.3.3,协议转换,126,华为,2.3,边缘层,7
+58,用友,1.3.4.1,企业资源计划ERP,80,Salesforce,1.3.4,企业运营管理,7
+47,首自信,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,7
+137,美林数据,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,7
+47,首自信,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,7
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+47,首自信,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,7
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+168,中控技术,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,7
+79,PTC,2.1.3.7,制造类API,108,百度,2.1.3,工业物联网,7
+23,和利时,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,7
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+123,海得控制,1.1.2,工业控制器,126,华为,1.1,工业自动化,7
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+123,海得控制,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,7
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+125,华数机器人,1.2.3,数据互通,106,阿里巴巴,1.2,工业互联网网络,7
+25,华大九天,1.3.1.7,电子设计自动化EDA,99,Siemens,1.3.1,设计研发,7
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+117,格创东智,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,7
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+163,优也科技,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,7
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+166,中国电子科技网络信息安全,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,7
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+78,OutSystems,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,7
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+135,浪潮,1.1.3,工业服务器,105,Intel,1.1,工业自动化,6
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+131,九物互联,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,6
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+131,九物互联,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,6
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+38,牛刀,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,6
+23,和利时,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,6
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+37,绿盟,1.4.4.3,接入认证,170,Pseudo1,1,供给,6
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+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,6
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+143,沈阳自动化研究所,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,6
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+22,航天云网,2.3.3,协议转换,124,海尔,2.3,边缘层,6
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+136,美的,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,6
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+26,寄云科技,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,5
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+78,OutSystems,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,5
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+127,华为海思,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,5
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+22,航天云网,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,5
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+113,飞腾信息,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,5
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+13,东方国信,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,5
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+129,华中数控,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,5
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+60,宇动源,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,5
+22,航天云网,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,5
+82,Uptake,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,5
+22,航天云网,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,5
+13,东方国信,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,5
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+3,艾克斯特,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,5
+82,Uptake,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,5
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+13,东方国信,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,5
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+128,华伍股份,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,5
+69,紫光集团,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,5
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+89,Rockwell,1.1.2,工业控制器,126,华为,1.1,工业自动化,5
+33,蓝谷信息,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,5
+81,SAP,2.1.4.1,工业大数据存储,170,Pseudo1,1,供给,5
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+46,适创科技,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,5
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+143,沈阳自动化研究所,2.1.1.1,算法建模工具,80,Salesforce,2.1.1,开发工具,5
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+103,STMicroelectronics ,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,5
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+117,格创东智,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,5
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+135,浪潮,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,5
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+117,格创东智,2.1.4.1.3,实时数据库,81,SAP,2.1.4.1,工业大数据存储,5
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+22,航天云网,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,5
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+16,东土科技,2.3.1,工业数据接入,155,小米,2.3,边缘层,5
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+22,航天云网,2.3.1,工业数据接入,124,海尔,2.3,边缘层,5
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+81,SAP,2.1.4.2,工业大数据管理,98,Microsoft Azure,2,工业互联网平台,5
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+115,富士康,1.1.3,工业服务器,126,华为,1.1,工业自动化,5
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+78,OutSystems,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,4
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+31,昆仑数据,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,4
+115,富士康,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,4
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+123,海得控制,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,4
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+129,华中数控,1.1.2,工业控制器,126,华为,1.1,工业自动化,4
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+62,云道智造,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,4
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+169,中芯国际,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,4
+101,Analog Devices,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,4
+47,首自信,2.1.1.3,流程开发工具,106,阿里巴巴,2.1.1,开发工具,4
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+53,天融信,1.4.4.4,工业应用行为监控,170,Pseudo1,1,供给,4
+47,首自信,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,4
+6,安世亚太,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,4
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,29,京东工业品,1.3,工业软件,4
+130,金蝶,1.3.4.3,人力资源管理HRM,29,京东工业品,1.3,工业软件,4
+99,Siemens,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,4
+60,宇动源,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,4
+101,Analog Devices,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,4
+60,宇动源,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,4
+38,牛刀,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,4
+60,宇动源,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,4
+131,九物互联,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,4
+131,九物互联,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,4
+101,Analog Devices,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,4
+144,树根互联,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,4
+130,金蝶,1.3.4.3,人力资源管理HRM,106,阿里巴巴,1.3,工业软件,4
+45,石化盈科,1.3.4.1,企业资源计划ERP,80,Salesforce,1.3.4,企业运营管理,4
+57,亚控科技,2.3.1,工业数据接入,155,小米,2.3,边缘层,4
+45,石化盈科,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,4
+57,亚控科技,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,4
+44,圣邦微电子,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,4
+57,亚控科技,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,4
+44,圣邦微电子,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,4
+104,Infineon,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,4
+13,东方国信,2.3.3,协议转换,99,Siemens,2.3,边缘层,4
+149,天泽智云,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,4
+91,Moxa,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,4
+58,用友,1.3.4.2,客户关系管理CRM,29,京东工业品,1.3,工业软件,4
+103,STMicroelectronics ,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,4
+168,中控技术,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,4
+165,智能云科,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,4
+131,九物互联,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,4
+89,Rockwell,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,4
+47,首自信,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,4
+60,宇动源,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,4
+62,云道智造,2.1.2.4,行业机理模型,81,SAP,2.1.2,工业模型库,4
+62,云道智造,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,4
+87,Texas Instruments,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,4
+50,索为系统,1.3.1.5,产品数据管理PDM,29,京东工业品,1.3,工业软件,4
+62,云道智造,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,4
+167,中环股份,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,4
+165,智能云科,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,4
+87,Texas Instruments,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,4
+136,美的,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,4
+135,浪潮,2.2,IaaS,98,Microsoft Azure,2,工业互联网平台,4
+62,云道智造,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,4
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,93,Cadence,1.3.1,设计研发,4
+36,龙芯中科,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,4
+36,龙芯中科,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,4
+35,凌昊智能,1.1.3,工业服务器,126,华为,1.1,工业自动化,4
+64,中电智科,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,4
+149,天泽智云,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,4
+49,数码大方,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,4
+65,中国电信,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,4
+62,云道智造,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,4
+45,石化盈科,1.3.4.2,客户关系管理CRM,106,阿里巴巴,1.3,工业软件,4
+163,优也科技,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,4
+49,数码大方,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,4
+62,云道智造,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,4
+20,海基科技,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,4
+137,美林数据,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,4
+148,腾讯,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,4
+62,云道智造,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,4
+62,云道智造,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,4
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,170,Pseudo1,1,供给,3
+49,数码大方,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,3
+45,石化盈科,1.3.4.2,客户关系管理CRM,29,京东工业品,1.3,工业软件,3
+45,石化盈科,1.3.4.2,客户关系管理CRM,170,Pseudo1,1,供给,3
+131,九物互联,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,3
+23,和利时,2.3.2,边缘数据处理,126,华为,2.3,边缘层,3
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,106,阿里巴巴,1.3,工业软件,3
+23,和利时,2.3.3,协议转换,126,华为,2.3,边缘层,3
+135,浪潮,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,3
+53,天融信,1.4.1.4,入侵检测系统,170,Pseudo1,1,供给,3
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,3
+79,PTC,2.1.4.1,工业大数据存储,102,Amazon AWS,2,工业互联网平台,3
+117,格创东智,2.1.1.4,组态建模工具,106,阿里巴巴,2.1.1,开发工具,3
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,29,京东工业品,1.3,工业软件,3
+49,数码大方,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,3
+79,PTC,2.1.4.1,工业大数据存储,98,Microsoft Azure,2,工业互联网平台,3
+16,东土科技,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,3
+99,Siemens,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,3
+117,格创东智,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,3
+22,航天云网,2.3.1,工业数据接入,126,华为,2.3,边缘层,3
+99,Siemens,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,3
+133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,126,华为,1.4,工业互联网安全,3
+168,中控技术,1.1.2,工业控制器,106,阿里巴巴,1.1,工业自动化,3
+49,数码大方,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,3
+16,东土科技,2.3.3,协议转换,99,Siemens,2.3,边缘层,3
+47,首自信,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,3
+131,九物互联,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,3
+79,PTC,2.1.4.1,工业大数据存储,170,Pseudo1,1,供给,3
+22,航天云网,2.3.2,边缘数据处理,126,华为,2.3,边缘层,3
+23,和利时,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,3
 117,格创东智,2.1.1.1,算法建模工具,80,Salesforce,2.1.1,开发工具,3
-79,PTC,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,3
-79,PTC,2.1.3.4,应用管理服务,74,HoneyWell,2.1.3,工业物联网,3
-79,PTC,2.1.3.7,制造类API,148,腾讯,2.1.3,工业物联网,3
-79,PTC,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,3
-32,兰光创新,1.2.3,数据互通,106,阿里巴巴,1.2,工业互联网网络,3
-103,STMicroelectronics ,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,3
-169,中芯国际,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
-47,首自信,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,2
-74,HoneyWell,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,2
-72,ANSYS,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,2
-57,亚控科技,2.3.2,边缘数据处理,155,小米,2.3,边缘层,2
-22,航天云网,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,2
-71,Altair,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,2
-47,首自信,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,2
-70,ABB,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,2
-22,航天云网,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,2
-104,Infineon,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
-57,亚控科技,2.3.3,协议转换,155,小米,2.3,边缘层,2
-57,亚控科技,2.3.3,协议转换,99,Siemens,2.3,边缘层,2
-47,首自信,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,2
-22,航天云网,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,2
-22,航天云网,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,2
-48,曙光信息,1.2.2,标识解析,126,华为,1.2,工业互联网网络,2
-145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,2
-49,数码大方,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,2
-78,OutSystems,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,2
-57,亚控科技,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,2
-6,安世亚太,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,2
-78,OutSystems,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,2
-79,PTC,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,2
-168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,2
-135,浪潮,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,2
-135,浪潮,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,2
-167,中环股份,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,2
-79,PTC,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,2
-168,中控技术,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,2
-22,航天云网,2.3.3,协议转换,99,Siemens,2.3,边缘层,2
-79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,2
-138,启明信息,1.3.1.5,产品数据管理PDM,100,Synopsys,1.3.1,设计研发,2
-56,芯愿景,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
-14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,75,IBM,1.3.3,生产制造,2
-167,中环股份,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
-166,中国电子科技网络信息安全,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,2
-103,STMicroelectronics ,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
-47,首自信,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,2
-117,格创东智,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,2
-22,航天云网,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,2
-47,首自信,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,2
-49,数码大方,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,2
-165,智能云科,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,2
-58,用友,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,2
-103,STMicroelectronics ,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
-153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,39,Autodesk,1.3.1,设计研发,2
-60,宇动源,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,2
-49,数码大方,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,2
-60,宇动源,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,2
-115,富士康,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,2
-16,东土科技,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,2
-16,东土科技,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,2
-161,研华科技,2.3.3,协议转换,95,Schneider,2.3,边缘层,2
-6,安世亚太,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,2
-6,安世亚太,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,2
-16,东土科技,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,2
-16,东土科技,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,2
-6,安世亚太,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,2
-6,安世亚太,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,2
-161,研华科技,2.3.3,协议转换,155,小米,2.3,边缘层,2
-16,东土科技,2.3.3,协议转换,95,Schneider,2.3,边缘层,2
-6,安世亚太,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,2
-169,中芯国际,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
-6,安世亚太,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,2
-49,数码大方,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,2
-153,武汉开目,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,2
-61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,2
-135,浪潮,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,2
-49,数码大方,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,2
-147,拓邦股份,1.1.2,工业控制器,105,Intel,1.1,工业自动化,2
-168,中控技术,2.3.3,协议转换,126,华为,2.3,边缘层,2
-147,拓邦股份,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,2
-64,中电智科,1.1.2,工业控制器,105,Intel,1.1,工业自动化,2
-149,天泽智云,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,2
-101,Analog Devices,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
-149,天泽智云,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,2
-62,云道智造,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,2
-22,航天云网,1.2.2,标识解析,126,华为,1.2,工业互联网网络,2
-49,数码大方,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,2
-62,云道智造,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,2
-106,阿里巴巴,1.3,工业软件,170,Pseudo1,1,供给,2
-22,航天云网,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,2
-150,唯捷创芯,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
-49,数码大方,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,2
-33,蓝谷信息,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,2
-22,航天云网,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,2
-79,PTC,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,2
-150,唯捷创芯,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,2
-13,东方国信,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,2
-82,Uptake,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,2
-38,牛刀,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,2
-26,寄云科技,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,2
-13,东方国信,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,2
-13,东方国信,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,2
-9,北京航天测控,1.3.3.6,运维保障系统MRO,97,General Electric,1.3.3,生产制造,2
-42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,39,Autodesk,1.3.1,设计研发,2
-26,寄云科技,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,2
-13,东方国信,2.1.3.7,制造类API,148,腾讯,2.1.3,工业物联网,2
-13,东方国信,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,2
-127,华为海思,1.1.3,工业服务器,126,华为,1.1,工业自动化,2
-24,华大电子,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
-90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,99,Siemens,1.3.1,设计研发,2
-79,PTC,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,2
-44,圣邦微电子,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
-127,华为海思,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
-13,东方国信,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,2
-111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,2
-31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,2
-46,适创科技,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,2
-29,京东工业品,1.3,工业软件,170,Pseudo1,1,供给,2
+96,Cisco,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,3
+117,格创东智,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,3
+161,研华科技,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,3
+53,天融信,1.4.1.4,入侵检测系统,126,华为,1.4,工业互联网安全,3
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,106,阿里巴巴,1.3,工业软件,3
+16,东土科技,2.3.1,工业数据接入,126,华为,2.3,边缘层,3
+23,和利时,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,3
+165,智能云科,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,3
+57,亚控科技,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,3
+150,唯捷创芯,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,3
+82,Uptake,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,3
+127,华为海思,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,3
+152,卫士通,1.4.4.1,身份鉴别与访问控制,170,Pseudo1,1,供给,3
+89,Rockwell,1.2.1,网络互联,126,华为,1.2,工业互联网网络,3
+60,宇动源,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,3
+38,牛刀,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,3
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,106,阿里巴巴,1.3,工业软件,3
+150,唯捷创芯,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,3
+60,宇动源,2.1.1.3,流程开发工具,106,阿里巴巴,2.1.1,开发工具,3
+150,唯捷创芯,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,3
+38,牛刀,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,3
+41,启明星辰,1.4.1.5,统一威胁管理系统,170,Pseudo1,1,供给,3
+113,飞腾信息,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,3
+129,华中数控,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,3
+149,天泽智云,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,3
+149,天泽智云,2.1.2.4,行业机理模型,81,SAP,2.1.2,工业模型库,3
+149,天泽智云,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,3
+69,紫光集团,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,3
+69,紫光集团,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,3
+22,航天云网,1.2.2,标识解析,126,华为,1.2,工业互联网网络,3
+22,航天云网,1.2.1,网络互联,126,华为,1.2,工业互联网网络,3
+36,龙芯中科,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,3
+41,启明星辰,1.4.1.5,统一威胁管理系统,126,华为,1.4,工业互联网安全,3
+27,江南天安,1.4.4.2,密钥管理,170,Pseudo1,1,供给,3
+78,OutSystems,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,3
+106,阿里巴巴,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,3
+58,用友,1.3.4.3,人力资源管理HRM,29,京东工业品,1.3,工业软件,3
+58,用友,1.3.4.2,客户关系管理CRM,106,阿里巴巴,1.3,工业软件,3
+144,树根互联,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,3
+144,树根互联,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,3
+78,OutSystems,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,3
+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,3
+161,研华科技,2.3.3,协议转换,99,Siemens,2.3,边缘层,3
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,106,阿里巴巴,1.3,工业软件,3
+104,Infineon,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,3
+154,西格数据,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,3
+144,树根互联,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,3
+13,东方国信,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,3
+22,航天云网,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,3
+130,金蝶,1.3.4.2,客户关系管理CRM,170,Pseudo1,1,供给,3
+13,东方国信,2.3.1,工业数据接入,126,华为,2.3,边缘层,3
+13,东方国信,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,3
+87,Texas Instruments,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
 87,Texas Instruments,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
-13,东方国信,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,2
-3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,2
-88,HPE,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,2
-13,东方国信,2.1.3.3,工业引擎服务,106,阿里巴巴,2.1.3,工业物联网,2
-4,爱创科技,1.2.2,标识解析,97,General Electric,1.2,工业互联网网络,2
-13,东方国信,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,2
-82,Uptake,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,2
-113,飞腾信息,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
-89,Rockwell,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,2
-13,东方国信,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,2
-13,东方国信,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,2
-13,东方国信,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,2
-129,华中数控,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,2
-79,PTC,2.3.1,工业数据接入,126,华为,2.3,边缘层,2
-127,华为海思,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
-79,PTC,2.3.1,工业数据接入,124,海尔,2.3,边缘层,2
-123,海得控制,1.1.2,工业控制器,126,华为,1.1,工业自动化,2
-131,九物互联,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,2
-38,牛刀,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,2
-79,PTC,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,2
-23,和利时,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,2
-99,Siemens,1.1.2,工业控制器,105,Intel,1.1,工业自动化,2
-23,和利时,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,2
-120,广州数控,1.2.3,数据互通,126,华为,1.2,工业互联网网络,2
-99,Siemens,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,2
-35,凌昊智能,1.1.3,工业服务器,105,Intel,1.1,工业自动化,2
-135,浪潮,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,2
-113,飞腾信息,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,2
-79,PTC,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,2
-34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,2
-46,适创科技,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,2
-117,格创东智,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,2
-79,PTC,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,2
-13,东方国信,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,2
-23,和利时,2.3.2,边缘数据处理,155,小米,2.3,边缘层,2
-95,Schneider,1.2.3,数据互通,126,华为,1.2,工业互联网网络,2
-79,PTC,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,2
-95,Schneider,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,2
-38,牛刀,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,2
-79,PTC,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,2
-79,PTC,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,2
-79,PTC,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,2
-109,宝信软件,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,1
-104,Infineon,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
-49,数码大方,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,1
-36,龙芯中科,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+93,Cadence,1.3.1,设计研发,170,Pseudo1,1,供给,2
+112,东华测试,1.3.3.7,故障预测与健康管理PHM,29,京东工业品,1.3,工业软件,2
+85,Dassault,1.3.1,设计研发,170,Pseudo1,1,供给,2
+104,Infineon,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+88,HPE,1.1.3,工业服务器,170,Pseudo1,1,供给,2
+85,Dassault,2.1.1,开发工具,98,Microsoft Azure,2,工业互联网平台,2
+85,Dassault,2.1.1,开发工具,170,Pseudo1,1,供给,2
+97,General Electric,1.3.3,生产制造,170,Pseudo1,1,供给,2
+117,格创东智,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,2
+100,Synopsys,1.3.1,设计研发,170,Pseudo1,1,供给,2
+82,Uptake,2.1.2.4,行业机理模型,81,SAP,2.1.2,工业模型库,2
+107,安恒信息,1.4.3.3,APT检测,126,华为,1.4,工业互联网安全,2
+99,Siemens,1.3.1,设计研发,170,Pseudo1,1,供给,2
+96,Cisco,1.2.3,数据互通,170,Pseudo1,1,供给,2
+82,Uptake,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,2
+89,Rockwell,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,2
+168,中控技术,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,2
+169,中芯国际,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,2
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,106,阿里巴巴,1.3,工业软件,2
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,170,Pseudo1,1,供给,2
+107,安恒信息,1.4.3.3,APT检测,170,Pseudo1,1,供给,2
+169,中芯国际,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,2
+101,Analog Devices,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,2
+85,Dassault,2.1.1,开发工具,102,Amazon AWS,2,工业互联网平台,2
+3,艾克斯特,1.3.1.5,产品数据管理PDM,106,阿里巴巴,1.3,工业软件,2
+6,安世亚太,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,2
+15,东软集团,1.3.3.5,企业资产管理系统EAM,29,京东工业品,1.3,工业软件,2
+39,Autodesk,1.3.1,设计研发,170,Pseudo1,1,供给,2
+58,用友,1.3.4.2,客户关系管理CRM,170,Pseudo1,1,供给,2
+26,寄云科技,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,2
+149,天泽智云,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,2
+37,绿盟,1.4.1.4,入侵检测系统,170,Pseudo1,1,供给,2
+3,艾克斯特,1.3.1.5,产品数据管理PDM,29,京东工业品,1.3,工业软件,2
+140,山石网科,1.4.5.3,数据审计系统,126,华为,1.4,工业互联网安全,2
+74,HoneyWell,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,2
+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,106,阿里巴巴,1.3,工业软件,2
+140,山石网科,1.4.5.3,数据审计系统,170,Pseudo1,1,供给,2
+13,东方国信,2.1.3.1,物联网服务,99,Siemens,2.1,PaaS,2
+24,华大电子,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
+43,神舟软件,1.3.1.5,产品数据管理PDM,29,京东工业品,1.3,工业软件,2
+69,紫光集团,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+13,东方国信,2.1.3.3,工业引擎服务,99,Siemens,2.1,PaaS,2
+56,芯愿景,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
+37,绿盟,1.4.1.4,入侵检测系统,126,华为,1.4,工业互联网安全,2
+37,绿盟,1.4.1.2,下一代防火墙,170,Pseudo1,1,供给,2
+56,芯愿景,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
+56,芯愿景,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,2
+33,蓝谷信息,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,2
+37,绿盟,1.4.1.2,下一代防火墙,126,华为,1.4,工业互联网安全,2
+14,东华软件,1.3.4.3,人力资源管理HRM,106,阿里巴巴,1.3,工业软件,2
+131,九物互联,2.1.1.1,算法建模工具,80,Salesforce,2.1.1,开发工具,2
+79,PTC,2.1.2,工业模型库,102,Amazon AWS,2,工业互联网平台,2
+58,用友,1.3.4.3,人力资源管理HRM,106,阿里巴巴,1.3,工业软件,2
+79,PTC,2.1.4.2,工业大数据管理,98,Microsoft Azure,2,工业互联网平台,2
+79,PTC,2.1.4.2,工业大数据管理,170,Pseudo1,1,供给,2
+117,格创东智,2.1.1.3,流程开发工具,106,阿里巴巴,2.1.1,开发工具,2
+79,PTC,2.1.4.2,工业大数据管理,102,Amazon AWS,2,工业互联网平台,2
+117,格创东智,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,2
+146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,106,阿里巴巴,1.3,工业软件,2
+49,数码大方,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,2
+50,索为系统,1.3.1.5,产品数据管理PDM,106,阿里巴巴,1.3,工业软件,2
+49,数码大方,1.3.1.6,产品生命周期管理PLM,29,京东工业品,1.3,工业软件,2
+23,和利时,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,2
+79,PTC,2.1.3.1,物联网服务,99,Siemens,2.1,PaaS,2
+6,安世亚太,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,2
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,170,Pseudo1,1,供给,2
+79,PTC,2.1.2,工业模型库,170,Pseudo1,1,供给,2
+79,PTC,2.1.2,工业模型库,98,Microsoft Azure,2,工业互联网平台,2
+24,华大电子,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+124,海尔,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,2
+43,神舟软件,1.3.1.5,产品数据管理PDM,106,阿里巴巴,1.3,工业软件,1
 108,百度,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,1
-49,数码大方,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
-35,凌昊智能,1.1.3,工业服务器,126,华为,1.1,工业自动化,1
-49,数码大方,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
-104,Infineon,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
-56,芯愿景,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
-38,牛刀,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,1
-39,Autodesk,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,1
-36,龙芯中科,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
-36,龙芯中科,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
-103,STMicroelectronics ,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
-38,牛刀,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,1
-35,凌昊智能,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
-38,牛刀,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
-50,索为系统,1.3.1.5,产品数据管理PDM,85,Dassault,1.3.1,设计研发,1
-38,牛刀,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
-50,索为系统,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,1
-46,适创科技,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,1
-39,Autodesk,1.3.1,设计研发,29,京东工业品,1.3,工业软件,1
-45,石化盈科,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,1
-45,石化盈科,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,1
-45,石化盈科,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,1
-47,首自信,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,1
-47,首自信,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
-45,石化盈科,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,1
-45,石化盈科,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,1
-47,首自信,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,1
-47,首自信,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
-47,首自信,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
-47,首自信,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
-47,首自信,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,1
-47,首自信,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
-45,石化盈科,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,1
-47,首自信,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,1
-47,首自信,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,1
-47,首自信,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,1
-45,石化盈科,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,1
-46,适创科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,1
-47,首自信,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,1
-44,圣邦微电子,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
-49,数码大方,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
-49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,1
-4,爱创科技,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,1
-49,数码大方,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
-111,鼎捷软件,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,1
-49,数码大方,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,1
-111,鼎捷软件,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,1
-111,鼎捷软件,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,1
-49,数码大方,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,1
-49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
-111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,1
-47,首自信,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
-49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,1
-42,山大华天,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,1
-42,山大华天,1.3.1.3,计算机辅助制造CAM,99,Siemens,1.3.1,设计研发,1
-42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,1
-42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
-47,首自信,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,1
-43,神舟软件,1.3.1.5,产品数据管理PDM,93,Cadence,1.3.1,设计研发,1
-43,神舟软件,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,1
-56,芯愿景,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
-119,广联达,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,1
-56,芯愿景,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
-82,Uptake,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,1
-81,SAP,2.1.4.2,工业大数据管理,115,富士康,2.1.4,工业大数据,1
-81,SAP,2.1.4.2,工业大数据管理,102,Amazon AWS,2.1.4,工业大数据,1
-81,SAP,2.1.4.1,工业大数据存储,102,Amazon AWS,2.1.4,工业大数据,1
-80,Salesforce,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,1
-79,PTC,2.3.3,协议转换,95,Schneider,2.3,边缘层,1
-79,PTC,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
-79,PTC,2.3.3,协议转换,155,小米,2.3,边缘层,1
-79,PTC,2.3.3,协议转换,102,Amazon AWS,2,工业互联网平台,1
-79,PTC,2.3.2,边缘数据处理,126,华为,2.3,边缘层,1
-79,PTC,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,1
-79,PTC,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
-79,PTC,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
-79,PTC,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,1
-79,PTC,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
-79,PTC,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,1
-79,PTC,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,1
-79,PTC,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,1
-79,PTC,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,1
-79,PTC,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,1
-79,PTC,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,1
-79,PTC,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,1
-79,PTC,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,1
-79,PTC,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,1
-79,PTC,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,1
-79,PTC,2.1.3.2,平台基础服务,74,HoneyWell,2.1.3,工业物联网,1
-79,PTC,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,1
-79,PTC,1.3.1.6,产品生命周期管理PLM,93,Cadence,1.3.1,设计研发,1
-81,SAP,2.1.4.2,工业大数据管理,84,Bosch,2.1.4,工业大数据,1
-82,Uptake,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
-79,PTC,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,1
-82,Uptake,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
-10,北京英贝思,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,1
-10,北京英贝思,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,1
-99,Siemens,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
-100,Synopsys,1.3.1,设计研发,29,京东工业品,1.3,工业软件,1
-97,General Electric,1.3.3,生产制造,29,京东工业品,1.3,工业软件,1
-96,Cisco,1.2.3,数据互通,126,华为,1.2,工业互联网网络,1
-95,Schneider,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,1
-93,Cadence,1.3.1,设计研发,29,京东工业品,1.3,工业软件,1
-93,Cadence,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,1
-92,Omron,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,1
-91,Moxa,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
-90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,1
-9,北京航天测控,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,1
-89,Rockwell,1.3.3.1,制造执行系统MES,75,IBM,1.3.3,生产制造,1
-89,Rockwell,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,1
-89,Rockwell,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
-89,Rockwell,1.1.2,工业控制器,126,华为,1.1,工业自动化,1
-88,HPE,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
-87,Texas Instruments,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
-85,Dassault,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,1
-83,Emerson,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,1
-83,Emerson,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,1
-82,Uptake,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
-82,Uptake,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
-82,Uptake,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,1
-82,Uptake,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,1
-82,Uptake,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,1
-79,PTC,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,1
-79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
-56,芯愿景,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
-62,云道智造,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,1
-62,云道智造,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,1
-62,云道智造,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,1
-62,云道智造,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
-60,宇动源,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
-60,宇动源,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,1
-60,宇动源,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,1
-60,宇动源,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
-60,宇动源,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,1
-60,宇动源,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,1
-60,宇动源,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,1
-6,安世亚太,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,1
-6,安世亚太,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
-6,安世亚太,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
-6,安世亚太,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,1
-6,安世亚太,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,1
-6,安世亚太,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
-103,STMicroelectronics ,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
-58,用友,1.3.2,采购供应,170,Pseudo1,1,供给,1
-58,用友,1.2.2,标识解析,67,中国移动,1.2,工业互联网网络,1
-58,用友,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,1
-57,亚控科技,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
-57,亚控科技,2.3.2,边缘数据处理,126,华为,2.3,边缘层,1
-57,亚控科技,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
-57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,1
-57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,1
-56,芯愿景,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,1
-56,芯愿景,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,1
-62,云道智造,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,1
-62,云道智造,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
-79,PTC,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,1
-62,云道智造,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,1
-79,PTC,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,1
-79,PTC,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
-78,OutSystems,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,1
-78,OutSystems,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,1
-78,OutSystems,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,1
-78,OutSystems,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,1
-78,OutSystems,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
-77,Oracle,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,1
-77,Oracle,1.3.3.6,运维保障系统MRO,97,General Electric,1.3.3,生产制造,1
-72,ANSYS,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
-71,Altair,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,1
-71,Altair,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
-70,ABB,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,1
-69,紫光集团,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
-69,紫光集团,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
-68,中望软件,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,1
-68,中望软件,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,1
-68,中望软件,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,1
-68,中望软件,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
-66,中国联通,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
-65,中国电信,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
-65,中国电信,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
-65,中国电信,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1
-64,中电智科,1.1.2,工业控制器,126,华为,1.1,工业自动化,1
-101,Analog Devices,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
-62,云道智造,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
-62,云道智造,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,1
-33,蓝谷信息,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
-23,和利时,2.3.3,协议转换,99,Siemens,2.3,边缘层,1
-33,蓝谷信息,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
-137,美林数据,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,1
-135,浪潮,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,1
-135,浪潮,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,1
-135,浪潮,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,1
-135,浪潮,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
-135,浪潮,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,1
-135,浪潮,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,1
-137,美林数据,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,1
-135,浪潮,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,1
-139,容知日新,1.3.3.7,故障预测与健康管理PHM,99,Siemens,1.3.3,生产制造,1
-14,东华软件,1.3.4.3,人力资源管理HRM,80,Salesforce,1.3.4,企业运营管理,1
-117,格创东智,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,1
-117,格创东智,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
-141,上海新华控制,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,1
-141,上海新华控制,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,1
-135,浪潮,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,1
-135,浪潮,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,1
-116,概伦电子,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,1
-117,格创东智,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,1
-131,九物互联,2.1.1.4,组态建模工具,106,阿里巴巴,2.1.1,开发工具,1
-132,科远智慧,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,1
-133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,40,奇安信,1.4.4,平台安全,1
-134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,1
-134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,1
-135,浪潮,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
-117,格创东智,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
-135,浪潮,2.1.3.4,应用管理服务,106,阿里巴巴,2.1.3,工业物联网,1
-135,浪潮,1.3.2.1,供应链管理SCM,170,Pseudo1,1,供给,1
-117,格创东智,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,1
-135,浪潮,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,1
-135,浪潮,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,1
-135,浪潮,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,1
-135,浪潮,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,1
-117,格创东智,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,1
-116,概伦电子,1.3.1.7,电子设计自动化EDA,39,Autodesk,1.3.1,设计研发,1
-150,唯捷创芯,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
-149,天泽智云,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,1
-146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
-146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,1
-146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,1
-149,天泽智云,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,1
-149,天泽智云,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
-149,天泽智云,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
-149,天泽智云,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,1
-145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,1
-149,天泽智云,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
-149,天泽智云,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
-149,天泽智云,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,1
-149,天泽智云,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
-15,东软集团,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,1
-15,东软集团,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,1
-145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,1
-144,树根互联,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
-143,沈阳自动化研究所,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
-143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
-144,树根互联,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
-144,树根互联,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,1
-144,树根互联,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
-144,树根互联,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,1
-144,树根互联,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
-144,树根互联,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,1
-144,树根互联,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,1
-144,树根互联,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
-131,九物互联,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,1
-131,九物互联,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,1
-131,九物互联,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,1
-13,东方国信,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,1
-128,华伍股份,1.1.2,工业控制器,126,华为,1.1,工业自动化,1
-129,华中数控,1.1.2,工业控制器,105,Intel,1.1,工业自动化,1
-129,华中数控,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,1
-129,华中数控,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,1
-13,东方国信,1.2.2,标识解析,126,华为,1.2,工业互联网网络,1
-13,东方国信,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,1
-13,东方国信,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,1
-127,华为海思,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
-13,东方国信,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,1
-13,东方国信,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,1
-13,东方国信,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,1
-13,东方国信,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,1
-13,东方国信,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,1
-13,东方国信,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,1
-128,华伍股份,1.1.2,工业控制器,106,阿里巴巴,1.1,工业自动化,1
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,106,阿里巴巴,1.3,工业软件,1
+13,东方国信,2.3.2,边缘数据处理,98,Microsoft Azure,2,工业互联网平台,1
+44,圣邦微电子,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,106,阿里巴巴,1.3,工业软件,1
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,29,京东工业品,1.3,工业软件,1
+143,沈阳自动化研究所,2.1.1.3,流程开发工具,99,Siemens,2.1,PaaS,1
+58,用友,1.3.1.6,产品生命周期管理PLM,29,京东工业品,1.3,工业软件,1
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,106,阿里巴巴,1.3,工业软件,1
+58,用友,2.1.2,工业模型库,98,Microsoft Azure,2,工业互联网平台,1
+43,神舟软件,1.3.1.5,产品数据管理PDM,170,Pseudo1,1,供给,1
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,106,阿里巴巴,1.3,工业软件,1
+168,中控技术,2.3.1,工业数据接入,98,Microsoft Azure,2,工业互联网平台,1
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,29,京东工业品,1.3,工业软件,1
+42,山大华天,1.3.1.3,计算机辅助制造CAM,106,阿里巴巴,1.3,工业软件,1
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,29,京东工业品,1.3,工业软件,1
+58,用友,2.1.2,工业模型库,102,Amazon AWS,2,工业互联网平台,1
+42,山大华天,1.3.1.3,计算机辅助制造CAM,29,京东工业品,1.3,工业软件,1
+22,航天云网,2.1.4.1.2,分布式数据库,102,Amazon AWS,2.1.4,工业大数据,1
+95,Schneider,1.2.3,数据互通,170,Pseudo1,1,供给,1
+56,芯愿景,1.3.1.7,电子设计自动化EDA,106,阿里巴巴,1.3,工业软件,1
+135,浪潮,2.1.3.7,制造类API,99,Siemens,2.1,PaaS,1
+50,索为系统,1.3.1.5,产品数据管理PDM,170,Pseudo1,1,供给,1
+135,浪潮,2.1.3.3,工业引擎服务,99,Siemens,2.1,PaaS,1
+167,中环股份,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+49,数码大方,1.3.1.6,产品生命周期管理PLM,106,阿里巴巴,1.3,工业软件,1
+53,天融信,1.4.3.3,APT检测,126,华为,1.4,工业互联网安全,1
+138,启明信息,1.3.1.5,产品数据管理PDM,106,阿里巴巴,1.3,工业软件,1
+53,天融信,1.4.3.3,APT检测,170,Pseudo1,1,供给,1
+99,Siemens,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
+135,浪潮,1.1.3,工业服务器,170,Pseudo1,1,供给,1
+14,东华软件,1.3.4.3,人力资源管理HRM,170,Pseudo1,1,供给,1
+14,东华软件,1.3.4.3,人力资源管理HRM,29,京东工业品,1.3,工业软件,1
+53,天融信,1.4.5.3,数据审计系统,126,华为,1.4,工业互联网安全,1
+53,天融信,1.4.5.3,数据审计系统,170,Pseudo1,1,供给,1
+140,山石网科,1.4.1.4,入侵检测系统,126,华为,1.4,工业互联网安全,1
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,29,京东工业品,1.3,工业软件,1
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,106,阿里巴巴,1.3,工业软件,1
+140,山石网科,1.4.1.4,入侵检测系统,170,Pseudo1,1,供给,1
+140,山石网科,1.4.1.5,统一威胁管理系统,126,华为,1.4,工业互联网安全,1
+103,STMicroelectronics ,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
+140,山石网科,1.4.1.5,统一威胁管理系统,170,Pseudo1,1,供给,1
+41,启明星辰,1.4.1.2,下一代防火墙,170,Pseudo1,1,供给,1
+46,适创科技,1.3.1.2,计算机辅助工程CAE,29,京东工业品,1.3,工业软件,1
+46,适创科技,1.3.1.2,计算机辅助工程CAE,106,阿里巴巴,1.3,工业软件,1
+131,九物互联,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
+96,Cisco,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1
+56,芯愿景,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
+168,中控技术,1.3.3.1,制造执行系统MES,29,京东工业品,1.3,工业软件,1
+89,Rockwell,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,1
+41,启明星辰,1.4.1.2,下一代防火墙,126,华为,1.4,工业互联网安全,1
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,106,阿里巴巴,1.3,工业软件,1
+22,航天云网,1.3.3.6,运维保障系统MRO,29,京东工业品,1.3,工业软件,1
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,29,京东工业品,1.3,工业软件,1
+113,飞腾信息,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
+5,安华金和,1.4.5.3,数据审计系统,170,Pseudo1,1,供给,1
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,29,京东工业品,1.3,工业软件,1
+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,106,阿里巴巴,1.3,工业软件,1
+77,Oracle,1.3.3.6,运维保障系统MRO,106,阿里巴巴,1.3,工业软件,1
 127,华为海思,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
-117,格创东智,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,1
-123,海得控制,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,1
-119,广联达,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,1
-12,大唐软件,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1
-12,大唐软件,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
-12,大唐软件,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,1
-120,广州数控,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,1
-123,海得控制,1.1.2,工业控制器,105,Intel,1.1,工业自动化,1
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,106,阿里巴巴,1.3,工业软件,1
+78,OutSystems,2.1.1.4,组态建模工具,99,Siemens,2.1,PaaS,1
+78,OutSystems,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,1
+26,寄云科技,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,1
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,29,京东工业品,1.3,工业软件,1
+22,航天云网,2.1.1.3,流程开发工具,99,Siemens,2.1,PaaS,1
+124,海尔,2.3,边缘层,170,Pseudo1,1,供给,1
+79,PTC,1.3.1.6,产品生命周期管理PLM,29,京东工业品,1.3,工业软件,1
+25,华大九天,1.3.1.7,电子设计自动化EDA,106,阿里巴巴,1.3,工业软件,1
 124,海尔,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1
-126,华为,2.3,边缘层,102,Amazon AWS,2,工业互联网平台,1
-124,海尔,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
-124,海尔,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,1
-125,华数机器人,1.2.3,数据互通,126,华为,1.2,工业互联网网络,1
-118,工邦邦,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,1
-126,华为,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,1
-126,华为,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,1
-13,东方国信,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,1
-13,东方国信,2.1.3.3,工业引擎服务,97,General Electric,2.1.3,工业物联网,1
-13,东方国信,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,1
-13,东方国信,2.3.3,协议转换,124,海尔,2.3,边缘层,1
-13,东方国信,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,1
-13,东方国信,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
-13,东方国信,2.3.2,边缘数据处理,126,华为,2.3,边缘层,1
-13,东方国信,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
-13,东方国信,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,1
-13,东方国信,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
-13,东方国信,2.3.3,协议转换,155,小米,2.3,边缘层,1
-13,东方国信,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,1
-117,格创东智,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,1
-130,金蝶,1.3.2,采购供应,170,Pseudo1,1,供给,1
-117,格创东智,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,1
-130,金蝶,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,1
-117,格创东智,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,1
-130,金蝶,1.3.5,仓储物流,170,Pseudo1,1,供给,1
-13,东方国信,2.3.1,工业数据接入,155,小米,2.3,边缘层,1
-13,东方国信,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
-13,东方国信,2.3.1,工业数据接入,124,海尔,2.3,边缘层,1
-13,东方国信,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,1
-13,东方国信,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,1
-13,东方国信,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,1
-13,东方国信,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,1
-13,东方国信,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,1
-13,东方国信,2.1.3.7,制造类API,126,华为,2.1.3,工业物联网,1
-13,东方国信,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,1
-13,东方国信,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
-13,东方国信,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
-13,东方国信,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,1
-13,东方国信,2.1.3.5,容器服务,73,FANUC,2.1.3,工业物联网,1
-13,东方国信,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,1
-13,东方国信,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,1
-13,东方国信,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,1
-150,唯捷创芯,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
-153,武汉开目,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
-33,蓝谷信息,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,1
-23,和利时,1.3.3.4,可编程逻揖控制系统PLC,99,Siemens,1.3.3,生产制造,1
-22,航天云网,2.3.3,协议转换,95,Schneider,2.3,边缘层,1
-23,和利时,1.3.3.1,制造执行系统MES,75,IBM,1.3.3,生产制造,1
-23,和利时,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,1
-23,和利时,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,1
-23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,1
-23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,1
-115,富士康,1.1.3,工业服务器,105,Intel,1.1,工业自动化,1
-22,航天云网,2.3.3,协议转换,126,华为,2.3,边缘层,1
-113,飞腾信息,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
-23,和利时,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
-23,和利时,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,1
-23,和利时,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
-23,和利时,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
-23,和利时,2.3.3,协议转换,124,海尔,2.3,边缘层,1
-1,51WORLD,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,1
-22,航天云网,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,1
-23,和利时,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
-22,航天云网,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,1
-22,航天云网,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,1
-22,航天云网,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,1
-22,航天云网,2.1.3.3,工业引擎服务,106,阿里巴巴,2.1.3,工业物联网,1
-22,航天云网,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,1
-22,航天云网,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,1
-22,航天云网,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,1
-22,航天云网,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
-22,航天云网,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
-22,航天云网,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
-22,航天云网,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,1
-22,航天云网,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
-22,航天云网,2.3.1,工业数据接入,155,小米,2.3,边缘层,1
-22,航天云网,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
-22,航天云网,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,1
-23,和利时,2.3.3,协议转换,126,华为,2.3,边缘层,1
-23,和利时,2.3.3,协议转换,95,Schneider,2.3,边缘层,1
-153,武汉开目,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,1
-31,昆仑数据,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,1
-3,艾克斯特,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,1
-3,艾克斯特,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,1
-3,艾克斯特,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,1
-3,艾克斯特,1.3.4.1,企业资源计划ERP,80,Salesforce,1.3.4,企业运营管理,1
-31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,1
-31,昆仑数据,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,1
-31,昆仑数据,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,1
-26,寄云科技,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
-32,兰光创新,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,1
-33,蓝谷信息,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,1
-33,蓝谷信息,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,1
-33,蓝谷信息,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,1
-33,蓝谷信息,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,1
-33,蓝谷信息,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,1
-3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
-26,寄云科技,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,1
-119,广联达,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,1
-26,寄云科技,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,1
-24,华大电子,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
-24,华大电子,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
-25,华大九天,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,1
-25,华大九天,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,1
-26,寄云科技,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,1
-26,寄云科技,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
-22,航天云网,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,1
-22,航天云网,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
-22,航天云网,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,1
-163,优也科技,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,1
-161,研华科技,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
-161,研华科技,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,1
-161,研华科技,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,1
-161,研华科技,2.3.3,协议转换,124,海尔,2.3,边缘层,1
-163,优也科技,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,1
-163,优也科技,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,1
-163,优也科技,2.1.4.2.2,数据安全管理,81,SAP,2.1.4.2,工业大数据管理,1
-161,研华科技,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
-164,震坤行,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,1
-164,震坤行,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,1
-165,智能云科,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,1
-165,智能云科,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
-165,智能云科,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,1
-165,智能云科,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,1
-161,研华科技,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
-161,研华科技,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
-22,航天云网,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
-116,概伦电子,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,1
-153,武汉开目,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,1
-153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,1
-154,西格数据,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,1
-154,西格数据,2.1.4.2.2,数据安全管理,81,SAP,2.1.4.2,工业大数据管理,1
-156,芯禾科技,1.3.1.7,电子设计自动化EDA,39,Autodesk,1.3.1,设计研发,1
-156,芯禾科技,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,1
-16,东土科技,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,1
-161,研华科技,2.3.1,工业数据接入,124,海尔,2.3,边缘层,1
-16,东土科技,2.3.1,工业数据接入,124,海尔,2.3,边缘层,1
-16,东土科技,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
-16,东土科技,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
-16,东土科技,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,1
-16,东土科技,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,1
-16,东土科技,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
-165,智能云科,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
-165,智能云科,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,1
-165,智能云科,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
-20,海基科技,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,1
-169,中芯国际,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
-169,中芯国际,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
-18,国能智深,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,1
-2,706所,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,1
-2,706所,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
-20,海基科技,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
-20,海基科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,1
-165,智能云科,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,1
-21,Hexagon,1.3.1.3,计算机辅助制造CAM,85,Dassault,1.3.1,设计研发,1
-22,航天云网,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,1
-22,航天云网,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,1
-22,航天云网,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,1
-22,航天云网,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,1
-22,航天云网,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,1
-168,中控技术,2.3.3,协议转换,99,Siemens,2.3,边缘层,1
-168,中控技术,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
-168,中控技术,2.3.3,协议转换,124,海尔,2.3,边缘层,1
-168,中控技术,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
-168,中控技术,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,1
-168,中控技术,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
-168,中控技术,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
-168,中控技术,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,1
-168,中控技术,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
-168,中控技术,2.3.1,工业数据接入,155,小米,2.3,边缘层,1
-168,中控技术,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,1
-168,中控技术,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,1
-168,中控技术,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,1
-168,中控技术,1.1.2,工业控制器,105,Intel,1.1,工业自动化,1
-167,中环股份,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
-166,中国电子科技网络信息安全,1.2.3,数据互通,106,阿里巴巴,1.2,工业互联网网络,1
-165,智能云科,2.1.2.4,行业机理模型,81,SAP,2.1.2,工业模型库,1
-22,航天云网,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
+24,华大电子,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
+158,信大捷安,1.4.4.1,身份鉴别与访问控制,126,华为,1.4,工业互联网安全,1
+116,概伦电子,1.3.1.7,电子设计自动化EDA,106,阿里巴巴,1.3,工业软件,1
+158,信大捷安,1.4.4.1,身份鉴别与访问控制,170,Pseudo1,1,供给,1
+159,徐工集团,2.1.2,工业模型库,102,Amazon AWS,2,工业互联网平台,1
+159,徐工集团,2.1.2,工业模型库,170,Pseudo1,1,供给,1
+159,徐工集团,2.1.2,工业模型库,98,Microsoft Azure,2,工业互联网平台,1
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,29,京东工业品,1.3,工业软件,1
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,106,阿里巴巴,1.3,工业软件,1
+23,和利时,1.1.2,工业控制器,126,华为,1.1,工业自动化,1
+16,东土科技,1.1.3,工业服务器,170,Pseudo1,1,供给,1
+22,航天云网,1.3.3.6,运维保障系统MRO,106,阿里巴巴,1.3,工业软件,1
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,106,阿里巴巴,1.3,工业软件,1
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,29,京东工业品,1.3,工业软件,1
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,106,阿里巴巴,1.3,工业软件,1
+40,奇安信,1.4.3.3,APT检测,170,Pseudo1,1,供给,1
+40,奇安信,1.4.3.3,APT检测,126,华为,1.4,工业互联网安全,1
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,170,Pseudo1,1,供给,1
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,29,京东工业品,1.3,工业软件,1
+4,爱创科技,1.2.2,标识解析,170,Pseudo1,1,供给,1
+89,Rockwell,1.3.3.1,制造执行系统MES,106,阿里巴巴,1.3,工业软件,1
+148,腾讯,2.1.1,开发工具,102,Amazon AWS,2,工业互联网平台,1
+148,腾讯,2.1.1,开发工具,170,Pseudo1,1,供给,1
+163,优也科技,2.1.4.1.3,实时数据库,84,Bosch,2.1.4,工业大数据,1
+22,航天云网,2.1.4.1.2,分布式数据库,84,Bosch,2.1.4,工业大数据,1
+148,腾讯,2.1.1,开发工具,98,Microsoft Azure,2,工业互联网平台,1
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,106,阿里巴巴,1.3,工业软件,1
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,29,京东工业品,1.3,工业软件,1
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,29,京东工业品,1.3,工业软件,1
+22,航天云网,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,1
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,106,阿里巴巴,1.3,工业软件,1
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,29,京东工业品,1.3,工业软件,1
+149,天泽智云,2.1.2.1,数据算法模型,99,Siemens,2.1,PaaS,1
+163,优也科技,2.1.4.1.3,实时数据库,115,富士康,2.1.4,工业大数据,1
+63,长扬科技,1.4.2.4,安全隔离与信息交换系统,126,华为,1.4,工业互联网安全,1
+13,东方国信,2.1.3.7,制造类API,99,Siemens,2.1,PaaS,1
+163,优也科技,2.1.4.1.3,实时数据库,102,Amazon AWS,2.1.4,工业大数据,1
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,106,阿里巴巴,1.3,工业软件,1
+65,中国电信,1.2.1,网络互联,170,Pseudo1,1,供给,1
+68,中望软件,1.3.1.1,计算机辅助设计CAD,29,京东工业品,1.3,工业软件,1
+32,兰光创新,1.2.3,数据互通,170,Pseudo1,1,供给,1
+68,中望软件,1.3.1.3,计算机辅助制造CAM,106,阿里巴巴,1.3,工业软件,1
+68,中望软件,1.3.1.3,计算机辅助制造CAM,29,京东工业品,1.3,工业软件,1
+5,安华金和,1.4.5.3,数据审计系统,126,华为,1.4,工业互联网安全,1
diff --git a/analysis/count_dcp_network.png b/analysis/count_dcp_network.png
new file mode 100644
index 0000000..da3864c
Binary files /dev/null and b/analysis/count_dcp_network.png differ
diff --git a/analysis/count_dcp_prod.csv b/analysis/count_dcp_prod.csv
index ab522b0..7475df0 100644
--- a/analysis/count_dcp_prod.csv
+++ b/analysis/count_dcp_prod.csv
@@ -1,98 +1,227 @@
 up_id_product,up_name_product,down_id_product,down_name_product,count
-1.4,工业互联网安全,1,供给,118
-1.4.3,网络安全,1.4,工业互联网安全,96
-1.4.5,数据安全,1.4,工业互联网安全,92
-1.4.2,控制安全,1.4,工业互联网安全,92
-2.1,PaaS,2,工业互联网平台,77
-1.4.4.5,安全态势感知,1.4.4,平台安全,76
-1.3.2.1,供应链管理SCM,1.3.2,采购供应,76
-1.3.2,采购供应,1.3,工业软件,74
-1.3.5,仓储物流,1.3,工业软件,72
-1.1.1,工业计算芯片,1.1,工业自动化,67
-1.4.5.8,数据加密,1.4.5,数据安全,50
-1.4.5.1,恶意代码检测系统,1.4.5,数据安全,50
+1.4,工业互联网安全,1,供给,926
+1.3,工业软件,1,供给,422
+2.1.3.5,容器服务,2.1.3,工业物联网,397
+2.1.3.4,应用管理服务,2.1.3,工业物联网,396
+2.1.3.3,工业引擎服务,2.1.3,工业物联网,396
+2.1.3.6,微服务,2.1.3,工业物联网,396
+2.1.3.1,物联网服务,2.1.3,工业物联网,390
+2.1.3.7,制造类API,2.1.3,工业物联网,390
+2.1.3.2,平台基础服务,2.1.3,工业物联网,389
+2.3.2,边缘数据处理,2.3,边缘层,332
+2.3.1,工业数据接入,2.3,边缘层,323
+1.3.1.2,计算机辅助工程CAE,1.3.1,设计研发,323
+2.3.3,协议转换,2.3,边缘层,321
+1.3.1.1,计算机辅助设计CAD,1.3.1,设计研发,317
+2.1.2.4,行业机理模型,2.1.2,工业模型库,307
+1.3.1.7,电子设计自动化EDA,1.3.1,设计研发,306
+1.3.1.5,产品数据管理PDM,1.3.1,设计研发,304
+2.1.2.3,研发仿真模型,2.1.2,工业模型库,303
+2.1.2.1,数据算法模型,2.1.2,工业模型库,302
+1.3.1.3,计算机辅助制造CAM,1.3.1,设计研发,302
+2.1.2.2,业务流程模型,2.1.2,工业模型库,301
+1.3.1.6,产品生命周期管理PLM,1.3.1,设计研发,301
+1.4.4,平台安全,1.4,工业互联网安全,301
+1.3.2,采购供应,1.3,工业软件,300
+1.3.1.4,计算机辅助工艺过程设计CAPP,1.3.1,设计研发,299
+1.4.5,数据安全,1,供给,290
+1.4.5,数据安全,1.4,工业互联网安全,290
+1.1.3,工业服务器,1.1,工业自动化,278
+1.1.2,工业控制器,1.1,工业自动化,276
+1.1.1,工业计算芯片,1.1,工业自动化,268
+1.3.1,设计研发,1.3,工业软件,241
+2,工业互联网平台,1,供给,237
+2.1.1.1,算法建模工具,2.1.1,开发工具,231
+1.2.3,数据互通,1.2,工业互联网网络,229
+2.1.1.2,低代码开发工具,2.1.1,开发工具,225
+1.2.2,标识解析,1.2,工业互联网网络,222
+2.1.1.4,组态建模工具,2.1.1,开发工具,221
+1.2.1,网络互联,1.2,工业互联网网络,218
+2.1.1.3,流程开发工具,2.1.1,开发工具,214
+2.1.1.5,数字孪生建模工具,2.1.1,开发工具,212
+1.4.3,网络安全,1.4,工业互联网安全,211
+1.4.3,网络安全,1,供给,211
+1.4.2,控制安全,1.4,工业互联网安全,205
+1.4.2,控制安全,1,供给,204
+1.4.4,平台安全,1,供给,199
+1.3.3.4,可编程逻揖控制系统PLC,1.3.3,生产制造,177
+1.3.3.1,制造执行系统MES,1.3.3,生产制造,176
+1.3.3.2,分布式控制系统DCS,1.3.3,生产制造,175
+1.3.3.5,企业资产管理系统EAM,1.3.3,生产制造,174
+1.3.3,生产制造,1.3,工业软件,174
+1.3.4.3,人力资源管理HRM,1.3.4,企业运营管理,173
+1.3.4.2,客户关系管理CRM,1.3.4,企业运营管理,171
+1.3.3.3,数据采集与监视控制系统SCADA,1.3.3,生产制造,170
+1.3.3.6,运维保障系统MRO,1.3.3,生产制造,169
+1.3.4,企业运营管理,1.3,工业软件,167
+1.3.3.7,故障预测与健康管理PHM,1.3.3,生产制造,166
+1.3.4.1,企业资源计划ERP,1.3.4,企业运营管理,166
+2.1.4.1,工业大数据存储,2.1.4,工业大数据,161
+2.1,PaaS,2,工业互联网平台,158
+2.1.4.2,工业大数据管理,2.1.4,工业大数据,156
+1.3.5,仓储物流,1.3,工业软件,150
+1.4.4.1,身份鉴别与访问控制,1.4.4,平台安全,147
+2.1.4,工业大数据,2.1,PaaS,139
+1.4.4.4,工业应用行为监控,1.4.4,平台安全,136
+1.4.4.2,密钥管理,1.4.4,平台安全,136
+1.4.4.3,接入认证,1.4.4,平台安全,136
+2.1.4.1.2,分布式数据库,2.1.4.1,工业大数据存储,130
+2.1.4.1.4,时序数据库,2.1.4.1,工业大数据存储,129
+2.1.4.2.1,数据质量管理,2.1.4.2,工业大数据管理,127
+2.1.4.2.2,数据安全管理,2.1.4.2,工业大数据管理,125
+2.1.4.1.3,实时数据库,2.1.4.1,工业大数据存储,124
+2.1.4.1.1,关系型数据库,2.1.4.1,工业大数据存储,122
+1.3.2,采购供应,1,供给,117
+2.3,边缘层,2,工业互联网平台,109
+1.4.4.5,安全态势感知,1.4.4,平台安全,100
+1.3.2.1,供应链管理SCM,1.3,工业软件,100
+1.3.2.1,供应链管理SCM,1.3.2,采购供应,100
+2.2,IaaS,2,工业互联网平台,91
+1.4.2.1,工控安全监测与审计,1.4.2,控制安全,90
+1.2,工业互联网网络,1,供给,90
+1.1,工业自动化,1,供给,89
+2.1.4,工业大数据,2,工业互联网平台,88
+2.1.3,工业物联网,2.1,PaaS,87
+1.4.2.4,安全隔离与信息交换系统,1.4.2,控制安全,85
+1.4.1.3,防毒墙,1.4.1,设备安全,83
+1.4.1.1,工业防火墙,1.4.1,设备安全,81
+2.1,PaaS,1,供给,77
+1.4.3.3,APT检测,1.4.3,网络安全,76
+1.3.5,仓储物流,1,供给,75
+1.4.5.4,数据脱敏,1.4.5,数据安全,70
+1.4.5.5,敏感数据发现与监控,1.4.5,数据安全,70
+1.4.5.3,数据审计系统,1.4.5,数据安全,70
+1.4.5.2,数据防泄漏系统,1.4.5,数据安全,70
+1.4.5.6,数据容灾备份,1.4.5,数据安全,70
+1.3.5.1,仓储物流管理WMS,1.3.5,仓储物流,70
+1.4.5.7,数据恢复,1.4.5,数据安全,70
+1.4.5.9,数据防火墙,1.4.5,数据安全,70
+1.4.1.2,下一代防火墙,1.4.1,设备安全,68
+1.4.3.5,负载均衡,1.4.3,网络安全,68
+1.4.3.4,攻击溯源,1.4.3,网络安全,68
+1.4.3.1,网络漏洞扫描和补丁管理,1.4.3,网络安全,68
+1.4.1.5,统一威胁管理系统,1.4.1,设备安全,68
+1.4.1.4,入侵检测系统,1.4.1,设备安全,68
+1.4.2.2,工控主机卫士,1.4.2,控制安全,68
+1.4.2.6,隐私计算,1.4.2,控制安全,68
+1.4.2.5,安全日志与审计,1.4.2,控制安全,68
+1.4.1,设备安全,1,供给,63
+1.4.1,设备安全,1.4,工业互联网安全,63
+2.1.1,开发工具,2.1,PaaS,61
+2.1.2,工业模型库,2.1,PaaS,59
+1.4.4.5,安全态势感知,1.4,工业互联网安全,50
+1.4.4.5,安全态势感知,1,供给,50
 1.4.3.6,沙箱类设备,1.4.3,网络安全,50
-1.4.2.7,工控原生安全,1.4.2,控制安全,50
+1.3.5.1,仓储物流管理WMS,1.3,工业软件,50
+1.4.2.3,工控漏洞扫描,1,供给,50
+1.4.2.3,工控漏洞扫描,1.4,工业互联网安全,50
+1.4.5.1,恶意代码检测系统,1,供给,50
 1.4.2.3,工控漏洞扫描,1.4.2,控制安全,50
-1.4.1,设备安全,1.4,工业互联网安全,50
+1.3.2.1,供应链管理SCM,1,供给,50
+1.4.5.1,恶意代码检测系统,1.4,工业互联网安全,50
+1.4.3.6,沙箱类设备,1.4,工业互联网安全,50
+1.4.5.8,数据加密,1,供给,50
+1.4.2.7,工控原生安全,1.4.2,控制安全,50
+1.4.2.7,工控原生安全,1.4,工业互联网安全,50
+1.4.3.2,流量检测,1,供给,50
+1.4.3.2,流量检测,1.4,工业互联网安全,50
 1.4.3.2,流量检测,1.4.3,网络安全,50
-2.3.3,协议转换,2.3,边缘层,37
-1.3.2.1,供应链管理SCM,1.3,工业软件,37
-2.3.1,工业数据接入,2.3,边缘层,33
-2.1.3.6,微服务,2.1.3,工业物联网,33
-2.3.2,边缘数据处理,2.3,边缘层,30
-2.1.3.4,应用管理服务,2.1.3,工业物联网,30
-2.1.2.4,行业机理模型,2.1.2,工业模型库,30
-2.1.2.2,业务流程模型,2.1.2,工业模型库,28
-2.1.3.7,制造类API,2.1.3,工业物联网,28
-1.3.1.1,计算机辅助设计CAD,1.3.1,设计研发,28
-2.1.2.1,数据算法模型,2.1.2,工业模型库,27
-1.3.1.2,计算机辅助工程CAE,1.3.1,设计研发,26
-2.1.3.1,物联网服务,2.1.3,工业物联网,25
-1.1.2,工业控制器,1.1,工业自动化,24
-2.1.3.5,容器服务,2.1.3,工业物联网,24
-1.4.3.6,沙箱类设备,1.4,工业互联网安全,23
-2.1.1.2,低代码开发工具,2.1.1,开发工具,23
-1.1.3,工业服务器,1.1,工业自动化,23
-1.4.3.2,流量检测,1.4,工业互联网安全,23
-2.1.3.3,工业引擎服务,2.1.3,工业物联网,23
-1.4.5.1,恶意代码检测系统,1.4,工业互联网安全,21
-1.4.5.8,数据加密,1.4,工业互联网安全,21
-1.4.2.3,工控漏洞扫描,1.4,工业互联网安全,21
-2.1.3.2,平台基础服务,2.1.3,工业物联网,21
-1.4.2.7,工控原生安全,1.4,工业互联网安全,21
-1.4.3,网络安全,1,供给,21
-1.3.1.4,计算机辅助工艺过程设计CAPP,1.3.1,设计研发,20
-1.4.5,数据安全,1,供给,19
-1.4.2,控制安全,1,供给,19
-2.1.2.3,研发仿真模型,2.1.2,工业模型库,18
-2.1.1.5,数字孪生建模工具,2.1.1,开发工具,18
-1.3.1.6,产品生命周期管理PLM,1.3.1,设计研发,18
-1.2.3,数据互通,1.2,工业互联网网络,17
-2.1.1.1,算法建模工具,2.1.1,开发工具,15
-2.1.1.4,组态建模工具,2.1.1,开发工具,14
-1.3.3.2,分布式控制系统DCS,1.3.3,生产制造,14
-1.2.2,标识解析,1.2,工业互联网网络,13
-1.2.1,网络互联,1.2,工业互联网网络,13
-2.1.1.3,流程开发工具,2.1.1,开发工具,12
-1.3.1.7,电子设计自动化EDA,1.3.1,设计研发,12
-1.3.3.3,数据采集与监视控制系统SCADA,1.3.3,生产制造,11
-2,工业互联网平台,1,供给,10
-1.3.3.6,运维保障系统MRO,1.3.3,生产制造,10
-1.3.3.1,制造执行系统MES,1.3.3,生产制造,10
-1.4.4,平台安全,1.4,工业互联网安全,10
-1.4.1,设备安全,1,供给,9
-1.3.1,设计研发,1.3,工业软件,8
-1.3.3.4,可编程逻揖控制系统PLC,1.3.3,生产制造,7
-1.3.4.1,企业资源计划ERP,1.3.4,企业运营管理,6
-1.3.3.5,企业资产管理系统EAM,1.3.3,生产制造,6
-1.4.3.6,沙箱类设备,1,供给,6
-1.4.3.2,流量检测,1,供给,6
-1.4.5.1,恶意代码检测系统,1,供给,5
-1.4.4.5,安全态势感知,1.4,工业互联网安全,5
-2.1,PaaS,1,供给,5
-1.4.2.7,工控原生安全,1,供给,5
-1.3.1.5,产品数据管理PDM,1.3.1,设计研发,5
-1.4.5.8,数据加密,1,供给,5
-1.4.2.3,工控漏洞扫描,1,供给,5
-2.1.4.2.2,数据安全管理,2.1.4.2,工业大数据管理,5
-2.1.4.2.1,数据质量管理,2.1.4.2,工业大数据管理,5
-1.3,工业软件,1,供给,4
-2.1.4.1.4,时序数据库,2.1.4.1,工业大数据存储,4
-2.3,边缘层,2,工业互联网平台,3
-2.2,IaaS,2,工业互联网平台,3
-2.1.4.1.1,关系型数据库,2.1.4.1,工业大数据存储,3
-2.1.4.1.2,分布式数据库,2.1.4.1,工业大数据存储,3
-2.1.4.1.3,实时数据库,2.1.4.1,工业大数据存储,3
-2.1.4.2,工业大数据管理,2.1.4,工业大数据,3
-1.3.1.3,计算机辅助制造CAM,1.3.1,设计研发,2
-1.3.2,采购供应,1,供给,2
-1.3.3,生产制造,1.3,工业软件,1
-2.3.3,协议转换,2,工业互联网平台,1
-1.3.4.3,人力资源管理HRM,1.3.4,企业运营管理,1
-2.1.4.1,工业大数据存储,2.1.4,工业大数据,1
-1.3.3.7,故障预测与健康管理PHM,1.3.3,生产制造,1
-1.3.4,企业运营管理,1.3,工业软件,1
-1.3.5,仓储物流,1,供给,1
-1.4.4.1,身份鉴别与访问控制,1.4.4,平台安全,1
-1.3.2.1,供应链管理SCM,1,供给,1
+1.4.5.8,数据加密,1.4.5,数据安全,50
+1.4.5.8,数据加密,1.4,工业互联网安全,50
+1.4.3.6,沙箱类设备,1,供给,50
+1.4.5.1,恶意代码检测系统,1.4.5,数据安全,50
+1.4.2.7,工控原生安全,1,供给,50
+2.1.4,工业大数据,1,供给,43
+1.4.4.2,密钥管理,1.4,工业互联网安全,30
+1.4.5.4,数据脱敏,1,供给,28
+1.4.5.4,数据脱敏,1.4,工业互联网安全,28
+1.4.4.4,工业应用行为监控,1.4,工业互联网安全,26
+1.4.5.5,敏感数据发现与监控,1,供给,25
+1.4.5.7,数据恢复,1.4,工业互联网安全,25
+1.4.5.7,数据恢复,1,供给,25
+1.3.5.1,仓储物流管理WMS,1,供给,25
+1.3.4.2,客户关系管理CRM,1.3,工业软件,25
+1.4.5.5,敏感数据发现与监控,1.4,工业互联网安全,25
+1.3.4,企业运营管理,1,供给,24
+1.4.5.9,数据防火墙,1.4,工业互联网安全,23
+1.4.5.9,数据防火墙,1,供给,23
+1.4.2.5,安全日志与审计,1.4,工业互联网安全,22
+1.4.2.5,安全日志与审计,1,供给,22
+1.3.3,生产制造,1,供给,21
+1.4.4.3,接入认证,1.4,工业互联网安全,21
+1.4.5.6,数据容灾备份,1.4,工业互联网安全,21
+1.4.5.6,数据容灾备份,1,供给,21
+1.4.3.5,负载均衡,1.4,工业互联网安全,19
+1.4.3.4,攻击溯源,1.4,工业互联网安全,19
+1.4.3.1,网络漏洞扫描和补丁管理,1.4,工业互联网安全,19
+1.4.3.1,网络漏洞扫描和补丁管理,1,供给,19
+1.4.3.4,攻击溯源,1,供给,19
+2.1.4.1,工业大数据存储,2.1,PaaS,19
+1.4.3.5,负载均衡,1,供给,19
+2.1.4.2,工业大数据管理,2.1,PaaS,18
+1.3.3.7,故障预测与健康管理PHM,1.3,工业软件,18
+1.4.4.4,工业应用行为监控,1,供给,17
+1.4.2.2,工控主机卫士,1,供给,17
+1.4.2.2,工控主机卫士,1.4,工业互联网安全,17
+2.1.4.1,工业大数据存储,2,工业互联网平台,16
+1.3.4.3,人力资源管理HRM,1.3,工业软件,16
+1.4.2.6,隐私计算,1,供给,15
+1.4.2.6,隐私计算,1.4,工业互联网安全,15
+1.4.5.2,数据防泄漏系统,1,供给,14
+2.1.4.2,工业大数据管理,2,工业互联网平台,14
+1.3.1.5,产品数据管理PDM,1.3,工业软件,14
+1.4.5.2,数据防泄漏系统,1.4,工业互联网安全,14
+1.3.3.3,数据采集与监视控制系统SCADA,1.3,工业软件,13
+1.4.4.2,密钥管理,1,供给,12
+1.4.4.1,身份鉴别与访问控制,1.4,工业互联网安全,12
+1.4.4.3,接入认证,1,供给,12
+1.3.1.7,电子设计自动化EDA,1.3,工业软件,12
+1.3.1,设计研发,1,供给,10
+1.3.1.3,计算机辅助制造CAM,1.3,工业软件,10
+1.3.1.6,产品生命周期管理PLM,1.3,工业软件,10
+1.3.4.2,客户关系管理CRM,1,供给,8
+2.1.2,工业模型库,2,工业互联网平台,8
+2.1.4.1,工业大数据存储,1,供给,8
+1.3.3.5,企业资产管理系统EAM,1.3,工业软件,7
+2.1.4.2,工业大数据管理,1,供给,7
+2.1.1,开发工具,2,工业互联网平台,6
+1.4.1.4,入侵检测系统,1.4,工业互联网安全,6
+1.4.1.4,入侵检测系统,1,供给,6
+1.3.1.2,计算机辅助工程CAE,1.3,工业软件,4
+1.4.1.5,统一威胁管理系统,1,供给,4
+1.4.4.1,身份鉴别与访问控制,1,供给,4
+2.1.3.1,物联网服务,2.1,PaaS,4
+1.4.3.3,APT检测,1,供给,4
+1.4.5.3,数据审计系统,1,供给,4
+1.4.1.5,统一威胁管理系统,1.4,工业互联网安全,4
+1.3.3.7,故障预测与健康管理PHM,1,供给,4
+1.3.1.4,计算机辅助工艺过程设计CAPP,1.3,工业软件,4
+1.4.5.3,数据审计系统,1.4,工业互联网安全,4
+1.4.3.3,APT检测,1.4,工业互联网安全,4
+1.2.3,数据互通,1,供给,4
+1.1.3,工业服务器,1,供给,4
+1.3.1.1,计算机辅助设计CAD,1.3,工业软件,3
+1.3.3.6,运维保障系统MRO,1.3,工业软件,3
+1.4.1.2,下一代防火墙,1,供给,3
+2.1.1,开发工具,1,供给,3
+1.3.3.3,数据采集与监视控制系统SCADA,1,供给,3
+2.1.3.3,工业引擎服务,2.1,PaaS,3
+2.1.2,工业模型库,1,供给,3
+2.1.4.1.3,实时数据库,2.1.4,工业大数据,3
+1.4.1.2,下一代防火墙,1.4,工业互联网安全,3
+2.1.1.3,流程开发工具,2.1,PaaS,2
+2.1.4.1.2,分布式数据库,2.1.4,工业大数据,2
+1.3.1.5,产品数据管理PDM,1,供给,2
+2.1.3.7,制造类API,2.1,PaaS,2
+1.3.3.1,制造执行系统MES,1.3,工业软件,2
+1.3.3.4,可编程逻揖控制系统PLC,1.3,工业软件,2
+2.1.1.4,组态建模工具,2.1,PaaS,1
+2.1.2.1,数据算法模型,2.1,PaaS,1
+1.3.4.3,人力资源管理HRM,1,供给,1
+2.3,边缘层,1,供给,1
+1.2.2,标识解析,1,供给,1
+2.3.1,工业数据接入,2,工业互联网平台,1
+1.2.1,网络互联,1,供给,1
+2.3.2,边缘数据处理,2,工业互联网平台,1
+1.3.3.5,企业资产管理系统EAM,1,供给,1
+1.4.2.4,安全隔离与信息交换系统,1.4,工业互联网安全,1
diff --git a/analysis/count_dcp_prod_network.png b/analysis/count_dcp_prod_network.png
new file mode 100644
index 0000000..e137d04
Binary files /dev/null and b/analysis/count_dcp_prod_network.png differ
diff --git a/analysis/count_firm.csv b/analysis/count_firm.csv
index 1caf1b0..07c560e 100644
--- a/analysis/count_firm.csv
+++ b/analysis/count_firm.csv
@@ -1,144 +1,172 @@
 id_firm,Name,count
-126,华为,468
-142,深信服,300
-41,启明星辰,200
-53,天融信,150
-106,阿里巴巴,146
-170,Pseudo1,125
-99,Siemens,120
-79,PTC,117
-130,金蝶,91
-13,东方国信,80
-135,浪潮,73
-23,和利时,71
-58,用友,62
-97,General Electric,54
-29,京东工业品,52
-63,长扬科技,50
-85,Dassault,50
-157,新华三,50
-140,山石网科,50
-148,腾讯,49
-102,Amazon AWS,47
-22,航天云网,46
-40,奇安信,39
-0,360科技,38
-98,Microsoft Azure,38
-84,Bosch,35
-81,SAP,35
-74,HoneyWell,32
-100,Synopsys,29
-86,Dell EMC,28
-80,Salesforce,28
-108,百度,25
-105,Intel,25
-49,数码大方,24
-47,首自信,24
-95,Schneider,22
-39,Autodesk,21
-168,中控技术,20
-6,安世亚太,20
-16,东土科技,20
-94,Mitsubishi,19
-75,IBM,19
-73,FANUC,18
-124,海尔,18
-117,格创东智,17
-26,寄云科技,17
-155,小米,17
-159,徐工集团,16
-57,亚控科技,13
-149,天泽智云,13
-93,Cadence,13
-62,云道智造,13
-82,Uptake,12
-78,OutSystems,12
-161,研华科技,12
-60,宇动源,11
-165,智能云科,11
-33,蓝谷信息,10
-42,山大华天,10
-67,中国移动,10
-131,九物互联,10
-38,牛刀,10
-103,STMicroelectronics ,9
-144,树根互联,9
-56,芯愿景,8
-143,沈阳自动化研究所,8
-127,华为海思,8
-153,武汉开目,8
-3,艾克斯特,7
-45,石化盈科,7
-68,中望软件,7
-31,昆仑数据,6
-46,适创科技,6
-111,鼎捷软件,6
-89,Rockwell,6
-150,唯捷创芯,6
-169,中芯国际,6
-69,紫光集团,5
-113,飞腾信息,5
-167,中环股份,5
-129,华中数控,5
-43,神舟软件,5
-71,Altair,4
-104,Infineon,4
-77,Oracle,4
-123,海得控制,4
-145,思普软件,4
-35,凌昊智能,4
-163,优也科技,4
-24,华大电子,4
-32,兰光创新,4
-115,富士康,4
-147,拓邦股份,4
-9,北京航天测控,3
-88,HPE,3
-87,Texas Instruments,3
-120,广州数控,3
-12,大唐软件,3
-64,中电智科,3
-90,Mentor Graphics,3
-101,Analog Devices,3
-116,概伦电子,3
-166,中国电子科技网络信息安全,3
-119,广联达,3
-70,ABB,3
-20,海基科技,3
-65,中国电信,3
-72,ANSYS,3
-4,爱创科技,3
-36,龙芯中科,3
-44,圣邦微电子,3
-146,苏州浩辰,3
-14,东华软件,3
-83,Emerson,2
-138,启明信息,2
-10,北京英贝思,2
-128,华伍股份,2
-15,东软集团,2
-154,西格数据,2
-156,芯禾科技,2
-48,曙光信息,2
-50,索为系统,2
-141,上海新华控制,2
-61,元年科技,2
-164,震坤行,2
-2,706所,2
-134,朗坤智慧,2
-137,美林数据,2
-25,华大九天,2
-34,力控科技,2
-132,科远智慧,1
-92,Omron,1
-21,Hexagon,1
-96,Cisco,1
-18,国能智深,1
-118,工邦邦,1
-91,Moxa,1
-125,华数机器人,1
-133,蓝盾股份,1
-109,宝信软件,1
-139,容知日新,1
-66,中国联通,1
-1,51WORLD,1
+126,华为,1955
+170,Pseudo1,1525
+106,阿里巴巴,1446
+99,Siemens,1342
+79,PTC,1271
+97,General Electric,991
+142,深信服,969
+81,SAP,883
+41,启明星辰,842
+85,Dassault,705
+148,腾讯,697
+22,航天云网,657
+58,用友,546
+84,Bosch,543
+13,东方国信,501
+80,Salesforce,499
+39,Autodesk,486
+93,Cadence,485
+53,天融信,484
+73,FANUC,472
+100,Synopsys,459
+75,IBM,454
+29,京东工业品,453
+108,百度,441
+40,奇安信,426
+157,新华三,418
+74,HoneyWell,400
+135,浪潮,396
+0,360科技,352
+102,Amazon AWS,313
+130,金蝶,307
+23,和利时,272
+47,首自信,267
+26,寄云科技,252
+159,徐工集团,243
+98,Microsoft Azure,233
+77,Oracle,227
+49,数码大方,221
+95,Schneider,216
+105,Intel,201
+124,海尔,195
+67,中国移动,191
+140,山石网科,190
+37,绿盟,188
+155,小米,182
+86,Dell EMC,179
+45,石化盈科,178
+168,中控技术,175
+94,Mitsubishi,174
+117,格创东智,160
+115,富士康,155
+55,威努特,153
+6,安世亚太,148
+5,安华金和,128
+143,沈阳自动化研究所,126
+3,艾克斯特,124
+62,云道智造,119
+78,OutSystems,115
+38,牛刀,112
+33,蓝谷信息,111
+42,山大华天,109
+60,宇动源,107
+57,亚控科技,107
+165,智能云科,106
+144,树根互联,106
+16,东土科技,104
+31,昆仑数据,103
+68,中望软件,101
+82,Uptake,100
+149,天泽智云,98
+137,美林数据,93
+163,优也科技,92
+154,西格数据,92
+161,研华科技,90
+131,九物互联,86
+111,鼎捷软件,79
+63,长扬科技,76
+43,神舟软件,75
+14,东华软件,72
+162,壹进制,70
+153,武汉开目,69
+54,网御星云,68
+9,北京航天测控,65
+89,Rockwell,64
+133,蓝盾股份,52
+70,ABB,51
+129,华中数控,47
+127,华为海思,47
+56,芯愿景,46
+27,江南天安,45
+76,MasterCAM,45
+11,北信源,45
+50,索为系统,45
+96,Cisco,45
+116,概伦电子,44
+152,卫士通,43
+138,启明信息,43
+90,Mentor Graphics,42
+21,Hexagon,42
+112,东华测试,41
+25,华大九天,39
+71,Altair,38
+158,信大捷安,38
+72,ANSYS,38
+146,苏州浩辰,38
+48,曙光信息,38
+46,适创科技,38
+88,HPE,37
+139,容知日新,37
+156,芯禾科技,37
+15,东软集团,36
+20,海基科技,36
+120,广州数控,36
+10,北京英贝思,36
+145,思普软件,35
+151,唯智信息,35
+52,天空卫士,35
+119,广联达,35
+114,富勒科技,35
+92,Omron,34
+30,可信华泰,34
+4,爱创科技,34
+109,宝信软件,34
+110,晨科软件,34
+122,国民技术,34
+59,优特捷,34
+164,震坤行,34
+61,元年科技,33
+147,拓邦股份,33
+160,亚信科技,33
+107,安恒信息,33
+2,706所,33
+134,朗坤智慧,32
+35,凌昊智能,32
+1,51WORLD,31
+64,中电智科,31
+118,工邦邦,31
+166,中国电子科技网络信息安全,31
+34,力控科技,31
+8,梆梆安全,30
+125,华数机器人,30
+128,华伍股份,29
+66,中国联通,28
+123,海得控制,28
+32,兰光创新,28
+17,国保金泰,28
+51,天地和兴,28
+19,国泰网信,28
+121,广州智臣,28
+12,大唐软件,27
+132,科远智慧,27
+83,Emerson,27
+65,中国电信,25
+7,百望,24
+136,美的,24
+141,上海新华控制,24
+18,国能智深,24
+169,中芯国际,22
+44,圣邦微电子,21
+103,STMicroelectronics ,21
+167,中环股份,21
+36,龙芯中科,21
+91,Moxa,20
+28,金山云,20
+69,紫光集团,20
+113,飞腾信息,19
+87,Texas Instruments,18
+104,Infineon,18
+101,Analog Devices,18
+24,华大电子,17
+150,唯捷创芯,17
diff --git a/analysis/count_firm_prod.csv b/analysis/count_firm_prod.csv
index b3ab1fd..7d1f831 100644
--- a/analysis/count_firm_prod.csv
+++ b/analysis/count_firm_prod.csv
@@ -1,358 +1,476 @@
 id_firm,name_firm,id_product,name_product,count
-126,华为,1.4,工业互联网安全,385
-142,深信服,1.4.3,网络安全,150
-41,启明星辰,1.4.5,数据安全,150
-142,深信服,1.4.2,控制安全,150
-170,Pseudo1,1,供给,125
-106,阿里巴巴,1.3,工业软件,67
-29,京东工业品,1.3,工业软件,52
-53,天融信,1.4.2.3,工控漏洞扫描,50
-41,启明星辰,1.4.3.2,流量检测,50
+170,Pseudo1,1,供给,1525
+126,华为,1.4,工业互联网安全,1012
+41,启明星辰,1.4.5,数据安全,640
+142,深信服,1.4.2,控制安全,529
+39,Autodesk,1.3.1,设计研发,486
+93,Cadence,1.3.1,设计研发,485
+73,FANUC,2.1.3,工业物联网,472
+100,Synopsys,1.3.1,设计研发,459
+75,IBM,1.3.3,生产制造,454
+29,京东工业品,1.3,工业软件,453
+108,百度,2.1.3,工业物联网,434
+106,阿里巴巴,1.3,工业软件,433
+142,深信服,1.4.3,网络安全,430
+97,General Electric,1.3.3,生产制造,424
+99,Siemens,1.3.3,生产制造,418
+157,新华三,1.4.1,设备安全,418
+85,Dassault,1.3.1,设计研发,412
+99,Siemens,1.3.1,设计研发,404
+148,腾讯,2.1.3,工业物联网,402
+97,General Electric,2.1.3,工业物联网,385
+74,HoneyWell,2.1.3,工业物联网,383
+106,阿里巴巴,2.1.3,工业物联网,372
+126,华为,2.1.3,工业物联网,362
+40,奇安信,1.4.4,平台安全,357
+0,360科技,1.4.4,平台安全,352
+99,Siemens,2.1,PaaS,323
+79,PTC,2.1.4.1,工业大数据存储,317
+80,Salesforce,2.1.1,开发工具,310
+85,Dassault,2.1.1,开发工具,293
+148,腾讯,2.1.1,开发工具,285
+58,用友,2.1.2,工业模型库,281
+79,PTC,2.1.2,工业模型库,280
+81,SAP,2.1.4.1,工业大数据存储,278
+106,阿里巴巴,2.1.1,开发工具,271
+159,徐工集团,2.1.2,工业模型库,243
+81,SAP,2.1.2,工业模型库,235
+98,Microsoft Azure,2,工业互联网平台,233
+84,Bosch,2.1.2,工业模型库,230
+81,SAP,1.3.4,企业运营管理,209
+102,Amazon AWS,2,工业互联网平台,203
+105,Intel,1.1,工业自动化,201
+77,Oracle,1.3.4,企业运营管理,194
+67,中国移动,1.2,工业互联网网络,191
+126,华为,1.2,工业互联网网络,190
+95,Schneider,2.3,边缘层,190
+80,Salesforce,1.3.4,企业运营管理,189
+106,阿里巴巴,1.2,工业互联网网络,188
+84,Bosch,2.3,边缘层,182
+97,General Electric,1.2,工业互联网网络,182
+155,小米,2.3,边缘层,182
+79,PTC,2.1.4.2,工业大数据管理,181
+124,海尔,2.3,边缘层,179
+86,Dell EMC,1.1,工业自动化,179
+126,华为,1.1,工业自动化,178
+94,Mitsubishi,1.1,工业自动化,174
+126,华为,2.3,边缘层,174
+106,阿里巴巴,1.1,工业自动化,172
+99,Siemens,2.3,边缘层,166
+81,SAP,2.1.4.2,工业大数据管理,161
+115,富士康,2.1.4,工业大数据,131
+84,Bosch,2.1.4,工业大数据,131
+130,金蝶,1.3.5,仓储物流,120
+102,Amazon AWS,2.1.4,工业大数据,110
+130,金蝶,1.3.2,采购供应,88
+58,用友,1.3.2,采购供应,88
 23,和利时,1.4.2.7,工控原生安全,50
-63,长扬科技,1.4.4.5,安全态势感知,50
-157,新华三,1.4.1,设备安全,50
-53,天融信,1.4.5.8,数据加密,50
-140,山石网科,1.4.5.1,恶意代码检测系统,50
-135,浪潮,1.3.2.1,供应链管理SCM,50
-130,金蝶,1.3.5,仓储物流,50
-99,Siemens,2.1,PaaS,50
 53,天融信,1.4.3.6,沙箱类设备,50
-102,Amazon AWS,2,工业互联网平台,45
-130,金蝶,1.3.2,采购供应,40
-40,奇安信,1.4.4,平台安全,39
-0,360科技,1.4.4,平台安全,38
-98,Microsoft Azure,2,工业互联网平台,38
-58,用友,1.3.2,采购供应,36
-148,腾讯,2.1.3,工业物联网,32
-74,HoneyWell,2.1.3,工业物联网,30
-100,Synopsys,1.3.1,设计研发,29
-85,Dassault,1.3.1,设计研发,29
-86,Dell EMC,1.1,工业自动化,28
-99,Siemens,1.3.1,设计研发,27
-97,General Electric,2.1.3,工业物联网,27
-106,阿里巴巴,2.1.3,工业物联网,27
-126,华为,2.1.3,工业物联网,26
-105,Intel,1.1,工业自动化,25
-80,Salesforce,2.1.1,开发工具,25
-79,PTC,2.1.2,工业模型库,25
-108,百度,2.1.3,工业物联网,24
-84,Bosch,2.1.2,工业模型库,22
-58,用友,2.1.2,工业模型库,22
-39,Autodesk,1.3.1,设计研发,21
-97,General Electric,1.3.3,生产制造,21
-106,阿里巴巴,1.1,工业自动化,21
-85,Dassault,2.1.1,开发工具,21
-126,华为,1.1,工业自动化,21
-99,Siemens,1.3.3,生产制造,20
-99,Siemens,2.3,边缘层,20
-106,阿里巴巴,2.1.1,开发工具,19
-126,华为,2.3,边缘层,19
-75,IBM,1.3.3,生产制造,19
-94,Mitsubishi,1.1,工业自动化,19
-73,FANUC,2.1.3,工业物联网,18
-81,SAP,2.1.2,工业模型库,18
-95,Schneider,2.3,边缘层,18
-155,小米,2.3,边缘层,17
-148,腾讯,2.1.1,开发工具,17
-124,海尔,2.3,边缘层,16
-159,徐工集团,2.1.2,工业模型库,16
-126,华为,1.2,工业互联网网络,15
-93,Cadence,1.3.1,设计研发,13
-106,阿里巴巴,1.2,工业互联网网络,12
-84,Bosch,2.3,边缘层,12
-13,东方国信,2.1.3.7,制造类API,11
-13,东方国信,2.1.3.4,应用管理服务,11
-13,东方国信,2.1.3.5,容器服务,10
-79,PTC,2.1.3.2,平台基础服务,10
-67,中国移动,1.2,工业互联网网络,10
-79,PTC,2.1.3.1,物联网服务,9
-79,PTC,2.1.3.4,应用管理服务,9
-103,STMicroelectronics ,1.1.1,工业计算芯片,9
-13,东方国信,2.1.3.1,物联网服务,9
-13,东方国信,2.1.3.3,工业引擎服务,8
-79,PTC,2.1.3.5,容器服务,8
-79,PTC,2.1.4.1,工业大数据存储,7
-13,东方国信,2.1.3.6,微服务,7
-79,PTC,2.1.3.7,制造类API,7
-81,SAP,2.1.4.1,工业大数据存储,7
-81,SAP,2.1.4.2,工业大数据管理,7
-79,PTC,2.1.3.6,微服务,7
-79,PTC,2.3.3,协议转换,6
-79,PTC,2.1.3.3,工业引擎服务,6
-16,东土科技,2.3.1,工业数据接入,6
-150,唯捷创芯,1.1.1,工业计算芯片,6
-49,数码大方,1.3.1.1,计算机辅助设计CAD,6
-79,PTC,2.3.1,工业数据接入,6
-47,首自信,2.1.3.6,微服务,6
-56,芯愿景,1.1.1,工业计算芯片,6
-169,中芯国际,1.1.1,工业计算芯片,6
-97,General Electric,1.2,工业互联网网络,6
-46,适创科技,1.3.1.2,计算机辅助工程CAE,6
-47,首自信,2.1.2.1,数据算法模型,6
-13,东方国信,2.1.3.2,平台基础服务,6
-16,东土科技,1.1.3,工业服务器,5
-161,研华科技,2.3.3,协议转换,5
-16,东土科技,2.3.3,协议转换,5
-22,航天云网,2.1.3.6,微服务,5
-168,中控技术,2.3.3,协议转换,5
-13,东方国信,2.3.2,边缘数据处理,5
-22,航天云网,2.3.1,工业数据接入,5
-153,武汉开目,1.3.1.1,计算机辅助设计CAD,5
-69,紫光集团,1.1.1,工业计算芯片,5
-22,航天云网,2.3.3,协议转换,5
-127,华为海思,1.1.1,工业计算芯片,5
-6,安世亚太,2.1.2.1,数据算法模型,5
-79,PTC,2.1.4.2,工业大数据管理,5
-23,和利时,2.3.3,协议转换,5
-42,山大华天,1.3.1.1,计算机辅助设计CAD,5
-113,飞腾信息,1.1.1,工业计算芯片,5
-167,中环股份,1.1.1,工业计算芯片,5
-78,OutSystems,2.1.1.5,数字孪生建模工具,4
-32,兰光创新,1.2.3,数据互通,4
-68,中望软件,1.3.1.2,计算机辅助工程CAE,4
-78,OutSystems,2.1.1.2,低代码开发工具,4
-6,安世亚太,2.1.2.4,行业机理模型,4
-165,智能云科,2.1.2.2,业务流程模型,4
-24,华大电子,1.1.1,工业计算芯片,4
-62,云道智造,2.1.2.2,业务流程模型,4
-23,和利时,2.3.2,边缘数据处理,4
-16,东土科技,2.3.2,边缘数据处理,4
-6,安世亚太,2.1.2.3,研发仿真模型,4
-22,航天云网,2.1.3.4,应用管理服务,4
-71,Altair,1.3.1.2,计算机辅助工程CAE,4
-161,研华科技,2.3.2,边缘数据处理,4
-168,中控技术,2.3.2,边缘数据处理,4
-35,凌昊智能,1.1.3,工业服务器,4
-33,蓝谷信息,2.1.2.4,行业机理模型,4
-57,亚控科技,2.3.3,协议转换,4
-129,华中数控,1.1.2,工业控制器,4
-13,东方国信,2.3.1,工业数据接入,4
-104,Infineon,1.1.1,工业计算芯片,4
-131,九物互联,2.1.1.2,低代码开发工具,4
-95,Schneider,1.2.3,数据互通,4
-149,天泽智云,2.1.2.4,行业机理模型,4
-42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,4
-135,浪潮,2.1.3.7,制造类API,4
-117,格创东智,2.1.1.4,组态建模工具,4
-82,Uptake,2.1.2.4,行业机理模型,4
-57,亚控科技,2.3.2,边缘数据处理,4
-43,神舟软件,1.3.1.6,产品生命周期管理PLM,4
-145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,4
-123,海得控制,1.1.2,工业控制器,4
-38,牛刀,2.1.1.5,数字孪生建模工具,4
-149,天泽智云,2.1.2.3,研发仿真模型,4
-147,拓邦股份,1.1.2,工业控制器,4
-6,安世亚太,2.1.2.2,业务流程模型,4
-4,爱创科技,1.2.2,标识解析,3
-36,龙芯中科,1.1.1,工业计算芯片,3
-33,蓝谷信息,2.1.2.2,业务流程模型,3
-115,富士康,1.1.3,工业服务器,3
-49,数码大方,2.1.2.1,数据算法模型,3
-49,数码大方,1.3.3.1,制造执行系统MES,3
-23,和利时,2.1.3.6,微服务,3
-49,数码大方,2.1.2.2,业务流程模型,3
-64,中电智科,1.1.2,工业控制器,3
-49,数码大方,1.3.1.6,产品生命周期管理PLM,3
-49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,3
-47,首自信,2.1.2.4,行业机理模型,3
-47,首自信,2.1.1.2,低代码开发工具,3
-62,云道智造,2.1.2.4,行业机理模型,3
-117,格创东智,2.1.1.2,低代码开发工具,3
-60,宇动源,2.1.1.1,算法建模工具,3
-26,寄云科技,2.1.3.1,物联网服务,3
-117,格创东智,2.1.1.1,算法建模工具,3
-26,寄云科技,2.1.3.3,工业引擎服务,3
-44,圣邦微电子,1.1.1,工业计算芯片,3
-62,云道智造,1.3.1.2,计算机辅助工程CAE,3
-26,寄云科技,2.1.3.6,微服务,3
-6,安世亚太,1.3.1.2,计算机辅助工程CAE,3
-57,亚控科技,2.3.1,工业数据接入,3
-3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,3
-116,概伦电子,1.3.1.7,电子设计自动化EDA,3
-3,艾克斯特,1.3.1.6,产品生命周期管理PLM,3
-31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,3
-60,宇动源,2.1.1.2,低代码开发工具,3
-65,中国电信,1.2.1,网络互联,3
-23,和利时,2.3.1,工业数据接入,3
-68,中望软件,1.3.1.1,计算机辅助设计CAD,3
-87,Texas Instruments,1.1.1,工业计算芯片,3
-149,天泽智云,2.1.2.2,业务流程模型,3
-120,广州数控,1.2.3,数据互通,3
-146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,3
-144,树根互联,2.1.2.4,行业机理模型,3
-22,航天云网,2.1.3.7,制造类API,3
-80,Salesforce,1.3.4,企业运营管理,3
-81,SAP,1.3.4,企业运营管理,3
-82,Uptake,2.1.2.1,数据算法模型,3
-82,Uptake,2.1.2.2,业务流程模型,3
-111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,3
-135,浪潮,2.1.3.4,应用管理服务,3
-12,大唐软件,1.2.1,网络互联,3
-135,浪潮,2.1.3.3,工业引擎服务,3
-88,HPE,1.1.3,工业服务器,3
-89,Rockwell,1.1.2,工业控制器,3
-9,北京航天测控,1.3.3.6,运维保障系统MRO,3
-135,浪潮,1.1.3,工业服务器,3
-90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,3
-131,九物互联,2.1.1.4,组态建模工具,3
-13,东方国信,1.2.2,标识解析,3
-101,Analog Devices,1.1.1,工业计算芯片,3
-127,华为海思,1.1.3,工业服务器,3
-153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,3
-79,PTC,2.3.2,边缘数据处理,3
-161,研华科技,2.3.1,工业数据接入,3
-168,中控技术,1.3.3.2,分布式控制系统DCS,3
-22,航天云网,2.1.3.3,工业引擎服务,3
-72,ANSYS,1.3.1.2,计算机辅助工程CAE,3
-22,航天云网,1.2.2,标识解析,3
-20,海基科技,1.3.1.2,计算机辅助工程CAE,3
-119,广联达,1.3.1.1,计算机辅助设计CAD,3
-168,中控技术,2.3.1,工业数据接入,3
-79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,3
-165,智能云科,2.1.2.1,数据算法模型,3
-79,PTC,1.3.1.6,产品生命周期管理PLM,3
-79,PTC,1.3.1.1,计算机辅助设计CAD,3
-166,中国电子科技网络信息安全,1.2.3,数据互通,3
-165,智能云科,2.1.2.4,行业机理模型,3
-58,用友,1.3.1.6,产品生命周期管理PLM,2
-117,格创东智,2.1.1.3,流程开发工具,2
-70,ABB,1.3.3.2,分布式控制系统DCS,2
-10,北京英贝思,1.3.3.5,企业资产管理系统EAM,2
-102,Amazon AWS,2.1.4,工业大数据,2
-99,Siemens,1.1.2,工业控制器,2
-74,HoneyWell,1.3.3.2,分布式控制系统DCS,2
-77,Oracle,1.3.3.6,运维保障系统MRO,2
-77,Oracle,1.3.4,企业运营管理,2
-49,数码大方,2.1.2.4,行业机理模型,2
-50,索为系统,1.3.1.5,产品数据管理PDM,2
-89,Rockwell,1.2.1,网络互联,2
-58,用友,1.2.2,标识解析,2
-78,OutSystems,2.1.1.1,算法建模工具,2
-56,芯愿景,1.3.1.7,电子设计自动化EDA,2
-111,鼎捷软件,1.3.4.1,企业资源计划ERP,2
-57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,2
-78,OutSystems,2.1.1.3,流程开发工具,2
-61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,2
-83,Emerson,1.3.3.2,分布式控制系统DCS,2
-82,Uptake,2.1.2.3,研发仿真模型,2
-60,宇动源,2.1.1.5,数字孪生建模工具,2
-60,宇动源,2.1.1.4,组态建模工具,2
-62,云道智造,2.1.2.1,数据算法模型,2
-33,蓝谷信息,2.1.2.3,研发仿真模型,2
-48,曙光信息,1.2.2,标识解析,2
-22,航天云网,1.3.3.6,运维保障系统MRO,2
-26,寄云科技,2.1.3.2,平台基础服务,2
-144,树根互联,2.1.2.2,业务流程模型,2
-25,华大九天,1.3.1.7,电子设计自动化EDA,2
-23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,2
-138,启明信息,1.3.1.5,产品数据管理PDM,2
-23,和利时,1.3.3.1,制造执行系统MES,2
-22,航天云网,2.3.2,边缘数据处理,2
-22,航天云网,2.1.3.5,容器服务,2
-14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,2
-22,航天云网,2.1.3.1,物联网服务,2
-22,航天云网,2.1.1.5,数字孪生建模工具,2
-22,航天云网,2.1.1.3,流程开发工具,2
-22,航天云网,2.1.1.2,低代码开发工具,2
-22,航天云网,2.1.1.1,算法建模工具,2
-141,上海新华控制,1.3.3.2,分布式控制系统DCS,2
-26,寄云科技,2.1.3.5,容器服务,2
-2,706所,1.1.3,工业服务器,2
-124,海尔,1.2.1,网络互联,2
-168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,2
-143,沈阳自动化研究所,2.1.1.2,低代码开发工具,2
-168,中控技术,1.1.2,工业控制器,2
-143,沈阳自动化研究所,2.1.1.3,流程开发工具,2
-164,震坤行,1.3.3.6,运维保障系统MRO,2
-163,优也科技,2.1.4.2.2,数据安全管理,2
-143,沈阳自动化研究所,2.1.1.4,组态建模工具,2
-156,芯禾科技,1.3.1.7,电子设计自动化EDA,2
-143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,2
-144,树根互联,2.1.2.1,数据算法模型,2
-15,东软集团,1.3.3.5,企业资产管理系统EAM,2
-149,天泽智云,2.1.2.1,数据算法模型,2
-26,寄云科技,2.1.3.4,应用管理服务,2
-144,树根互联,2.1.2.3,研发仿真模型,2
-26,寄云科技,2.1.3.7,制造类API,2
-135,浪潮,2.1.3.1,物联网服务,2
-117,格创东智,2.1.1.5,数字孪生建模工具,2
-134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,2
-13,东方国信,2.3.3,协议转换,2
-38,牛刀,2.1.1.2,低代码开发工具,2
-38,牛刀,2.1.1.1,算法建模工具,2
-13,东方国信,2.1.4.1.4,时序数据库,2
-34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,2
-135,浪潮,2.1.3.2,平台基础服务,2
-128,华伍股份,1.1.2,工业控制器,2
-47,首自信,2.1.2.2,业务流程模型,2
-135,浪潮,2.1.3.5,容器服务,2
-135,浪潮,2.1.3.6,微服务,2
-131,九物互联,2.1.1.1,算法建模工具,2
-99,Siemens,1.2.1,网络互联,1
-13,东方国信,2.1.4.1.2,分布式数据库,1
-131,九物互联,2.1.1.3,流程开发工具,1
-111,鼎捷软件,1.3.3.1,制造执行系统MES,1
-129,华中数控,1.2.3,数据互通,1
-13,东方国信,2.1.4.2.1,数据质量管理,1
-126,华为,2.1.1.5,数字孪生建模工具,1
-130,金蝶,1.3.4.1,企业资源计划ERP,1
-96,Cisco,1.2.3,数据互通,1
-91,Moxa,1.2.1,网络互联,1
-132,科远智慧,1.3.3.2,分布式控制系统DCS,1
-133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,1
-92,Omron,1.3.3.4,可编程逻揖控制系统PLC,1
-108,百度,2.2,IaaS,1
-14,东华软件,1.3.4.3,人力资源管理HRM,1
-125,华数机器人,1.2.3,数据互通,1
-139,容知日新,1.3.3.7,故障预测与健康管理PHM,1
-89,Rockwell,1.3.3.1,制造执行系统MES,1
-135,浪潮,1.3.4.1,企业资源计划ERP,1
-137,美林数据,2.1.4.2.1,数据质量管理,1
-137,美林数据,2.1.4.1.3,实时数据库,1
-84,Bosch,2.1.4,工业大数据,1
-135,浪潮,2.2,IaaS,1
-109,宝信软件,1.3.3.1,制造执行系统MES,1
-18,国能智深,1.3.3.2,分布式控制系统DCS,1
-154,西格数据,2.1.4.1.2,分布式数据库,1
-45,石化盈科,1.3.4.1,企业资源计划ERP,1
-115,富士康,2.1.4,工业大数据,1
-38,牛刀,2.1.1.3,流程开发工具,1
-38,牛刀,2.1.1.4,组态建模工具,1
-117,格创东智,2.1.4.2.1,数据质量管理,1
-117,格创东智,2.1.4.1.1,关系型数据库,1
-42,山大华天,1.3.1.3,计算机辅助制造CAM,1
-43,神舟软件,1.3.1.5,产品数据管理PDM,1
-45,石化盈科,1.3.3.1,制造执行系统MES,1
-45,石化盈科,2.1.4.1.2,分布式数据库,1
-31,昆仑数据,2.1.4.2.1,数据质量管理,1
-45,石化盈科,2.1.4.1.3,实时数据库,1
-45,石化盈科,2.1.4.1.4,时序数据库,1
-45,石化盈科,2.1.4.2.1,数据质量管理,1
-45,石化盈科,2.1.4.2.2,数据安全管理,1
-49,数码大方,2.1.2.3,研发仿真模型,1
-47,首自信,2.1.1.1,算法建模工具,1
-47,首自信,2.1.1.3,流程开发工具,1
-47,首自信,2.1.1.4,组态建模工具,1
-33,蓝谷信息,2.1.2.1,数据算法模型,1
-31,昆仑数据,2.1.4.1.3,实时数据库,1
-154,西格数据,2.1.4.2.2,数据安全管理,1
-70,ABB,1.3.3.4,可编程逻揖控制系统PLC,1
-163,优也科技,2.1.4.1.1,关系型数据库,1
-163,优也科技,2.1.4.1.4,时序数据库,1
-165,智能云科,2.1.2.3,研发仿真模型,1
-168,中控技术,1.3.3.1,制造执行系统MES,1
-47,首自信,2.1.2.3,研发仿真模型,1
-21,Hexagon,1.3.1.3,计算机辅助制造CAM,1
-22,航天云网,2.1.1.4,组态建模工具,1
-22,航天云网,2.1.3.2,平台基础服务,1
-23,和利时,1.3.3.2,分布式控制系统DCS,1
-31,昆仑数据,2.1.4.1.1,关系型数据库,1
-66,中国联通,1.2.1,网络互联,1
-23,和利时,1.3.3.4,可编程逻揖控制系统PLC,1
-118,工邦邦,1.3.3.6,运维保障系统MRO,1
-1,51WORLD,2.1.1.5,数字孪生建模工具,1
-62,云道智造,2.1.2.3,研发仿真模型,1
-117,格创东智,2.1.4.2.2,数据安全管理,1
-3,艾克斯特,1.3.4.1,企业资源计划ERP,1
-60,宇动源,2.1.1.3,流程开发工具,1
-126,华为,2.2,IaaS,1
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+63,长扬科技,1.4.4.5,安全态势感知,50
+135,浪潮,1.3.2.1,供应链管理SCM,50
+41,启明星辰,1.4.3.2,流量检测,50
+140,山石网科,1.4.5.1,恶意代码检测系统,50
+53,天融信,1.4.5.8,数据加密,50
+13,东方国信,2.1.3.4,应用管理服务,46
+54,网御星云,1.4.4.3,接入认证,45
+53,天融信,1.4.4.4,工业应用行为监控,45
+11,北信源,1.4.4.2,密钥管理,45
+76,MasterCAM,1.3.1.3,计算机辅助制造CAM,45
+50,索为系统,1.3.1.5,产品数据管理PDM,45
+37,绿盟,1.4.4.3,接入认证,45
+79,PTC,2.1.3.2,平台基础服务,45
+55,威努特,1.4.4.4,工业应用行为监控,45
+27,江南天安,1.4.4.2,密钥管理,45
+116,概伦电子,1.3.1.7,电子设计自动化EDA,44
+135,浪潮,2.1.3.2,平台基础服务,44
+79,PTC,2.1.3.1,物联网服务,44
+135,浪潮,2.1.3.3,工业引擎服务,44
+152,卫士通,1.4.4.1,身份鉴别与访问控制,43
+22,航天云网,2.1.3.4,应用管理服务,43
+22,航天云网,2.1.3.5,容器服务,43
+22,航天云网,2.1.3.3,工业引擎服务,43
+13,东方国信,2.1.3.1,物联网服务,43
+79,PTC,2.1.3.5,容器服务,43
+79,PTC,2.1.3.4,应用管理服务,43
+14,东华软件,1.3.4.3,人力资源管理HRM,43
+13,东方国信,2.1.3.7,制造类API,43
+22,航天云网,2.1.3.7,制造类API,43
+13,东方国信,2.1.3.5,容器服务,43
+138,启明信息,1.3.1.5,产品数据管理PDM,43
+13,东方国信,2.1.3.2,平台基础服务,43
+13,东方国信,2.1.3.3,工业引擎服务,43
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,42
+135,浪潮,2.1.3.7,制造类API,42
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,42
+79,PTC,2.1.3.3,工业引擎服务,42
+135,浪潮,2.1.3.5,容器服务,42
+79,PTC,2.1.3.7,制造类API,42
+135,浪潮,2.1.3.1,物联网服务,42
+79,PTC,2.1.3.6,微服务,42
+112,东华测试,1.3.3.7,故障预测与健康管理PHM,41
+26,寄云科技,2.1.3.4,应用管理服务,41
+22,航天云网,2.1.3.1,物联网服务,41
+45,石化盈科,1.3.4.2,客户关系管理CRM,41
+22,航天云网,2.1.3.2,平台基础服务,40
+43,神舟软件,1.3.1.5,产品数据管理PDM,40
+42,山大华天,1.3.1.3,计算机辅助制造CAM,40
+58,用友,1.3.4.3,人力资源管理HRM,40
+130,金蝶,1.3.4.2,客户关系管理CRM,40
+26,寄云科技,2.1.3.5,容器服务,40
+26,寄云科技,2.1.3.2,平台基础服务,40
+58,用友,1.3.4.2,客户关系管理CRM,40
+56,芯愿景,1.3.1.7,电子设计自动化EDA,39
+25,华大九天,1.3.1.7,电子设计自动化EDA,39
+68,中望软件,1.3.1.3,计算机辅助制造CAM,39
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,38
+48,曙光信息,1.2.2,标识解析,38
+46,适创科技,1.3.1.2,计算机辅助工程CAE,38
+135,浪潮,2.1.3.4,应用管理服务,38
+133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,38
+3,艾克斯特,1.3.1.5,产品数据管理PDM,38
+130,金蝶,1.3.4.3,人力资源管理HRM,38
+71,Altair,1.3.1.2,计算机辅助工程CAE,38
+146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,38
+158,信大捷安,1.4.4.1,身份鉴别与访问控制,38
+22,航天云网,2.1.3.6,微服务,38
+58,用友,1.3.1.6,产品生命周期管理PLM,38
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,38
+23,和利时,2.1.3.6,微服务,38
+88,HPE,1.1.3,工业服务器,37
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,37
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,37
+58,用友,1.2.2,标识解析,36
+120,广州数控,1.2.3,数据互通,36
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,36
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,36
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,36
+15,东软集团,1.3.3.5,企业资产管理系统EAM,36
+20,海基科技,1.3.1.2,计算机辅助工程CAE,36
+26,寄云科技,2.1.3.7,制造类API,36
+135,浪潮,2.1.3.6,微服务,36
+26,寄云科技,2.1.3.3,工业引擎服务,35
+42,山大华天,1.3.1.1,计算机辅助设计CAD,35
+162,壹进制,1.4.5.7,数据恢复,35
+37,绿盟,1.4.5.2,数据防泄漏系统,35
+162,壹进制,1.4.5.6,数据容灾备份,35
+119,广联达,1.3.1.1,计算机辅助设计CAD,35
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,35
+140,山石网科,1.4.5.4,数据脱敏,35
+168,中控技术,2.3.1,工业数据接入,35
+114,富勒科技,1.3.5.1,仓储物流管理WMS,35
+140,山石网科,1.4.5.9,数据防火墙,35
+53,天融信,1.4.5.7,数据恢复,35
+5,安华金和,1.4.5.5,敏感数据发现与监控,35
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,35
+53,天融信,1.4.5.2,数据防泄漏系统,35
+5,安华金和,1.4.5.9,数据防火墙,35
+151,唯智信息,1.3.5.1,仓储物流管理WMS,35
+53,天融信,1.4.5.6,数据容灾备份,35
+52,天空卫士,1.4.5.5,敏感数据发现与监控,35
+5,安华金和,1.4.5.4,数据脱敏,35
+47,首自信,2.1.3.6,微服务,35
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,34
+55,威努特,1.4.2.2,工控主机卫士,34
+79,PTC,2.3.1,工业数据接入,34
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,34
+109,宝信软件,1.3.3.1,制造执行系统MES,34
+59,优特捷,1.4.2.5,安全日志与审计,34
+53,天融信,1.4.3.5,负载均衡,34
+53,天融信,1.4.3.4,攻击溯源,34
+30,可信华泰,1.4.2.6,隐私计算,34
+122,国民技术,1.4.2.6,隐私计算,34
+37,绿盟,1.4.3.1,网络漏洞扫描和补丁管理,34
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,34
+37,绿盟,1.4.2.2,工控主机卫士,34
+4,爱创科技,1.2.2,标识解析,34
+40,奇安信,1.4.2.5,安全日志与审计,34
+47,首自信,2.1.1.2,低代码开发工具,34
+41,启明星辰,1.4.3.1,网络漏洞扫描和补丁管理,34
+41,启明星辰,1.4.3.4,攻击溯源,34
+41,启明星辰,1.4.3.5,负载均衡,34
+164,震坤行,1.3.3.6,运维保障系统MRO,34
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,34
+2,706所,1.1.3,工业服务器,33
+107,安恒信息,1.4.3.3,APT检测,33
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,33
+49,数码大方,1.3.1.6,产品生命周期管理PLM,33
+13,东方国信,2.1.3.6,微服务,33
+160,亚信科技,1.4.1.3,防毒墙,33
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,33
+26,寄云科技,2.1.3.1,物联网服务,33
+68,中望软件,1.3.1.1,计算机辅助设计CAD,33
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,33
+147,拓邦股份,1.1.2,工业控制器,33
+79,PTC,1.3.1.1,计算机辅助设计CAD,33
+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,33
+77,Oracle,1.3.3.6,运维保障系统MRO,33
+23,和利时,2.3.2,边缘数据处理,32
+6,安世亚太,2.1.2.4,行业机理模型,32
+79,PTC,1.3.1.6,产品生命周期管理PLM,32
+47,首自信,2.1.2.3,研发仿真模型,32
+35,凌昊智能,1.1.3,工业服务器,32
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,32
+168,中控技术,2.3.3,协议转换,32
+127,华为海思,1.1.3,工业服务器,32
+118,工邦邦,1.3.3.6,运维保障系统MRO,31
+64,中电智科,1.1.2,工业控制器,31
+13,东方国信,2.3.1,工业数据接入,31
+161,研华科技,2.3.1,工业数据接入,31
+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,31
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,31
+1,51WORLD,2.1.1.5,数字孪生建模工具,31
+22,航天云网,2.3.2,边缘数据处理,31
+70,ABB,1.3.3.4,可编程逻揖控制系统PLC,31
+144,树根互联,2.1.2.1,数据算法模型,31
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,31
+143,沈阳自动化研究所,2.1.1.4,组态建模工具,31
+166,中国电子科技网络信息安全,1.2.3,数据互通,31
+57,亚控科技,2.3.1,工业数据接入,31
+161,研华科技,2.3.3,协议转换,31
+47,首自信,2.1.2.4,行业机理模型,30
+49,数码大方,2.1.2.3,研发仿真模型,30
+47,首自信,2.1.2.2,业务流程模型,30
+13,东方国信,2.3.2,边缘数据处理,30
+13,东方国信,1.2.2,标识解析,30
+79,PTC,2.3.2,边缘数据处理,30
+8,梆梆安全,1.4.1.1,工业防火墙,30
+33,蓝谷信息,2.1.2.2,业务流程模型,30
+144,树根互联,2.1.2.2,业务流程模型,30
+125,华数机器人,1.2.3,数据互通,30
+165,智能云科,2.1.2.3,研发仿真模型,30
+47,首自信,2.1.1.4,组态建模工具,30
+47,首自信,2.1.1.1,算法建模工具,29
+78,OutSystems,2.1.1.4,组态建模工具,29
+13,东方国信,2.3.3,协议转换,29
+6,安世亚太,2.1.2.2,业务流程模型,29
+128,华伍股份,1.1.2,工业控制器,29
+49,数码大方,1.3.1.1,计算机辅助设计CAD,29
+68,中望软件,1.3.1.2,计算机辅助工程CAE,29
+22,航天云网,2.3.3,协议转换,29
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,29
+168,中控技术,2.3.2,边缘数据处理,28
+121,广州智臣,1.4.2.4,安全隔离与信息交换系统,28
+62,云道智造,1.3.1.2,计算机辅助工程CAE,28
+96,Cisco,1.2.3,数据互通,28
+33,蓝谷信息,2.1.2.3,研发仿真模型,28
+123,海得控制,1.1.2,工业控制器,28
+32,兰光创新,1.2.3,数据互通,28
+17,国保金泰,1.4.2.4,安全隔离与信息交换系统,28
+51,天地和兴,1.4.2.1,工控安全监测与审计,28
+6,安世亚太,2.1.2.1,数据算法模型,28
+66,中国联通,1.2.1,网络互联,28
+161,研华科技,2.3.2,边缘数据处理,28
+19,国泰网信,1.4.2.1,工控安全监测与审计,28
+23,和利时,2.3.3,协议转换,28
+149,天泽智云,2.1.2.2,业务流程模型,27
+79,PTC,2.3.3,协议转换,27
+78,OutSystems,2.1.1.1,算法建模工具,27
+126,华为,2.1.1.5,数字孪生建模工具,27
+165,智能云科,2.1.2.4,行业机理模型,27
+132,科远智慧,1.3.3.2,分布式控制系统DCS,27
+135,浪潮,1.1.3,工业服务器,27
+22,航天云网,2.1.1.1,算法建模工具,27
+26,寄云科技,2.1.3.6,微服务,27
+23,和利时,1.3.3.4,可编程逻揖控制系统PLC,27
+33,蓝谷信息,2.1.2.4,行业机理模型,27
+9,北京航天测控,1.3.3.6,运维保障系统MRO,27
+12,大唐软件,1.2.1,网络互联,27
+83,Emerson,1.3.3.2,分布式控制系统DCS,27
+47,首自信,2.1.2.1,数据算法模型,26
+16,东土科技,1.1.3,工业服务器,26
+82,Uptake,2.1.2.4,行业机理模型,26
+16,东土科技,2.3.1,工业数据接入,26
+53,天融信,1.4.1.5,统一威胁管理系统,26
+78,OutSystems,2.1.1.2,低代码开发工具,26
+95,Schneider,1.2.3,数据互通,26
+16,东土科技,2.3.2,边缘数据处理,26
+49,数码大方,2.1.2.1,数据算法模型,26
+16,东土科技,2.3.3,协议转换,26
+168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,26
+22,航天云网,1.2.2,标识解析,26
+165,智能云科,2.1.2.2,业务流程模型,26
+38,牛刀,2.1.1.1,算法建模工具,26
+149,天泽智云,2.1.2.4,行业机理模型,26
+22,航天云网,2.1.1.4,组态建模工具,26
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,26
+33,蓝谷信息,2.1.2.1,数据算法模型,26
+82,Uptake,2.1.2.3,研发仿真模型,26
+6,安世亚太,2.1.2.3,研发仿真模型,25
+82,Uptake,2.1.2.1,数据算法模型,25
+22,航天云网,2.1.1.3,流程开发工具,25
+53,天融信,1.4.5.3,数据审计系统,25
+129,华中数控,1.1.2,工业控制器,25
+143,沈阳自动化研究所,2.1.1.1,算法建模工具,25
+23,和利时,2.3.1,工业数据接入,25
+57,亚控科技,2.3.3,协议转换,25
+65,中国电信,1.2.1,网络互联,25
+41,启明星辰,1.4.1.2,下一代防火墙,25
+49,数码大方,2.1.2.2,业务流程模型,25
+140,山石网科,1.4.1.4,入侵检测系统,25
+41,启明星辰,1.4.1.5,统一威胁管理系统,25
+57,亚控科技,2.3.2,边缘数据处理,25
+111,鼎捷软件,1.3.4.1,企业资源计划ERP,25
+49,数码大方,2.1.2.4,行业机理模型,24
+22,航天云网,2.1.1.2,低代码开发工具,24
+62,云道智造,2.1.2.1,数据算法模型,24
+7,百望,2.2,IaaS,24
+168,中控技术,1.1.2,工业控制器,24
+18,国能智深,1.3.3.2,分布式控制系统DCS,24
+143,沈阳自动化研究所,2.1.1.3,流程开发工具,24
+53,天融信,1.4.1.4,入侵检测系统,24
+149,天泽智云,2.1.2.3,研发仿真模型,24
+45,石化盈科,1.3.4.1,企业资源计划ERP,24
+136,美的,1.2.1,网络互联,24
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,24
+141,上海新华控制,1.3.3.2,分布式控制系统DCS,24
+135,浪潮,1.3.4.1,企业资源计划ERP,24
+22,航天云网,2.3.1,工业数据接入,24
+115,富士康,1.1.3,工业服务器,24
+38,牛刀,2.1.1.3,流程开发工具,24
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,24
+62,云道智造,2.1.2.4,行业机理模型,23
+62,云道智造,2.1.2.3,研发仿真模型,23
+144,树根互联,2.1.2.3,研发仿真模型,23
+5,安华金和,1.4.5.3,数据审计系统,23
+89,Rockwell,1.3.3.1,制造执行系统MES,23
+58,用友,1.3.4.1,企业资源计划ERP,23
+60,宇动源,2.1.1.5,数字孪生建模工具,23
+111,鼎捷软件,1.3.3.1,制造执行系统MES,23
+82,Uptake,2.1.2.2,业务流程模型,23
+60,宇动源,2.1.1.1,算法建模工具,23
+165,智能云科,2.1.2.1,数据算法模型,23
+22,航天云网,2.1.4.1.3,实时数据库,22
+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,22
+55,威努特,1.4.1.2,下一代防火墙,22
+60,宇动源,2.1.1.2,低代码开发工具,22
+144,树根互联,2.1.2.4,行业机理模型,22
+169,中芯国际,1.1.1,工业计算芯片,22
+53,天融信,1.4.3.3,APT检测,22
+129,华中数控,1.2.3,数据互通,22
+89,Rockwell,1.1.2,工业控制器,22
+78,OutSystems,2.1.1.3,流程开发工具,22
+3,艾克斯特,1.3.4.1,企业资源计划ERP,22
+131,九物互联,2.1.1.3,流程开发工具,22
+140,山石网科,1.4.5.3,数据审计系统,22
+38,牛刀,2.1.1.4,组态建模工具,22
+99,Siemens,1.1.2,工业控制器,22
+37,绿盟,1.4.1.2,下一代防火墙,21
+103,STMicroelectronics ,1.1.1,工业计算芯片,21
+62,云道智造,2.1.2.2,业务流程模型,21
+149,天泽智云,2.1.2.1,数据算法模型,21
+36,龙芯中科,1.1.1,工业计算芯片,21
+47,首自信,2.1.1.3,流程开发工具,21
+45,石化盈科,2.1.4.1.2,分布式数据库,21
+44,圣邦微电子,1.1.1,工业计算芯片,21
+167,中环股份,1.1.1,工业计算芯片,21
+40,奇安信,1.4.3.3,APT检测,21
+130,金蝶,1.3.4.1,企业资源计划ERP,21
+49,数码大方,1.3.3.1,制造执行系统MES,21
+60,宇动源,2.1.1.3,流程开发工具,20
+91,Moxa,1.2.1,网络互联,20
+69,紫光集团,1.1.1,工业计算芯片,20
+70,ABB,1.3.3.2,分布式控制系统DCS,20
+22,航天云网,2.1.1.5,数字孪生建模工具,20
+38,牛刀,2.1.1.2,低代码开发工具,20
+55,威努特,1.4.2.1,工控安全监测与审计,20
+28,金山云,2.2,IaaS,20
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,20
+22,航天云网,2.1.4.2.1,数据质量管理,20
+38,牛刀,2.1.1.5,数字孪生建模工具,20
+23,和利时,1.3.3.1,制造执行系统MES,19
+45,石化盈科,2.1.4.1.4,时序数据库,19
+89,Rockwell,1.2.1,网络互联,19
+37,绿盟,1.4.1.4,入侵检测系统,19
+113,飞腾信息,1.1.1,工业计算芯片,19
+13,东方国信,2.1.4.1.4,时序数据库,19
+23,和利时,1.1.2,工业控制器,19
+60,宇动源,2.1.1.4,组态建模工具,19
+163,优也科技,2.1.4.2.1,数据质量管理,19
+117,格创东智,2.1.1.2,低代码开发工具,19
+117,格创东智,2.1.1.3,流程开发工具,19
+87,Texas Instruments,1.1.1,工业计算芯片,18
+131,九物互联,2.1.1.1,算法建模工具,18
+137,美林数据,2.1.4.2.2,数据安全管理,18
+131,九物互联,2.1.1.4,组态建模工具,18
+154,西格数据,2.1.4.1.2,分布式数据库,18
+63,长扬科技,1.4.1.1,工业防火墙,18
+104,Infineon,1.1.1,工业计算芯片,18
+101,Analog Devices,1.1.1,工业计算芯片,18
+22,航天云网,1.3.3.6,运维保障系统MRO,18
+45,石化盈科,1.3.3.1,制造执行系统MES,18
+168,中控技术,1.3.3.1,制造执行系统MES,17
+24,华大电子,1.1.1,工业计算芯片,17
+13,东方国信,2.1.4.2.2,数据安全管理,17
+31,昆仑数据,2.1.4.2.1,数据质量管理,17
+13,东方国信,2.1.4.1.2,分布式数据库,17
+96,Cisco,1.2.1,网络互联,17
+22,航天云网,2.1.4.2.2,数据安全管理,17
+22,航天云网,2.1.4.1.2,分布式数据库,17
+117,格创东智,2.1.1.1,算法建模工具,17
+163,优也科技,2.1.4.1.4,时序数据库,17
+140,山石网科,1.4.1.5,统一威胁管理系统,17
+154,西格数据,2.1.4.1.4,时序数据库,17
+55,威努特,1.4.1.1,工业防火墙,17
+74,HoneyWell,1.3.3.2,分布式控制系统DCS,17
+117,格创东智,2.1.1.5,数字孪生建模工具,17
+150,唯捷创芯,1.1.1,工业计算芯片,17
+137,美林数据,2.1.4.1.4,时序数据库,17
+163,优也科技,2.1.4.1.1,关系型数据库,16
+124,海尔,1.2.1,网络互联,16
+45,石化盈科,2.1.4.1.1,关系型数据库,16
+117,格创东智,2.1.1.4,组态建模工具,16
+22,航天云网,2.1.4.1.1,关系型数据库,16
+31,昆仑数据,2.1.4.1.1,关系型数据库,16
+137,美林数据,2.1.4.2.1,数据质量管理,16
+163,优也科技,2.1.4.1.2,分布式数据库,15
+137,美林数据,2.1.4.1.2,分布式数据库,15
+55,威努特,1.4.1.3,防毒墙,15
+117,格创东智,2.1.4.1.1,关系型数据库,15
+131,九物互联,2.1.1.2,低代码开发工具,15
+31,昆仑数据,2.1.4.1.3,实时数据库,15
+154,西格数据,2.1.4.2.1,数据质量管理,15
+127,华为海思,1.1.1,工业计算芯片,15
+154,西格数据,2.1.4.2.2,数据安全管理,15
+137,美林数据,2.1.4.1.3,实时数据库,15
+22,航天云网,1.2.1,网络互联,15
+45,石化盈科,2.1.4.1.3,实时数据库,14
+45,石化盈科,2.1.4.2.1,数据质量管理,14
+40,奇安信,1.4.2.1,工控安全监测与审计,14
+163,优也科技,2.1.4.2.2,数据安全管理,14
+133,蓝盾股份,1.4.1.3,防毒墙,14
+154,西格数据,2.1.4.1.1,关系型数据库,14
+31,昆仑数据,2.1.4.2.2,数据安全管理,13
+168,中控技术,1.3.3.2,分布式控制系统DCS,13
+131,九物互联,2.1.1.5,数字孪生建模工具,13
+54,网御星云,1.4.2.4,安全隔离与信息交换系统,13
+154,西格数据,2.1.4.1.3,实时数据库,13
+13,东方国信,2.1.4.1.3,实时数据库,12
+117,格创东智,2.1.4.1.3,实时数据库,12
+13,东方国信,2.1.4.1.1,关系型数据库,12
+126,华为,2.2,IaaS,12
+117,格创东智,2.1.4.2.2,数据安全管理,12
+137,美林数据,2.1.4.1.1,关系型数据库,12
+31,昆仑数据,2.1.4.1.2,分布式数据库,11
+117,格创东智,2.1.4.2.1,数据质量管理,11
+45,石化盈科,2.1.4.2.2,数据安全管理,11
+53,天融信,1.4.1.3,防毒墙,11
+117,格创东智,2.1.4.1.2,分布式数据库,11
+78,OutSystems,2.1.1.5,数字孪生建模工具,11
+117,格创东智,2.1.4.1.4,时序数据库,11
+163,优也科技,2.1.4.1.3,实时数据库,11
+31,昆仑数据,2.1.4.1.4,时序数据库,11
+142,深信服,1.4.1.1,工业防火墙,10
+106,阿里巴巴,2.2,IaaS,10
+13,东方国信,2.1.4.2.1,数据质量管理,10
+54,网御星云,1.4.1.3,防毒墙,10
+148,腾讯,2.2,IaaS,10
+23,和利时,1.3.3.2,分布式控制系统DCS,10
+22,航天云网,2.1.4.1.4,时序数据库,9
+99,Siemens,1.2.1,网络互联,9
+53,天融信,1.4.2.4,安全隔离与信息交换系统,8
+63,长扬科技,1.4.2.4,安全隔离与信息交换系统,8
+135,浪潮,2.2,IaaS,7
+56,芯愿景,1.1.1,工业计算芯片,7
+108,百度,2.2,IaaS,7
+140,山石网科,1.4.1.1,工业防火墙,6
diff --git a/analysis/count_prod.csv b/analysis/count_prod.csv
index 494f17d..caea6ca 100644
--- a/analysis/count_prod.csv
+++ b/analysis/count_prod.csv
@@ -1,82 +1,108 @@
 id_product,Name,count
-1.4,工业互联网安全,385
-2.1.3,工业物联网,184
-1.4.5,数据安全,150
-1.4.3,网络安全,150
-1.4.2,控制安全,150
-1,供给,125
-1.3,工业软件,119
-1.3.1,设计研发,119
-1.1,工业自动化,114
-2.1.2,工业模型库,103
-2.3,边缘层,102
-2,工业互联网平台,83
-2.1.1,开发工具,82
-1.4.4,平台安全,77
-1.3.2,采购供应,76
-1.1.1,工业计算芯片,67
-1.3.3,生产制造,60
-2.1,PaaS,50
-1.3.5,仓储物流,50
+2.1.3,工业物联网,2810
+1.3.1,设计研发,2246
+1,供给,1525
+1.3.3,生产制造,1296
+2.1.2,工业模型库,1269
+2.1.1,开发工具,1159
+2.3,边缘层,1073
+1.4,工业互联网安全,1012
+1.1,工业自动化,904
+1.3,工业软件,886
+1.2,工业互联网网络,751
+1.4.4,平台安全,709
+1.4.5,数据安全,640
+2.1.4.1,工业大数据存储,595
+1.3.4,企业运营管理,592
+1.4.2,控制安全,529
+2,工业互联网平台,436
+1.4.3,网络安全,430
+1.4.1,设备安全,418
+2.1.4,工业大数据,372
+2.1.4.2,工业大数据管理,342
+2.1,PaaS,323
+1.1.1,工业计算芯片,255
+2.1.3.6,微服务,249
+2.1.2.3,研发仿真模型,241
+2.1.2.2,业务流程模型,241
+1.3.1.2,计算机辅助工程CAE,241
+2.1.2.4,行业机理模型,237
+2.3.1,工业数据接入,237
+1.3.1.1,计算机辅助设计CAD,236
+1.1.2,工业控制器,233
+2.1.2.1,数据算法模型,230
+2.3.2,边缘数据处理,230
+2.3.3,协议转换,227
+2.1.3.2,平台基础服务,212
+2.1.3.4,应用管理服务,211
+1.1.3,工业服务器,211
+2.1.3.5,容器服务,211
+2.1.3.3,工业引擎服务,207
+1.3.1.4,计算机辅助工艺过程设计CAPP,207
+2.1.3.7,制造类API,206
+2.1.3.1,物联网服务,203
+1.3.1.7,电子设计自动化EDA,201
+1.2.3,数据互通,201
+1.3.1.6,产品生命周期管理PLM,200
+1.2.1,网络互联,200
+2.1.1.1,算法建模工具,192
+2.1.1.4,组态建模工具,191
+2.1.1.5,数字孪生建模工具,184
+2.1.1.2,低代码开发工具,184
+2.1.1.3,流程开发工具,177
+1.3.2,采购供应,176
+1.3.1.5,产品数据管理PDM,166
+1.3.1.3,计算机辅助制造CAM,166
+1.2.2,标识解析,164
+1.3.3.2,分布式控制系统DCS,162
+1.3.3.1,制造执行系统MES,155
+1.3.3.4,可编程逻揖控制系统PLC,147
+1.3.3.6,运维保障系统MRO,143
+1.3.4.1,企业资源计划ERP,139
+1.3.3.5,企业资产管理系统EAM,138
+1.3.3.3,数据采集与监视控制系统SCADA,134
+2.1.4.1.2,分布式数据库,125
+2.1.4.2.1,数据质量管理,122
+1.3.4.3,人力资源管理HRM,121
+1.3.4.2,客户关系管理CRM,121
+2.1.4.1.4,时序数据库,120
+1.3.5,仓储物流,120
+1.4.4.1,身份鉴别与访问控制,119
+2.1.4.2.2,数据安全管理,117
+2.1.4.1.1,关系型数据库,117
+1.3.3.7,故障预测与健康管理PHM,116
+2.1.4.1.3,实时数据库,114
+1.4.2.1,工控安全监测与审计,90
+2.2,IaaS,90
+1.4.4.3,接入认证,90
+1.4.4.4,工业应用行为监控,90
+1.4.4.2,密钥管理,90
+1.4.2.4,安全隔离与信息交换系统,85
+1.4.1.3,防毒墙,83
+1.4.1.1,工业防火墙,81
+1.4.3.3,APT检测,76
+1.4.5.9,数据防火墙,70
+1.4.5.7,数据恢复,70
+1.4.5.6,数据容灾备份,70
+1.4.5.5,敏感数据发现与监控,70
+1.4.5.4,数据脱敏,70
+1.4.5.3,数据审计系统,70
+1.4.5.2,数据防泄漏系统,70
+1.3.5.1,仓储物流管理WMS,70
+1.4.3.4,攻击溯源,68
+1.4.3.5,负载均衡,68
+1.4.3.1,网络漏洞扫描和补丁管理,68
+1.4.2.6,隐私计算,68
+1.4.2.5,安全日志与审计,68
+1.4.2.2,工控主机卫士,68
+1.4.1.5,统一威胁管理系统,68
+1.4.1.4,入侵检测系统,68
+1.4.1.2,下一代防火墙,68
 1.3.2.1,供应链管理SCM,50
-1.4.5.8,数据加密,50
 1.4.5.1,恶意代码检测系统,50
 1.4.4.5,安全态势感知,50
 1.4.3.6,沙箱类设备,50
 1.4.3.2,流量检测,50
 1.4.2.7,工控原生安全,50
 1.4.2.3,工控漏洞扫描,50
-1.4.1,设备安全,50
-1.2,工业互联网网络,43
-2.3.3,协议转换,37
-2.3.1,工业数据接入,33
-2.1.3.6,微服务,33
-2.3.2,边缘数据处理,30
-2.1.2.4,行业机理模型,30
-2.1.3.4,应用管理服务,29
-1.3.1.1,计算机辅助设计CAD,28
-2.1.2.2,业务流程模型,28
-2.1.2.1,数据算法模型,27
-2.1.3.7,制造类API,27
-1.3.1.2,计算机辅助工程CAE,26
-2.1.3.1,物联网服务,25
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-2.1.3.2,平台基础服务,21
-1.3.1.4,计算机辅助工艺过程设计CAPP,20
-2.1.2.3,研发仿真模型,18
-2.1.1.5,数字孪生建模工具,18
-1.3.1.6,产品生命周期管理PLM,18
-1.2.3,数据互通,17
-2.1.1.1,算法建模工具,15
-2.1.1.4,组态建模工具,14
-2.1.4.1,工业大数据存储,14
-1.3.3.2,分布式控制系统DCS,14
-1.2.2,标识解析,13
-1.2.1,网络互联,13
-2.1.4.2,工业大数据管理,12
-1.3.1.7,电子设计自动化EDA,12
-2.1.1.3,流程开发工具,12
-1.3.3.3,数据采集与监视控制系统SCADA,11
-1.3.3.1,制造执行系统MES,10
-1.3.3.6,运维保障系统MRO,10
-1.3.4,企业运营管理,8
-1.3.3.4,可编程逻揖控制系统PLC,7
-1.3.3.5,企业资产管理系统EAM,6
-1.3.4.1,企业资源计划ERP,6
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-2.1.4.2.2,数据安全管理,5
-1.3.1.5,产品数据管理PDM,5
-2.1.4,工业大数据,4
-2.1.4.1.4,时序数据库,4
-2.1.4.1.1,关系型数据库,3
-2.1.4.1.2,分布式数据库,3
-2.1.4.1.3,实时数据库,3
-2.2,IaaS,3
-1.3.1.3,计算机辅助制造CAM,2
-1.3.3.7,故障预测与健康管理PHM,1
-1.4.4.1,身份鉴别与访问控制,1
-1.3.4.3,人力资源管理HRM,1
+1.4.5.8,数据加密,50
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diff --git a/analysis/dissertation_count.csv b/analysis/dissertation_count.csv
new file mode 100644
index 0000000..9b72392
--- /dev/null
+++ b/analysis/dissertation_count.csv
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diff --git a/analysis/dissertation_count_dcp.csv b/analysis/dissertation_count_dcp.csv
new file mode 100644
index 0000000..ca63932
--- /dev/null
+++ b/analysis/dissertation_count_dcp.csv
@@ -0,0 +1,700 @@
+up_id_firm,up_name_firm,up_id_product,up_name_product,down_id_firm,down_name_firm,down_id_product,down_name_product,count
+126,华为,1.4,工业互联网安全,170,Pseudo1,1,供给,118
+142,深信服,1.4.3,网络安全,126,华为,1.4,工业互联网安全,96
+41,启明星辰,1.4.5,数据安全,126,华为,1.4,工业互联网安全,92
+142,深信服,1.4.2,控制安全,126,华为,1.4,工业互联网安全,92
+53,天融信,1.4.3.6,沙箱类设备,142,深信服,1.4.3,网络安全,50
+23,和利时,1.4.2.7,工控原生安全,142,深信服,1.4.2,控制安全,50
+157,新华三,1.4.1,设备安全,126,华为,1.4,工业互联网安全,50
+53,天融信,1.4.5.8,数据加密,41,启明星辰,1.4.5,数据安全,50
+41,启明星辰,1.4.3.2,流量检测,142,深信服,1.4.3,网络安全,50
+53,天融信,1.4.2.3,工控漏洞扫描,142,深信服,1.4.2,控制安全,50
+140,山石网科,1.4.5.1,恶意代码检测系统,41,启明星辰,1.4.5,数据安全,50
+99,Siemens,2.1,PaaS,102,Amazon AWS,2,工业互联网平台,41
+135,浪潮,1.3.2.1,供应链管理SCM,130,金蝶,1.3.2,采购供应,40
+130,金蝶,1.3.5,仓储物流,106,阿里巴巴,1.3,工业软件,39
+63,长扬科技,1.4.4.5,安全态势感知,0,360科技,1.4.4,平台安全,38
+63,长扬科技,1.4.4.5,安全态势感知,40,奇安信,1.4.4,平台安全,38
+135,浪潮,1.3.2.1,供应链管理SCM,58,用友,1.3.2,采购供应,36
+99,Siemens,2.1,PaaS,98,Microsoft Azure,2,工业互联网平台,36
+130,金蝶,1.3.5,仓储物流,29,京东工业品,1.3,工业软件,33
+130,金蝶,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,23
+135,浪潮,1.3.2.1,供应链管理SCM,106,阿里巴巴,1.3,工业软件,23
+53,天融信,1.4.3.6,沙箱类设备,126,华为,1.4,工业互联网安全,23
+41,启明星辰,1.4.3.2,流量检测,126,华为,1.4,工业互联网安全,23
+58,用友,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,23
+53,天融信,1.4.5.8,数据加密,126,华为,1.4,工业互联网安全,21
+140,山石网科,1.4.5.1,恶意代码检测系统,126,华为,1.4,工业互联网安全,21
+142,深信服,1.4.3,网络安全,170,Pseudo1,1,供给,21
+53,天融信,1.4.2.3,工控漏洞扫描,126,华为,1.4,工业互联网安全,21
+23,和利时,1.4.2.7,工控原生安全,126,华为,1.4,工业互联网安全,21
+41,启明星辰,1.4.5,数据安全,170,Pseudo1,1,供给,19
+142,深信服,1.4.2,控制安全,170,Pseudo1,1,供给,19
+130,金蝶,1.3.2,采购供应,29,京东工业品,1.3,工业软件,14
+58,用友,1.3.2,采购供应,29,京东工业品,1.3,工业软件,14
+135,浪潮,1.3.2.1,供应链管理SCM,29,京东工业品,1.3,工业软件,14
+157,新华三,1.4.1,设备安全,170,Pseudo1,1,供给,9
+41,启明星辰,1.4.3.2,流量检测,170,Pseudo1,1,供给,6
+53,天融信,1.4.3.6,沙箱类设备,170,Pseudo1,1,供给,6
+99,Siemens,2.1,PaaS,170,Pseudo1,1,供给,5
+53,天融信,1.4.2.3,工控漏洞扫描,170,Pseudo1,1,供给,5
+40,奇安信,1.4.4,平台安全,126,华为,1.4,工业互联网安全,5
+98,Microsoft Azure,2,工业互联网平台,170,Pseudo1,1,供给,5
+63,长扬科技,1.4.4.5,安全态势感知,126,华为,1.4,工业互联网安全,5
+140,山石网科,1.4.5.1,恶意代码检测系统,170,Pseudo1,1,供给,5
+23,和利时,1.4.2.7,工控原生安全,170,Pseudo1,1,供给,5
+53,天融信,1.4.5.8,数据加密,170,Pseudo1,1,供给,5
+0,360科技,1.4.4,平台安全,126,华为,1.4,工业互联网安全,5
+102,Amazon AWS,2,工业互联网平台,170,Pseudo1,1,供给,5
+131,九物互联,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,4
+42,山大华天,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,4
+13,东方国信,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,4
+79,PTC,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,4
+78,OutSystems,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,3
+68,中望软件,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,3
+13,东方国信,2.1.3.4,应用管理服务,74,HoneyWell,2.1.3,工业物联网,3
+13,东方国信,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,3
+13,东方国信,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,3
+13,东方国信,2.1.3.7,制造类API,108,百度,2.1.3,工业物联网,3
+79,PTC,2.3.3,协议转换,126,华为,2.3,边缘层,3
+69,紫光集团,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,3
+79,PTC,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,3
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,3
+16,东土科技,2.3.3,协议转换,126,华为,2.3,边缘层,3
+79,PTC,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,3
+117,格创东智,2.1.1.1,算法建模工具,80,Salesforce,2.1.1,开发工具,3
+79,PTC,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,3
+79,PTC,2.1.3.4,应用管理服务,74,HoneyWell,2.1.3,工业物联网,3
+79,PTC,2.1.3.7,制造类API,148,腾讯,2.1.3,工业物联网,3
+79,PTC,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,3
+32,兰光创新,1.2.3,数据互通,106,阿里巴巴,1.2,工业互联网网络,3
+103,STMicroelectronics ,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,3
+169,中芯国际,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+47,首自信,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,2
+74,HoneyWell,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,2
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,2
+57,亚控科技,2.3.2,边缘数据处理,155,小米,2.3,边缘层,2
+22,航天云网,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,2
+71,Altair,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,2
+47,首自信,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,2
+70,ABB,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,2
+22,航天云网,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,2
+104,Infineon,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+57,亚控科技,2.3.3,协议转换,155,小米,2.3,边缘层,2
+57,亚控科技,2.3.3,协议转换,99,Siemens,2.3,边缘层,2
+47,首自信,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,2
+22,航天云网,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,2
+22,航天云网,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,2
+48,曙光信息,1.2.2,标识解析,126,华为,1.2,工业互联网网络,2
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,2
+49,数码大方,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,2
+78,OutSystems,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,2
+57,亚控科技,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,2
+6,安世亚太,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,2
+78,OutSystems,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,2
+79,PTC,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,2
+168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,2
+135,浪潮,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,2
+135,浪潮,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,2
+167,中环股份,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,2
+79,PTC,2.1.3.1,物联网服务,73,FANUC,2.1.3,工业物联网,2
+168,中控技术,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,2
+22,航天云网,2.3.3,协议转换,99,Siemens,2.3,边缘层,2
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,2
+138,启明信息,1.3.1.5,产品数据管理PDM,100,Synopsys,1.3.1,设计研发,2
+56,芯愿景,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,75,IBM,1.3.3,生产制造,2
+167,中环股份,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
+166,中国电子科技网络信息安全,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,2
+103,STMicroelectronics ,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
+47,首自信,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,2
+117,格创东智,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,2
+22,航天云网,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,2
+47,首自信,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,2
+49,数码大方,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,2
+165,智能云科,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,2
+58,用友,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,2
+103,STMicroelectronics ,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,39,Autodesk,1.3.1,设计研发,2
+60,宇动源,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,2
+49,数码大方,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,2
+60,宇动源,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,2
+115,富士康,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,2
+16,东土科技,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,2
+16,东土科技,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,2
+161,研华科技,2.3.3,协议转换,95,Schneider,2.3,边缘层,2
+6,安世亚太,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,2
+6,安世亚太,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,2
+16,东土科技,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,2
+16,东土科技,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,2
+6,安世亚太,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,2
+6,安世亚太,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,2
+161,研华科技,2.3.3,协议转换,155,小米,2.3,边缘层,2
+16,东土科技,2.3.3,协议转换,95,Schneider,2.3,边缘层,2
+6,安世亚太,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,2
+169,中芯国际,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
+6,安世亚太,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,2
+49,数码大方,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,2
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,2
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,2
+135,浪潮,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,2
+49,数码大方,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,2
+147,拓邦股份,1.1.2,工业控制器,105,Intel,1.1,工业自动化,2
+168,中控技术,2.3.3,协议转换,126,华为,2.3,边缘层,2
+147,拓邦股份,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,2
+64,中电智科,1.1.2,工业控制器,105,Intel,1.1,工业自动化,2
+149,天泽智云,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,2
+101,Analog Devices,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
+149,天泽智云,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,2
+62,云道智造,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,2
+22,航天云网,1.2.2,标识解析,126,华为,1.2,工业互联网网络,2
+49,数码大方,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,2
+62,云道智造,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,2
+106,阿里巴巴,1.3,工业软件,170,Pseudo1,1,供给,2
+22,航天云网,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,2
+150,唯捷创芯,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
+49,数码大方,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,2
+33,蓝谷信息,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,2
+22,航天云网,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,2
+79,PTC,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,2
+150,唯捷创芯,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,2
+13,东方国信,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,2
+82,Uptake,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,2
+38,牛刀,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,2
+26,寄云科技,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,2
+13,东方国信,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,2
+13,东方国信,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,2
+9,北京航天测控,1.3.3.6,运维保障系统MRO,97,General Electric,1.3.3,生产制造,2
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,39,Autodesk,1.3.1,设计研发,2
+26,寄云科技,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,2
+13,东方国信,2.1.3.7,制造类API,148,腾讯,2.1.3,工业物联网,2
+13,东方国信,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,2
+127,华为海思,1.1.3,工业服务器,126,华为,1.1,工业自动化,2
+24,华大电子,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,2
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,99,Siemens,1.3.1,设计研发,2
+79,PTC,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,2
+44,圣邦微电子,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+127,华为海思,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
+13,东方国信,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,2
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,2
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,2
+46,适创科技,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,2
+29,京东工业品,1.3,工业软件,170,Pseudo1,1,供给,2
+87,Texas Instruments,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
+13,东方国信,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,2
+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,2
+88,HPE,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,2
+13,东方国信,2.1.3.3,工业引擎服务,106,阿里巴巴,2.1.3,工业物联网,2
+4,爱创科技,1.2.2,标识解析,97,General Electric,1.2,工业互联网网络,2
+13,东方国信,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,2
+82,Uptake,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,2
+113,飞腾信息,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,2
+89,Rockwell,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,2
+13,东方国信,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,2
+13,东方国信,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,2
+13,东方国信,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,2
+129,华中数控,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,2
+79,PTC,2.3.1,工业数据接入,126,华为,2.3,边缘层,2
+127,华为海思,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,2
+79,PTC,2.3.1,工业数据接入,124,海尔,2.3,边缘层,2
+123,海得控制,1.1.2,工业控制器,126,华为,1.1,工业自动化,2
+131,九物互联,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,2
+38,牛刀,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,2
+79,PTC,2.1.3.4,应用管理服务,148,腾讯,2.1.3,工业物联网,2
+23,和利时,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,2
+99,Siemens,1.1.2,工业控制器,105,Intel,1.1,工业自动化,2
+23,和利时,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,2
+120,广州数控,1.2.3,数据互通,126,华为,1.2,工业互联网网络,2
+99,Siemens,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,2
+35,凌昊智能,1.1.3,工业服务器,105,Intel,1.1,工业自动化,2
+135,浪潮,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,2
+113,飞腾信息,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,2
+79,PTC,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,2
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,2
+46,适创科技,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,2
+117,格创东智,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,2
+79,PTC,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,2
+13,东方国信,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,2
+23,和利时,2.3.2,边缘数据处理,155,小米,2.3,边缘层,2
+95,Schneider,1.2.3,数据互通,126,华为,1.2,工业互联网网络,2
+79,PTC,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,2
+95,Schneider,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,2
+38,牛刀,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,2
+79,PTC,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,2
+79,PTC,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,2
+79,PTC,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,2
+109,宝信软件,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,1
+104,Infineon,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
+49,数码大方,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,1
+36,龙芯中科,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+108,百度,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,1
+49,数码大方,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
+35,凌昊智能,1.1.3,工业服务器,126,华为,1.1,工业自动化,1
+49,数码大方,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
+104,Infineon,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+56,芯愿景,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
+38,牛刀,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,1
+39,Autodesk,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,1
+36,龙芯中科,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
+36,龙芯中科,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
+103,STMicroelectronics ,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+38,牛刀,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,1
+35,凌昊智能,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
+38,牛刀,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
+50,索为系统,1.3.1.5,产品数据管理PDM,85,Dassault,1.3.1,设计研发,1
+38,牛刀,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
+50,索为系统,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,1
+46,适创科技,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,1
+39,Autodesk,1.3.1,设计研发,29,京东工业品,1.3,工业软件,1
+45,石化盈科,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,1
+45,石化盈科,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,1
+45,石化盈科,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,1
+47,首自信,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,1
+47,首自信,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
+45,石化盈科,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,1
+45,石化盈科,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,1
+47,首自信,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,1
+47,首自信,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
+47,首自信,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
+47,首自信,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
+47,首自信,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,1
+47,首自信,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
+45,石化盈科,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,1
+47,首自信,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,1
+47,首自信,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,1
+47,首自信,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,1
+45,石化盈科,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,1
+46,适创科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,1
+47,首自信,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,1
+44,圣邦微电子,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+49,数码大方,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,1
+4,爱创科技,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,1
+49,数码大方,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
+111,鼎捷软件,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,1
+49,数码大方,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,1
+111,鼎捷软件,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,1
+111,鼎捷软件,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,1
+49,数码大方,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,1
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,1
+47,首自信,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,1
+42,山大华天,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,1
+42,山大华天,1.3.1.3,计算机辅助制造CAM,99,Siemens,1.3.1,设计研发,1
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,1
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
+47,首自信,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,1
+43,神舟软件,1.3.1.5,产品数据管理PDM,93,Cadence,1.3.1,设计研发,1
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,1
+56,芯愿景,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+119,广联达,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,1
+56,芯愿景,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+82,Uptake,2.1.2.1,数据算法模型,159,徐工集团,2.1.2,工业模型库,1
+81,SAP,2.1.4.2,工业大数据管理,115,富士康,2.1.4,工业大数据,1
+81,SAP,2.1.4.2,工业大数据管理,102,Amazon AWS,2.1.4,工业大数据,1
+81,SAP,2.1.4.1,工业大数据存储,102,Amazon AWS,2.1.4,工业大数据,1
+80,Salesforce,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,1
+79,PTC,2.3.3,协议转换,95,Schneider,2.3,边缘层,1
+79,PTC,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
+79,PTC,2.3.3,协议转换,155,小米,2.3,边缘层,1
+79,PTC,2.3.3,协议转换,102,Amazon AWS,2,工业互联网平台,1
+79,PTC,2.3.2,边缘数据处理,126,华为,2.3,边缘层,1
+79,PTC,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,1
+79,PTC,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
+79,PTC,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
+79,PTC,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,1
+79,PTC,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
+79,PTC,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,1
+79,PTC,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,1
+79,PTC,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,1
+79,PTC,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,1
+79,PTC,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,1
+79,PTC,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,1
+79,PTC,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,1
+79,PTC,2.1.3.3,工业引擎服务,148,腾讯,2.1.3,工业物联网,1
+79,PTC,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,1
+79,PTC,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,1
+79,PTC,2.1.3.2,平台基础服务,74,HoneyWell,2.1.3,工业物联网,1
+79,PTC,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,1
+79,PTC,1.3.1.6,产品生命周期管理PLM,93,Cadence,1.3.1,设计研发,1
+81,SAP,2.1.4.2,工业大数据管理,84,Bosch,2.1.4,工业大数据,1
+82,Uptake,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
+79,PTC,1.3.1.6,产品生命周期管理PLM,100,Synopsys,1.3.1,设计研发,1
+82,Uptake,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,1
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,1
+99,Siemens,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
+100,Synopsys,1.3.1,设计研发,29,京东工业品,1.3,工业软件,1
+97,General Electric,1.3.3,生产制造,29,京东工业品,1.3,工业软件,1
+96,Cisco,1.2.3,数据互通,126,华为,1.2,工业互联网网络,1
+95,Schneider,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,1
+93,Cadence,1.3.1,设计研发,29,京东工业品,1.3,工业软件,1
+93,Cadence,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,1
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,97,General Electric,1.3.3,生产制造,1
+91,Moxa,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,1
+9,北京航天测控,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,1
+89,Rockwell,1.3.3.1,制造执行系统MES,75,IBM,1.3.3,生产制造,1
+89,Rockwell,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,1
+89,Rockwell,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,1
+89,Rockwell,1.1.2,工业控制器,126,华为,1.1,工业自动化,1
+88,HPE,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
+87,Texas Instruments,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+85,Dassault,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,1
+83,Emerson,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,1
+83,Emerson,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,1
+82,Uptake,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
+82,Uptake,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
+82,Uptake,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,1
+82,Uptake,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,1
+82,Uptake,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,1
+79,PTC,1.3.1.6,产品生命周期管理PLM,85,Dassault,1.3.1,设计研发,1
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
+56,芯愿景,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,1
+62,云道智造,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,1
+62,云道智造,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,1
+62,云道智造,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,1
+62,云道智造,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
+60,宇动源,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
+60,宇动源,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,1
+60,宇动源,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,1
+60,宇动源,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
+60,宇动源,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,1
+60,宇动源,2.1.1.1,算法建模工具,148,腾讯,2.1.1,开发工具,1
+60,宇动源,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,1
+6,安世亚太,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,1
+6,安世亚太,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
+6,安世亚太,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,1
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,1
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+103,STMicroelectronics ,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
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+79,PTC,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,1
+79,PTC,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
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+78,OutSystems,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,1
+78,OutSystems,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
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+77,Oracle,1.3.3.6,运维保障系统MRO,97,General Electric,1.3.3,生产制造,1
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+71,Altair,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
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+68,中望软件,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,1
+68,中望软件,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,1
+68,中望软件,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
+66,中国联通,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
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+65,中国电信,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
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+101,Analog Devices,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
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+23,和利时,2.3.3,协议转换,99,Siemens,2.3,边缘层,1
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+135,浪潮,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,1
+135,浪潮,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
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+135,浪潮,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,1
+137,美林数据,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,1
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+139,容知日新,1.3.3.7,故障预测与健康管理PHM,99,Siemens,1.3.3,生产制造,1
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+117,格创东智,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,1
+117,格创东智,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
+141,上海新华控制,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,1
+141,上海新华控制,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,1
+135,浪潮,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,1
+135,浪潮,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,1
+116,概伦电子,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,1
+117,格创东智,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,1
+131,九物互联,2.1.1.4,组态建模工具,106,阿里巴巴,2.1.1,开发工具,1
+132,科远智慧,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,1
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+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,1
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,1
+135,浪潮,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
+117,格创东智,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
+135,浪潮,2.1.3.4,应用管理服务,106,阿里巴巴,2.1.3,工业物联网,1
+135,浪潮,1.3.2.1,供应链管理SCM,170,Pseudo1,1,供给,1
+117,格创东智,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,1
+135,浪潮,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,1
+135,浪潮,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,1
+135,浪潮,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,1
+135,浪潮,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,1
+117,格创东智,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,1
+116,概伦电子,1.3.1.7,电子设计自动化EDA,39,Autodesk,1.3.1,设计研发,1
+150,唯捷创芯,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+149,天泽智云,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,1
+146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
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+149,天泽智云,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,1
+149,天泽智云,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
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+149,天泽智云,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,1
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,1
+149,天泽智云,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
+149,天泽智云,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
+149,天泽智云,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,1
+149,天泽智云,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
+15,东软集团,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,1
+15,东软集团,1.3.3.5,企业资产管理系统EAM,99,Siemens,1.3.3,生产制造,1
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,1
+144,树根互联,2.1.2.4,行业机理模型,84,Bosch,2.1.2,工业模型库,1
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,1
+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,1
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,1
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+143,沈阳自动化研究所,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,1
+143,沈阳自动化研究所,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
+144,树根互联,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,1
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+144,树根互联,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
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+144,树根互联,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,1
+144,树根互联,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,1
+131,九物互联,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,1
+131,九物互联,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,1
+131,九物互联,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,1
+13,东方国信,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,1
+128,华伍股份,1.1.2,工业控制器,126,华为,1.1,工业自动化,1
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+129,华中数控,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,1
+129,华中数控,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,1
+13,东方国信,1.2.2,标识解析,126,华为,1.2,工业互联网网络,1
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+127,华为海思,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
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+13,东方国信,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,1
+13,东方国信,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,1
+13,东方国信,2.1.3.2,平台基础服务,97,General Electric,2.1.3,工业物联网,1
+13,东方国信,2.1.3.3,工业引擎服务,126,华为,2.1.3,工业物联网,1
+13,东方国信,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,1
+128,华伍股份,1.1.2,工业控制器,106,阿里巴巴,1.1,工业自动化,1
+127,华为海思,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,1
+117,格创东智,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,1
+123,海得控制,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,1
+119,广联达,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,1
+12,大唐软件,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1
+12,大唐软件,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
+12,大唐软件,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,1
+120,广州数控,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,1
+123,海得控制,1.1.2,工业控制器,105,Intel,1.1,工业自动化,1
+124,海尔,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1
+126,华为,2.3,边缘层,102,Amazon AWS,2,工业互联网平台,1
+124,海尔,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1
+124,海尔,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,1
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+118,工邦邦,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,1
+126,华为,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,1
+126,华为,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,1
+13,东方国信,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,1
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+13,东方国信,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,1
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+13,东方国信,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
+13,东方国信,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,1
+13,东方国信,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
+13,东方国信,2.3.3,协议转换,155,小米,2.3,边缘层,1
+13,东方国信,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,1
+117,格创东智,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,1
+130,金蝶,1.3.2,采购供应,170,Pseudo1,1,供给,1
+117,格创东智,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,1
+130,金蝶,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,1
+117,格创东智,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,1
+130,金蝶,1.3.5,仓储物流,170,Pseudo1,1,供给,1
+13,东方国信,2.3.1,工业数据接入,155,小米,2.3,边缘层,1
+13,东方国信,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
+13,东方国信,2.3.1,工业数据接入,124,海尔,2.3,边缘层,1
+13,东方国信,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,1
+13,东方国信,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,1
+13,东方国信,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,1
+13,东方国信,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,1
+13,东方国信,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,1
+13,东方国信,2.1.3.7,制造类API,126,华为,2.1.3,工业物联网,1
+13,东方国信,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,1
+13,东方国信,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
+13,东方国信,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
+13,东方国信,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,1
+13,东方国信,2.1.3.5,容器服务,73,FANUC,2.1.3,工业物联网,1
+13,东方国信,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,1
+13,东方国信,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,1
+13,东方国信,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,1
+150,唯捷创芯,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,1
+33,蓝谷信息,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,1
+23,和利时,1.3.3.4,可编程逻揖控制系统PLC,99,Siemens,1.3.3,生产制造,1
+22,航天云网,2.3.3,协议转换,95,Schneider,2.3,边缘层,1
+23,和利时,1.3.3.1,制造执行系统MES,75,IBM,1.3.3,生产制造,1
+23,和利时,1.3.3.1,制造执行系统MES,99,Siemens,1.3.3,生产制造,1
+23,和利时,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,1
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,1
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,1
+115,富士康,1.1.3,工业服务器,105,Intel,1.1,工业自动化,1
+22,航天云网,2.3.3,协议转换,126,华为,2.3,边缘层,1
+113,飞腾信息,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+23,和利时,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
+23,和利时,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,1
+23,和利时,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
+23,和利时,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
+23,和利时,2.3.3,协议转换,124,海尔,2.3,边缘层,1
+1,51WORLD,2.1.1.5,数字孪生建模工具,148,腾讯,2.1.1,开发工具,1
+22,航天云网,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,1
+23,和利时,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
+22,航天云网,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,1
+22,航天云网,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,1
+22,航天云网,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,1
+22,航天云网,2.1.3.3,工业引擎服务,106,阿里巴巴,2.1.3,工业物联网,1
+22,航天云网,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,1
+22,航天云网,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,1
+22,航天云网,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,1
+22,航天云网,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
+22,航天云网,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
+22,航天云网,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,1
+22,航天云网,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,1
+22,航天云网,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
+22,航天云网,2.3.1,工业数据接入,155,小米,2.3,边缘层,1
+22,航天云网,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
+22,航天云网,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,1
+23,和利时,2.3.3,协议转换,126,华为,2.3,边缘层,1
+23,和利时,2.3.3,协议转换,95,Schneider,2.3,边缘层,1
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,1
+31,昆仑数据,2.1.4.1.3,实时数据库,79,PTC,2.1.4.1,工业大数据存储,1
+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,1
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+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,1
+3,艾克斯特,1.3.4.1,企业资源计划ERP,80,Salesforce,1.3.4,企业运营管理,1
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,1
+31,昆仑数据,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,1
+31,昆仑数据,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,1
+26,寄云科技,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,1
+32,兰光创新,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,1
+33,蓝谷信息,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,1
+33,蓝谷信息,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,1
+33,蓝谷信息,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,1
+33,蓝谷信息,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,1
+33,蓝谷信息,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,1
+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,1
+26,寄云科技,2.1.3.7,制造类API,73,FANUC,2.1.3,工业物联网,1
+119,广联达,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,1
+26,寄云科技,2.1.3.2,平台基础服务,73,FANUC,2.1.3,工业物联网,1
+24,华大电子,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
+24,华大电子,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+25,华大九天,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,1
+25,华大九天,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,1
+26,寄云科技,2.1.3.1,物联网服务,148,腾讯,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.5,容器服务,97,General Electric,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,1
+26,寄云科技,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,1
+22,航天云网,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,1
+22,航天云网,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,1
+22,航天云网,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,1
+163,优也科技,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,1
+161,研华科技,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
+161,研华科技,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,1
+161,研华科技,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,1
+161,研华科技,2.3.3,协议转换,124,海尔,2.3,边缘层,1
+163,优也科技,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,1
+163,优也科技,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,1
+163,优也科技,2.1.4.2.2,数据安全管理,81,SAP,2.1.4.2,工业大数据管理,1
+161,研华科技,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
+164,震坤行,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,1
+164,震坤行,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,1
+165,智能云科,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,1
+165,智能云科,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,1
+165,智能云科,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,1
+165,智能云科,2.1.2.2,业务流程模型,79,PTC,2.1.2,工业模型库,1
+161,研华科技,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
+161,研华科技,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
+22,航天云网,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,1
+116,概伦电子,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,1
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,1
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,1
+154,西格数据,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,1
+154,西格数据,2.1.4.2.2,数据安全管理,81,SAP,2.1.4.2,工业大数据管理,1
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,39,Autodesk,1.3.1,设计研发,1
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,1
+16,东土科技,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,1
+161,研华科技,2.3.1,工业数据接入,124,海尔,2.3,边缘层,1
+16,东土科技,2.3.1,工业数据接入,124,海尔,2.3,边缘层,1
+16,东土科技,2.3.1,工业数据接入,126,华为,2.3,边缘层,1
+16,东土科技,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
+16,东土科技,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,1
+16,东土科技,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,1
+16,东土科技,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
+165,智能云科,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,1
+165,智能云科,2.1.2.3,研发仿真模型,79,PTC,2.1.2,工业模型库,1
+165,智能云科,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,1
+20,海基科技,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,1
+169,中芯国际,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1
+169,中芯国际,1.1.1,工业计算芯片,126,华为,1.1,工业自动化,1
+18,国能智深,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,1
+2,706所,1.1.3,工业服务器,106,阿里巴巴,1.1,工业自动化,1
+2,706所,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,1
+20,海基科技,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,1
+20,海基科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,1
+165,智能云科,2.1.2.4,行业机理模型,79,PTC,2.1.2,工业模型库,1
+21,Hexagon,1.3.1.3,计算机辅助制造CAM,85,Dassault,1.3.1,设计研发,1
+22,航天云网,1.2.2,标识解析,106,阿里巴巴,1.2,工业互联网网络,1
+22,航天云网,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,1
+22,航天云网,2.1.1.1,算法建模工具,85,Dassault,2.1.1,开发工具,1
+22,航天云网,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,1
+22,航天云网,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,1
+168,中控技术,2.3.3,协议转换,99,Siemens,2.3,边缘层,1
+168,中控技术,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
+168,中控技术,2.3.3,协议转换,124,海尔,2.3,边缘层,1
+168,中控技术,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,1
+168,中控技术,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,1
+168,中控技术,2.3.2,边缘数据处理,155,小米,2.3,边缘层,1
+168,中控技术,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,1
+168,中控技术,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,1
+168,中控技术,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,1
+168,中控技术,2.3.1,工业数据接入,155,小米,2.3,边缘层,1
+168,中控技术,1.3.3.2,分布式控制系统DCS,97,General Electric,1.3.3,生产制造,1
+168,中控技术,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,1
+168,中控技术,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,1
+168,中控技术,1.1.2,工业控制器,105,Intel,1.1,工业自动化,1
+167,中环股份,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,1
+166,中国电子科技网络信息安全,1.2.3,数据互通,106,阿里巴巴,1.2,工业互联网网络,1
+165,智能云科,2.1.2.4,行业机理模型,81,SAP,2.1.2,工业模型库,1
+22,航天云网,2.3.3,协议转换,84,Bosch,2.3,边缘层,1
diff --git a/analysis/count_dcp_network20230407.png b/analysis/dissertation_count_dcp_network20230407.png
similarity index 100%
rename from analysis/count_dcp_network20230407.png
rename to analysis/dissertation_count_dcp_network20230407.png
diff --git a/analysis/count_dcp_network20230526_de.png b/analysis/dissertation_count_dcp_network20230526_de.png
similarity index 100%
rename from analysis/count_dcp_network20230526_de.png
rename to analysis/dissertation_count_dcp_network20230526_de.png
diff --git a/analysis/dissertation_count_dcp_prod.csv b/analysis/dissertation_count_dcp_prod.csv
new file mode 100644
index 0000000..ab522b0
--- /dev/null
+++ b/analysis/dissertation_count_dcp_prod.csv
@@ -0,0 +1,98 @@
+up_id_product,up_name_product,down_id_product,down_name_product,count
+1.4,工业互联网安全,1,供给,118
+1.4.3,网络安全,1.4,工业互联网安全,96
+1.4.5,数据安全,1.4,工业互联网安全,92
+1.4.2,控制安全,1.4,工业互联网安全,92
+2.1,PaaS,2,工业互联网平台,77
+1.4.4.5,安全态势感知,1.4.4,平台安全,76
+1.3.2.1,供应链管理SCM,1.3.2,采购供应,76
+1.3.2,采购供应,1.3,工业软件,74
+1.3.5,仓储物流,1.3,工业软件,72
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+1.4.5.8,数据加密,1.4.5,数据安全,50
+1.4.5.1,恶意代码检测系统,1.4.5,数据安全,50
+1.4.3.6,沙箱类设备,1.4.3,网络安全,50
+1.4.2.7,工控原生安全,1.4.2,控制安全,50
+1.4.2.3,工控漏洞扫描,1.4.2,控制安全,50
+1.4.1,设备安全,1.4,工业互联网安全,50
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+2.1.2.2,业务流程模型,2.1.2,工业模型库,28
+2.1.3.7,制造类API,2.1.3,工业物联网,28
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+2.1.2.1,数据算法模型,2.1.2,工业模型库,27
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+2.1.3.1,物联网服务,2.1.3,工业物联网,25
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+1.4.5.8,数据加密,1.4,工业互联网安全,21
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+2.1.3.2,平台基础服务,2.1.3,工业物联网,21
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+1.3.1.4,计算机辅助工艺过程设计CAPP,1.3.1,设计研发,20
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+2.1.2.3,研发仿真模型,2.1.2,工业模型库,18
+2.1.1.5,数字孪生建模工具,2.1.1,开发工具,18
+1.3.1.6,产品生命周期管理PLM,1.3.1,设计研发,18
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+1.3.3.3,数据采集与监视控制系统SCADA,1.3.3,生产制造,11
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+2.1,PaaS,1,供给,5
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+1.3.1.5,产品数据管理PDM,1.3.1,设计研发,5
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+2.1.4.2.2,数据安全管理,2.1.4.2,工业大数据管理,5
+2.1.4.2.1,数据质量管理,2.1.4.2,工业大数据管理,5
+1.3,工业软件,1,供给,4
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+2.3,边缘层,2,工业互联网平台,3
+2.2,IaaS,2,工业互联网平台,3
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+1.3.3.7,故障预测与健康管理PHM,1.3.3,生产制造,1
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diff --git a/analysis/count_dcp_prod_network20230407.png b/analysis/dissertation_count_dcp_prod_network20230407.png
similarity index 100%
rename from analysis/count_dcp_prod_network20230407.png
rename to analysis/dissertation_count_dcp_prod_network20230407.png
diff --git a/analysis/dissertation_count_firm.csv b/analysis/dissertation_count_firm.csv
new file mode 100644
index 0000000..1caf1b0
--- /dev/null
+++ b/analysis/dissertation_count_firm.csv
@@ -0,0 +1,144 @@
+id_firm,Name,count
+126,华为,468
+142,深信服,300
+41,启明星辰,200
+53,天融信,150
+106,阿里巴巴,146
+170,Pseudo1,125
+99,Siemens,120
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+130,金蝶,91
+13,东方国信,80
+135,浪潮,73
+23,和利时,71
+58,用友,62
+97,General Electric,54
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+63,长扬科技,50
+85,Dassault,50
+157,新华三,50
+140,山石网科,50
+148,腾讯,49
+102,Amazon AWS,47
+22,航天云网,46
+40,奇安信,39
+0,360科技,38
+98,Microsoft Azure,38
+84,Bosch,35
+81,SAP,35
+74,HoneyWell,32
+100,Synopsys,29
+86,Dell EMC,28
+80,Salesforce,28
+108,百度,25
+105,Intel,25
+49,数码大方,24
+47,首自信,24
+95,Schneider,22
+39,Autodesk,21
+168,中控技术,20
+6,安世亚太,20
+16,东土科技,20
+94,Mitsubishi,19
+75,IBM,19
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+124,海尔,18
+117,格创东智,17
+26,寄云科技,17
+155,小米,17
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+149,天泽智云,13
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+82,Uptake,12
+78,OutSystems,12
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+60,宇动源,11
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+42,山大华天,10
+67,中国移动,10
+131,九物互联,10
+38,牛刀,10
+103,STMicroelectronics ,9
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+143,沈阳自动化研究所,8
+127,华为海思,8
+153,武汉开目,8
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+68,中望软件,7
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+111,鼎捷软件,6
+89,Rockwell,6
+150,唯捷创芯,6
+169,中芯国际,6
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+65,中国电信,3
+72,ANSYS,3
+4,爱创科技,3
+36,龙芯中科,3
+44,圣邦微电子,3
+146,苏州浩辰,3
+14,东华软件,3
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+156,芯禾科技,2
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+61,元年科技,2
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diff --git a/analysis/dissertation_count_firm_prod.csv b/analysis/dissertation_count_firm_prod.csv
new file mode 100644
index 0000000..b3ab1fd
--- /dev/null
+++ b/analysis/dissertation_count_firm_prod.csv
@@ -0,0 +1,358 @@
+id_firm,name_firm,id_product,name_product,count
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+142,深信服,1.4.2,控制安全,150
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+41,启明星辰,1.4.3.2,流量检测,50
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+135,浪潮,1.3.2.1,供应链管理SCM,50
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+0,360科技,1.4.4,平台安全,38
+98,Microsoft Azure,2,工业互联网平台,38
+58,用友,1.3.2,采购供应,36
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+74,HoneyWell,2.1.3,工业物联网,30
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+85,Dassault,1.3.1,设计研发,29
+86,Dell EMC,1.1,工业自动化,28
+99,Siemens,1.3.1,设计研发,27
+97,General Electric,2.1.3,工业物联网,27
+106,阿里巴巴,2.1.3,工业物联网,27
+126,华为,2.1.3,工业物联网,26
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+80,Salesforce,2.1.1,开发工具,25
+79,PTC,2.1.2,工业模型库,25
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+58,用友,2.1.2,工业模型库,22
+39,Autodesk,1.3.1,设计研发,21
+97,General Electric,1.3.3,生产制造,21
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+85,Dassault,2.1.1,开发工具,21
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+99,Siemens,2.3,边缘层,20
+106,阿里巴巴,2.1.1,开发工具,19
+126,华为,2.3,边缘层,19
+75,IBM,1.3.3,生产制造,19
+94,Mitsubishi,1.1,工业自动化,19
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+95,Schneider,2.3,边缘层,18
+155,小米,2.3,边缘层,17
+148,腾讯,2.1.1,开发工具,17
+124,海尔,2.3,边缘层,16
+159,徐工集团,2.1.2,工业模型库,16
+126,华为,1.2,工业互联网网络,15
+93,Cadence,1.3.1,设计研发,13
+106,阿里巴巴,1.2,工业互联网网络,12
+84,Bosch,2.3,边缘层,12
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+67,中国移动,1.2,工业互联网网络,10
+79,PTC,2.1.3.1,物联网服务,9
+79,PTC,2.1.3.4,应用管理服务,9
+103,STMicroelectronics ,1.1.1,工业计算芯片,9
+13,东方国信,2.1.3.1,物联网服务,9
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+79,PTC,2.1.4.1,工业大数据存储,7
+13,东方国信,2.1.3.6,微服务,7
+79,PTC,2.1.3.7,制造类API,7
+81,SAP,2.1.4.1,工业大数据存储,7
+81,SAP,2.1.4.2,工业大数据管理,7
+79,PTC,2.1.3.6,微服务,7
+79,PTC,2.3.3,协议转换,6
+79,PTC,2.1.3.3,工业引擎服务,6
+16,东土科技,2.3.1,工业数据接入,6
+150,唯捷创芯,1.1.1,工业计算芯片,6
+49,数码大方,1.3.1.1,计算机辅助设计CAD,6
+79,PTC,2.3.1,工业数据接入,6
+47,首自信,2.1.3.6,微服务,6
+56,芯愿景,1.1.1,工业计算芯片,6
+169,中芯国际,1.1.1,工业计算芯片,6
+97,General Electric,1.2,工业互联网网络,6
+46,适创科技,1.3.1.2,计算机辅助工程CAE,6
+47,首自信,2.1.2.1,数据算法模型,6
+13,东方国信,2.1.3.2,平台基础服务,6
+16,东土科技,1.1.3,工业服务器,5
+161,研华科技,2.3.3,协议转换,5
+16,东土科技,2.3.3,协议转换,5
+22,航天云网,2.1.3.6,微服务,5
+168,中控技术,2.3.3,协议转换,5
+13,东方国信,2.3.2,边缘数据处理,5
+22,航天云网,2.3.1,工业数据接入,5
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,5
+69,紫光集团,1.1.1,工业计算芯片,5
+22,航天云网,2.3.3,协议转换,5
+127,华为海思,1.1.1,工业计算芯片,5
+6,安世亚太,2.1.2.1,数据算法模型,5
+79,PTC,2.1.4.2,工业大数据管理,5
+23,和利时,2.3.3,协议转换,5
+42,山大华天,1.3.1.1,计算机辅助设计CAD,5
+113,飞腾信息,1.1.1,工业计算芯片,5
+167,中环股份,1.1.1,工业计算芯片,5
+78,OutSystems,2.1.1.5,数字孪生建模工具,4
+32,兰光创新,1.2.3,数据互通,4
+68,中望软件,1.3.1.2,计算机辅助工程CAE,4
+78,OutSystems,2.1.1.2,低代码开发工具,4
+6,安世亚太,2.1.2.4,行业机理模型,4
+165,智能云科,2.1.2.2,业务流程模型,4
+24,华大电子,1.1.1,工业计算芯片,4
+62,云道智造,2.1.2.2,业务流程模型,4
+23,和利时,2.3.2,边缘数据处理,4
+16,东土科技,2.3.2,边缘数据处理,4
+6,安世亚太,2.1.2.3,研发仿真模型,4
+22,航天云网,2.1.3.4,应用管理服务,4
+71,Altair,1.3.1.2,计算机辅助工程CAE,4
+161,研华科技,2.3.2,边缘数据处理,4
+168,中控技术,2.3.2,边缘数据处理,4
+35,凌昊智能,1.1.3,工业服务器,4
+33,蓝谷信息,2.1.2.4,行业机理模型,4
+57,亚控科技,2.3.3,协议转换,4
+129,华中数控,1.1.2,工业控制器,4
+13,东方国信,2.3.1,工业数据接入,4
+104,Infineon,1.1.1,工业计算芯片,4
+131,九物互联,2.1.1.2,低代码开发工具,4
+95,Schneider,1.2.3,数据互通,4
+149,天泽智云,2.1.2.4,行业机理模型,4
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,4
+135,浪潮,2.1.3.7,制造类API,4
+117,格创东智,2.1.1.4,组态建模工具,4
+82,Uptake,2.1.2.4,行业机理模型,4
+57,亚控科技,2.3.2,边缘数据处理,4
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,4
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,4
+123,海得控制,1.1.2,工业控制器,4
+38,牛刀,2.1.1.5,数字孪生建模工具,4
+149,天泽智云,2.1.2.3,研发仿真模型,4
+147,拓邦股份,1.1.2,工业控制器,4
+6,安世亚太,2.1.2.2,业务流程模型,4
+4,爱创科技,1.2.2,标识解析,3
+36,龙芯中科,1.1.1,工业计算芯片,3
+33,蓝谷信息,2.1.2.2,业务流程模型,3
+115,富士康,1.1.3,工业服务器,3
+49,数码大方,2.1.2.1,数据算法模型,3
+49,数码大方,1.3.3.1,制造执行系统MES,3
+23,和利时,2.1.3.6,微服务,3
+49,数码大方,2.1.2.2,业务流程模型,3
+64,中电智科,1.1.2,工业控制器,3
+49,数码大方,1.3.1.6,产品生命周期管理PLM,3
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,3
+47,首自信,2.1.2.4,行业机理模型,3
+47,首自信,2.1.1.2,低代码开发工具,3
+62,云道智造,2.1.2.4,行业机理模型,3
+117,格创东智,2.1.1.2,低代码开发工具,3
+60,宇动源,2.1.1.1,算法建模工具,3
+26,寄云科技,2.1.3.1,物联网服务,3
+117,格创东智,2.1.1.1,算法建模工具,3
+26,寄云科技,2.1.3.3,工业引擎服务,3
+44,圣邦微电子,1.1.1,工业计算芯片,3
+62,云道智造,1.3.1.2,计算机辅助工程CAE,3
+26,寄云科技,2.1.3.6,微服务,3
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,3
+57,亚控科技,2.3.1,工业数据接入,3
+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,3
+116,概伦电子,1.3.1.7,电子设计自动化EDA,3
+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,3
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,3
+60,宇动源,2.1.1.2,低代码开发工具,3
+65,中国电信,1.2.1,网络互联,3
+23,和利时,2.3.1,工业数据接入,3
+68,中望软件,1.3.1.1,计算机辅助设计CAD,3
+87,Texas Instruments,1.1.1,工业计算芯片,3
+149,天泽智云,2.1.2.2,业务流程模型,3
+120,广州数控,1.2.3,数据互通,3
+146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,3
+144,树根互联,2.1.2.4,行业机理模型,3
+22,航天云网,2.1.3.7,制造类API,3
+80,Salesforce,1.3.4,企业运营管理,3
+81,SAP,1.3.4,企业运营管理,3
+82,Uptake,2.1.2.1,数据算法模型,3
+82,Uptake,2.1.2.2,业务流程模型,3
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,3
+135,浪潮,2.1.3.4,应用管理服务,3
+12,大唐软件,1.2.1,网络互联,3
+135,浪潮,2.1.3.3,工业引擎服务,3
+88,HPE,1.1.3,工业服务器,3
+89,Rockwell,1.1.2,工业控制器,3
+9,北京航天测控,1.3.3.6,运维保障系统MRO,3
+135,浪潮,1.1.3,工业服务器,3
+90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,3
+131,九物互联,2.1.1.4,组态建模工具,3
+13,东方国信,1.2.2,标识解析,3
+101,Analog Devices,1.1.1,工业计算芯片,3
+127,华为海思,1.1.3,工业服务器,3
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,3
+79,PTC,2.3.2,边缘数据处理,3
+161,研华科技,2.3.1,工业数据接入,3
+168,中控技术,1.3.3.2,分布式控制系统DCS,3
+22,航天云网,2.1.3.3,工业引擎服务,3
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,3
+22,航天云网,1.2.2,标识解析,3
+20,海基科技,1.3.1.2,计算机辅助工程CAE,3
+119,广联达,1.3.1.1,计算机辅助设计CAD,3
+168,中控技术,2.3.1,工业数据接入,3
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,3
+165,智能云科,2.1.2.1,数据算法模型,3
+79,PTC,1.3.1.6,产品生命周期管理PLM,3
+79,PTC,1.3.1.1,计算机辅助设计CAD,3
+166,中国电子科技网络信息安全,1.2.3,数据互通,3
+165,智能云科,2.1.2.4,行业机理模型,3
+58,用友,1.3.1.6,产品生命周期管理PLM,2
+117,格创东智,2.1.1.3,流程开发工具,2
+70,ABB,1.3.3.2,分布式控制系统DCS,2
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,2
+102,Amazon AWS,2.1.4,工业大数据,2
+99,Siemens,1.1.2,工业控制器,2
+74,HoneyWell,1.3.3.2,分布式控制系统DCS,2
+77,Oracle,1.3.3.6,运维保障系统MRO,2
+77,Oracle,1.3.4,企业运营管理,2
+49,数码大方,2.1.2.4,行业机理模型,2
+50,索为系统,1.3.1.5,产品数据管理PDM,2
+89,Rockwell,1.2.1,网络互联,2
+58,用友,1.2.2,标识解析,2
+78,OutSystems,2.1.1.1,算法建模工具,2
+56,芯愿景,1.3.1.7,电子设计自动化EDA,2
+111,鼎捷软件,1.3.4.1,企业资源计划ERP,2
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,2
+78,OutSystems,2.1.1.3,流程开发工具,2
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,2
+83,Emerson,1.3.3.2,分布式控制系统DCS,2
+82,Uptake,2.1.2.3,研发仿真模型,2
+60,宇动源,2.1.1.5,数字孪生建模工具,2
+60,宇动源,2.1.1.4,组态建模工具,2
+62,云道智造,2.1.2.1,数据算法模型,2
+33,蓝谷信息,2.1.2.3,研发仿真模型,2
+48,曙光信息,1.2.2,标识解析,2
+22,航天云网,1.3.3.6,运维保障系统MRO,2
+26,寄云科技,2.1.3.2,平台基础服务,2
+144,树根互联,2.1.2.2,业务流程模型,2
+25,华大九天,1.3.1.7,电子设计自动化EDA,2
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,2
+138,启明信息,1.3.1.5,产品数据管理PDM,2
+23,和利时,1.3.3.1,制造执行系统MES,2
+22,航天云网,2.3.2,边缘数据处理,2
+22,航天云网,2.1.3.5,容器服务,2
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,2
+22,航天云网,2.1.3.1,物联网服务,2
+22,航天云网,2.1.1.5,数字孪生建模工具,2
+22,航天云网,2.1.1.3,流程开发工具,2
+22,航天云网,2.1.1.2,低代码开发工具,2
+22,航天云网,2.1.1.1,算法建模工具,2
+141,上海新华控制,1.3.3.2,分布式控制系统DCS,2
+26,寄云科技,2.1.3.5,容器服务,2
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+124,海尔,1.2.1,网络互联,2
+168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,2
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,2
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+143,沈阳自动化研究所,2.1.1.3,流程开发工具,2
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+163,优也科技,2.1.4.2.2,数据安全管理,2
+143,沈阳自动化研究所,2.1.1.4,组态建模工具,2
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,2
+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,2
+144,树根互联,2.1.2.1,数据算法模型,2
+15,东软集团,1.3.3.5,企业资产管理系统EAM,2
+149,天泽智云,2.1.2.1,数据算法模型,2
+26,寄云科技,2.1.3.4,应用管理服务,2
+144,树根互联,2.1.2.3,研发仿真模型,2
+26,寄云科技,2.1.3.7,制造类API,2
+135,浪潮,2.1.3.1,物联网服务,2
+117,格创东智,2.1.1.5,数字孪生建模工具,2
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,2
+13,东方国信,2.3.3,协议转换,2
+38,牛刀,2.1.1.2,低代码开发工具,2
+38,牛刀,2.1.1.1,算法建模工具,2
+13,东方国信,2.1.4.1.4,时序数据库,2
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,2
+135,浪潮,2.1.3.2,平台基础服务,2
+128,华伍股份,1.1.2,工业控制器,2
+47,首自信,2.1.2.2,业务流程模型,2
+135,浪潮,2.1.3.5,容器服务,2
+135,浪潮,2.1.3.6,微服务,2
+131,九物互联,2.1.1.1,算法建模工具,2
+99,Siemens,1.2.1,网络互联,1
+13,东方国信,2.1.4.1.2,分布式数据库,1
+131,九物互联,2.1.1.3,流程开发工具,1
+111,鼎捷软件,1.3.3.1,制造执行系统MES,1
+129,华中数控,1.2.3,数据互通,1
+13,东方国信,2.1.4.2.1,数据质量管理,1
+126,华为,2.1.1.5,数字孪生建模工具,1
+130,金蝶,1.3.4.1,企业资源计划ERP,1
+96,Cisco,1.2.3,数据互通,1
+91,Moxa,1.2.1,网络互联,1
+132,科远智慧,1.3.3.2,分布式控制系统DCS,1
+133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,1
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,1
+108,百度,2.2,IaaS,1
+14,东华软件,1.3.4.3,人力资源管理HRM,1
+125,华数机器人,1.2.3,数据互通,1
+139,容知日新,1.3.3.7,故障预测与健康管理PHM,1
+89,Rockwell,1.3.3.1,制造执行系统MES,1
+135,浪潮,1.3.4.1,企业资源计划ERP,1
+137,美林数据,2.1.4.2.1,数据质量管理,1
+137,美林数据,2.1.4.1.3,实时数据库,1
+84,Bosch,2.1.4,工业大数据,1
+135,浪潮,2.2,IaaS,1
+109,宝信软件,1.3.3.1,制造执行系统MES,1
+18,国能智深,1.3.3.2,分布式控制系统DCS,1
+154,西格数据,2.1.4.1.2,分布式数据库,1
+45,石化盈科,1.3.4.1,企业资源计划ERP,1
+115,富士康,2.1.4,工业大数据,1
+38,牛刀,2.1.1.3,流程开发工具,1
+38,牛刀,2.1.1.4,组态建模工具,1
+117,格创东智,2.1.4.2.1,数据质量管理,1
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+42,山大华天,1.3.1.3,计算机辅助制造CAM,1
+43,神舟软件,1.3.1.5,产品数据管理PDM,1
+45,石化盈科,1.3.3.1,制造执行系统MES,1
+45,石化盈科,2.1.4.1.2,分布式数据库,1
+31,昆仑数据,2.1.4.2.1,数据质量管理,1
+45,石化盈科,2.1.4.1.3,实时数据库,1
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+49,数码大方,2.1.2.3,研发仿真模型,1
+47,首自信,2.1.1.1,算法建模工具,1
+47,首自信,2.1.1.3,流程开发工具,1
+47,首自信,2.1.1.4,组态建模工具,1
+33,蓝谷信息,2.1.2.1,数据算法模型,1
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+154,西格数据,2.1.4.2.2,数据安全管理,1
+70,ABB,1.3.3.4,可编程逻揖控制系统PLC,1
+163,优也科技,2.1.4.1.1,关系型数据库,1
+163,优也科技,2.1.4.1.4,时序数据库,1
+165,智能云科,2.1.2.3,研发仿真模型,1
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+47,首自信,2.1.2.3,研发仿真模型,1
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+22,航天云网,2.1.1.4,组态建模工具,1
+22,航天云网,2.1.3.2,平台基础服务,1
+23,和利时,1.3.3.2,分布式控制系统DCS,1
+31,昆仑数据,2.1.4.1.1,关系型数据库,1
+66,中国联通,1.2.1,网络互联,1
+23,和利时,1.3.3.4,可编程逻揖控制系统PLC,1
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+62,云道智造,2.1.2.3,研发仿真模型,1
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+3,艾克斯特,1.3.4.1,企业资源计划ERP,1
+60,宇动源,2.1.1.3,流程开发工具,1
+126,华为,2.2,IaaS,1
diff --git a/analysis/dissertation_count_prod.csv b/analysis/dissertation_count_prod.csv
new file mode 100644
index 0000000..494f17d
--- /dev/null
+++ b/analysis/dissertation_count_prod.csv
@@ -0,0 +1,82 @@
+id_product,Name,count
+1.4,工业互联网安全,385
+2.1.3,工业物联网,184
+1.4.5,数据安全,150
+1.4.3,网络安全,150
+1.4.2,控制安全,150
+1,供给,125
+1.3,工业软件,119
+1.3.1,设计研发,119
+1.1,工业自动化,114
+2.1.2,工业模型库,103
+2.3,边缘层,102
+2,工业互联网平台,83
+2.1.1,开发工具,82
+1.4.4,平台安全,77
+1.3.2,采购供应,76
+1.1.1,工业计算芯片,67
+1.3.3,生产制造,60
+2.1,PaaS,50
+1.3.5,仓储物流,50
+1.3.2.1,供应链管理SCM,50
+1.4.5.8,数据加密,50
+1.4.5.1,恶意代码检测系统,50
+1.4.4.5,安全态势感知,50
+1.4.3.6,沙箱类设备,50
+1.4.3.2,流量检测,50
+1.4.2.7,工控原生安全,50
+1.4.2.3,工控漏洞扫描,50
+1.4.1,设备安全,50
+1.2,工业互联网网络,43
+2.3.3,协议转换,37
+2.3.1,工业数据接入,33
+2.1.3.6,微服务,33
+2.3.2,边缘数据处理,30
+2.1.2.4,行业机理模型,30
+2.1.3.4,应用管理服务,29
+1.3.1.1,计算机辅助设计CAD,28
+2.1.2.2,业务流程模型,28
+2.1.2.1,数据算法模型,27
+2.1.3.7,制造类API,27
+1.3.1.2,计算机辅助工程CAE,26
+2.1.3.1,物联网服务,25
+1.1.2,工业控制器,24
+2.1.3.5,容器服务,24
+2.1.3.3,工业引擎服务,23
+2.1.1.2,低代码开发工具,23
+1.1.3,工业服务器,23
+2.1.3.2,平台基础服务,21
+1.3.1.4,计算机辅助工艺过程设计CAPP,20
+2.1.2.3,研发仿真模型,18
+2.1.1.5,数字孪生建模工具,18
+1.3.1.6,产品生命周期管理PLM,18
+1.2.3,数据互通,17
+2.1.1.1,算法建模工具,15
+2.1.1.4,组态建模工具,14
+2.1.4.1,工业大数据存储,14
+1.3.3.2,分布式控制系统DCS,14
+1.2.2,标识解析,13
+1.2.1,网络互联,13
+2.1.4.2,工业大数据管理,12
+1.3.1.7,电子设计自动化EDA,12
+2.1.1.3,流程开发工具,12
+1.3.3.3,数据采集与监视控制系统SCADA,11
+1.3.3.1,制造执行系统MES,10
+1.3.3.6,运维保障系统MRO,10
+1.3.4,企业运营管理,8
+1.3.3.4,可编程逻揖控制系统PLC,7
+1.3.3.5,企业资产管理系统EAM,6
+1.3.4.1,企业资源计划ERP,6
+2.1.4.2.1,数据质量管理,5
+2.1.4.2.2,数据安全管理,5
+1.3.1.5,产品数据管理PDM,5
+2.1.4,工业大数据,4
+2.1.4.1.4,时序数据库,4
+2.1.4.1.1,关系型数据库,3
+2.1.4.1.2,分布式数据库,3
+2.1.4.1.3,实时数据库,3
+2.2,IaaS,3
+1.3.1.3,计算机辅助制造CAM,2
+1.3.3.7,故障预测与健康管理PHM,1
+1.4.4.1,身份鉴别与访问控制,1
+1.3.4.3,人力资源管理HRM,1
diff --git a/analysis/count_prod.xlsx b/analysis/dissertation_count_prod.xlsx
similarity index 100%
rename from analysis/count_prod.xlsx
rename to analysis/dissertation_count_prod.xlsx
diff --git a/analysis/count_prod_network20230406.png b/analysis/dissertation_count_prod_network20230406.png
similarity index 100%
rename from analysis/count_prod_network20230406.png
rename to analysis/dissertation_count_prod_network20230406.png
diff --git a/analysis/dissertation_count_prod_pie.png b/analysis/dissertation_count_prod_pie.png
new file mode 100644
index 0000000..b7e8737
Binary files /dev/null and b/analysis/dissertation_count_prod_pie.png differ
diff --git a/analysis/dissertation_g_bom_exp_id_1.png b/analysis/dissertation_g_bom_exp_id_1.png
new file mode 100644
index 0000000..7ff2bf1
Binary files /dev/null and b/analysis/dissertation_g_bom_exp_id_1.png differ
diff --git a/analysis/g_firm_sample_id_1.png b/analysis/dissertation_g_firm_sample_id_1.png
similarity index 100%
rename from analysis/g_firm_sample_id_1.png
rename to analysis/dissertation_g_firm_sample_id_1.png
diff --git a/analysis/dissertation_g_firm_sample_id_1_de.png b/analysis/dissertation_g_firm_sample_id_1_de.png
new file mode 100644
index 0000000..175abef
Binary files /dev/null and b/analysis/dissertation_g_firm_sample_id_1_de.png differ
diff --git a/analysis/experiment_result-L27.csv b/analysis/experiment_result-L27.csv
new file mode 100644
index 0000000..7708325
--- /dev/null
+++ b/analysis/experiment_result-L27.csv
@@ -0,0 +1,28 @@
+,n_max_trial,crit_supplier,firm_pref_request,firm_pref_accept,netw_pref_cust_n,netw_pref_cust_size,cap_limit,diff_new_conn,diff_remove,X10,X11,X12,X13,n_disrupt_s,n_disrupt_t
+0,15,2.0,2.0,2.0,0.5,2.0,4,0.5,0.5,0,0,0,0,888.0,2114.0
+1,15,2.0,2.0,2.0,1.0,1.0,2,1.0,1.0,1,1,1,1,1297.0,2810.0
+2,15,2.0,2.0,2.0,2.0,0.5,1,2.0,2.0,2,2,2,2,1826.0,3809.0
+3,15,1.0,1.0,1.0,0.5,2.0,4,1.0,1.0,1,2,2,2,1372.0,3055.0
+4,15,1.0,1.0,1.0,1.0,1.0,2,2.0,2.0,2,0,0,0,2118.0,4519.0
+5,15,1.0,1.0,1.0,2.0,0.5,1,0.5,0.5,0,1,1,1,815.0,2073.0
+6,15,0.5,0.5,0.5,0.5,2.0,4,2.0,2.0,2,1,1,1,2378.0,5528.0
+7,15,0.5,0.5,0.5,1.0,1.0,2,0.5,0.5,0,2,2,2,968.0,2300.0
+8,15,0.5,0.5,0.5,2.0,0.5,1,1.0,1.0,1,0,0,0,1531.0,3317.0
+9,10,2.0,1.0,0.5,0.5,1.0,1,0.5,1.0,2,0,1,2,881.0,1972.0
+10,10,2.0,1.0,0.5,1.0,0.5,4,1.0,2.0,0,1,2,0,1298.0,2763.0
+11,10,2.0,1.0,0.5,2.0,2.0,2,2.0,0.5,1,2,0,1,1717.0,3837.0
+12,10,1.0,0.5,2.0,0.5,1.0,1,1.0,2.0,0,2,0,1,1327.0,2855.0
+13,10,1.0,0.5,2.0,1.0,0.5,4,2.0,0.5,1,0,1,2,2126.0,4788.0
+14,10,1.0,0.5,2.0,2.0,2.0,2,0.5,1.0,2,1,2,0,801.0,1814.0
+15,10,0.5,2.0,1.0,0.5,1.0,1,2.0,0.5,1,1,2,0,2442.0,5980.0
+16,10,0.5,2.0,1.0,1.0,0.5,4,0.5,1.0,2,2,0,1,991.0,2186.0
+17,10,0.5,2.0,1.0,2.0,2.0,2,1.0,2.0,0,0,1,2,1311.0,2776.0
+18,5,2.0,0.5,1.0,0.5,0.5,2,0.5,2.0,1,0,2,1,879.0,1909.0
+19,5,2.0,0.5,1.0,1.0,2.0,1,1.0,0.5,2,1,0,2,1354.0,3132.0
+20,5,2.0,0.5,1.0,2.0,1.0,4,2.0,1.0,0,2,1,0,1727.0,3673.0
+21,5,1.0,2.0,0.5,0.5,0.5,2,1.0,0.5,2,2,1,0,1379.0,3184.0
+22,5,1.0,2.0,0.5,1.0,2.0,1,2.0,1.0,0,0,2,1,2145.0,4658.0
+23,5,1.0,2.0,0.5,2.0,1.0,4,0.5,2.0,1,1,0,2,810.0,1764.0
+24,5,0.5,1.0,2.0,0.5,0.5,2,2.0,1.0,0,1,0,2,2412.0,5783.0
+25,5,0.5,1.0,2.0,1.0,2.0,1,0.5,2.0,1,2,1,0,915.0,1973.0
+26,5,0.5,1.0,2.0,2.0,1.0,4,1.0,0.5,2,0,2,1,1336.0,3087.0
diff --git a/analysis/experiment_result.csv b/analysis/experiment_result.csv
index 7708325..dcdbc05 100644
--- a/analysis/experiment_result.csv
+++ b/analysis/experiment_result.csv
@@ -1,28 +1,37 @@
-,n_max_trial,crit_supplier,firm_pref_request,firm_pref_accept,netw_pref_cust_n,netw_pref_cust_size,cap_limit,diff_new_conn,diff_remove,X10,X11,X12,X13,n_disrupt_s,n_disrupt_t
-0,15,2.0,2.0,2.0,0.5,2.0,4,0.5,0.5,0,0,0,0,888.0,2114.0
-1,15,2.0,2.0,2.0,1.0,1.0,2,1.0,1.0,1,1,1,1,1297.0,2810.0
-2,15,2.0,2.0,2.0,2.0,0.5,1,2.0,2.0,2,2,2,2,1826.0,3809.0
-3,15,1.0,1.0,1.0,0.5,2.0,4,1.0,1.0,1,2,2,2,1372.0,3055.0
-4,15,1.0,1.0,1.0,1.0,1.0,2,2.0,2.0,2,0,0,0,2118.0,4519.0
-5,15,1.0,1.0,1.0,2.0,0.5,1,0.5,0.5,0,1,1,1,815.0,2073.0
-6,15,0.5,0.5,0.5,0.5,2.0,4,2.0,2.0,2,1,1,1,2378.0,5528.0
-7,15,0.5,0.5,0.5,1.0,1.0,2,0.5,0.5,0,2,2,2,968.0,2300.0
-8,15,0.5,0.5,0.5,2.0,0.5,1,1.0,1.0,1,0,0,0,1531.0,3317.0
-9,10,2.0,1.0,0.5,0.5,1.0,1,0.5,1.0,2,0,1,2,881.0,1972.0
-10,10,2.0,1.0,0.5,1.0,0.5,4,1.0,2.0,0,1,2,0,1298.0,2763.0
-11,10,2.0,1.0,0.5,2.0,2.0,2,2.0,0.5,1,2,0,1,1717.0,3837.0
-12,10,1.0,0.5,2.0,0.5,1.0,1,1.0,2.0,0,2,0,1,1327.0,2855.0
-13,10,1.0,0.5,2.0,1.0,0.5,4,2.0,0.5,1,0,1,2,2126.0,4788.0
-14,10,1.0,0.5,2.0,2.0,2.0,2,0.5,1.0,2,1,2,0,801.0,1814.0
-15,10,0.5,2.0,1.0,0.5,1.0,1,2.0,0.5,1,1,2,0,2442.0,5980.0
-16,10,0.5,2.0,1.0,1.0,0.5,4,0.5,1.0,2,2,0,1,991.0,2186.0
-17,10,0.5,2.0,1.0,2.0,2.0,2,1.0,2.0,0,0,1,2,1311.0,2776.0
-18,5,2.0,0.5,1.0,0.5,0.5,2,0.5,2.0,1,0,2,1,879.0,1909.0
-19,5,2.0,0.5,1.0,1.0,2.0,1,1.0,0.5,2,1,0,2,1354.0,3132.0
-20,5,2.0,0.5,1.0,2.0,1.0,4,2.0,1.0,0,2,1,0,1727.0,3673.0
-21,5,1.0,2.0,0.5,0.5,0.5,2,1.0,0.5,2,2,1,0,1379.0,3184.0
-22,5,1.0,2.0,0.5,1.0,2.0,1,2.0,1.0,0,0,2,1,2145.0,4658.0
-23,5,1.0,2.0,0.5,2.0,1.0,4,0.5,2.0,1,1,0,2,810.0,1764.0
-24,5,0.5,1.0,2.0,0.5,0.5,2,2.0,1.0,0,1,0,2,2412.0,5783.0
-25,5,0.5,1.0,2.0,1.0,2.0,1,0.5,2.0,1,2,1,0,915.0,1973.0
-26,5,0.5,1.0,2.0,2.0,1.0,4,1.0,0.5,2,0,2,1,1336.0,3087.0
+idx_scenario,n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n,mean_count_firm_prod,mean_count_firm,mean_count_prod,mean_max_ts_firm_prod,mean_max_ts_firm,mean_max_ts_prod,mean_n_remove_firm_prod,mean_n_all_prod_remove_firm,mean_end_ts
+0,7,1,1,uniform,5.0000,0.3000,3,3,2.6375,2.6375,2.0861,1.0861,1.0861,1.0861,0.6034,0.2116,1.5507
+1,5,1,1,uniform,10.0000,0.5000,5,2,2.7680,2.7657,2.2021,1.2021,1.2021,1.2021,0.8602,0.3032,2.2992
+2,3,1,1,uniform,15.0000,0.7000,7,1,2.5724,2.5693,2.1728,1.1728,1.1728,1.1728,0.9326,0.3135,3.0844
+3,7,1,1,uniform,5.0000,0.3000,3,2,2.6731,2.6731,2.1181,1.1181,1.1181,1.1181,0.6080,0.2147,1.5562
+4,5,1,1,uniform,10.0000,0.5000,5,1,2.5528,2.5499,2.1568,1.1568,1.1568,1.1568,0.8128,0.2853,2.3352
+5,3,1,1,uniform,15.0000,0.7000,7,3,2.7758,2.7731,2.2036,1.2036,1.2036,1.2036,1.0053,0.3469,3.1764
+6,7,1,1,normal,5.0000,0.5000,7,3,2.8051,2.8051,2.1349,1.1349,1.1349,1.1349,0.6017,0.2112,2.1840
+7,5,1,1,normal,10.0000,0.7000,3,2,2.4440,2.4432,2.0097,1.0097,1.0097,1.0097,0.6482,0.2261,1.5912
+8,3,1,1,normal,15.0000,0.3000,5,1,2.5905,2.5857,2.1907,1.1907,1.1907,1.1907,0.8535,0.3027,2.5069
+9,7,1,0,uniform,5.0000,0.7000,5,3,2.6484,2.6484,2.0897,1.0897,1.0897,1.0897,0.6034,0.2116,1.8699
+10,5,1,0,uniform,10.0000,0.3000,7,2,2.7800,2.7777,2.2126,1.2126,1.2126,1.2126,0.8669,0.3067,2.8625
+11,3,1,0,uniform,15.0000,0.5000,3,1,2.6061,2.6008,2.2017,1.2017,1.2017,1.2017,1.0899,0.3779,2.0444
+12,7,1,0,normal,10.0000,0.7000,3,1,2.4703,2.4701,2.0848,1.0848,1.0848,1.0848,0.6754,0.2326,1.6291
+13,5,1,0,normal,15.0000,0.3000,5,3,2.8619,2.8602,2.1882,1.1882,1.1882,1.1882,0.8069,0.2745,2.2118
+14,3,1,0,normal,5.0000,0.5000,7,2,2.4358,2.4358,2.0008,1.0008,1.0008,1.0008,0.6013,0.2105,2.1909
+15,7,1,0,normal,10.0000,0.7000,5,3,2.8232,2.8225,2.1522,1.1522,1.1522,1.1522,0.6636,0.2312,1.9735
+16,5,1,0,normal,15.0000,0.3000,7,2,2.4954,2.4939,2.0549,1.0549,1.0549,1.0549,0.7598,0.2646,2.6013
+17,3,1,0,normal,5.0000,0.5000,3,1,2.4886,2.4880,2.1011,1.1011,1.1011,1.1011,0.7004,0.2467,1.6741
+18,7,0,1,normal,10.0000,0.3000,7,1,2.5133,2.5112,2.1253,1.1253,1.1253,1.1253,0.6949,0.2459,2.6966
+19,5,0,1,normal,15.0000,0.5000,3,3,2.8387,2.8366,2.1686,1.1686,1.1686,1.1686,0.8318,0.2914,1.7528
+20,3,0,1,normal,5.0000,0.7000,5,2,2.4606,2.4606,1.9937,0.9937,0.9937,0.9937,0.6004,0.2105,1.8640
+21,7,0,1,normal,10.0000,0.5000,7,1,2.4653,2.4642,2.0829,1.0829,1.0829,1.0829,0.6514,0.2267,2.4522
+22,5,0,1,normal,15.0000,0.7000,3,3,2.8364,2.8343,2.1667,1.1667,1.1667,1.1667,0.8267,0.2888,1.7461
+23,3,0,1,normal,5.0000,0.3000,5,2,2.4608,2.4608,1.9939,0.9939,0.9939,0.9939,0.6006,0.2107,1.8651
+24,7,0,1,uniform,15.0000,0.5000,3,2,2.5840,2.5794,2.1474,1.1474,1.1474,1.1474,0.9568,0.3301,1.8722
+25,5,0,1,uniform,5.0000,0.7000,5,1,2.4339,2.4339,2.0541,1.0541,1.0541,1.0541,0.6048,0.2118,1.9189
+26,3,0,1,uniform,10.0000,0.3000,7,3,2.7619,2.7602,2.1701,1.1701,1.1701,1.1701,0.8429,0.2994,2.8086
+27,7,0,0,normal,15.0000,0.5000,5,2,2.5179,2.5160,2.0465,1.0465,1.0465,1.0465,0.7621,0.2688,2.1512
+28,5,0,0,normal,5.0000,0.7000,7,1,2.4286,2.4284,2.0486,1.0486,1.0486,1.0486,0.6006,0.2105,2.2440
+29,3,0,0,normal,10.0000,0.3000,3,3,2.7964,2.7962,2.1312,1.1312,1.1312,1.1312,0.6960,0.2406,1.6377
+30,7,0,0,uniform,15.0000,0.7000,7,2,2.5851,2.5806,2.1476,1.1476,1.1476,1.1476,0.9295,0.3154,2.9756
+31,5,0,0,uniform,5.0000,0.3000,3,1,2.4966,2.4952,2.1103,1.1103,1.1103,1.1103,0.8017,0.2952,1.7958
+32,3,0,0,uniform,10.0000,0.5000,5,3,2.7703,2.7686,2.1771,1.1771,1.1771,1.1771,0.8387,0.2956,2.3099
+33,7,0,0,uniform,15.0000,0.3000,5,1,2.6002,2.5941,2.2002,1.2002,1.2002,1.2002,1.0322,0.3707,2.7615
+34,5,0,0,uniform,5.0000,0.5000,7,3,2.6827,2.6827,2.0994,1.0994,1.0994,1.0994,0.6025,0.2122,2.1867
+35,3,0,0,uniform,10.0000,0.7000,3,2,2.5514,2.5495,2.1181,1.1181,1.1181,1.1181,0.8352,0.2867,1.7676
diff --git a/analysis/g_bom_exp_id_1.png b/analysis/g_bom_exp_id_1.png
index 7ff2bf1..ad12be6 100644
Binary files a/analysis/g_bom_exp_id_1.png and b/analysis/g_bom_exp_id_1.png differ
diff --git a/analysis/g_firm_sample_id_1_de.png b/analysis/g_firm_sample_id_1_de.png
index 175abef..de74bae 100644
Binary files a/analysis/g_firm_sample_id_1_de.png and b/analysis/g_firm_sample_id_1_de.png differ
diff --git a/analysis_count.py b/analysis_count.py
index eb7e27c..9a8a70d 100644
--- a/analysis_count.py
+++ b/analysis_count.py
@@ -5,4 +5,4 @@ print(count)
 print(len(count['s_id'].unique()))
 count_max_ts = count.groupby('s_id')['ts'].max()
 print(count_max_ts.value_counts())
-print(count_max_ts.value_counts()/1593)
\ No newline at end of file
+print(count_max_ts.value_counts()/1593)
diff --git a/analysis_firm_network.py b/analysis_firm_network.py
index 5cd6beb..a6dab56 100644
--- a/analysis_firm_network.py
+++ b/analysis_firm_network.py
@@ -6,7 +6,6 @@ plt.rcParams['font.sans-serif'] = 'SimHei'
 
 # count firm category
 count_firm = pd.read_csv("analysis\\count_firm.csv")
-count_firm = count_firm[count_firm['count'] > 4]
 print(count_firm.describe())
 
 count_dcp = pd.read_csv("analysis\\count_dcp.csv",
@@ -15,7 +14,7 @@ count_dcp = pd.read_csv("analysis\\count_dcp.csv",
                             'down_id_firm': str
                         })
 # print(count_dcp)
-count_dcp = count_dcp[count_dcp['count'] > 2]
+count_dcp = count_dcp[count_dcp['count'] > 35]
 
 list_firm = count_dcp['up_id_firm'].tolist(
 ) + count_dcp['down_id_firm'].tolist()
@@ -53,7 +52,6 @@ for _, row in count_dcp.iterrows():
             'up_name_product': row['up_name_product'],
             'down_id_product': row['down_id_product'],
             'down_name_product': row['down_name_product'],
-            # 'edge_label': f"{row['up_id_product']} {row['up_name_product']} - {row['down_id_product']} {row['down_name_product']}",
             'edge_label': f"{row['up_id_product']} - {row['down_id_product']}",
             'edge_width': k * (row['count'] - count_min),
             'count': row['count']
@@ -87,7 +85,7 @@ nx.draw(G_firm,
         pos,
         node_size=node_size,
         labels=node_label,
-        font_size=6,
+        font_size=8,
         width=3,
         edge_color=colors,
         edge_cmap=cmap,
@@ -96,7 +94,9 @@ nx.draw(G_firm,
 nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=6)
 sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
 sm._A = []
-position = fig.add_axes([0.9, 0.05, 0.01, 0.3])
-plt.colorbar(sm, fraction=0.01, cax=position)
-plt.savefig("analysis\\count_dcp_network20230526_de")
+position = fig.add_axes([0.95, 0.05, 0.01, 0.3])
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=10)
+cb.outline.set_visible(False)
+plt.savefig("analysis\\count_dcp_network")
 plt.close()
diff --git a/analysis_prod_network.py b/analysis_prod_network.py
index 6742eaf..c4f6bf3 100644
--- a/analysis_prod_network.py
+++ b/analysis_prod_network.py
@@ -1,8 +1,6 @@
 import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
 import networkx as nx
-import math
 
 plt.rcParams['font.sans-serif'] = 'SimHei'
 
@@ -32,7 +30,7 @@ for code in G.nodes:
     index_list = count_prod[count_prod['id_product'] == code].index.tolist()
     index = index_list[0] if len(index_list) == 1 else -1
     node_attr['count'] = count_prod['count'].get(index, 0)
-    node_attr['node_size'] = 5 * count_prod['count'].get(index, 0)
+    node_attr['node_size'] = count_prod['count'].get(index, 0)
     node_attr['node_color'] = count_prod['count'].get(index, 0)
     labels_dict[code] = node_attr
 nx.set_node_attributes(G, labels_dict)
@@ -62,8 +60,10 @@ nx.draw(G,
 sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
 sm._A = []
 position = fig.add_axes([0.01, 0.05, 0.01, 0.3])
-plt.colorbar(sm, fraction=0.01, cax=position)
-# plt.savefig("analysis\\count_prod_network")
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=8)
+cb.outline.set_visible(False)
+plt.savefig("analysis\\count_prod_network")
 plt.close()
 
 # dcp_prod
@@ -72,13 +72,17 @@ count_dcp = pd.read_csv("analysis\\count_dcp.csv",
                             'up_id_firm': str,
                             'down_id_firm': str
                         })
-count_dcp_prod = count_dcp.groupby(['up_id_product','up_name_product', 'down_id_product', 'down_name_product'])['count'].sum()
+count_dcp_prod = count_dcp.groupby(
+    ['up_id_product',
+     'up_name_product',
+     'down_id_product',
+     'down_name_product'])['count'].sum()
 count_dcp_prod = count_dcp_prod.reset_index()
 count_dcp_prod.sort_values('count', inplace=True, ascending=False)
 count_dcp_prod.to_csv('analysis\\count_dcp_prod.csv',
-                       index=False,
-                       encoding='utf-8-sig')
-count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 2]
+                      index=False,
+                      encoding='utf-8-sig')
+count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 50]
 # print(count_dcp_prod)
 
 list_prod = count_dcp_prod['up_id_product'].tolist(
@@ -116,6 +120,8 @@ for _, row in count_dcp_prod.iterrows():
 # dcp_networkx
 pos = nx.nx_agraph.graphviz_layout(g_bom, prog="dot", args="")
 node_labels = nx.get_node_attributes(g_bom, 'Name')
+# rename node 1
+node_labels['1'] = '解决方案'
 temp = {}
 for key, value in node_labels.items():
     temp[key] = key + " " + value
@@ -126,28 +132,7 @@ vmin = min(colors)
 vmax = max(colors)
 cmap = plt.cm.Blues
 
-# dct_row = {}
-# for node, p in pos.items():
-#     if p[1] not in dct_row.keys():
-#         dct_row[p[1]] = {node: p}
-#     else:
-#         dct_row[p[1]][node] = p
-# dct_row = dict(sorted(dct_row.items(), key=lambda d: d[0], reverse=True))
-# dct_up = dct_row[max(dct_row.keys())]
-# dct_up = dict(sorted(dct_up.items(), key=lambda d: d[1][0], reverse=True))
-# h = list(dct_row.keys())[0] - list(dct_row.keys())[1]
-# n = len(dct_up.items())
-# arr_h = np.linspace(list(dct_row.keys())[0]-h/2, list(dct_row.keys())[0]+2*h, num=n)
-# dct_up_new = {}
-# for index, (node, p) in enumerate(dct_up.items()):
-#     dct_up_new[node] = (p[0], arr_h[index])
-# pos_new = {}
-# for row, dct in dct_row.items():
-#     if row == list(dct_row.keys())[0]:
-#         pos_new.update(dct_up_new)
-#     else:
-#         pos_new.update(dct)
-pos_new ={}
+pos_new = {}
 for node, p in pos.items():
     pos_new[node] = (p[1], p[0])
 
@@ -157,8 +142,8 @@ nx.draw(g_bom,
         pos_new,
         node_size=50,
         labels=node_labels,
-        font_size=6,
-        width = 1.5,
+        font_size=5,
+        width=1.5,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
@@ -170,7 +155,9 @@ axis.set_ylim([1.2*y for y in axis.get_ylim()])
 
 sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
 sm._A = []
-position=fig.add_axes([0.1, 0.4, 0.01, 0.2])
-plt.colorbar(sm, fraction=0.01, cax=position)
-# plt.savefig("analysis\\count_dcp_prod_network")
-plt.close()
\ No newline at end of file
+position = fig.add_axes([0.75, 0.1, 0.01, 0.2])
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=8)
+cb.outline.set_visible(False)
+plt.savefig("analysis\\count_dcp_prod_network")
+plt.close()
diff --git a/sum_result.py b/analysis_sum_result.py
similarity index 81%
rename from sum_result.py
rename to analysis_sum_result.py
index 73223fe..395d02f 100644
--- a/sum_result.py
+++ b/analysis_sum_result.py
@@ -10,20 +10,13 @@ Firm['Code'] = Firm['Code'].astype('string')
 Firm.fillna(0, inplace=True)
 BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
 
-result = pd.read_sql(sql='select * from iiabmdb_dissertation.not_test_result where ts > 0;',
+with open('SQL_analysis_risk.sql', 'r') as f:
+    str_sql = f.read()
+result = pd.read_sql(sql=str_sql,
                      con=engine)
-lst_s_id = list(set(result['s_id'].to_list()))
-for s_id in lst_s_id:
-    query = pd.read_sql(
-        sql=f'select * from iiabmdb_dissertation.not_test_result where ts = 0 and s_id = {s_id};',
-        con=engine)
-    result = pd.concat([result, query])
-result.set_index('id', inplace=True)
-result.sort_index(inplace=True)
-result['id_firm'] = result['id_firm'].astype('string')
-# result.to_csv('analysis\\count.csv',
-#               index=False,
-#               encoding='utf-8-sig')
+result.to_csv('analysis\\count.csv',
+              index=False,
+              encoding='utf-8-sig')
 print(result)
 
 # G bom
@@ -31,17 +24,20 @@ plt.rcParams['font.sans-serif'] = 'SimHei'
 
 exp_id = 1
 G_bom_str = pd.read_sql(
-    sql=f'select g_bom from iiabmdb_dissertation.not_test_experiment where id = {exp_id};',
+    sql=f'select g_bom from iiabmdb.without_exp_experiment '
+        f'where id = {exp_id};',
     con=engine)['g_bom'].tolist()[0]
 G_bom = nx.adjacency_graph(json.loads(G_bom_str))
 pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="")
 node_labels = nx.get_node_attributes(G_bom, 'Name')
+# rename node 1
+node_labels['1'] = '解决方案'
 plt.figure(figsize=(12, 12), dpi=300)
 nx.draw_networkx_nodes(G_bom, pos)
 nx.draw_networkx_edges(G_bom, pos)
 nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6)
 # plt.show()
-# plt.savefig(f"analysis\\g_bom_exp_id_{exp_id}.png")
+plt.savefig(f"analysis\\g_bom_exp_id_{exp_id}.png")
 plt.close()
 
 # G firm
@@ -49,7 +45,7 @@ plt.rcParams['font.sans-serif'] = 'SimHei'
 
 sample_id = 1
 G_firm_str = pd.read_sql(
-    sql=f'select g_firm from iiabmdb_dissertation.not_test_sample where id = {exp_id};',
+    sql=f'select g_firm from iiabmdb.without_exp_sample where id = {exp_id};',
     con=engine)['g_firm'].tolist()[0]
 G_firm = nx.adjacency_graph(json.loads(G_firm_str))
 pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
@@ -91,9 +87,9 @@ count_firm_prod.rename(columns={'Name': 'name_product'}, inplace=True)
 count_firm_prod = count_firm_prod[[
     'id_firm', 'name_firm', 'id_product', 'name_product', 'count'
 ]]
-# count_firm_prod.to_csv('analysis\\count_firm_prod.csv',
-#                        index=False,
-#                        encoding='utf-8-sig')
+count_firm_prod.to_csv('analysis\\count_firm_prod.csv',
+                       index=False,
+                       encoding='utf-8-sig')
 print(count_firm_prod)
 
 # count firm
@@ -107,9 +103,9 @@ count_firm = pd.merge(count_firm,
 count_firm.drop('Code', axis=1, inplace=True)
 count_firm.sort_values('count', inplace=True, ascending=False)
 count_firm = count_firm[['id_firm', 'Name', 'count']]
-# count_firm.to_csv('analysis\\count_firm.csv',
-#                   index=False,
-#                   encoding='utf-8-sig')
+count_firm.to_csv('analysis\\count_firm.csv',
+                  index=False,
+                  encoding='utf-8-sig')
 print(count_firm)
 
 # count product
@@ -123,14 +119,13 @@ count_prod = pd.merge(count_prod,
 count_prod.drop('Code', axis=1, inplace=True)
 count_prod.sort_values('count', inplace=True, ascending=False)
 count_prod = count_prod[['id_product', 'Name', 'count']]
-# count_prod.to_csv('analysis\\count_prod.csv',
-#                   index=False,
-#                   encoding='utf-8-sig')
+count_prod.to_csv('analysis\\count_prod.csv',
+                  index=False,
+                  encoding='utf-8-sig')
 print(count_prod)
 
 # DCP disruption causing probability
-result_disrupt_ts_above_0 = result[(result['ts'] > 0)
-                                   & (result['is_disrupted'] == 1)]
+result_disrupt_ts_above_0 = result[result['ts'] > 0]
 print(result_disrupt_ts_above_0)
 result_dcp = pd.DataFrame(columns=[
     's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
@@ -188,5 +183,5 @@ count_dcp = count_dcp[[
     'down_id_firm', 'down_name_firm', 'down_id_product', 'down_name_product',
     'count'
 ]]
-# count_dcp.to_csv('analysis\\count_dcp.csv', index=False, encoding='utf-8-sig')
+count_dcp.to_csv('analysis\\count_dcp.csv', index=False, encoding='utf-8-sig')
 print(count_dcp)
diff --git a/anova.py b/anova.py
index 032b0b0..40eb89c 100644
--- a/anova.py
+++ b/anova.py
@@ -110,49 +110,16 @@ def anova(lst_col_seg, n_level, oa_file, result_file, alpha=0.1):
 
 if __name__ == '__main__':
     # prep data
-    str_sql = """
-    select * from
-    (select distinct idx_scenario, n_max_trial, crit_supplier,
-    firm_pref_request, firm_pref_accept, netw_pref_cust_n,
-    netw_pref_cust_size, cap_limit, diff_new_conn, diff_remove
-    from iiabmdb.with_exp_experiment) as a
-    inner join
-    (
-    select idx_scenario,
-    sum(n_disrupt_s) as n_disrupt_s, sum(n_disrupt_t) as n_disrupt_t from
-    iiabmdb.with_exp_experiment as a
-    inner join
-    (
-    select e_id, count(n_s_disrupt_t) as n_disrupt_s,
-    sum(n_s_disrupt_t) as n_disrupt_t from
-    iiabmdb.with_exp_sample as a
-    inner join
-    (select a.s_id as s_id, count(id) as n_s_disrupt_t from
-    iiabmdb.with_exp_result as a
-    inner join
-    (select distinct s_id from iiabmdb.with_exp_result where ts > 0) as b
-    on a.s_id = b.s_id
-    group by s_id
-    ) as b
-    on a.id = b.s_id
-    group by e_id
-    ) as b
-    on a.id = b.e_id
-    group by idx_scenario) as b
-    on a.idx_scenario = b.idx_scenario;
-
-    """
-    result = pd.read_sql(sql=str_sql,
-                         con=engine)
+    result = pd.read_csv("experiment_result.csv", index_col=None)
     result.drop('idx_scenario', 1, inplace=True)
     df_oa = pd.read_csv("oa_with_exp.csv", index_col=None)
-    result = pd.concat(
+    scenario_result = pd.concat(
         [result.iloc[:, 0:10],
          df_oa.iloc[:, -4:],
          result.iloc[:, -2:]], axis=1)
     result.to_csv('analysis\\experiment_result.csv')
 
-    # 9 factors (X), 4 for error (E), and 2 indicators (Y)
-    the_lst_col_seg = [10, 3, 2]
+    # 10 factors (X), 13 for error (E), and 9 indicators (Y)
+    the_lst_col_seg = [10, 13, 9]
     the_n_level = 3
     anova(the_lst_col_seg, the_n_level, "oa25.txt", result, 0.1)
diff --git a/anova.xlsx b/anova.xlsx
deleted file mode 100644
index 30fa7a8..0000000
Binary files a/anova.xlsx and /dev/null differ
diff --git a/anova_visualization.ipynb b/anova_visualization.ipynb
new file mode 100644
index 0000000..802b8e5
--- /dev/null
+++ b/anova_visualization.ipynb
@@ -0,0 +1,573 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>自变量</th>\n",
+       "      <th>level</th>\n",
+       "      <th>系统恢复用时R1</th>\n",
+       "      <th>产业-企业边累计扰乱次数R2</th>\n",
+       "      <th>产业-企业边最大传导深度R3</th>\n",
+       "      <th>产业-企业边断裂总数R4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>新供应关系构成概率P7</td>\n",
+       "      <td>低</td>\n",
+       "      <td>2.240</td>\n",
+       "      <td>2.672</td>\n",
+       "      <td>1.143</td>\n",
+       "      <td>0.7640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>新供应关系构成概率P7</td>\n",
+       "      <td>中</td>\n",
+       "      <td>2.132</td>\n",
+       "      <td>2.674</td>\n",
+       "      <td>1.143</td>\n",
+       "      <td>0.7859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>新供应关系构成概率P7</td>\n",
+       "      <td>高</td>\n",
+       "      <td>2.179</td>\n",
+       "      <td>2.649</td>\n",
+       "      <td>1.124</td>\n",
+       "      <td>0.7575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>是否已有连接偏好P4</td>\n",
+       "      <td>不倾向</td>\n",
+       "      <td>2.177</td>\n",
+       "      <td>2.668</td>\n",
+       "      <td>1.141</td>\n",
+       "      <td>0.7804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>是否已有连接偏好P4</td>\n",
+       "      <td>倾向</td>\n",
+       "      <td>2.191</td>\n",
+       "      <td>2.663</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>0.7579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>是否规模偏好P2</td>\n",
+       "      <td>不倾向</td>\n",
+       "      <td>2.171</td>\n",
+       "      <td>2.669</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>0.7726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>是否规模偏好P2</td>\n",
+       "      <td>倾向</td>\n",
+       "      <td>2.196</td>\n",
+       "      <td>2.661</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>0.7657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>最大尝试时间步P8</td>\n",
+       "      <td>低</td>\n",
+       "      <td>1.726</td>\n",
+       "      <td>2.646</td>\n",
+       "      <td>1.123</td>\n",
+       "      <td>0.7782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>最大尝试时间步P8</td>\n",
+       "      <td>中</td>\n",
+       "      <td>2.186</td>\n",
+       "      <td>2.682</td>\n",
+       "      <td>1.144</td>\n",
+       "      <td>0.7599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>最大尝试时间步P8</td>\n",
+       "      <td>高</td>\n",
+       "      <td>2.640</td>\n",
+       "      <td>2.667</td>\n",
+       "      <td>1.143</td>\n",
+       "      <td>0.7694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>最大尝试次数P3</td>\n",
+       "      <td>低</td>\n",
+       "      <td>2.286</td>\n",
+       "      <td>2.691</td>\n",
+       "      <td>1.154</td>\n",
+       "      <td>0.8254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>最大尝试次数P3</td>\n",
+       "      <td>中</td>\n",
+       "      <td>2.124</td>\n",
+       "      <td>2.652</td>\n",
+       "      <td>1.127</td>\n",
+       "      <td>0.7431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>最大尝试次数P3</td>\n",
+       "      <td>高</td>\n",
+       "      <td>2.141</td>\n",
+       "      <td>2.652</td>\n",
+       "      <td>1.130</td>\n",
+       "      <td>0.7390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>采购策略P1</td>\n",
+       "      <td>单供应商</td>\n",
+       "      <td>2.261</td>\n",
+       "      <td>2.519</td>\n",
+       "      <td>1.121</td>\n",
+       "      <td>0.7919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>采购策略P1</td>\n",
+       "      <td>双供应商</td>\n",
+       "      <td>2.146</td>\n",
+       "      <td>2.650</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>0.7615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>采购策略P1</td>\n",
+       "      <td>三供应商</td>\n",
+       "      <td>2.144</td>\n",
+       "      <td>2.826</td>\n",
+       "      <td>1.156</td>\n",
+       "      <td>0.7541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>额外产能分布P5</td>\n",
+       "      <td>均匀分布</td>\n",
+       "      <td>2.316</td>\n",
+       "      <td>2.681</td>\n",
+       "      <td>1.158</td>\n",
+       "      <td>0.8403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>额外产能分布P5</td>\n",
+       "      <td>正态分布</td>\n",
+       "      <td>2.052</td>\n",
+       "      <td>2.650</td>\n",
+       "      <td>1.115</td>\n",
+       "      <td>0.6980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>额外产能分布参数P6</td>\n",
+       "      <td>低</td>\n",
+       "      <td>2.436</td>\n",
+       "      <td>2.705</td>\n",
+       "      <td>1.171</td>\n",
+       "      <td>0.9121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>额外产能分布参数P6</td>\n",
+       "      <td>中</td>\n",
+       "      <td>2.202</td>\n",
+       "      <td>2.666</td>\n",
+       "      <td>1.142</td>\n",
+       "      <td>0.7655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>额外产能分布参数P6</td>\n",
+       "      <td>高</td>\n",
+       "      <td>1.914</td>\n",
+       "      <td>2.624</td>\n",
+       "      <td>1.098</td>\n",
+       "      <td>0.6299</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            自变量 level  系统恢复用时R1  产业-企业边累计扰乱次数R2  产业-企业边最大传导深度R3  产业-企业边断裂总数R4\n",
+       "15  新供应关系构成概率P7     低     2.240           2.672           1.143        0.7640\n",
+       "16  新供应关系构成概率P7     中     2.132           2.674           1.143        0.7859\n",
+       "17  新供应关系构成概率P7     高     2.179           2.649           1.124        0.7575\n",
+       "9    是否已有连接偏好P4   不倾向     2.177           2.668           1.141        0.7804\n",
+       "8    是否已有连接偏好P4    倾向     2.191           2.663           1.133        0.7579\n",
+       "4      是否规模偏好P2   不倾向     2.171           2.669           1.137        0.7726\n",
+       "3      是否规模偏好P2    倾向     2.196           2.661           1.137        0.7657\n",
+       "18    最大尝试时间步P8     低     1.726           2.646           1.123        0.7782\n",
+       "19    最大尝试时间步P8     中     2.186           2.682           1.144        0.7599\n",
+       "20    最大尝试时间步P8     高     2.640           2.667           1.143        0.7694\n",
+       "7      最大尝试次数P3     低     2.286           2.691           1.154        0.8254\n",
+       "6      最大尝试次数P3     中     2.124           2.652           1.127        0.7431\n",
+       "5      最大尝试次数P3     高     2.141           2.652           1.130        0.7390\n",
+       "2        采购策略P1  单供应商     2.261           2.519           1.121        0.7919\n",
+       "1        采购策略P1  双供应商     2.146           2.650           1.133        0.7615\n",
+       "0        采购策略P1  三供应商     2.144           2.826           1.156        0.7541\n",
+       "10     额外产能分布P5  均匀分布     2.316           2.681           1.158        0.8403\n",
+       "11     额外产能分布P5  正态分布     2.052           2.650           1.115        0.6980\n",
+       "14   额外产能分布参数P6     低     2.436           2.705           1.171        0.9121\n",
+       "13   额外产能分布参数P6     中     2.202           2.666           1.142        0.7655\n",
+       "12   额外产能分布参数P6     高     1.914           2.624           1.098        0.6299"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "config = {\"figure.dpi\": 300,\n",
+    "          \"font.family\": 'serif',\n",
+    "          \"font.serif\": ['SimSun']}\n",
+    "df = pd.read_csv('analysis/anova_visualization.csv', encoding='utf-8-sig')\n",
+    "df['sort_index'] = df['level'].map({'不倾向':0,\n",
+    "                                    '倾向':1,\n",
+    "                                    '低':0,\n",
+    "                                    '中':1,\n",
+    "                                    '高':2,\n",
+    "                                    '单供应商':0,\n",
+    "                                    '双供应商':1,\n",
+    "                                    '三供应商':2})\n",
+    "df.sort_values(['自变量', 'sort_index'], inplace=True)\n",
+    "df.drop(columns='sort_index', inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\1808291987.py:10: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
+      "  ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_name = '采购策略P1'\n",
+    "y_choose = [0, 1, 2]\n",
+    "y_prop = pd.DataFrame({'y_name': ['系统恢复用时R1', '产业-企业边累计扰乱次数R2', '产业-企业边最大传导深度R3', '产业-企业边断裂总数R4'],\n",
+    "                       'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
+    "                       'palette': sns.color_palette(\"deep\")[0:4]})\n",
+    "df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
+    ").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
+    "df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
+    "sns.set_theme(style=\"whitegrid\", rc=config)\n",
+    "ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
+    "                  markers=['o'],\n",
+    "                  dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
+    "                  palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
+    "                  legend='brief')\n",
+    "ax.set_title(x_name)\n",
+    "for item in df_x.groupby('响应变量'):\n",
+    "    for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
+    "        ax.text(x, y+0.05, f'{m:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\1224603408.py:10: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
+      "  ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_name = '最大尝试次数P3'\n",
+    "y_choose = [0, 1, 2, 3]\n",
+    "y_prop = pd.DataFrame({'y_name': ['系统恢复用时R1', '产业-企业边累计扰乱次数R2', '产业-企业边最大传导深度R3', '产业-企业边断裂总数R4'],\n",
+    "                       'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
+    "                       'palette': sns.color_palette(\"deep\")[0:4]})\n",
+    "df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
+    ").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
+    "df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
+    "sns.set_theme(style=\"whitegrid\", rc=config)\n",
+    "ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
+    "                  markers=['o'],\n",
+    "                  dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
+    "                  palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
+    "                  legend='brief')\n",
+    "ax.set_title(x_name)\n",
+    "for item in df_x.groupby('响应变量'):\n",
+    "    for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
+    "        ax.text(x, y+0.05, f'{m:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\2665207915.py:10: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
+      "  ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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tlMArbMFdCG06HQHP6qujykGXS46DB+U4eKgVaKyBA9Gr/P7q69qz8Guf8wDrn9xLGZeONLQWISAAAABw7LId9m+c+HhjP+AYGxtb43rR5uWXX5YkjRw50tC/Y4qLi72eHw/vE8EdAAAAcIwymUyyxMXJEhcnhXh4dsc7b5MkuV0uOUvLPBV/RsMzt8tV0ZLU5ZKrvDykvVSxxBsP21yl4TxzMDz/UAst/CyVq7xcrvJy2QsKPdfjmzQxvNbKG/6u8vwCWRISKtp7Vrb19LT/TIivqBJM8G39WTEn8dB1g//4BQAAAHB0Dg+Q4uLiDM0/fPzhAVc0WbNmjb7//nuZTCade+652rt3b9Bzj/Z9Ojy4i4b3ieAOAAAAgExms6yJFQGPjHUW8cw/9d/TJEkuh6Pa2X8l1dp7lvi2/vSMqWoLWqKEFsbPXQjc8tNYeOZ2u+UsKTV8/3DcW6qhcjDENqiu0lK5Sktlr3ZYu1HxTZvqlNf+aWhO0YaNshcVVQsBq50HGBMT8l4AAACAE0VZWZnX8xiDf4+2Wr3jn8PXiyZvvPGGJKlPnz5q2rSpoeDuaN+nw8dHw/tEcAcAAAAgrMxWq8zJyYpJTq6zezbo01sJzZp5gkJHZVBotF2lq6zM69zAoxFKcBeo3WhIlYNhavESyuvY+b+Z2v/Djz7Xmw4/R+1uuNbQWvt/XC5zjNVTKWhNTKhoI5oQL5PFYnhvAAAAwLHg8JaP5QY7mxw+PjGEf1PUhezsbC1YsECSNGTIEMPzw/0+JYTw759wI7iLMk6nU59//rlmzZqlX375RcXFxWratKm6dOmiK6+8Uv369Yv0FlVWVqaePXvKdRQfaMycOVOdO3f2+7VBgwZp586dIa379ttvq2/fviHvCwAAAMemjEsuDs9CbreaDj+nsiqwomrQUa0q0GkrqQj3ghBam87wVA667Ha5HQ7D9/d77xCr/fyulWC85ebG556Xq9R/FaQ5Ls5zDqAloTLUq/bckljV8jNecenpanBqH8P3BwAAACLh8ADJaCXYsRLcvffee56sYeDAgYbnH/66jL5Ph4+vV6+e4T2EG8FdFNmxY4duvvlm/fLLL+rVq5emTJmitm3batu2bZo2bZrGjh2rYcOG6fHHH4/oL54tW7YcVWiXkJCg9PT0MO7okEBhIAAAABAMS0LCESvC3E5nRavPkmrtP222Q9cqg774ZsZbfgaqkjMa3AUKAEMR0ll9gYI7o2cnOp0BQzupokLSVVYm+4H8I66V3Kmj4eAu+6P/qnTXblkS4r3O/0vO6qCkdm0NrQUAAAAYcXgGcLTBXTQEUoez2+363//+J0lq2rSp2rVrZ7hi7nh8nwjuosSOHTt0xRVXaO/evTrzzDP18ssvy1LZ9qVp06bq27evJk2apA8++EA7duzQu+++G7GSzU2bNnk9j42NVZMmTXx65h7u4MGD2rt3r8aPH69GjRrVODYtLU3169c3tK/09HSlpKQYmgMAAAAYZbJYZE2qJ2tS+P9Bl3XrzbIXFXnOBKyo+LMpuUN7Q+uEq02mJFkSwlc5aDUYAobrvEEptJafB1asVNFv632ut7zyz4aCO7fbrY3PTvEEf9VDwOrn/1V8LcHz2HyEf2MBAADg+HX4Z+j79+83NP/AYedc11YxzdH47rvvlJeXJ0nq3r17SGscj+8T/wqIAuXl5Ro/frz27t2rRo0a6dlnn/WEdtVNmjRJP/74o3799Vfdd999evbZZyOw20PBXVJSkiZOnKjzzz9fcXFxR5w3fvx4bdu2TX/5y1+OOHb06NG66aabjnqvAAAAwLEkuWNWWNaJqV9fXR+eXBn+HaoC9Jz/V+15VUhY1RL08Aq30M7qC1Q5aCwEjHzlYHgqIF1lZdr37XeG72+OjT0U5lWGeyldOqv1VVcaWsftdstkMhm+PwAAACKnXbt2Xs93795taP6ePXtqXC8azJ8/3/M4Kyu0fwu1b+/9Q47Hw/tEcBcFpk+frg0bNkiSbrzxRiUlJfkdZ7Vadf311+uee+7R559/rvPOO0+DBg2qy61KkjZv3iyLxaLXXntNvXv3DmrO0qVL9dVXX2n69OmKiYmp5R0CAAAAJzZLXJzSup8U0ly30ylnaakn3AvlXDpzTKxMMTFy2+3e+zLc8jPSlYP+K/6MVw6GFkC6ysvlKi+XvaDg0L1DaN2z5o4JKtmZ41PR1+y84Wp4WvBnhLvdbkkiBAQAAKgDhwdIu3btMjR/7969nsdpaWlq2LBhWPYVTt9++63ncdu2obWiT09PV1pamvLz8yUd3ft0NPsIJ4K7CDt48KCmTZsmSUpOTtbIkSNrHD98+HA9+OCDKi0t1ZQpU3TmmWfKbDbXxVY9Nm3apBEjRgQd2rlcLj3++OM644wz1K9fv1reHQAAAICjYbJYZK1XL6SAqMopr/1TkuSy2yvP/iuRs8SmuEbG2s647A5Zk5LksNmkozhnWwp35aDB4C7AmX+hMHpOoKSKKsvKCkvlHbresL+xf585ioq07JpxsiYmep/7V/U8IbGi9WfVtcQEWRIqvmb1el4RINIKFAAAILCkpCS1atVK27dvlyRt3LjR0PwtW7Z4Hnfr1i2sewuHjRs3Kjc31/M8LS0t5LU6d+6sH374wbOuEdXfp8zMzICFVXWJvyVH2AcffOBJgs8555wjtpxMSEhQ//79tXDhQm3cuFHff/+9Bg4cWAc7rWCz2bRz505deWXwrVk++eQTbdq0KWKtPQEAAABEhjkmRuaYGMWkJIc0P6ldW/V9999yu91ylZdXa+95eItPW0VA6GkDWu1rlZWDcY2M/YSx2+0OGLgZDc8cYTxz0Gi1nxQ4OLQafB3OkhLJ5ZLj4EE5Dh40vI/qGv1poDrefquhOQdW/SyTyXToPMBqFYSmOv6BVgAAgLowaNAgTZ8+XZK0Zs2aoOc5nU5Pl7+qdaLNypUrvZ4nJ4f2bwap4vVVBXe///67ioqKgl5v3bp1nseDBw8OeQ/hRHAXYXPmzPE8Pv3004Oa07t3by1cuFCSNG/evDoN7kpLSzV69GiddFJwbXdKSko0depUXXjhhT69ZgEAAAAgGCaTSZa4OFni4qSj+ElcQ1wuNTl7iJy2Eq+KNafNJqvBDxXCelZfKJWDgc7qM9ryM5yVgyG0Lt384j9Vvj/P53qHm29U40FnBb2Os6RE+WvWesK/Q9WACTLHxdEKFAAARI2hQ4d6grv8/Hxt2bIlqDPYNm3apNLKs6tNJpOGDBlSm9sMyerVq72eO53OkNcaOnSoHnvsMbndbrndbq1cuVJnnHHGEecVFBRo27ZtnufR8j4R3EXQ7t279csvv3ie9+rVK6h5Xbp08Tz+6quv9NBDD4V9b4E0aNBA9913X9Dj33rrLe3fv1833HBDLe4KAAAAAMLLZLGo3Q3XhWWtmNRUNRk6xNMy1GGrVjlYUlEl6A7ygwqjwZ3L4ZCrvDwsa0Vr5aDRELBs716tf+xJ/180m6sFeodafVqqhXuesC8hQfXaZCq5Y5bRlwIAABCUk08+WZmZmdq6daskafbs2brpppuOOG/BggWexwMGDFCTJk1qa4shq14RKEl79uwJea1mzZqpX79++v777yVVFEwFE9wtWrTIc45zu3bt1LNnz5D3EE4EdxG0atUqzy+K9PR0NW7cOKh5HTp08Dzet2+fdu/eraZNm9bKHo9GXl6epk2bpnPPPVeZmZmR3g4AAAAARES91q3U/sa/Bfy62+2W224/FOiV2DwtPg+v+Evu3MnQvWuq9jNccRfOykGDbTrdLpeclT817rNWQryhtRw1VQ66XHIWF8tZXCz/cae3Zhecbzi42/TiyzJZzF4tP62JCUrr2VNx6Y0MrQUAAI5vZrNZt9xyi2655RZJ0qeffqobb7zxiB0C5s6d63l888031zh29uzZevHFF7V3714NGDBADzzwgBo0aHDUez+SqjCySnFx8VGtd+utt3qCu7lz52rSpElKPMLfOb/88kvP45tuuknmKGm/TnAXQZs2bfI8zsjICHpew4YNZbVa5XA4JEmbN2+OyuDutdde08GDB/XXv/71qNZZunSpZs+erVWrVmn37t0qLS1Venq6Tj75ZF1++eU69dRTw7RjAAAAAKh7JpNJpthYxcbGSmmpYV3bEh+vrg89cCgIrDwP0GGzKbaBsXP/ArXcDGlfBqv9nKVlUuUPvvqsFcpZfWFitHLQ7XZr76Kv/VZYdnngPkPBXUlOjrI/+MirKvBQlWDl+X+JlV9LqPiaOT6eVqAAABxjzjnnHPXo0UOrV69Wdna2Zs2apREjRgQcv2TJEm3cuFGSdPbZZ6t79+4Bx65cuVK33367XC6XpIoga//+/ZoxY0at/p2huLj4qIO6w3Xv3l3Dhw/XnDlzZLPZNH36dI0fPz7g+C1btmjx4sWSpK5du+qcc84J636OBsFdBG3evNnzuHnz5kHPM5lMatSokXbv3i2p4hdYXZ5zF4zc3Fy999576tOnjzp1MvYToVWcTqcmTZqkjz/+WJKUkJCgBg0ayOVyaefOndq5c6c+++wzXX755Zo8ebIsFks4X0KdczgcJ+Q/oKoC6CNdAwAAABCael06B/ya3W4Pep3ETp2Udd89cnnafPr5n62k4uslpZUhYcU19+F/x4+NNXTv8sLCgF9zx8YYW6uoKOixRxQXZ+jervLygG1R3THGXodtd672fv1N0OMlSSZTZZBXEfCZKx83OusMNTrd2OcKbrf7hPw3LAAAteFIn5E+/vjj+vOf/6yioiJNmTJFp59+upL9nLtst9v13HPPSaro8jdx4sQa/34xb948T2hXZfny5dq8eXOtdtHLz8/3ueZ0OmW324/qs+EHHnhAq1evVk5Ojt58801ddNFFAbOX5557Ti6XS4mJiXr66aej6u81BHcRlJd36FDt1FRjP1WZnJzsCe4KCgrCuq9weO2111RWVqarr7465DXuv/9+ff7557r++ut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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_name = '额外产能分布P5'\n",
+    "y_choose = [0, 1, 2, 3]\n",
+    "y_prop = pd.DataFrame({'y_name': ['系统恢复用时R1', '产业-企业边累计扰乱次数R2', '产业-企业边最大传导深度R3', '产业-企业边断裂总数R4'],\n",
+    "                       'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
+    "                       'palette': sns.color_palette(\"deep\")[0:4]})\n",
+    "df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
+    ").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
+    "df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
+    "sns.set_theme(style=\"whitegrid\", rc=config)\n",
+    "ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
+    "                  markers=['o'],\n",
+    "                  dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
+    "                  palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
+    "                  legend='brief')\n",
+    "ax.set_title(x_name)\n",
+    "for item in df_x.groupby('响应变量'):\n",
+    "    for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
+    "        ax.text(x, y+0.05, f'{m:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\759130329.py:10: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
+      "  ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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hnc5YW7JkyR7fS04ck7hgwQIdeuih09LfvvrVr36lW2+9VY8//riam5t144036stf/rI+/OEPKycnR93d3fr3v/+thx9+WE6nU9/97nfldDo1MjISd5+srKwkfQUzi+8gZjGfz6dLL71U9fX1uv/++3XyyScnu6W4cG3H3zR7MvH16enpiWxrxlit1n0O10wm005vEsxm86zezTdx1+FEs7lnAAAA7D8S9X7UbDbv9F7cZrMRrgEADlg2m01LF6Zp6cLx8W+Dw0HVtXhU09yvbc1u1TS71dU3+D532TvDgbDea+jXew39Rs2RblNVmUuLKrK1qNylReUu5WbxA96YvSbzefHE95+J+Hx5uthsNt1xxx363Oc+p9/85jd67bXXdMstt+iWW24xfn3ZsmW6/PLLdcYZZxhB48QfgrPZbKqoqEhK/zON7yBmKY/Ho0svvVR1dXX62c9+pmOPPTbZLUmSsrOz1draKinW42QFAoG4nW45OTkJ7w0AAAAAAADAvklPs+mQqjwdUpVn1Dz+EdWMBm01zf2qaXbL7ZvaD95Phm8wqHXburVuW7dRy3GmxYK2CpcWlcdCN0d6yvvcBcC+WL58uX7wgx8oGo2qv79fHo9HNptNBQUFxi68iVPqJuYEB9JUiAPjq5xj+vv7dckll6ipqUm//OUvtWLFimS3ZFi4cKE2bdokSWpra5v0dd3d3YpO2E++cOHChPcGAAAAAAAAIPGyMlO14uBCrTi4UJIUjUbV4x42graa5n7VNrs1MBxK+Np93mG9+W6H3ny3w6gV52aoanRn26JylxaWuWRP5aNuIJFMJpNycnL2uFGmr6/PeLxo0aLpbmvW4E+cWWZsx9pYsHbEEUcku6U4VVVVxuP29vZJX9fZ2Wk8Tk9PV2lpaUL7AgBMn9bWVpWUlDD3fgYMDAzM2XNJAQAAABw4TCaT8rPtys+267hDSyRJkUhU7b0DqmnqN3a51bV6FAiGE75+e++A2nsH9O//xCZsmU1SWaFjNGyL7W6bX+KUzWpJ+NoAxg0PDxvHSEnS0UcfnbxmZhjh2izi9/t12WWXqaGhQf/3f/+nww8/PNkt7WTiYYtbt25VJBKZ1DkHNTU1xuNly5bxAS0AzCH33XefGhsb9d3vfldLlixJdjt79NBDDyklJUWf/exn59QogtbWVn3605/Weeedp0svvVQulyvZLe2koaFB8+bNm7b7BwIB3X///briiivkcDimbR0AAAAAiWc2m1San6nS/Ex96APlkqRwOKKmTp+2NY2Pk2xs9yocie7hblMTiUpNHT41dfj0j7eaJUlWi1nzSpxaVO5S9WjoVlbokMXM55JAorS0tBiPLRaLTj755CR2M7PmzidO+7lAIKCrr75aNTU1+sUvfjGpYC0YDOr//b//p7vuukt2+8wc7LlixQq5XC653W75/X7V1taqurp6j9e9++67xuNTTz11OlsEACTY5s2btW3bNn3mM5/R5z73OX3ta1+b1cHH4YcfrvPPP1+///3vdfPNN+vII49MdkuTUlpaqmuvvVa33HKLfv/73+vLX/6yLr744lkVEF5zzTUqLCzUl770pWk5D/aee+7R//3f/+npp5/WnXfeqaOOOirhawAAAACYORaLWfNLsjS/JEsfO6ZSkhQIhrW9zRN3hltLl1/RxOZtCoUjqm12q7bZrWdHa6kpFi0szTJ2ty2qcKk4N4ONAMBeqqurMx4ffPDBysrKSmI3M2v2fFpzAItGo/rWt76ltWvX6ic/+cmkP0javHmzVq9ePWPBmiRZrVadeuqpevTRRyVJr7/++qTCtbfffltSbMs44RoAzB0ej0e1tbWSpHA4rO7u7kntWE6mww47TGeddZaeeOIJffGLX9QXvvAF3XDDDbLZbMlubY8+//nP680339Tzzz+vu+66Sy+88IIeeOAB5efnJ7s1SZLdbteqVau0atWqaV2nra1NV155pZ588kmVl5dP61oAAAAAZlaKzaKDKnN0UOX4OU6Dw0HVtXhU09yvbaOhW1ffYMLXHgmE9d72Pr23ffyMqEy7bcL5bdmqrnApN2vmPm8F5rINGzYYjw+0z/0J12aBBx54QM8884zuuOMOnXTSSZO+7rnnnlNFRcU0drZrF198sR5//HGFw2H97W9/08UXX/y+r6+pqTES7NNOO43z1gBgDlmzZo0ikYik2J/hP/zhD2d9uCZJX/3qV/XMM88oGAzqt7/9rYLBoG6++eZktzUpN954o15//XX5fD6tX79eX/jCF/TXv/5VmZmZe3W/aDQqj8eTkDGTaWlpkqSCggI9/PDD+3y/Hf3+97/X73//e0nSz3/+c4I1AAAA4ACRnmbTIVV5OqQqz6h5/CNxu9tqmt1y+0YSvrZ/KKj/bOvWf7Z1G7UcZ+r47rbybC2qcMmRnpLwtYHZJBgM6sknn9TAwIDOOOMM5eTkvO/r/X6/Nm3aJEmaN2/enJkclCiEa0n26quv6sc//rG+/OUv65xzzpn0da2trXrkkUd04okn7vLX29radNNNN+mtt95SWVmZ/vu/TILB/AABAABJREFU/1vHHXdcQnpetGiRzjrrLD322GN67733tHbtWn3gAx/Y7ev/+te/SorNXL322msT0gMAYGa8+uqrkqSDDjpId95555wI1qTYiMXTTjtNTz75pKTY30X/8z//o9TU1EnfIxQKJWUkY35+vj73uc/p5z//uaTYOWdr166d0g/gjIlGo1q5cqXWrFmj3/zmNyosLNyn3sb+fdhsNi1cuHCf7rUr2dnZxuO5cL4fAAAAgOmTlZmqFQcXasXBse9jotGoetzDqmnuV22LWzVNbtW0uDUwFEz42n3eEb35bofefLfDqBXnZhijJBeVZ2thaZbSUvl4HTPj73//u+6//351d3fr+OOP13e/+909hl9TddNNNxkT615++WU99NBD7/v6l156ScFg7Pffueeee8CNV+V3fxJ1d3frhhtu0DHHHKOvfvWrk75u3bp1uuGGGzQwMLDbnWs33HCD1qxZI0natm2brr76aj377LMqLi5OSO/XX3+91qxZo5aWFt1xxx3685//vMvfPJ2dnfrjH/8oSbr22mu1YMGChKwPAJh+4XBYL730kmw2m+66664pBVOzwac//WkjXLNYLLJYLJO+1ufz6Utf+pLOOussXXDBBdPV4m6df/75RrhWWFi4Vz/9NRas/fnPf5YkXXjhhfscsE3l3yEAAAAAJJLJZFJ+tl352XYdd2iJJCkSiaqjd2B0lGS/aprcqmv1KBAMJ3z99t4BtfcO6F//aZUkmU1SeaFjdKRkbJfb/BKnbFa+b0K86IQDBaN7cbjgO++8o2984xvGZKHnnntOvb29+u1vf5vQQOvZZ581Hr/xxhsaHh42JtjsqKWlRU899ZQk6ZhjjtHhhx++z+vv67+nmUa4lkT333+/+vv7VVtbq9NPP31S13i9XvX29hrPdxeujW3HHDM0NKSNGzcmLFzLycnRgw8+qM9//vPasGGD7rjjDv33f/933GsCgYC+9rWvaXh4WB/96Ed11VVXJWRtAMDMWLNmjfr7+/WVr3xFBx100JSuDQaDe3XG2T//+U95vV6dffbZU752RytWrFBGRoYGBgb06U9/etK70Hw+ny677DKtX79e69evVzQa1ec///n3vWb79u3Gm9xEyc/Pl9vt1nXXXaf29vYpX//www8bwZoU2wG3rwHb2Jv2YDAYd2hxovT39xuP58IbaQAAAADJZTabVJKfqZL8TH3oiDJJUjgcUVOnL26kZEObV+FIYr/HiESlxg6fGjt8+sdbzZIkq8WseSVOLSp3qXo0dCsrdMhiPrB29GBcIBBQV1eX8by5uVnHHHPMlO7x0ksv7fSZw1tvvaWGhgbNnz8/IX1KUnp6ugYGBiRJkUhEvb29uzziKRAI6L//+781NDSk8vJyfelLX0rI+i0tLcbj5ubmhNxzOhGuJVFPT4+k2A627u7uPbx613YXri1btszYuSbFxjctXrx4r9bYnerqav3lL3/Rtddeq4cfflhtbW267LLLVFRUpM2bN+u+++7T5s2bddFFF+mGG2444LaFAsBMePTRR+Vyud730NhoNKpvfetbOu200/ShD33IqD/zzDM64YQTlJWVtcvrHn/8cc2bN2+vfjji5ptvltls1o033jilkG1oaEg33HCDnnvuOd188837tMvKarXq4osvVnt7u775zW9O6pqJwZoU+3d36623StL7BmyrV6/WLbfcMi2B0PXXX5+wezU0NOiLX/yifvvb3+7Vv9uxsaBdXV2T/sGgvUW4BgAAAGBvWCxmzS/J0vySLH306EpJUiAY1vY2j2qb3aO73Nxq6fIp0d92hMIR1Ta7Vdvs1tgeoLQUixaWuUbPb3Opqtyl4twMPis9QDzxxBMKhULG8z/96U86++yz9+oHkneU6B/yPf/883X//fcbz5uamnYK1/r6+nTNNdfoP//5jyorK3XDDTcoPT19n9d+7bXX4n6w+Mknn9Sll14qp9O5z/eeLoRrc9zuwrU777zTOHOtsLBQ3/jGN3b72n0xb948/fWvf9Vzzz2nxx9/XF/96lfl8XhUVFSkww8/XLfffruWLl2a8HUBADH9/f369re/rY997GO69NJLdzm276WXXtITTzyhv/3tb7r66qt1zTXXyGw2a9WqVbrzzjv1ve99T8cff3zcNX6/Xy+88IIeeOABpaRM7dDmNWvW6NFHH1U0GlVdXZ3uv//+Sc8BH3tz+fLLL+vll1+e0rrv57HHHtvra6PRqG655RZJuw/YLrjgAjkcjv/P3l2HRZm1YQC/h24BBUTFQhC7c11bv7W7FQNFxVi7xW5FFDtWRVRcXezORV27sDsoAemegZnvD3ZmQQaYGYZQ7991ee3wvuc950zAwtzznIOIiAhUqVIFJUqUgImJCQwNc/6D6fbt23B0dAQA/P7773BxcZHbbtOmTVi3bh0AwNPTEw0aNFD1LqlMGniVLFkSly9fVnv/Hh4e2LBhAwD1/5FAREREREQ/Lx1tTVQsY46KZczR4d9jCUkivAuMTtu7zT8Sb/yjEBKRoPaxk4SpePY+HM/e/7camZG+9r/LSaZVt9mXNkXRIvpqH5vyV0xMDL58+QKxWIzIyEhcv34dnp6eGdo8efIEvXv3xsCBA1GuXDkYGRlBW1s72wq01q1bY9euXRn+Tra3t0fZsmXVOv/Ro0cjKioKXl5ekEgkcHNzw++//w4zMzN8+fIFd+/exZ9//gmhUIiBAweiVatWKm0hkpCQgICAAEgkEsTExODevXvYuXNnhjaBgYHo0aMHhgwZAnt7e9kHw+3t7dVyX9WB4VoB2rRpU571XaJECdleLXlNR0cHnTt3RufOnfNlPCIi+o/0l5hz587h8uXLcoOc1NS0teYlEgm8vb3RvXt3lCpVCtra2ggJCcGwYcOy7D+7c4q4d+8eVqxYgRUrVijUPv0nt7p27QpnZ+dcja9OOYVknTp1yqeZFJz8rCaTvm6JiIiIiIjygoGeNqrZFkM122KyY9FxyXgb8O9ykv+GbpGxyWofOy5RhEevw/Do9X+rmZmb6Mr2brOzMUMFG1OYGCr3YVcqWBcvXsy0dZI8z58/x6xZs2Rf5/QB1tq1a2PNmjVYv349QkJCUK9ePbi6uqp9X3RNTU3MmTMHPXv2xJ9//ok7d+5g7NixEIlEMDY2RoUKFTBs2DB069YNFhYWePLkiUrjPHnyRPYh4+x8/vxZ9mFnqVevXqk0Zl5guEZERPQdSx9G7d69G3Xr1s3UZu3atdiyZQsA4I8//kCpUmnr0Esr0ooVK5bpk1S5ERAQIAvF2rRpI1tWURHp74+pqSlsbW3VNi/KvfysJhOJRPk2FhEREREREQAUMdJFHQcr1HFIW0ZfIpEgPDpJVtn25nMU3gREIT5R/X+vRMQk4/azL7j97IvsWPGiBukCN1PYljKFvi7f0i+sunfvju7du+dJ3+3bt8/z7RmkHBwc4Orqmm0boVCocv8NGjQoVCGZqvidSERE9B3T0lLuf+XplxmQfsJJU1Mzz0Ksli1bKrWsZPpw7Udbf/7Vq1fw9PTEggULlH7eCgtp4CUSifDu3Tu19x8ZGZlpLCIiIiIiooIiEAhQzFQfxUz10ahaCQBpgVvw1/i0sM0/rbrtXWA0koXqX33jS3gCvoQn4NqjQACAhgAoZWUsq26zszFFuRIm0NZSbwUTEeXs+3xnh4iIiAAgVxvgFsaA50cO11avXg1fX1+EhoZi3bp1atnw19fXF9euXcOIESNgaWmphllmT7pUY2hoaJ5/Yi4pKSlP+yciIiIiIlKFQCBACQsjlLAwQrPaaSvDpKaK4R8ahzefI/H638DtY1AMUsXqXVpfLAE+f4nF5y+xuHTXHwCgpSlAWWuT/yrcSpvBxsoYmho/1t/URIVN4XtXjYiIiBSWm4BM3Wtzq4OGhkZBTyFPXLx4Eb6+vgDSArFBgwZh27ZtKFq0aK76bdiwIa5cuYI2bdqgb9++cHZ2znWf2UlOTttrIKf14FXl4eGBDRs2QFtbO1/3dyMiIiIiIsoNTU0NlLU2QVlrE7RpUAYAIBSl4kNQdIYKt4DQOKj7T52UVAneBkTjbUA0ztxMO6arownbkkXSBW6msC5q+MN9iJWoIDFcIyIi+o7lJowq7OGaKr/037x5EyEhIejatatK48fFxWH69OmYNWsWSpYsqVIf34qPj8fSpUtlXxsYGKBKlSqIiYnJdRCmo6ODefPmoU6dOpg7dy7+/PNPDBw4EMOHD0eRIkVyO/VMpOGaubm52vsG0vb/Gzt2LPr06ZNlJd7hw4fRvn17tVT+ERERERER5RUdbU1ULGOOimX++/spIUmEd4HRaXu3/buPW0hEgtrHTham4vmHCDz/ECE7ZqSvjQr/7t1mZ2MK+9JmKFpEX+1jE/0sGK4RERF9x3ITrqmjSkwsFqu12ix9X9/2m5CQkGOgYmZmhtGjR6NatWoq7SP35MkTXLx4ETdu3MCoUaMwbNgwpfaMk2fFihUIDExbH9/U1BR79uyBg4NDrvr8VseOHWFvb4/Ro0dj27ZtOHjwIEaMGAFHR0fo6uqqbZxatWph4sSJaN68udr6TK9fv35ZnpNIJFiyZAn27t2Lc+fOYfPmzYVyaVMiIiIiIqKsGOhpo5ptMVSzLSY7Fh2XjLcB/1a3/Ru6RcYmq33suEQRHr0Ow6PXYbJj5ia6qFDKDHalTWX7uJkY5u5vYKKfBd+RICIi+o7lJtjKqjLs5s2bEAqFaNasWbbX37p1Cx4eHvDw8FBbJVNW4ZpIJELv3r2xd+9emJmZZXm9g4MDatSogYkTJ+Lw4cNKB2NPnjwBACQmJmLt2rU4duwY3N3dUbFiRSXvSZq///4bBw8eBAAYGxtj586dag/WpOzt7XH48GGMGzcOd+/exerVq3HgwAFMmTJFof3R3r9/n+NSjIMHDwYAfPjwQS1zVsamTZtw8uRJAGlLa86aNQsrVqzgsiZERERERPRdK2KkizoOVqjjYAUg7YOF4dFJssq2N5+j8CYgCvGJIrWPHRGTjDvPv+DO8y+yY1bmBrKgza60KWxLFoGBnur7vRP9qBiuERERfcfyYlnIuLg4jB07Fj179sSUKVPkVoslJSVhzpw58Pf3R8+ePbFlyxbY29urPBeprMI1bW1t/PLLL5g1axY2b96cbR+DBw/G6NGj4ebmhhkzZig1/tOnTzN8XbRoUZQvX16pPqS+fv2KmTNnAkhbCnL79u2oWrWqSn0pyszMDH/88QdmzpyJkydPIjAwUBY0Lly4EKVKlcry2jt37mD+/PnfzV5nx44dQ9GiRTF9+vSCngoREREREZHaCAQCFDPVRzFTfTSqVgJAWuAW/DUer//du+3N5yi8C4yGUJSq9vFDIhIQEpGA64+D/p0PUMrSOG0pSRtTVLAxRbkSRaCjXfi2miDKTwzXiIiIflJZVfzo6ekBSNvb6vDhwzn2ExgYiAULFsDLyyvXVUTpr/82/Bs4cCDatm0Lb29v9O3bN8s+WrRogdKlS2PPnj1o2bIl6tevr/D40so1IG0/s2XLlkFbW/lP6KWkpGDSpEkIDw+HiYkJtm3bhlq1aindjyp0dHSwevVqmJiYYP/+/QCAGzduoFu3btiyZQvq1Kkj97q+ffvCwsICMTExqFy5MqysrFCkSBFWhhERERERERUwgUCAEhZGKGFhhOa10z40mZoqhn9oHN58/rfCzT8SH4NjkJKq3g9MSiSAf0gs/ENicfmePwBAS1OAMtYmadVt/+7fZmNpBE1N9W0bQVTYMVwjIiL6juUm+Miq6i39UoozZ85E69atM7URCoVo164dAKB27drYunWrWkIYkei/ZS6+nZ+NjQ1atWqF5cuXo379+llWlAkEAnTv3h3u7u6YMWMGTpw4AUNDwxzHDgsLQ1BQkOzr/v37w8bGRqX7sWTJEty+fRvm5ubYuXMnKleurFI/qhIIBJg3bx40NDTg5eUFAIiJicGkSZNw5syZLPeua9WqVZZ9+vv7w8rKKtd70OUkKSkJQUFBKlcMEhERERER/Qw0NTVQ1toEZa1N0KZBGQCAUJSKj8ExePM58t8qtygEhMZC3QuUpKRK8C4gGu8ConH2ZtoxXR1N2JYsIgvc7EqbwrqoIT+wST8shmtERETfsdwsC5kVXV1d2W0zMzO5SwkmJ/+3uXL9+vVhZGSklrGTkpJkt+UtWzlw4EBcuHAB06dPx4EDB6ClJf9XmW7dumH9+vUIDAzE2rVrMWfOnBzHvnXrluy2jo4Ohg8frsI9AA4cOID9+/fDysoKu3btgq2trUr9qMOcOXPw9etXnD17FgDw5csXnD9/Hl27dlW6r7Nnz+LAgQNwcXFB165ds3zsc2vmzJm4dOkSJk2ahMGDB/MPMSIiIiIiIgXpaGvCvrQZ7EubocO/xxKSRHgXGJ22d5t/JN4GROFLeILax04WpuL5hwg8/xAhO2akr40KNqayPdzsS5uiaBF9tY+dXwQCAYoVKwZdXV0IBAJIJJI8eV+Gvg8M14iIiL5judkfK6vQoiB/MUxMTJTdlheuNWjQAGXKlIGfnx+2bt2KMWPGyO2nePHi+OWXX3Dt2jXs27cPnTp1Qo0aNbId++bNm7LbXbt2hYWFhdLzv337NpYsWQJ7e3ts374dxYsXV7oPVYWEhMDKyirDMYFAgOXLl+P9+/d4/fo1gLQKNlXo6OggMDAQs2fPxuzZs3M935wsW7YMT548wZo1a/J8LCIiIiIioh+VgZ42qtkWQzXbYrJjMfFCvJXu3/bvfyNikrPpRTVxiSI8eh2GR6/DZMfMTXT/q26zMUMFG1OYGObtCinqoqWlhTJlyhT0NKiQYLhGRET0HcuLcE1eqJVf0leuyauMEggE6NmzJ9asWYPNmzejVatWcHBwkNtXu3btcO3aNYjFYixcuBCHDh3KNjhMX7nm6Oio9Nz9/PwwduxY1K1bFx4eHjA2Nla6D1XdvXsXw4cPx4wZM9CvX78M5/T19bFx40b0798f4eHh+PXXX1UaI/1ykCtWrEC1atVyvObcuXNYt24dAGDnzp2wtrbO8Zrhw4cjKCgIdnZ2mDFjhkpzJSIiIiIioqyZGOqgtoMlajtYyo6FRyfi9ef/Are3/lGISxRl04tqImKScfvZF9x+9kV2zMrcQBa22ZU2hW3JIjDQU37/c6L8xHCNiIjoO5abcC0ryoZr6pxD+so1fX35S0V0794d69atg0gkwpw5c/Dnn3/KDc1atGgBTU1NpKam4unTpzh16hQ6deokt09/f38EBgYCSFvm0s7OTql5+/n5YdiwYWjbti0WLFgAbe20PwKCg4ORkJD9chvBwcGy25GRkXj37p3cdpGRkRmukbYLCwvDuHHjkJSUhPnz5yM+Pj7TkpalS5fG4cOH8erVK5QrV06p+yaVPlyztrZWaLnL9NV/NjY2Cn3CTxqqVq1aVaXqQSIiIiIiIlJe0SL6aFRNH42qpX0oUiKRIDg8/t/lJNNCt3eB0UgWpqp97JCIBIREJOD647R90AUCoJSlMexsTGFvYwq70mYoV8IE2loF92FgIG1Puw9BMfj0JQbJwlTo6miiTHETlCthAh3tgp0b5T+Ga0RERJQr6gzX0leuGRgYyG1TrFgxNGrUCNeuXcOTJ0+wf/9+DBw4MFM7c3Nz1K5dG3fv3gUAbN26NctwLX3V2reVXznx8/ODs7MzJk6ciAEDBmQ4FxYWhmHDhiE2Nlahvjw9PeHp6Zlju+nTp2d5btWqVRAIBHBycspwvHjx4rlapjJ9uEZEREREREQ/NoFAgBLFjFCimBGa1U7biz01VQz/0Di89Y/Ea/+00O1jUDRSUtX7wV+JBPAPiYV/SCwu3/MHAGhpClDW2gQVZEtKmqK0lTE0NfN+a4vXnyNx4tp73PALgihFnOm8tpYGfqleAp1+LQ/70mZ5Ph8qHBiuERERfcfE4sy/1GVHkSAsq+Ui1TWH7ERHR8tuGxkZZdlOuuQjAKxfvx6dO3eGiYlJpnb169eXhWtv3rzBkydP5C5n6OvrCyCt0qpNmzZKzXnz5s3YsGED6tatm+lc9erVceDAAYSHh8PBwQGmpqaycxcuXMCUKVNw9OhRlavJkpKS0L9/fzx79gwAYGlpiZEjR6JHjx4q9ZcdaTVefsmLqkwiIiIiIiJSnaamBspam6CstQla109bmUSUklbN9SbdHm7+IbFQ9590KakSvA2IxtuAaJz9d8t0XR1NlC9RBHal05aUtLcxhXUxQ6Xf18hKQlIKdp14irO3PmXbTpQixtUHAbj6IAC/NSyDoZ2qwkCP0cuPjs8wERHRd0zZYCt9+6x+2SzIcO3Ll//WXLe0tMyyXZs2bTBv3jyIRCJER0fj4MGDGDFiRKZ2NWrUyPD17du3M4VrQqEQ169fBwD07dtX6RBp8+bN2Z63s7OTu8xkmzZtULVqVcyaNQv79u3Ldj84eSQSCaZPny4L1kxMTLB7926FlmtURfrKNVX2pGvbtq1S7dX5uiIiIiIiIqK8oa2lCfvSZv9WbKV9cDQxOQXvAqTLSaaFbl/Cs98yQRXJwlS8+BiBFx8jZMcM9bVhV8r038AtLXQrWkRP6fc6wqMTMWfLPwgIjVPqurO3PuHp+3AsHtUYRYvI3+6CfgwM14iIiL5jygYQKSkpsttZVQblps/cSh+uWVtbZ9nOxMQEtWrVwp07dwCkVYHJC9eqV6+e4evQ0NBMbW7cuIGEhARoa2ujb9++qk5dJdOnT0fv3r2xe/duDBs2TKlrN27ciLNnzwJIqypbv359ngVrQMa9+FasWCG3AvBb586dw7p16wAAO3fuzPY5lRo+fDiCgoIYrhEREREREX2n9HW1UNW2GKraFpMdi4kX4m266rY3/pGIiElW+9jxiSI8ehOGR2/CZMfMjHVhZ2MmC9wqlDJFESPdLPtISEpRKViTCgiNw9ytN7F6fFNWsP3A+MwSERF9x5RdOk+dQZiUOkOQ4OBgAICenh6KFi2abduGDRvKwjU/Pz9ERUVlWHYRAMzMzGBqaoqoqCgAyHQeSAuAAKB9+/YoVqxYpvN5qXr16ujevTvc3NxQp06dTJV2WTl69Cg8PDwApFUaLlq0CI0aNcrLqUIoFMpuW1tbKxTkWVhYyG7b2NigTJkyOV6jpZX26ynDNSIiIiIioh+HiaEOajtYorbDf6vUhEcn4vXn/wK3t/5RiEsUqX3syNhk3Hn+BXee//eBXitzA1SwMYW9jSmqVSgGO5v/9krbdeKpysGalH9ILHadfIYxPRX7O5++PwzXiIiIvmPpw7IbN24gMDAwU5s3b97IbqcPSLKSmpoqux0WFoZ3795laiMSieTezi1p5VqFChVybNugQQPZbYlEgs+fP8sNzxwcHHDr1i0AyLQvmlAoxKVLlwCottShOkyfPh2+vr4YP348jhw5AnNz82zb//PPP5gzZ47s61mzZqFbt255PU2FXjvqlBdBMBERERERERUeRYvoo1E1fTSqlrbKiUQiQXB4PN58/m85yXeB0UgWpubQk/JCIhIQEpGAG4+DMK53TVm49vpzZI57rCnq7M2PaFO/9L9LZtKPhuEaERHRdyx9AHHq1Cno62dez/vr16+y28nJ/y25kFXVW/pwbdWqVVi1alW2c1BXuCYUChERkbZOeuXKlXNs7+DgAIFAILsfSUlJctu1bdsWt27dQqNGjVCvXr0M565du4aYmBjUrl0bVatWzeU9UE2RIkXg6uqKcePGYfz48fjjjz8y7G+W3rNnzzB27FjZYz5u3Lh8CwXVGaIWxvGIiIiIiIioYAkEApQoZoQSxYzQrHYpAEBqqhj+oXF48zkSb/7dx+1jUDRSUpVbyScrWpoaaFqzpOzrE9feq6VfqZPX32NS/zpq7ZMKB4ZrRERE37H04drSpUszVWYBwNq1a7FlyxYAioVr6ftcuXIlunTpkqlNcnKybD8zdVU0ffnyRTanSpUq5djeyMgI5cqVw/v3ab/4li5dWm67Pn36wMrKCs2aNcu0gfGpU6cAFFzVmlTbtm3RvXt3+Pj4YMqUKXB3d4eGhkaGNm/evMGwYcMQHx8PAHBxccHYsWPzbY7pn+fg4GC5FY3fCgv7b417f39/harRpG3Sv1aJiIiIiIjo56SpqYGy1iYoa22CNg3SthoQpaTiQ1CMrLrtjX8U/ENioeTOGQCAstYm0NNNi0mEolTc8AtS5/Rx/XEQxvaqCR1tzZwb03eF4RoREdF3TJml87S0tDKFS/IoG2qoKwR59eqV7Hb9+vUVuubXX3/F+/fvUbNmTRQvXlxuGy0tLbRu3TrT8bi4OFy6dAnW1tZo06aNapNWI1dXVzx58gTnzp3D/PnzsWDBAtnz9eHDBwwdOlS2d9zvv/8OFxeXfJ1f+nBt+vTpSl/v5OSkVPvExESlxyAiIiIiIqIfn7aWJuxLm/273GI5AEBCkgjvA6P/DdzSQrcv4Qk59lXBxlR2+0NQDEQp6t3/W5QixsfgGC4N+QNiuEZERPQdU2TpPG1tbXTv3h2jR4/OUN0lFsv/hVEaalhaWmZZDQYAJUqUQL9+/dCrVy8lZy3f06dPZeMqsucaAAwaNAjv3r3D/PnzlR7vwoULSEpKwsCBA6GlVfC/Eunr62PdunXo3bs3Dh48CJFIhMWLF+Pt27cYNmyYbHnPqVOnYvjw4fk+v/SvNU9Pzwx73mXl0KFDsv3hzp8/jzJlyuR4TZs2bZCamooOHTqoPlkiIiIiIiL6qRjoaaOqbTFUtS0mOxYTL8TbdNVtb/wjERGT8QPCVuYGstufvsTkydwYrv2YCv6dJCIiIlKZtJpIW1sbRkZGctuMGjVKbniU1bKQZmZmcHNzQ9u2baGtrS23ja6uLi5evAhNTfUta/Ds2TMAQJMmTRS+xsbGBjt37lRpvCNHjsDAwAC9e/dW6fq8YGtrCw8PDzg7O8PHxwehoaF4+vQpoqKioKWlhUWLFqF79+4FMjddXV106dIFbdu2Ra1atfJsnNWrV6NatWrQ0NBAXFxclq9rIiIiIiIiouyYGOqgtoMlajtYyo6FRyf+V932ORL6uv+9X5IsTJXXTa4li/KmXypYDNeIiIi+YxoaGujfvz9GjhyZ7bKI8mRVuVarVi2FwhN1BmspKSl4/PgxAKB9+/Zq6zcrQUFBuHPnDvr37w8TE5M8H08ZjRs3xpIlSzBt2jRcv34dQFpVm7u7O5o3b15g8+rfvz/69++f5+PUqFEDYrEY69evx4EDB+Dp6Qk7O7s8H5eIiIiIiIh+fEWL6KNoEX00rGoNIOMHj3V18mZfNF3ut/ZDYrhGRET0HXNyclJoHzV5sqpcKwh3795FTEwMLCws0Lhx4zwf78iRIwDSlpUsbCIiInD+/PkMx8zNzQukgis8PDzLEFYRcXFxstuRkZEwMDDIpnWa5ORkLFiwAL6+vgCAoUOHYv/+/dkuUUpERERERESkivTvqZQpnjcfvi1rXbg+1EvqwXCNiIjoO6ZqsAZkXblWEK5cuQIA6Ny5s1or4uSRSCQ4cuQImjdvjnLlymXZTigU4vTp0+jatWuezie98+fPY+HChQgLCwOQtq9dSEgIAgMDMXDgQPTt2xfjx4+Hubl5vs1nwYIFagli+/Tpo9J1YWFhGDJkCPbt2wdra+tcz4OIiIiIiIhInnIlTKCtpQFRivreL9HW0mC49oNiuEZERPSTSk3NmzW/ExMTlWovkUhw6dIl6OjoYPDgwXkyp/Tu3LkDf39/LF68OMs2IpEI48ePx5UrVxASEoKRI0fm6Zw+ffqExYsXy6q19PT0MGrUKDg5OeHhw4eYPHkywsLCcODAARw/fhzDhg3DwIEDYWpqmqfz6tevH/T19fHx40dUrVoVpUqVgrGxMYyMjBQKdo8dOyZ7nI8cOYJSpUqpPBddXV2VryUiIiIiIiLKiY62Jn6pXgJXHwSorc8mNUpAh8tC/pAYrhEREf2k8ipc++eff5Rqf+vWLQQEBKBPnz6wsrLKkzmld/jwYVSqVAkNGzaUe14sFmPatGmyajo3Nzfo6upiyJAhap9LaGgoNm3ahMOHD0MkEkFTUxNdunTBuHHjUKJECQBAgwYNcOLECSxbtgzHjh1DfHw8PDw8sGPHDnTp0gW9e/dGlSpV1D43qdxU7unp6cluGxoaFrr97YiIiIiIiIjS6/RrebWGax2blFdbX1S4MFwjIiL6SeUmXHN1dYWhoSEMDAygp6cHHR0daGho4MOHDzh48KCsnSLVTd7e3tDV1c1QHRYcHIyEhASV55cVoVCI8+fPY8SIEXj37p3cNjt27MDp06czHFu2bBl0dHTQv39/tczD398fu3btwl9//YWkpCRoa2ujW7duGDlypNylKs3MzLBy5Up069YNy5cvx8uXL5GYmAhvb294e3ujfPny6NChA5o1a4aqVavmarnQwuzQoUOIjIyEs7NzQU+FiIiIiIiIfkD2pc3wW8MyOHvrU677+q1RWdiXNlPDrKgwYrhGRET0k0pJSVH52t9++w0PHz7EtWvX8OjRoyz35LKxscm2n69fv+LSpUsYOXIkSpYsKTseFhYGJycnxMTEqDzH7Hh4eMDDw0OpaxYuXAhdXV306NFDpTElEglu3LiBgwcP4tKlS0hNTYWFhQUGDx6MAQMGKFS116hRIxw5cgTHjx/Hli1b8OHDBwDA+/fvZffJ3Nwc9erVQ/Xq1VGjRg3Y29ujSJEiKs25MPH09MTSpUsBAOXKlUObNm0KeEZERERERET0IxraqSqevg9HQGicyn3YWBljaMe8W2WGCh7DNSIiop+USCRS+drGjRujcePGGDNmDPz9/bF9+3b8+eefGUK24sWLo2bNmtn24+npCUtLy0yVSNWrV8fu3btx/fp1VKpUCeXKlYOJiQmMjIygqfl9rVX+/v17nD59GkeOHEFAQAB0dHTQokULdOvWDc2bN4eWlnK/jmloaKBr167o3LkzLl68CE9PT9y9e1d2PiIiAufOncO5c+dkx8zNzVG+fHlYW1vDwsICtra26NmzZ4Z+k5OTERCgvqUvgLSQVMrf31/lQPfcuXNYt26d7OupU6di3759ebocJhEREREREf2cDPS0sHhUY8zdehP+IbFKX29jZYxFIxvBQI/xy4+Mzy4REdFPKjeVa+nZ2Nhg4cKFaNiwISZNmgSJRAItLS0sXLgw2+AoMjIS+/btw/r166Grq5vpfJUqVb7b8OT58+e4cuUKzp8/j5cvX8LExASNGjXChAkT0KJFCxgZGeV6DA0NDbRt2xZt27bF27dv8ddff+H06dP48uVLprYRERGIiIiApqYmmjZtijJlymRqo62tjR07dsDHxyfXc5PHyclJbX0lJiZi1KhROHz4cL7s00dEREREREQ/l6JF9LF6fFPsOvFUqSUif2tUFkM7VmGw9hPgM0xERPSTEgqFsLCwwNChQ9XSX/v27XH06FEEBQVh3rx5qFevXrbtd+3ahd69e+OXX35Ry/gFKTg4GLdv38adO3dw7do1xMfHo3r16mjTpg3mzZuHGjVq5GnFXYUKFTB9+nRMmzYN9+/fx6VLl/D3339n2FeufPny2LBhA2xtbeX2oaGhgaVLl6JixYowNjZGpUqVUKxYMRgbG0NfXz/P5k5ERERERERUGBnoaWFMr5po06AMTl5/j+uPgyBKEWdqp62lgSY1SqBjk/LcY+0nIpBktUkK0U9MKBTiyZMnmY5Xq1YNOjo6ueo7JSUFb968yXDMzs5O6WXB8pNYLEZiYmKGY/r6+tDQ0CigGRGROjx58gQVK1bM9c+19L5+/YpixYop1Hb27NmYN2+eWscvCFeuXMH27dvh4OCASpUqoWrVqrC3ty8Uy1dKQ7979+5h+PDhKFu2bEFPiYhIJer6ffR7/F2ciIiIiAoHoSgVH4Nj8DE4BsmiVOhqa6KstQnKWptAR7vg3wPIT3n5/vn3guEakRwM1zJiuEZEREREBYnhGhERERFR4cFwDeA740REREREREREREREREQK4sfziIiIiIiIiIiIiIiIspGSkoLAwEDo6OhAQ0MDYrEYlpaWhWJbCMp/DNeIiIiIiIiIiIiIiIiyIZFI8PXr1wzHihUrxnDtJ8VlIYmIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iIiIiIiIiIiIiIiIgUxHCNiIiIiIiIiIiIiIiISEEM14iISC1SU1Pxzz//YNasWXB1dVVr3/7+/oiLi1Pp2idPniA2Nlat88nOsWPH8ObNG5WuDQwMRHx8vJpnlDtisbigp0BKunbtGgIDAwt6GkREREREREREPyyGa0REpLKgoCAcPXoUkyZNQuPGjTF06FD89ddfOHjwIHbv3q22cbZu3YrOnTvj7t27Sl/7+PFjtGrVCtu2bYNQKFTbnLJy7949dO7cGRMnTsTbt2+Vuvb48eP49ddfsXDhQqWvzQtxcXHo3Lkzbt26VdBTkevEiRMICQnJ1zGPHz+OFStWQCQS5eu4yrhz5w7+97//YdGiRQgLCyvo6RARERERERER/XAYrhERkUJiY2Nx584d7N69G5MnT0bz5s3RokULTJ8+HadOnUJUVFSG9qtWrVIpDPuWUCjEuXPnEBgYCEdHR6xYsQKpqakKXz9w4EA0bNgQa9asQZcuXfDkyZNczyk7urq6EIvFOH36NEaPHo3o6GiFrw0PD0d8fDz27duHDh06YMuWLXk405wdOHAAb968wZAhQ7BixYp8CSeV8fjxY3Ts2BFHjhzJl/EiIyOxdOlS/PHHHxgwYEChrQ7T1dWFSCSCl5cX2rRpo9agm4iIiIiIiIiIAK2CngARERUeycnJCAwMREBAAPz9/fHhwwe8f/8eHz58QFBQUKb2AoEAxYoVg6WlJaysrFC0aFGYmppCX18f2tra+PDhA+rVq5erOZ07dw4xMTGyr8uVKwdNTU2l+nB1dcWNGzfw/v17DBw4EH/88Qfq1KmTq3llRUdHBwBQpEgReHt7o0iRIgpfGxERIbvt6OgIZ2dntc9PUbGxsdixYwcAQCKRwMfHBxUqVECPHj3UNsbVq1dRt25dGBkZqXS9rq4uYmJiMGPGDMyYMUNt81LE48ePce7cOQwbNixfx1WEltZ/v941adIEnTt3LsDZEBERERERERH9eBiuERH9BCQSCaKiohAeHo6wsDB8/foVISEh+PLlC0JDQ/HlyxcEBQXh69evkEgksuu0tLRQqlQplC1bFo0aNUKJEiVQokQJWFtby/4rDZPyipeXl+z2vHnz0Lt3b6X7KFasGBwdHbFp0yYkJSVhw4YN2LVrlzqnKaOrqwsAMDIyQtGiRZW6NjQ0VHbbyckJGhoFV2C+adMmWTVivXr1sHPnTtl9U4fjx49jxowZqFatGnbu3KlSwJY+RLp48SIMDQ0Vum7//v3w8PCAtbU1fHx8FB7vy5cv6NatGwBg0KBBhTJYAzI+LjNmzIC5uXkBzoaIiIiIiIiI6MfDcI2I6Afn5uaGv//+GwKBAHp6etDT04O+vj4MDQ1haGgIGxsbVK5cGcbGxjA2NoaJiQlMTEzw+PFjrFixAg4ODhgzZgzs7e3zfe5+fn549OgRAKBXr17o27evyn317dsXW7ZsgVgsRkpKSrZtHzx4ABsbG1hYWCg9jrJVdem9f/8eAGBhYYHixYsrfX1SUhISEhJyHaa8fv0anp6eAAAzMzPMnz8f8fHxiI+Pz1W/Un///Tdmz56N1NRUPHr0CE5OTioFbNra2rLbRYoUgYmJiULX6evrAwA0NDSUeqwSEhJktytXrqzwda9evYKdnV2OYalQKMTatWsxatQopSoev5X+cSEiIiIiIiIiIvVjuEZE9IObNGkSJk2apNQ1X758gYuLCyQSCc6ePYtz586hZ8+emDt3rlqrl3KyYcMGAEDZsmUxe/bsXPVlZWWFGjVq4NGjR9lWHN27dw8jRoyAra0tvLy8oKenp9Q4qoZr0dHRCA8PBwBUrVpV6euTk5Ph4uKC0NBQeHp6qhywpaamYs6cObIAMjIyEh06dFCpL0U9evQIw4cPx44dO5QK2HITZOanrVu34tWrV3BxcUG7du2yDNmWLFkCb29vXLx4ERs3blQ50C7Iikd1SE1NRb9+/TB58mQ0aNCgoKdDRERERERERJQJwzUiyncSiQSJySkQpYihraUBfV0tCASCgp4W/Ss2NhYjRoxAZGQkgLQ9zlxcXNC+ffsMy83lNT8/P1nF3ZIlS2TVRt+KioqCWCxWqM9evXqhadOmqFGjRob9zaSkAUhCQgKePHmCadOmYd26dUq9PlUNfD58+CC7rWy4Jg3Wbty4AQAYPHgw9uzZo1LAtnPnTjx+/BgA0KNHDyxZsqTQfn9+L+Garq4u3r59q3DQ/fnzZwwcOBBnz55V6TnMTbjm4+ODmTNnKtRWX18fxsbGKF++PGrVqoUuXbqgXLlyKo8NAGKxGMuXL5e9BomIiIiIiIiICiOGa0SULz4Gx8D3YQBef47Eu4BoxCWKZOeM9LVhW6oI7EuboVmtUihjrdjSbqR+8fHxGD16NF6/fg1tbW04Oztj1KhReb6vmjzLly8HAAwdOhR169bNst3p06excOHCDHvF5WTdunUKtTt37hzc3NwwefJkhfvOKtjYv3+/wvP08PCAh4eHwmN+6/Xr1yoFbPfu3ZM9Nl27dsXixYsLbbAGoFDPLb30ofTNmzezbLd48WKcOnUKurq6cHd3V7n6MDehY8eOHdGqVSu8efMGzs7OsqVA+/Xrh9GjR8tC7ri4OISEhOD+/fv4888/cevWLWzevBndu3fHrFmzYGxsrPCYEokEkZGRuHXrFry8vHD//n2V509ERERERERElB8YrhFRnrr7/Av+uvIWz96HZ9kmLlGEx2++4vGbrzh06Q2qlC+Kni3tULeSVT7OlGJiYuDs7IyHDx/C2toa7u7uqFmzpkLXvn37Fnfv3kW/fv3UMpfTp0/j/v37qFSpEiZOnCi3zdq1azFo0CD0798fRYsWRWJiIipXroyiRYvC2Ng4QyB44sQJzJw5E9OnT8egQYPUMsesSAOfL1++YOnSpRg7dixMTEzQv39/lCpVCgKBALa2tpmWm1y1ahV8fHwAAMePH1dpv7dvGRoaKtw2JCQEEyZMQEpKCjp16oRu3bqpNbyKiYnBsmXLMGnSJLXcN+D7qVxLH65lF5hJX7PFihVD48aNVR4vu+ctNTUV0dHRWc5DR0cHOjo6qFu3LqpUqYI7d+4AAIYMGQIrq/9+JpuYmKBEiRKoVasWhgwZgmXLlsHLyws+Pj549+4ddu/eDQMDg2znOX/+fJw8eRKJiYk57oNIRERERERERFSYMFwjojwREy/E1iN+8H0YqPS1z96H49n7cDSrVQrO3arBxDD/q6Z+Nh8/fsTo0aPx/v17NGnSBKtWrVK4aiY4OBjOzs4IDAzEu3fvMHPmzFyFHlFRUVi6dCkMDAzg5uYmt2ruwYMH2LJlC3x8fLB27Vr873//y7bPP/74AyKRCIsXL8bjx4+xaNGiLJeZVMXXr19RrFixDMcsLS3x+vVr/Pbbb5gzZw7at2+Ppk2bZtnHq1evAAAlS5ZExYoVZcfFYjHCw8PVFkjJExUVheHDhyMsLAyDBw/GzJkz0b59e2hra2PMmDGoV69ervpPTU3FxIkTcffuXfzzzz/YuHGjSvvKfa/yOwTMKlxLSUnBlClT8OLFC3h6emYIy+RJP+/sloTV0tLC3LlzERgYiCtXruDx48fYsmVLjktgjh8/HsOHDweQ9jr38/PD7NmzkZSUlO11REREREREREQFjeEaEandh6BozN9+CxExuXuD9O+HAXjy7isWODdCWS4VmWfOnz+P2bNnIz4+HuPHj8fo0aMV3rPp06dPGDZsGAID00LUvXv34uPHj3B3d4eRkZFK81m8eDHCwsKwYsUKlC9fXm4bd3d3AEBoaCgGDx6M7du3Z1npc/r0aTx//hwAYGBgABsbGyQmJqotXEtNTUW/fv1w4cKFDMc1NDSwcuVKdOzYERMnTsS1a9ewcOFCaGtrZ+ojKSlJFq7VqVMn0/mWLVuiS5cuGDZsWJaPiari4uIwcuRIvHnzBjNnzsSQIUMAANra2nj16hXGjx+v1vG+fPmCAQMG4PDhw7Czs8tVX9/LspCFIVxLSUnB5MmTcfbsWQCAo6OjQgGbMiZPnowrV64ASFsGdfz48dmGcubm5hlC/NKlS+PSpUs4ffq02uZERERERERERJQXGK4RkVp9CIrGrE03MuyplhsRMUmYufE6lo1pwoBNzcLDw7Fy5UocPXoUpUuXxrZt21CrVi2Fr7958yYmTJiAqKgoAGnBVYcOHdC8eXMIhUKV5rRv3z6cOHEC3bt3R9euXeW2uXTpEm7fvg0gLQCaN29elsFaQkICVq9eDQCoUqUK1q1bBxsbG5XmlpWPHz8iJCRE7jlLS0tMnz4ds2bNgo+PDyQSiWwvufSePn0qWxbv2/3lNDQ0IBaLcejQIRw+fBitW7fG8uXLVQ4v0wsPD8fw4cMRGBiIjRs3olWrVrJz6UNAafCXG126dMHLly9hYGCATZs25TpYA76fcE2Veb569QqDBw9GZGRkrsZO/5ym9/HjR7UHbHZ2dihdujQ+f/6M2NhYPHv2DDVq1FCqj5yWkiQiIiIiIiIiKgwYrhGR2sTECzF/+y21BWtScYkizNt2Ex5TWnCJSDUIDg7Gn3/+iT179kAoFGLEiBEYM2aMQpVcQqEQSUlJ2LVrF7Zs2QKxWAwDAwMMHToUQ4cOhbGxscrzOn36NJYuXQoHBwfMmzdPbpu4uDgsWbIEQFr44+HhgRYtWmTZ59q1axEYGIhGjRph48aNGfYgk0gkaglnHj16hOTk5CzPd+/eHfv378fTp09x5MgR9OzZM1OAdvPmTdntX375JVMfOjo6SElJgZaWFtq2bauWYO3z588YMWIEjI2N4ePjg1KlSmU4L6/CTh3MzMzQqFGjPOm7sFK0EjS9ihUrYvfu3Th16hSqVKmCMmXKwMjISKF99I4fP45ly5YBAI4cOYLixYtn2Vady6MCQIUKFfD582cAyDJ0JiIiIiIiIiL63jFcIyK12XrEL9dLQWYlIiYJ2448wZSBmZfMI8UkJSWhT58+eP36NcRiMTQ1NdGqVSvExMTA1dUVSUlJsn+JiYly/5uampqhzw4dOmD69Om5qnxJSEjAmjVrsG/fPmhqamLYsGG4desWoqKiEBUVhejoaMTExCAmJgbv3r1DYGAgBAIBVq5cmW2w9s8//8DLywv169fHli1boKenl+H8/fv34e7ujtGjR8sNtBR1/fp1AIBIJJIbSAkEAowfPx7Ozs4AgICAgEzh2o0bNwAA9vb2mUIu4L+gq1y5cujcubPKc00/3rRp09CrVy+4uLjI3dcur8K1vKLKvnCBgYEZ9rfLK6qGuA4ODnBwcFD6uvQBnImJicL7J6pD+rFFIvV+0IKIiIiIiIiIqLBguEZEanH3+Rf4PgzM0zH+fhiAZrVLol7lrKswKGt6enqoU6cOXr58CSBtr7Dz589naGNoaIiyZcuidOnSKFmyJEqUKAFra2uYmZnhyJEj8PHxgUgkQpUqVTBr1qxMIZEq9PX14efnB4lEgpSUFEybNk12zsrKCjVq1ECVKlWgp6eH48ePAwAmTpyI9u3bZ9lnWFgYpk6dikqVKmHz5s2ZgjUgbfnFrl27Yvjw4ahWrRqmTJmC+vXrKzX36OhoXLlyBQKBINt9tZo2bYpSpUohIiICzZs3z3AuNjYWT548AZC2t5o80r51dXWVmp88wcHB2LlzJzw9PWFra5tlu/SBW0RERK7HFYvFue7jWxKJRHb78uXLCldh7d+/Hx4eHrC2toaPj4/C43358gXdunVTep6qVK59r+Li4mS3zczMCnAmRERERERERER5h+EaEanFX1fe5ts4DNdUN3LkSBw6dAhCoRACgQCVKlVCw4YNUadOHVSpUgXW1taZrrly5QqmTJmCgIAAWFhY4Pfff0ePHj3UFhgIBALMnTsXvXv3hkQiQe3atdGxY0e0atVKtpxdSEiILNTo2bMnRo4cmWV/ycnJGDNmDPT09LBt27Zsl1Ds2bMntLW1MWPGDAwaNAgtWrTA1KlTsw2d0tuyZQsSExNhZGSU7eMhEAjg4uICDQ0NmJqaZjh348YN2X5rWe2PFRsbCwDQ0sr9/7atra3xxx9/5NgufeVaYV3GMX0lpYWFhdwKPHmkIZyGhoZSVV0JCQnKTfBf38vecOrw4cMHAGn3WZWqOyIiIiIiIiKi7wHDNSLKtY/BMXj2Pjxfxnr2PhyfgmNQxtokX8b70VhZWWHkyJHQ0NBA165dUaJEiSzbfv78GcuWLcPly5ehq6sLZ2dnjBw5Ui37fX2revXqGDduHKpWrYpmzZplOJeSkoKJEyciPDwcTZs2xYIFC7LsRyQSYfLkyfj06RO8vb1RrFixHMfu0qULkpKS4OrqiitXruD69etwdnaGi4tLtmGWr68vdu3aBQAoU6ZMjuP06NFD7vGzZ88CAMqXL4/q1asDAFavXo0pU6YASKsEUmR5vStXrqBOnTowMVH/98arV69y3UeXLl1kVZPqkr4aTtFgrSDkNlyLi4tDSkpKpmC2sPn8+TM+fvwIAKhVq1a+LkdJRERERERERJSfGK4R/QBCIlSrplCUWCxGUlLGvdT0EiWySp0z/3zI0/G/dfqfj+jeokK+jpmelblBgY2tDmPHjs32fHx8PLZu3Ypdu3ZBKBSiXbt2mDJlity9wB48eIDo6Gg0bdo022URFTFmzBi5x5ctW4b79++jatWqcHd3zzLwiouLw5QpU3D9+nXs2rUL5cqVU3jsPn36IDIyEmvXroVIJMLGjRvx5MkTbN26VW5Fmlgsxrx582TLEiq7nKRUcnIy/v77bwCQVeZFRUXh4MGDsnAtKipK1l5a4fat2NhYzJgxQ1aVpo5QI6/2y1Ln8pBCoRDA97U/XHx8fJbnsnp+b9++jYULF2L58uWFtooQANatWye7ndX3MxERERERERHRj4DhGtEPYPiSCwU9hXx1+p8POJ3PgV56J9Z0KbCx81JqaioOHz4MDw8PhIWFoWbNmpgxYwZq1aqV5TUlS5bEqlWr4Orqip49e6Jv376wsrJS25x27doFLy8vlClTBtu3b4ehoaHcdidOnIC7uzsCAgLQq1cvxMXF4dSpU4iPj0dCQgLi4uKQkJCAhIQE2THp7djYWCQnJyM+Ph7a2tqyUMnX1xd+fn6oWbNmpvFSUlJkoZednR2cnJxUun9///03EhISoKmpiS5d0l5Xt2/fzhBsRUdHy24nJibK7cfDwwNRUVGIiopC//79sXv3btmSmqqSBlfqllWApArpHAtz1dq3ateurfQ1rVq1wrlz5zB06FCMGjUK48ePL1T7uInFYqxfvx4nT54EALi4uKBJkyYFPCsiIiIiIiIiorzDcI2I6CcnkUhw4cIFuLu74927dyhVqhTc3NzQoUOHHK+1srLCnj17MGvWLGzatAnbtm1D+/bt4ezsDDs7u1zN6/z581i5ciUsLCywc+fObKuxHj16hICAAADAoUOHcOjQoUxttLS0YGVlhZIlS6JkyZK4ePEigLTlGh0dHVG8eHEYGBigd+/eePHiBQDg69evcsfT0dHBiRMnkJycjPLly6u87J+Pjw+AtPBEGkqeOXMmQyWWv7+/7HZERESmPu7fv4+9e/fK7mODBg2yrY5SlDpDsPTUWREn3QNNV1dXbX3mNT8/vyzPzZkzB8ePH5d7bu7cubhx4wY2b94MPz8/rFu3DsbGxnk1TYVERETg1q1b2LVrF/z8/KCvr4+pU6diwIABBTovIiIiIiIiIqK8xnCNiOgnJRaLceHCBWzatAkvX76EqakpZs6cif79+ytVCaSjo4NVq1bBzMwMnp6eOH78OE6ePImuXbti2rRpMDMzU3pud+/exdSpU2FoaIgdO3bAxsYm2/ZOTk44ePCgLLgxMTFBnTp1cOXKFQBAw4YN8ccff2RYuvLIkSMAgKZNm8LBwUF23M3NDd26dUNSUlK2S0vKWyZTGSEhIfD19ZXNHwBCQ0Nx+fLlDHtrSfcp09bWRkREBOLi4mT73sXExGDGjBkQi8WoUKECVq5ciSpVquRqXlLpQzB5oZ6ypMtBSgMxdfgew7Xs5prd0qrGxsaYOnUqpk+fjhs3bmDAgAHw8vLKkz325OncuXOGarnk5GRZ5aCtrS2GDRuGQYMGZbuPIxERERERERHRj4LhGhHRTyYyMhLHjh2Dl5cX/P39YWhoCBcXFzg5OclCG2UJBAJMnToVycnJOHjwIMRiMXx8fHDt2jXs3LkTFStWVLiv+/fvw9nZGRKJBJs3b84QfGWlRIkS6NSpE4KCgjB48GD8+uuvAICqVavKziu6J1z58uUxadIkXL58Gba2tgrPW1lHjhxBamoq6tSpI1t6csOGDUhOToalpaWsnTRcK1euHOLi4vDu3TvUqFEDEokE06ZNw+fPn9GtWzfMmzcP+vr6aptf+nBNnft8CYVCJCcnqyUQi42NBQC13u/CrEuXLti2bRvevXuHV69eYeHChVi9enW+jL1t27YMS41u374d3t7eAIDly5ejevXq+TIPIiIiIiIiIqLCgOEaEdFP4NOnT7h58yauXr2K69evQyQSQV9fH3379kWfPn2gq6uL9+/fIz4+HvHx8YiLi5Pdlu5Rlv5ref8EAgGKFCmC4sWL48uXLwCAsLAwjBs3DmfOnFEo3Dp9+jRmzJiB1NRUbNiwAfXq1cvU5uvXrwgMDIS/vz8CAgIgkUgwevRozJ8/P0Ngk5SUJLutp6en1OPl6OiIPn36KHWNMkQiEQ4cOAAAGDlyJIC0+37w4EEAgL29vazt69evAQBlypSBRCKBn58fatSoATc3N1y7dg2urq55sgxfSkoKNDQ00LJlS0ycOBEuLi4YPnw4unbtqtIeZ5MmTULp0qXx22+/qa3STLofnYGBgVr6K+wEAgGcnJwwa9YsAMDJkycxZcqUXO+vp4jixYtnqNZ0dHSUhWv79+9nuEZEREREREREPxWGa0REP4GgoCDMmzcvw7HExER4e3vL3iCXx8DAACKRCCKRCMWLF0e/fv1QpEgRmJqaZvonrR5KSEhAz5498e7dOwBpwZ6/vz/Kli2b5TifP3/GunXrcPLkSQBAnz59EB8fj23btiEoKAiBgYEIDAxEUFAQEhMTYWNjg9q1a6N27dqoW7cugMzL7aWvvFK2skkgECgdyCnj1KlT+PLlC+rXr49mzZohICAAs2fPlp1v3rw5gP+CRCAtXDM0NMS1a9cgEAhw8OBB7NixQ61VZen16tULrVu3RqlSpTBt2jR8+vQJc+fOxd69e7Fjxw7ZHnGKGjt2LMzNzTMseZlb4eHhANKeX2WWrkxMTASQtlSlMtfFxMQoN8F/SSQSla6T57fffsOiRYuQmJgIiUSi1mU2lWFra4s6derg/v37OH36NKZPn67SErBERERERERERN8jhmtEP4Ads9vkaf9isThDFRCQVgkk3X9nzb77ePEx93syKapSOXNM7l8n38b7ETRq1AiNGjXCzZs3MxzX1taGnZ0d7O3tUaZMGZQpUwZWVlawsrKChYUF9PT0MGjQINy5cwclSpTAqFGjchzLwMAA69atQ/fu3WV7MhkbG2d7jUAgwPnz52VfHzx4UFbFBQBmZmZo1KgRhg0bhl9//VWhYCd9EJLT+Pntjz/+AABMnToVQFoFkjQksbe3R6tWrQBAtmccANSoUQPGxsbYtGkTPn78iAMHDuTpspVDhgwBADx//hwnTpwAANStWxfu7u4wMDBAfHy8wn35+flh/PjxKF68OHbu3Jlh2cvcCAsLA5C2R58qIWNwcHCehZPpqTNcMzQ0RJMmTXDhwgXUrFkT5cuXV1vfyurduzfu37+P5ORkHD58GCNGjCiwuRARERERERER5SeGa0Q/ACvzvF0STSwWIzFRkOGYvr6+LFyrals0X8O1quWL5vl9/hFNmjQJffr0Qa1atdCsWTPUr18fVapUUWmJv5zY2dnB2dkZGzZsQLVq1VC0aNFs29vY2GDw4MHYvn277JipqSk6deqE3377DbVr15a93hQl3Y8LAMzNzZW7A3no4sWLePXqFTp06CBbSq9Pnz7Q1taGsbEx2rRpI1tC8/LlywDSQtDGjRtDW1sbAoEAgwYNytNgTUoikWDx4sUQi8UAgHv37qFJkyYq9xcTE4P+/ftj165dsLGxydXcEhISZMtCdu/eHcuWLVP42p07d2LlypUoWbKk7DFWREBAgCz4LEgdOnTA9evXsWDBggKdx2+//YalS5ciOjoaBw4cgJOTk9Lfp0RERERERERE3yOGa0SUa01rlcKhS2/ybbxmtUrl3IgyqV69Om7cuJEpaJJIJEhKSlJ66cScjBw5Eh8+fEC/fv0Uaj9q1Cj4+PhAIBDAxcUFPXr0yNXSjF+/fpXdtrCwULkfdRKLxXB3d0eRIkVk+2YBaZV5Tk5OGdomJSXJKg3r1q0LIyMjAEDNmjVx/PhxDBo0KM/n6+npifv378PAwABVq1ZFxYoVZdV2ijp27BhcXV1l1Vs2NjYICAjIdbgmXS4TyLhHnSLUWUmmCGk4qa72v/32G6pVq5ZhD7SCoKenh86dO2Pv3r0IDAzE33//jRYtWhTonIiIiIiIiIiI8gM/XkxEuVbW2gRVymdfmaQuVcoXRRlrk3wZ60ckr4LL29sbTk5OSi3zd+/ePdmealnR0dGBm5sb6tWrp1CfRkZGcHd3x6lTpzBgwIBc73kWHBwsu53bIEddjh8/jjdv3mD69OkoVqxYtm19fX1le4N16NBBdrx58+bw8/PDixcv8nSuL168wJo1a6CpqYm1a9di/fr1OHfuHM6cOQNdXV2F/p07dw7z5s2DRCKBQCDA/PnzsWvXLrUsxfj+/XvZbQcHB6WuVTbsyi1lw7yc5icQCAo8WJPq3bu37Pa+ffsKcCZERERERERERPmH4RoRqUWPFhXyZZyeLe3yZZyfhb+/P1atWoX79+9jxIgRCgdsRkZGGDduHJycnODr66u2SqD69evD1NRULX19+vQJAKClpYXSpUurpc/cSEhIwNq1a9GkSRP06NEjx/ZeXl4AAEtLS3Tp0kV2vG3btgCAbdu25c1EAURFRWH8+PFITk7G3Llz0bx5c5iZmWH58uWYO3cufH19c+zj0KFDmD59OsRiMTQ1NbFs2TKFqxgV8eZNWrWslpYWatSoodS1hb1yLb/nlxv29vaoWbMmAOD69ev4/PlzwU6IiIiIiIiIiCgfMFwjIrWoV7k4mtYqmadjNKtVCnUrWeXpGD+TxMREjB07VhaoKROwOTg44PDhw7CwsMCIESPQoUMHHDp0CEKhMK+nLVdKSkqmY9LKLltbW+jq6ub3lDLZuHEjUlNTsXLlyhzbPn36FLdv3wYADBkyJMO+eDY2NqhSpQrOnj2LV69eqX2eQqEQLi4uCAgIwLx58zIEYr/88gv69u2LcePG4datW1n2sX37dsyZMwdisRh6enpYv349unXrptZ5Pn36FABQtWpVGBgotwejNOzKrxBL2XFSU1PzaCZ5Q1q9JpFIcODAgQKeDRERERERERFR3mO4RkRqM7JbdZib5G4pv6yYm+jBuVu1POn7Z5Samopp06bh5cuXANKWmWvXrh1mzJih8N5rBgYGWL58ORYvXgx/f3/MmTMHLVu2xI4dO5CQkJBnc09JScGTJ0+we/dujB8/Hu3atcOdO3cytBGJRHj8+DGAtP3KVKWu5QPfvXuHvXv3ws3NDUWL5ryEqjSAK126tNxqr549e0IsFmPx4sVqmZ+USCTChAkT8PjxY6xcuRL9+/fP1Gbq1KlwcHDAiBEjcO7cuUzXz5kzB6tXrwaQtpfcnj170Lp1a7XOUywW4/79+wCAVq1aKX29NLzKr3AtfVgWERGR5T9pOP29hWvt27eX7Qno4+ODpKSkAp4REREREREREVHeYrhGRGpjYqiDBc6NYKSvrdZ+jfS1scC5EUwMdXJuTDmSSCSYM2cOzp8/DwAoW7YsPD094e7ujurVq0NDQ7n/NfTq1Qv79u2DpaUlwsLCsGrVKrRs2RJ//PGHWirZoqKi8Pfff8Pd3R2Ojo6oW7cuHB0d8eTJE3Tt2hXHjx9H48aNM1xz7949xMbGAgCaNGmi0riPHz/GqFGjcj3/1NRUzJw5E7///jvq16+fY/vTp0/j9u3bEAgEWLp0qdyqrM6dO8PQ0BB37tyBt7d3rucIpAVjkyZNwsOHD7Fjxw506tRJbjsdHR1s2rQJlpaWmDBhArZs2QKJRIKQkBAMGTIEhw4dAvBfdaN0yUB1evr0KWJiYgAA//vf/5S+Xhqq5Ve4lj6kbdSoUZb/Tp06BQAFVgGqKn19fdnrJSoqCidPnizgGRERERERERER5S2tgp4AEf1YylqbYNmYJpi37SYiYnJfvWBuoocFzo1Q1tpEDbOjlJQUzJw5E8ePHweQtpzbrFmzFK5Wy0r16tVx6NAhODs749WrV4iMjMSKFStw4MABrFixArVr11aon7i4OLx8+RLPnj3D06dP4efnh48fPwJI21urSZMmWLJkCVq1agU9vayrJPfv3w8AMDU1VTlci4+Ph0gkUvq6b6uOtm7diipVqsDJySnHa0NCQrBgwQIAgKOjI+rVqye3nZGREXr16oXdu3dj+fLlqFWrFipWrKj0XKUSEhIwbtw4xMfH4+jRo7Cyyn751aJFi2Lbtm3o27cv1q5di3/++QevXr1CVFQUAKBjx45YvHhxrl9XWbl+/ToAoF69eihTpozS10uXEVVXZWJO0r8mslvKc8aMGThy5Mh3F64BQJ8+fWRLQu7fvx89e/ZUqR9pKA5A9noiIiIiIiIiIipsWLlGRGpX1toEHlNaoFmtUrnqp1mtUvCY0oLBmprExcVh9OjROH78OIyNjbFu3TosWrRIbQFI8eLFsX//ftSpU0d27PPnz3B0dMTVq1ezvXbChAlo3rw56tSpgwEDBmDp0qU4fvw4Pn78iCpVqsDV1RXXrl3D1q1b0aFDh2yDtRcvXuDixYsAgL59+2bYr0wZ0dHRGd7oV1T6qqi4uDh8+vQJc+bMyfE6oVCI33//HVFRUWjRogWmTZuWbfsRI0ZAX18fiYmJGDFiBIKCgpSeK5AW6A0bNgw1atTA3r17cwzWpGxtbbFs2TIAwO3btxEVFQVdXV0sWbIEa9asybNgDYCswkvespWKkIZqBRGuKSIlJUWlYFfd0s87p/tQqVIlVKuWtnTvs2fP8ODBA6XHi4+Pz3Dd5cuXle6DiIiIiIiIiCg/MFwjojxhYqiDKQPrwNWpAaqUz3mPqfSqlC+KecMbYsrAOlwKUk3evHmDHj16wNfXF1WrVsWRI0fw22+/KXStMkvnGRkZYceOHRkq1UQiEaZOnSpbxk8eOzs7BAcHy77W1NRE165d4ePjAx8fHwwYMADm5uY5ji8UCjF79myIxWIULVoUQ4cOVXju34qJiUFoaKjS10lDiNTUVBgZGWHFihXQ1NTM9hqJRIKZM2fi4cOHqF27Ntzd3aGllX1xebFixTB48GAAaQHZwIED8ebNG6Xm6u/vj0WLFmHRokUYP348tLUVX9L10qVLmfZ8EwqFuH79Ot6/f6/UPJTx7NkzvH37FmXLllVpSUjgv8o1ZUMvVSvKpOMpwtTUFFOnTs3xNZNXhEIhoqOj8eDBAzx79kx23NPTEyEhIYiLi8vy2t69e8tuu7q64uXLl4iJickyxExMTERMTAyCgoJw/fp1ODs7IywsTHb+6NGjmDNnDh4/foyvX78iJiYGycnJariXRERERERERES5w2UhiShP1atcHPUqF8en4Bj8/TAAbz5H4W1AFOIS/6vKMNLXRoVSprArbYpmtUqhDCvV1Or06dOYPXs2RCIRxowZg1GjRilVzRUREaHUeAYGBti6dSt69eolW9IxJiYGN27cQLt27eReM2jQIOzcuRPx8fFo1qwZZs+erfRyfxKJBLNmzcKzZ8+gqamJZcuWwdTUVKHr5ImIiEBoaCiio6NRpEgRhechDRIUDW7EYjFmz56NkydPomHDhvDw8Mi2Mi89FxcXnD17Fh8/fkRgYCD69OmD2bNno0ePHgpdb2VlhfXr1yu1z97jx4+xZs0a3L59GwBgZmaGkSNH4uvXr9izZw/OnDmDc+fOoXnz5hg4cCAaNWqk9D5+2fHx8QEAjB07VuUAStnnSOrDhw+y28rcJ2kVmqGhYbbtOnfujFmzZsHEJHc/AxMSElS+9uTJk5g5c2am415eXvDy8kLJkiWzrCjr0KEDli9fjvj4eLx58wZdunQBAJw4cQL29vaZ2i9YsABHjhzJdj6HDh2S7eMHpD3v48aNU+YuERERERERERGpHcM1IsoXZaxN4GhdGUBamJGYnAJRihjaWhrQ19WCQCAo4Bn+eJKSkrBmzRp4enqiTp06WLhwISpUqCC37fDhw5GamgorKytYWFjA3NwcJiYm+PjxI969ewcAOVZSpWdiYoJ169ahZ8+esmAhuzf8TUxMMHDgQFhaWmLgwIFK3Ms0QqEQM2bMwKlTp2BgYIAVK1agWbNm2V4jEAggkUgQEhIi9/ynT58gkUhw/PhxDBo0SOG5SAMbRaqVkpOTMXPmTJw6dQr9+/fH7NmzlXqcpcswDh48GCkpKYiPj8esWbNw5MgRjBkzBo0aNcr2ekVDVolEgmvXrmHnzp24desWgLQKq0GDBmHIkCEwMjICAHTv3h0LFizA7du3cfnyZVy+fBmWlpb47bff8Ouvv6JevXq5Wi4yKioKPj4+qFWrFjp27KhyP9LnRt5z9OzZM+zbtw+mpqYwNjaGkZERdHV1kZycjL1798raKRqASg0aNAguLi7ZtmncuLHC/T19+hT6+vqwsbHJ8DxKJBL4+vrKvlbm9QSkPYfdu3dX6hopQ0NDpZaDXL58OZYvX67SWEREREREREREBYnhGhHlO4FAAAM9xZefI+XdvXsXs2fPhoaGBjZs2IA2bdpk237btm3w8/PDyZMn4eXlJTcIs7OzU2oODg4OGDBgAHbv3g0AKF26dLbtJ06cqFLI+vnzZ0yePBl+fn4oV64c3N3d4eDgkON1VlZW+PLlC7Zs2QITExPY29tDR0cHqamp8PPzk+3rtWLFChgbG6Nr164KzUe6dGBOy9eFhIRg7NixeP/+PZYsWYKePXsq1P+36tatixkzZmRYovHu3bsYMmQIXF1dMWDAAJX6lc7x6NGjOHToEPz9/QEA5cuXx4ABA9CjR49MQZmtrS08PT1x+vRpbN++Hc+fP0doaCg8PT3h6ekJbW1tVKxYEVWrVoWdnR1sbGxQsmRJmJubo0iRIjlWou3fvx9CoRDz58/PVSAvDUDlPUfW1taoWbMmbt++jYMHD2a5DKIy3w9LlixROozLyfXr1+Ht7Y0vX77A3NwcFhYWMDQ0xNevX/Hp0ycAaeGppaWlWsclIiIiIiIiIiKGa0REP5T4+HisWbMGd+/exahRo9C5c2eFKlc0NDRQs2ZN1KxZEyNHjsTMmTNx7do12XlNTc0M+ykpavz48bh58yaMjIxQt27dbNsqG5aIxWLs378fbm5ukEgkmDRpEoYOHapwNVbLli2xf/9+REZGyl0GT0pHRwc1atRQeF6KhGsXL17EnDlzUKdOHWzYsAFWVlYK9y/PoEGDEBwcjJ07d6J06dIYP348mjdvDmNjY6X7CgoKwpUrV3D69Gncv38fEokEJiYm6N69O3r06JHj8wgA7du3R/v27XHnzh3s3r0bV65cgVgshkgkwtOnT/H06dNM1wgEAujo6MDMzAzu7u6oVatWhvNxcXHYu3cvfv/9d4XC0+xIK9bMzc2RkpKS4XvE3NwcvXv3Ru/evREfH489e/Zg06ZNsgpMAKhatSpsbW0VHk/dwRoAjBo1CqNGjcLTp0/x559/wsfHJ8McAah9SU4iIiIiIiIiIkrDcI2I6AeSmJiIJk2aYO7cuSpX9lhYWGDz5s3o3bs3nj9/Di0tLcyfP1+lQMPQ0BCHDx9GamqqWpf+vHPnDpYvX47g4GAMGzYM/fv3h7m5uVJ9TJ48GRoaGrh48SIiIiIy7b9lZGSEatWqYeLEiShXrpzC/UrDNYlEAqFQmCHsCw8Px8qVK/HixQssWbIErVq1UmrO2Zk2bRpKliyJjh07KrVHXFxcHO7fv49bt27h+vXreP36NQCgbNmy6NevH5o3b47GjRtDW1v5atP69eujfv36CAoKwqVLl3DlyhXcuXMnUwgEpAW406dPR58+feQGwlu3bkXlypUxfPhwpefxrVKlSmHhwoXo1q1btuGzoaEhXFxcYG9vjzFjxsiOpa8SLGhVq1ZF1apVMXjwYLi4uMj2OTQ0NMSUKVMKdnJERERERERERD8ogUQikRT0JIgKG6FQiCdPnmQ6Xq1aNYWrYrKSkpKCN2/eZDhmZ2en9L44+UksFiMxMTHDMX19fVZE/OD+/PNP3LhxA6NHj851pZC6fP36FfPnz4dYLEaHDh3QunVr6OrqFvS0Mpg5cyZCQ0MxZcoUVKpUSXb89u3b2Lx5MwYMGIDWrVsX2D6DCQkJOH/+PPz8/PDo0SO8fPkSxsbGsLW1RdWqVVG9enXUrl0bJUqUyJPx4+LicPPmTTx9+hSvX7/G69evERgYiBkzZmDIkCFyrwkODoaLiwv27NkDExOTPJlXTgYNGgSBQABXV9cs9y4saO/fv0fHjh1RsmRJuLu7o0qVKgU9JSIitVHX76Pf4+/iRERERFQ4iEQi+Pn5ZThWvXp1lT6Q/L3Ly/fPvxcM14jkYLiWEcM1KiySk5MhEolgZGRU0FPJUmhoqNx9rr6tYisoX79+xalTp2BlZQUrKyuUKVNG6ao/dYuPj4eurm6WPwcTEhIQGxub6+UzcyM+Ph6GhoYFNr6irl69iiZNmhTq/6cQEamC4RoRERERFTSGa/9huMZlIXO0YcMGjB07tqCnQUREAHR1dQtdpdq35AVrAArNLxbFihXD4MGDC3oaGeQUWhkYGMDAwCCfZiPf9xCsAUDz5s0LegpERERERERERD88lp1kY8OGDdi4cWNBT4OIiIiIiIiIiIiIiIgKiUIZroWEhBT0FLBp0yZs2LChoKdBREREREREREREREREhUihC9dCQkLg6OhYoHM4dOgQ1q9fX6BzICIiIiIiIiIiIiIiosKnUIVr0mDt8+fPBTqPNm3aoGLFigU6ByIiIiIiIiIiIiIiIip8Ck24Jg3WPn36VNBTgampKfbs2cOAjYiIiIiIiIiIiIiIiDIoNOHa9evXcwzWhEIhDhw4kC/zMTU1xYoVK/JlLCIiIiIiIiIiIiIiIvo+aBX0BKR69OiB2NhYLF++XO55iUSCyZMn49KlS6hQoQLq1auX53PS0dHJ8zGIiIiIiIiIiIiIiIjo+1FowjUAGDJkCEQiEdzc3DKdW7JkCS5cuAAAcHR0zO+pERERERERERERERERERWeZSGlWrVqlemYUCjE27dvAQACgQASiSTf/hERERERERERERERERFJFarKtazo6Ohgx44dmDNnDo4dO4YRI0ZkahMdHY1Dhw5h+PDhaj9HREREREREREREREREBHwn4RoAaGlpYcSIETh27BgmT56c6fy7d+9w6NChPDlHREREREREREREREREBBTCZSGzIxAICs05IiIiIiIiIiIiIiIi+vl8V+EaERERERERERERERERUUFiuEZERERERERERERERESkoAIN11JTUwtyeCIiIiIiIiIiIiIiIiKlFFi4lpycjCFDhiApKamgpkBERERERERERERERESkFK2CGvjPP//E3bt3MWzYMFSuXBn6+vrQ19dHQkICAODAgQPQ1dWFnp6e7L9fv34tqOkSERERERERERERERERFUy4JhKJsHPnTggEAjx8+BAPHz7McF4ikWDhwoUFMTUiIiIiIiIiIiIiIiKiLBVIuHb06FF8+fIl2zYSiUTucYFAkBdTIiIiIiIiIiIiIiIiIspRgYRrVapUgZeXF7S1taGhoQGJRAKxWIzk5GR8+vQJ8+bNg4eHB5KTk5GUlISkpCQkJycjKCgI+/btK4gpExERERERERERERERERVMuFa5cuUsz1lYWAAAWrdunenc+/fvGa4RERERERERERERERFRgdEo6AkQERERERERERERERERfS8YrhERUSbnzp3D48ePC3oapCY3btzA0aNHC3oaP61NmzbhwYMHBT0NykNxcXHw8/NT6VqhUIiHDx+qeUYkz7t373Dv3r2CnobM27dvVb42KCgIycnJapwNERERERERKYPhGhERZSIQCNCnTx9MmDABwcHBBT2dLF28eBH+/v45tjt27BhCQ0PzYUaF0+bNmzF9+nQ4OTkp9HgVhP3790MoFGbbJioqCkePHkVKSkqezycoKCjH+SjK19cX/fr1Q9euXXHo0CEkJiaqpd/shIWF5dtr/uHDh0hNTc1VHyEhIWqaTZpbt27hyZMnau0zO0lJSejVqxcGDRqEv//+W+nr+/bti8GDB+P27dt5MLv8sXv3bri6uuLVq1cFPZUsXbt2DQMGDMCQIUMKRcg2evRoDBw4EFeuXIFEIlHq2r179+K3337DqVOn8mh2RERERERElJ3vKlxLSkqCRCLB0aNHM/27ePEiAOTJOSKin03btm0xbNgwnDlzBu3bt8fBgwcLekpy3bhxA+3atcPs2bOzDY1Onz6NVq1aYf78+QgICMjHGRa8J0+e4O7duwCA69evo1OnToXyzdiVK1eidevW8PLyyjLUEovFmD59Otq2bYsDBw6oLfyS586dO2jXrh3OnTuX6750dHQAAC9evICnpyeio6Nz3WdOQkND8b///Q9bt25FTEwM4uPj8+TftWvXMGjQIIwaNQrx8fEqz3f79u1wcnJSW4Xfy5cv0bNnTzg6OqoUdilLT08PQNrrxtnZGcOGDVM4BNbR0YFAIMCtW7fg6OiIfv364evXr7LzSUlJeTJnZcTFxcHd3T3bCrvU1FQcPHgQnTt3xoABA3D9+nW1jB0bG4uDBw+qJVR//vw5AODmzZsYMGAAZs6cmes+c0NHRwd3797FqFGjMHHixEzn3717l+W1jx49QlBQECZNmoQ+ffqw2pyIiIiIiCifaRX0BLKSmpoKTU3NDMe2bt0KANn+IZwX54iIfkYTJkzApUuX8PHjR7i6uiIwMBCTJk0q6GlloK+vD5FIhMOHD+PYsWNwc3ND27ZtM7XT09ODUCjEgQMHcOjQIbi7u6NNmzYFMOP898cff2T4ukOHDmjRokUBzSZr+vr6CAkJwaJFi7Bnzx4cPnwYRYoUydQGAAIDAzF//nzs3r0b3t7eMDMzU/t8unbtilevXmH8+PFo2bIllixZAnNzc5X6Sv/7zJ49e1TuRxn6+vpISEiAm5sb3Nzc8nw8X19fDBkyBN7e3pl+f1PEnDlzsGjRIvTr1w8tW7bErFmzYGNjo/J8tLW1AQC3b9/G7du3MXnyZDg7O6vcn6LjAUClSpWwfPlyaGkp/mu2lpYWRCIRdHV10bdvXxQrVgxA2u/Dffr0QbNmzTB8+HCYmJiofe7ZefnyJXx8fHD06FFER0fjwIED8Pb2Rrly5TK11dXVld0Wi8Wws7NTyxwMDAxw6dIl7Nq1C/PmzUOjRo1U7kv6QQMAqFKlCqZOnaqOKWZp27ZtMDMzQ69eveSelz5mlSpVwqJFizKc+/jxIzp37gxnZ2e4uLhkeI2JRCI8e/ZM9rWGhgasrKzy4B4QERERERFRVgptuBYTE5PhzbJbt27JPj2u7LIpuSEQCPJtLCKiwkRHRwdz5szB8OHDAaR9wKFp06aoW7duAc/sPwYGBrLbgwcPzjIwk76BqaWlhVWrVv00wdqLFy9w9uxZ2dcmJiaoV69ehsetsJAGZ0WKFMGyZcsyBWtAxjfvK1asiI0bN+ZJsCY1adIk3L59G5cvX0aPHj3wxx9/yA0VcqKhkbZQgJaWVr4Ea0DGx2rnzp2oV69ejtdcuHABkydPBgCF9w/bs2cP1qxZAx0dHUycOFGlYE1q9uzZCA0Nxfnz53Hjxg3MnDkT/fr1U6mv9MFWx44dZT/H8kr64KNevXqwtLRU+nqRSAQHBwd06dJFdlxTUxNubm7o168fDh48iDFjxmDAgAG5epyzExoaivv37+PevXu4evVqpkrfqKgoDB8+HAcPHpQFgOnvg5Srq6vawh5NTU2sXbsWffv2xZAhQ9CrVy/MmTNHVi2oqE+fPiEoKEg212XLluX592ObNm3QpUsXnD9/HitWrMg0nvR16uDgAGNj4wznjhw5gpSUFGzatAnXrl3Dzp07ZT8Xnz17JttvrVixYti6dWu+B69EREREREQ/u0IbrkVHR2d4w0xXVxceHh7Q1dWFrq4udHR0oKOjAy0tLWhpaeVJCObv749Ro0apvV8iou/Fr7/+igoVKuDt27cAgKdPnyodrp04cQLPnz/H9OnT1T6/9G+gjxw5Msv/F0iDBn19fbRv317t8yisVqxYAbFYDACws7ODh4cHBg0ahNq1a6N06dIFPLuMpG/M161bN8vXmIaGhiyEaNCgQa4qmxSd0/z589G7d28EBQVhxIgROHr0KIyMjJTqR/qhIENDw7yYplzpvze0tbUzhG2KXKNIe+C/qjwLCws0btxYyVlmpKGhgWXLluHZs2ey6sSYmBiMHDlS6b7S35dffvlFFnDmFQ0NDWhpaam8dKH0cZQXmtna2mLVqlUYOXIklixZgqNHj2LVqlWwtbVVeb5xcXEICAjA69ev8fLlS7x69QqvXr1CWFhYhnb6+vooXrw4LCwsYGlpCVNTUxgbG8PPzw8tW7bM0DZ9uKZuhoaGcHNzQ48ePXDo0CE8e/YMO3bsQNGiRRXuw9fXV3a7Xbt2qFixYl5MNYNy5crB0dER27dvR/fu3bF9+/YMFX1ZPWaJiYnw9vYGANSsWRNr167N8IGDO3fuyG4PHTqUwRoREREREVEBKJThmkQiwatXr1C2bFnZsVq1ahXYfIRCoWy/FCLKPYlEAokwEZLUFAg0tSDQ0WeVaCHWuXNnuLm5wcDAQO6Si9k5efIkpk+fjtTUVIjFYrUvwato9UZevulbWP3999+4efMmgLQqxNWrV8ve6J0wYQL27dsnqxYrDJR5LkUiUR7P5j/Vq1dHixYtcPnyZfj7+8PT0xMuLi5K9SENOPMzXMuryqa8ZmRkhPnz52PEiBEAgHXr1qFLly4oXry4Uv0UxP3PTYCXvrpRnmbNmmHYsGHYuXMnnj17ht69e2PXrl2oXr26Qv37+/tj8eLFCA4ORnh4OFJSUmBoaAhjY2OYmJjA1NQUzZs3x82bNxEQEIDWrVtj0aJFSlV25fXPWTs7O4wZMwZubm54/vw5nJycsH//foUrcS9cuCC7PXjw4LyaZibOzs7Yv38/goOD4eTkhNOnT8sC+qxep3v27EFUVBTs7e2xc+fOTIG+NFzT1dVFz5498/YOEBERERERkVyFMlwD0jYc/9///lfQ0wCQtpG6Mp+MJaLMhKGfEPfsOpKD3iD5yweIk+Jk5zT0jKBbvBx0S9jBqMqv0LEsXBU136PExEQkJiaqpa9SpUpBIBBg1qxZ0NPTQ0REhELXXbt2DTNnzkRqaioAYPfu3RAIBJgxY4Za5gUo/mZuXletFDZJSUlYunSp7OtJkybBwcEBQFqVw4kTJzB58mRs2LCh0Dw2iu5PVRBBfN++fXH58mUAwKtXr5S+Xlq5lp9h5vf8gYWmTZuiTp06uH//PlJTUxEQEPBdhGu5GVP6fZjdz7Tx48fj3LlzCAgIQFxcHFxdXXH06FGF+re2tsaqVatgYGCQ7ffayJEjERAQAGNjY6WXTMyPx3zw4MHw8vJCaGgoXrx4gXXr1mX40Mbnz5/h7e2NadOmZbguKioK9+/fB5BWHVu1atU8n6uUiYkJunfvjr179yIkJAT79++X7f8n7zGLiorCjh07ULJkSezYsSNTsCYSifDgwQMAwP/+9z+Ymprman4BAQFo1aqVQm11dHRgbGyMkiVLolq1amjXrh3q1q2r9M+bmJgYHD16FL6+vnj58iWioqKgo6MDU1NTVKxYEQ0bNkT37t0zLZVJRERERERUmBRouObl5QULC4tMIZqNjQ369+9fQLPKSCKRIDw8nOEakYoS3txH1M0jSPJ/kWUbcVIcEj8+QeLHJ4j6xwd6NpVg2rgbDCrUyceZ/ljCw8Ph6OiIwMBAtfU5Z84czJkzJ1d97Nq1CwKBIE+WiMzO9xw0qGLt2rX4+PEjgLSlPYcMGSI7p62tjZUrV6JXr15YvHgxXF1dC2aS31D0OSqIMLBRo0YoUqQIoqOjFa4USk9aaVdQ4VpsbKxCoXh8fLzstqIhurpC/G8NGTIE9+/fR5EiRWBvb6/09QXxPZ+bMXOqXAMAPT09jBs3TvbzMyQkROH+tbS08nzpQEUD8tzQ09NDnz594OHhAQA4e/ZshnBt3759qFy5cqbrzp49K1uyM/3Pw/zSuXNn7N27FwCQkJAgOy7v55m7uzv09PSwa9cuufvWPXjwQPa92qtXr1zPrUSJErh79y6+fv2KKVOm4NmzZwDSVg1ZunSpbG+9xMREREZG4smTJzh27Bj27duHffv2ydqVL18+x7FSUlKwe/dubNy4EfXr10fv3r1RqVIlGBoaIjIyEo8fP8a+ffuwdOlSrFu3Dq6urujatWuu7yMREREREVFeKLBwLSQkBGvXroWJiQkaNWok+4Pf1NQUXl5eatsEXR1evHih0hs7RD+z1IRYfD2/A/HPrit9bZL/C3w5+AJGVX5F0bZO0DTgJ5eVVapUKXh6euLixYuoVKkSypQpAyMjIxgaGmZ6Azg1NTVXFQf8AELhcv/+fXh6egJI+7DKmjVrMj3nlSpVwu+//47Vq1dDS0sLs2bNKoipfjd0dHQwe/ZsfPr0SaU35qVv6isarh0+fBilS5dG/fr1lR7r2zEBYMyYMUpf36hRI5XHVoe2bdti5syZqFWrlkqhUEGEsPLCtZSUFEyePBkNGjTI9oNj0vnm9LO4Y8eOWL16NcLCwnL1+sgL+RVodurUSRaulSxZUnY8ISEBFy5cwMSJEzNdc/z4cQBpPxMVrdL61vPnz7F+/XqsXbtW6aC8evXqKFOmDIKCgjKERd++Tp8+fYqLFy9i9+7dKFOmjNy+rl27BgAoU6YM6tWrp/Ac4uLi5O4XqaGhARMTE5iYmKBFixaycK179+4ZAjMTExNYWVnBwcEBvXr1wrFjxzB79mw8fPgQ/fv3x759+7LdBzAhIQHjx4/HzZs3sXLlSnTo0CHDeXNzc9ja2qJbt25YsmQJ9u7di+nTpyM+Ph4DBgxQ+H4SERERERHllwIL15YuXYr4+HgkJCSgefPm6NKlCwYMGIAKFSoU1JSy9PXr14KeAtF3JTnkI754L0FqnGKVD1mJe3YNiZ+ewbrfHOhYyn+TibJWqlQphYKAuXPnoly5cnByclL6DemYmBi0bNkSPXv2xOjRo2WfcP/eff369bu8L7GxsZg5cybEYjH09fWxYcMGFClSRG5bJycn3L17F3v27IFYLMbs2bN/uAq/+Ph4CAQChfdkyk6XLl1UvlYadOnq6ubY9vDhw5gzZw709fWxdetWlQOU9OGap6cnGjRokOM1Z8+exe+//w5A8eUvd+7ciZUrV8qWvlSn3FQY5cVrOTIyMtv7KR0zOTlZVvk3f/58nDt3DufOnQOAHFdmyGmpWy0tLTg5OWHHjh2YMmWKMtPPc/n186NMmTIoX7483r9/L9ubDwCOHj2KJk2aQE9PL0P7wMBA2TKKAwcOVCl4ffnyJYYOHYqoqCiMHTsWmzdvVno/5tmzZyM5OTnDntLfPmY3b97Ezp07ZX8Pubu7IzY2FnPnzpW18fX1BZAWfin6mO/Zswd//PEHPD09swztgIzhbk6ViF26dMHXr1+xcuVKREZGYvbs2fD29s6y/cKFC3Ht2jWMHDkyU7CWnnQZ6sePH8PPzw9Lly5Fw4YNsw3uiIiIiIiICkKBhGt///237E0GiUSChIQEeHt7w9vbG/Xr18eAAQPQunVruX/8fvnyRel9N3Ljt99+w9ChQ/NtPKLvXXLIRwR7zcuwp1pupMZFIGivK0oMWsiALY84OzujS5cu8PX1hZubGywsLBS+9vr160hKSoKXlxd8fHzg6OgIFxcXhUIEeTw8PDB27NgCDXmEQiE6dOiAkSNHYujQod9N4CSRSDB16lR8+vQJAoEAS5cule2zJo+GhgZWr16NXr16Ye/evYiIiMDy5cuVfsNYnrdv3+L169do3759rvvKjStXrsDDwwOrV69GtWrV1Nr369ev4ejoiMjISIWvuX79OipWrKhQ24SEBIwcORJ//vkn7OzslJ5f+nAtPyQnJ+freDnJi8q1Y8eOYdmyZTm2O3jwIA4ePJjhmEQiwcKFCwHID9ikP2cUWVpx6NChGDBgAHR0dJCYmJivy40WFi4uLnj+/DlatGghOyZdTvBbf/31lywUXbZsmULPYXauX7+OyZMnw93dXW6lYUpKCmJiYjIdl/4MSr/kqvT7VCgUIiIiAj169JC12bNnD7Zs2SJrO3fuXPj7++PVq1cQCARo1qyZQsu3njhxQva4ODo65hiwKWPQoEHYvXs3QkND8fDhQ/j5+cldOvfly5c4cuQIAKBdu3Y59quhoYGhQ4di4sSJSElJwY4dO3L9vBEREREREalbgYRrjRs3xp49e3Dt2jVcu3YNr169kv3Re+fOHdy5cweWlpbo1asXevfuDUtLSwDAx48f0bdvX3h7e2f41GdeKVmyJNzc3PJ8HKIfRWpCLL54L1FbsCYlTopD8IHFKDXCjUtE5oGyZctixIgR8PDwQI8ePbB9+3aFA4Bjx47JblepUgWtWrVSOVgD0irhduzYkaEaIb/dv38fUVFRWLFiBXx9fbFixYpCtVRxVjZs2IArV64AAGbMmKFQsGViYoJNmzahb9++OHXqFIKDg7Fx40aYm5vnai42NjZwcXFB1apVUbp06Vz1lRu+vr74+PEj+vXrh3HjxmHEiBFqC13s7e2xa9cuHDlyBNWqVUO5cuVQpEgRGBoaZmrbo0cPBAUF4X//+x/mz5+fbb/r1q2TVX+0bt1a5WoN6T5v+UUoFObreDnJi1B8yJAhqF27NoyMjGBjY5Opyqxu3bqIjY2Fo6MjZs+erdIYiu5bpqOjg7dv38LZ2RndunXDuHHjVBpPVcHBwbC2ts7XMdPr1KkTOnXqJPv6xo0bEAgEqFGjRoZ2IpEIf/75JwCga9euKu/3OW/ePJw/f172dXBwMJ4+fZppPCAtSP22fU5OnTqFU6dOZXney8sLQNr+aNIxVNmL7MuXL2oN2HR0dNC0aVMcPnwYAHDr1i254dqZM2dktxX92ZR+aVrp/9uIiIiIiIgKkwIJ17S1tdGgQQM0aNAAU6ZMgb+/P86fP4/z58/j8ePHANL2ZNu4cSO2bNmC5s2byz5ZHxUVhV69euXJpuwSiQQSiQRisRgpKSkQiURITk6GUCjEqlWr0LFjR7WPSfQj+Xp+R66XgsxKalwEws/vhGXXCXnS/8/OyckJ3t7eCAkJwdChQ/HXX3/l+MZpaGgorl9P21PP1tYWW7ZskbufizKGDh2K9u3bo1GjRqhatWqu+lLVpUuXZLdv3ryJzp07Y8+ePdlWgRW0M2fOYOPGjQCAESNGyF1O7/79+6hdu3am0MHW1hZbt27FsGHD8ODBA/Ts2RMrV65E3bp1VZ6Prq4uevTogcmTJ+PAgQMKBwbqlJKSgr///htA2pu5bm5uuHv3LjZv3pzj0nvyCIXCTFV9lSpVQqVKlbK9TiwWIywsDABQvnz5HIPLuLj/PpwwefJklcPA9JVrsbGxClW4xMfHy24r0h4AEhMTARS+yrW8Ii84UAfp96Wi+1/eunUL48aNQ0xMDDZs2IDw8HC4urrmy15zy5Ytw5EjR7Br1y5UqVIlz8dTxN69e9G9e/dMxy9cuICwsDAYGxtj2rRpGb7/Hj9+jGrVquX4mH358kUW7pQuXRqLFy/OdplVbW1trF27Fvv370eJEiVgb2+PIkWKwMjISPb8rlu3Dps2bZJd061bNyxfvjzH+9m3b18AaeH+iRMnsmx38OBBuLq6AgB27NiBX3/9Nce+VZW+sjY0NFRumw8fPshu79q1C2vXrs2xXzMzMxgbGyM2NhaRkZGIjY2V++EFIiIiIiKiglJge66lZ2NjAycnJzg5OcHf3x/Hjh3DiRMn8OnTJ6SkpODSpUuyNzsFAgFiY2MRGxubr3M8c+YMwzWibCS8uY/4Z9fzdIy4Z9dgVOVXGNjVydNxfkb6+vro168f1q9fj/DwcCxbtgzr16/P9pp9+/YhJSUFRYoUwebNm3MdrAFpn8pv06YNpkyZgqNHj2baOyevicVi2bLFAFCrVi3MmjWrUAdr165dw9SpUyGRSNC/f/8s92FaunQpUlNTMWHCBDRv3jzDudq1a8PDwwOjR49GYGAgHB0dMWrUKLi4uKgcjPXr1w9btmzBxo0bZft45ad//vkHUVFRsq+dnZ0xbNgwlYK11atX49mzZ9i6davSy2aGhYXJKjVKlSqVY3vpPq+amppKLdH6rdTUVNntMWPGKH19+qoRRai7Ui45OTlXVbCqVK4FBwejePHieVL1dvHiRUyYMCHHx8nHxwc+Pj5K93/gwAFERkZi1apValnaNSsrVqzA7t27AaR9GKIwBGyfP3/GjRs3sGTJkkznpHP9/fffUbRo0Qznli1bhvj4ePz+++9o3bp1lv3v2bMHIpEIlSpVwq5du2BmZpbjnLS0tODo6Cj33M2bNzMs9wgAnz59wuPHj1G5cuUsf0Z9+vQJDx8+hK2tbYYgXJ704Xhufo4oIn3glVUFqzSEB4DTp09j6NChCgXV+vr6sr/5hEIhwzUiIiIiIipUCkW4lp6NjQ3Gjh2LsWPH4sGDB/D29sb58+eRlJQEIO3NEoFAAGNjYzRp0kRt40okEqSmpiIlJUVWtSYUCmX/goKCIBKJVHpTjuhnEHXzSP6Mc+sow7U80rNnT3h4eEAikeDSpUvZ7uWTnJyMgwcPQlNTE25ubmrbvwUAhg0bhq5du2LFihWYN2+e2vpVxL1792SfvNfW1sbKlSsLdFnDnNy7dw/jxo2DSCTCiBEjsgzWgLT78/TpU4wcORI1a9bE7NmzM7y5+euvv2LTpk0YN24ckpKSsHHjRpw/fx4TJ05Eq1atlJ6biYkJevbsia1bt6JZs2aoWbOmKndRZemXIWvQoAEmT56cZdvIyEjZ8tTfOnToELZv3w4g7Q16Dw8PpQJHf39/2W1FXkvh4eEAAEtLS4WrmORJX7nm6emZbaUNkFZxMnz4cDRr1izbx+pbO3fuxMqVKyGRSORW96li165dOHnyJPbu3QsDA4Nc96eIDx8+YODAgWjTpk2OS3eqonXr1ti1axdSUlJgb2+fKcBr164doqKiMixd6O7ujoMHD8LIyAgXLlxQaJxv+01NTcWkSZNw9uxZpeZ75MgR2R5ZWYmOji4UAZunpyeaNm2aKTy7evUqHj9+jIoVK8rd5y45ORmvX7/GmDFjUK1aNWzcuDHTEsCxsbE4ePAgSpcujR07digUrGUnJCQEU6dOhVgsRvPmzSESiXDjxg1ERkZi2LBhEIvFaNiwITp27Ii2bdtm+Lvj+PHjAIDBgwdj27ZtiIqKgqmpqdxx0u8FKV1eP6+k/8BjVo9PxYoV4evrK/s6qwq39MRiMaKjowGkVUObm5tn+XOaiIiIiIioIBS6cC292rVro3bt2pgzZw6OHj2Kffv24dOnTwDS/pCLjo7GokWLZPsPEFHBEIZ+QpL/i3wZK+nzcwhDP0PHsvAGHt8rKysrVK9eHY8fP4aOjk62b5IfPnwYkZGRmD59eo4fdIiJicHVq1fRuXNnheZRqVIlNGzYEPv370eLFi3QtGlTpe5Hbkj3jQGAPn36FOpg7c6dOxg9ejSSkpIwdepUDB8+PNv26d+kLVmypNy9S5s2bYqdO3di5MiRiIuLw5s3b+Di4oLatWtjyJAhaNGihVLhyeDBg7Fv3z5MnToVx44dy7egJC4uThYmCAQCTJ06Ndv2Pj4+WLlyZY79Xr16FZcvX0bbtm0VnsvTp08BABoaGqhcuXKO7aVLSBYvXlzhMeSRVkhZW1tnCh2+df/+fUyePBnBwcF49eoVACgcsBkYGKBTp07o0qWLwh9Aev78eYbKuvT++ecf2X6348ePx5YtW/J8WdHAwEAMHToUX79+xYEDB6Cnp4cZM2aofZx69epleU66HKi5ubls6UJp5Z5AIFB5H0RNTU2sWbNGtiRppUqVYGZmBhMTE7nh7dSpU3H9+nV06NABc+bMUWiMvKyUy0lcXBx8fHywZs2aTOek1deurq5y72tCQoLs9i+//CJ3b01vb29oa2tj27ZtKFasWK7mGh0djeHDhyMsLAxNmjSBh4eHbK+8mjVryj5cePnyZVy+fBmVKlWCl5cXjIyMkJqaCh8fH1hZWaFr1644cOAAgoKCsgzXQkJCAKQ9N7kNBHPy8eNH2e2sfsY5Ozvj69ev8PPzg729vULLVL5580a23Kw0kGa4RkREREREhUmhDtekTExM4OjoiEGDBuHy5cvYvXs37t69i3/++QedOnXC1KlTZXsQEP2MRFE5fwJYEQItbWgZyX8TJjU6LOOYyXqyfUqi75+Td0meiX5wDqYNu2R5Prv7oY7HSts0bz8FnhcuXbqEli1b5rjcWcuWLfH48WM4OTllWTUjEomwY8cOdO7cGcOGDcu2v4iICDg5OeH58+eyyhhFDBo0CLdu3cKcOXNw6tQpGBsbK3RdbsTGxsqWhDQ0NISLi0uej6mqs2fPYurUqdDT05PtTZoTafBRokQJWXghT926dfHnn3/CxcVF9qbpgwcP8ODBA5iYmKBNmzaoU6cOKlasCDs7u2yX7itVqhRatGiBixcvYvXq1bI9gPLaqVOnZG+ct2/fHtWqVcu2vZOTEywsLCAWi1GpUiUUK1YMxsbG0NHRgYeHBzZs2ABjY2Ns3Lgxxwqwb/n5+QFIq9zIaelUoVAoW8oypz0Pc1KiRAl4enqifv36WX7fJycnY/Pmzdi+fbus0s3a2hpv377F27dvUaFChRzH6devH/r166fU3F6/fo1Zs2ZlGbBJXbt2DV5eXhgyZAhCQ0Ph4uKSoRJQupqBWCzOdG365RcXLVqEFStWZDlOUlKSbIUEIK1yTldXFxMnTlTmbmVLKBTi4sWLaNq0aabXQVxcnOzxV8fyut/S0tJSeGlWaZCpo6OjcqCXnw4fPgwDA4NMH8I4c+YMnj17hl69emW5f6S0usvc3FxulbZQKMSBAwewbt06lCtXLlfzTExMxMiRI/H69Ws0a9YM69evzxRKlipVCgcOHMCYMWNw48YNvHjxArt378bYsWNx5coVBAUFYdmyZdDV1YWxsTECAwOzDLOk4VrJkiXzZJlTqdTUVNneqwYGBlkuJ2tiYqLQnnLpnT9/XnabS/MTEREREVFh9F2Ea1ICgQCtWrVCq1at4Ofnh02bNuHq1atYsGAB/v77byxdujTPP51JVBj5bxytln70SldBiUEL5Z77ukvxZcLyWuz9s4i9n/USV9ndD3U8VuVn/5XrPvLbtGnTULJkSYwZMybbSrOOHTsiLCwMgwcPznJPl+PHj8PIyAgzZ87Mdt+X6OhoODs7482bNwCAVatWQSQSYfTonJ+Dli1bolSpUggICMDSpUuxbNmyHK/JLR8fH9kb7KNGjcqx2qeg7N27F0uXLoWdnR3Wr18vtwJNHmm4psgbrba2tjh06BCmTp2Kq1evyo7HxMTgr7/+wl9//QVzc3NMmjQJvXr1yravQYMG4eLFi9i/fz/atm2Lhg0bKjTf3Dhw4ACAtMofRSuwcqqsLF68uNLBGgA8evQIALJ8gz896ZKQQO7DNWtr6yz7kEgkOHPmDNzc3ODv74+iRYuiY8eO6Ny5M6pWrSr3mjNnzuDVq1eYMGFCruYFAF27dkWJEiVw9+5dvHnzBq9evYKFhQVKlSqFyMhIXL58Gdra2ti6dSt++eUXAGlL223evBnHjh2DnZ0dIiMj8fTpU3z48AFA2hJyEokEEokEYrEYkZGRsp89c+fORffu3XM9b1UEBwfD29sbR44cQdu2beX+/JXuswfguwi0CovU1FR4enqiW7duGT4MEh8fj2XLlqF06dJZViCmpqYiJiYGAFC9enVZxWZ64eHh+P3333P9Mys6OhouLi54+PAhBg0ahJkzZ2b54RV9fX1s3rwZjo6OePTokewDDnv37kWVKlXQtWtXAGkh7OvXr9GmTRu5/QQGBgJIW24/L/3111+ysRwdHWFiYqKWfoVCIf78808A/1XHEhERERERFTbfVbiWXvXq1bFlyxa8ePEC69atw5UrV9ClSxesW7cOtWrVKujpEREVKvr6+nj16hXGjx+vUHsvL68c22T1CfXsuLu7IyUlRbYUVlY0NDTQp08frFmzBj4+PujYsaPsTfa8IBaL4enpCSCtemDIkCF5NpaqEhIS4OrqilOnTmHo0KGYMGGCUsuxKbu0nomJCbZu3Qpvb2+sWLFCVglmY2MDV1dXhZfrbNiwIWxtbfHu3Tu4urri+PHj0NPTU2ouyrh16xZevEhbpnbo0KEoWbJkno2Vk7dv38reeFbk9SutNgGQJ0teC4VCnD59Gjt37sTr169Rq1YtjBs3Du3atcv2tbRnzx4sX74cYrEY9vb2aN++fa7nUr9+fdSrVw/x8fHQ1taWVUD6+PjIwrVvHzMLC4sM1a/SoEGeixcvYsyYMbmepyokEgn++ecf7N+/H5cvX0bjxo2xa9cu2Nraym2f/nm3sLDIr2l+986fP4/AwED06NEjw3EPDw/ExMTA29s7y0rAkJAQ2RKDlStXxuXLlzO1sba2RpcuWVfJKyIoKAgjRozAp0+fMG/ePLl7v31LV1cXHh4e6N69O2xtbfH48WPcv38fhw8flq0YYG1tLVty9lsJCQmyPc3UuR/qty5fvoxFixYBSPs5P3bsWLX17eXlJbsPLi4uhfbDLkRERERE9HP7bsM1qUqVKmHLli24c+cOVq5cCUdHR8ycOVOhP16JiH4W0jfOS5QoIduLqrDr0aMH1q9fD5FIhLlz5+L06dN5FsqcP38eAQEBAIAZM2YU6B5C8rx58wbjx4+HQCDA3r17M1RBiUQihfa6UnQ/rG/17dsXzZo1w9q1a+Hr64sDBw4oHQD07t0by5Ytw6dPn7BhwwZMmTJFpbkoYteuXQDSKp2cnZ3zbBxFSJc1MzMzy3FvQgCyN5OB3FeuSaWmpuLp06c4ffo0Tpw4AZFIhM6dO2PNmjWwt7fP9tq4uDjMmzcPJ0+elB2bNWsW7OzsYGdnl+u5CQSCPFkGsaAdOHAAe/fuRbly5bBp06Ycl20NDg6W3Za37xfJ98cff6Bu3boZqncfP34MLy8vrF27Fg4ODlleKw29AaBs2bKIjY1FcnJytsvcKsvPzw9jxoyBmZkZDh8+nO18pKGZlKWlJQ4cOABjY2NMmzYNLi4uGa4vW7ZshmUT05PuTw1ALd+n6SUmJuLRo0c4ePAgzpw5AwDo1asXXF1dVf5/zLcCAwPh4eEBAPj111/h5OSkln6JiIiIiIjU7bsP16Tq16+Pw4cP4/jx41i5ciU+fvyIWbNmFfS0iIgKFYFAoNY3D/NS0aJF0bJlS5w7dw6BgYHYunWrwvsGKUMikWDLli0AgKZNm2a5zNa3OnXqlOFN8dy6d+9epmPS/e28vLwwYsQIDBw4MFMF2q5du/DixQuMHDky2zdvla1cS8/a2horV65EXFycSmFI165dsXr1aohEIuzevRvdu3dH+fLlVZ5PVp4/fy5bxnLmzJkwNDRU+xjKuHDhAgCgQ4cOCr3xHBb2396Wua1ci4mJke3xFBcXB3Nzc3Tu3Blt2rSBtrY24uLicPfuXaSmpsr9l5iYiI0bN+Lz588AgFq1aqFHjx5o2rQpA6B0JBIJfH194eXlhdjYWABplcJjxozBwIEDFXrepUv/AVB4mdef3Z07d+Dn55dhH6+4uDhMnjwZM2bMyPHnuPR1DaQFUJaWlnj8+DHq16+vlvnt3r0b69atg6OjI8aMGSP3Axvh4eEQCATo0aOH3OUrbWxscPPmTYjFYowaNSrDubJlyyIsLAwhISGZvh/TL3GpyL6JWVmwYAGWLl0q+zo1NVVWwWxpaYmuXbuif//+qFGjhspjfEskEmHSpElISEhArVq1sG7dukzBIxERERERUWHxw4RrUp07d0aLFi3g7u6OHTt2ZFg+iIjoZyddBut70aVLF5w7dw4AsHPnTvTp0wfFixdX6xiXLl3CixcvoKuri7lz5yp83fbt25GSkqLWuaR37949LF++HPXq1cOJEyey3ItJR0cHp0+fxpkzZ9C8eXOMHj061292BgUFyQ13VK0yMjU1RfPmzXHhwgWIRCKsWLECW7duzdUc5dm4cSMAoHHjxmpZujA3/Pz88Pz5cwBQeL+vL1++yG7nNlwzMTFBiRIl8ODBAwBAREQEdu/ejd27d2dqa2pqCgsLCxQrVgz6+vq4efMmEhMToaGhgd9++w0jR45E5cqVczWfH014eDh8fHxw8OBB+Pv7ZzjXrVs3DB06VOG+pPvGFS1aFMbGxmqd549q27ZtMDIywm+//SY7tmjRIvTs2RMDBw7M8XppAKWpqQlbW1tUrFgR169fz3W4lpycjClTpkBHRwfHjx/Pds8zR0dHSCQS9OvXL8vnPSkpCatXr84UMJUrVw5A2p6O//vf/zKce/nyJYC0+1apUiWV78v48eMz9H337l1ZCOjo6IgRI0ao3HdWFixYgEePHqFRo0bYtGkTDAwM1D4GERERERGRuvxw4RoAGBsbK/UGKRHRj04aqn1vnwBv2rQpTExMEBMTg+TkZGzcuFG2x4s6iMVirF+/HgAwZswYlC5dWuFr1R3yfevr16/YunVrjnvNSCsRJRIJrl+/jubNm+cqXDt69CjmzJmDVatWoV27dir3862OHTvKKrmuXr2KBw8eoHbt2mrr38/PD5cuXYKenh4WLFigtn5VJQ2xWrRogSpVqih0jTRcMzY2RpEiRXI9BxcXF9lyqrVr14adnR1KlCiB4sWLw9LSEpaWlihWrBh0dHQQGRkJT09PeHl5QSgUonPnzhg9enSmCkM/Pz84ODgUuqVT80NcXBx8fX1x8uRJ+Pr6QiQSAUgLJ8eMGQN3d3fEx8cr3a80DMmu8lSdVqxYgREjRmQZ2Gfl2rVruHr1KubMmQOBQJBHs8vZ06dPce3aNfTp0wf6+vqy46NGjZKFTjmRhmsODg7Q1dWFg4MDDh06hEmTJuVqbrq6upgwYUKW++tJffz4EW/fvgUAODs7w9nZGZMnT87UrkWLFnKvL1myJPT19XH16tVM4dqjR48AALa2trkKp8zMzFCqVCnZ19bW1nBzc0NoaCi8vb3h5OSk1t8pPDw8cOjQIbRp0wZubm4/5c8YIiIiIiL6vvyQ4RrRz8ZmzGa19CPQynr5qmJD12T4WldPT/amSugxdyQHvJJ3WZ7QLeUAyy5ZLw+Y3f1Q12P1vZEu5fS9fQpcW1sbLVu2xNGjRwGkBT+TJ0+GqampWvo/duwYXr16BQcHh0K3r0v6iozspH9z2d3dHa1bt1Z5zKNHj2LmzJkQi8WYMmUKNDQ0Mr1xq6pmzZpBT08PSUlJANLCJ3WGa6tXr4ZEIsH48eOVCknzQmBgoKzicty4cQpfJ11mNLdVa1K2trbYsmUL6tevn+F1kl5AQAB2796Nv/76CykpKejSpQuGDx+e5fKEDx48wNixYzFy5Ej07t1bbfssFXZCoRC9evXC+/fvZccEAgF69+6NSZMmwdTUVBbUKyMuLk62R5aiIWxu3bp1C8ePH8fChQtRq1atDOek1bhCoRARERGy4wEBAZgwYQLi4uIQHx+PJUuWQFNTM1/m+61169YBAHr27JnhuKLBWkpKCvz8/AAAderUAZC2h/Pz588RHh6e4wcacpJTsAYAp06dkt1u06aN3FDv6NGj6Ny5s9wAS0NDAzVq1MDVq1chFotlbZKTk/H06VMAyPTc5pampiZ69OiBzZs3IyAgAL6+vjnuJ6iorVu3YsOGDejatSuWLl1aYK8tIiIiIiIiZTBcI/oBaJta5vkYmkUsMo6pry97M0e/dJV8Ddf0S1dW+T7nx2NVGEkDje8tXAPSQhlpuCYUCnHr1i2Fg6fsJCUlYf369dDW1sby5ctztSdZQUr/JmTJkiVzbB8YGIiKFSvm2C4lJQWTJk2Cu/v/2bvvuBr7/w/gr9M47VTKLLJlb7c9yg4NZEaSyCjrzkgy05CRiBCRhDtZ2SsZt71HboSKREvz1Dnn90e/c31Lpzqzwvv5ePRw6lzX5/qcWa7Xeb8/m0Reh64sampq6Ny5M65fvw4AzL+ycO3aNfz7779o164dpkyZIrNxJeXj44OCggKYm5uLFZgkJiYCQLFqEWn16dNH6M8fPHiA/fv349y5c9DS0sKkSZMwadIkGBgYCN1eYMqUKcjMzMSqVasQEhKCv//+G6ampjKbb1XFZrPh5eWFcePGoaCgAIaGhvDy8kKnTp2kGvf+/ftMZbG0Y4lKRUUF3759g5OTU6nbnD59ulgAVNSxY8dgYGAgtNJKngoKCnDgwAFER0ejefPmaNOmjUTjPH/+nPnAyV9//QWgsIJNWVkZp06dwuTJk6WaJ4fDYaoaS3P8+HEAha1A3dzcmPkI7N+/Hxs3bsSFCxfg6+srNBzv3Lkzbt++jUePHjEfVLhz5w5z7O7du0t1O4QZPXo0duzYAR6Ph9DQUJmEawEBAdiyZQvGjh0LDw+PSq2KJIQQQgghhBBx/JpnEgkhVYpmy55IuxlRgcfrVWHH+h3weDwmXNPW1q7k2Yiva9euxb6Pj4+Xybg7d+5EYmIiFixYINW6NJVN3E/4165dGxERor9eZRk6du3alQnVsrOzkZKSInZrup9xOBysW7cO6urq8PHxqfSKh3v37uHMmTMwMDCAm5ubyPvl5+czbSFlGa4VlZmZidOnTyM8PBzPnz9HkyZNsGLFCowcORKqqqoijzN79mxkZmYiODgYTk5OGDx4MFatWiWTVpZVWZs2beDo6Ih///0X27dvl8n76e3btwEUVulWVLgmqDZs3rw5E/KUJz4+nglR+/XrhxkzZshtfgLv37/H7NmzoampCT6fjw8fPiAtLQ0AYGNjI/G4V69eBVAY+Pfo0QMAoKqqii5duuDIkSNSh2vv37/HlClTilX+leb79++lBuAAcPHiRcyaNQu7d+8uEToJ1oe7cOECE65FR0cDKPy9IAgOZalu3bro3r07YmJicP36dXz8+FGqSuFNmzZh+/btmDx5MpYuXSrDmRJCCCGEEEKI/FXpcO3kyZMwNzenTzASUsWxa9SHqpEJcj+9lPuxVOu1ALtG5bZ8+9UITkYCQM2aNStvIhKqXr06atasiaSkJACyWTfu06dP2LVrF7p27Ypp06ZJPV5lEjdMUlBQkDrQktTPVVyy+P0eHByMuLg4eHp6Vno7yLy8PGa9t9WrV0NXV1fkfePj48HlcgFAprcjOTkZV69excWLF3Hz5k1wuVz069cPCxcuFKmyhc/nIysrCxkZGfjx4wd+/PiBjIwMNG/eHHXr1kVCQgLOnj2LN2/e4ODBgzJr2VpVOTk5YdKkSTL7oIIgDOnSpQs0NDRkMmZ5pG3laWpqWiFzbdCgAQ4cOIDTp08jKCio2O+yzp07SzzuxYsXAQC9evUqFir36tULnp6eUq8H2axZM+zduxdXrlxBy5YtUa9ePWhpaTH32eLFixEVFYXmzZvj0KFDJX6npaenw8zMDHl5eRg4cCBWrVol9L2ybdu2YLPZiIiIgIuLC5SVlZl2tJ06dZLba9HGxgYxMTHg8/kICwuDq6urRONs2bIF27dvx9SpUyUegxBCCCGEEEIqU5UN13bu3ImNGzfi4cOHmD9/PpSUlJgvQkjVo9PNEl8qIFzT6WYh92P8bn71cA0AGjZsyIRr7dq1k3q81atXQ1tbGxs2bJBJWFeZKrtSSxwNGzZkLhsbG4sVPgkTHx+PwMBAWFlZwcrKStrpSW3t2rWIjY3FjBkz0K9fP7H2LbqWV/369aWax5cvX7B27Vo8f/4cCQkJAAorELt3746hQ4dCXV0dCQkJCA4OLhGaZWZmFvtZZmYmeDweWCwWqlWrBn19feZr4MCBuHHjBmJjY/H27VusWbMGvr6+Us29qlNSUpL6eSvw9u1b/PfffwCAIUOGyGRMUfxK73m6urqYOHEiLCws4ODggAcPHgCAxOuivXjxArGxsQBKrtnWt29feHp6Yv/+/VKvB9msWTOh7XdjY2Nx9uxZAMDy5cuFtns8fvw48vLyoKysDDc3t1KfbyoqKujevTuuXr2KkydPom7duszvSVm0Ti5N//79YWBggOTkZERERMDZ2Vmsyleg8P95AQEBFKwRQgghhBBCfmlVMqkKDw/Hxo0bmU9EhoWFFbteUVERysrKzBebzYaysjJUVFTAZrOhqqoKVVVVqKioQE1NDWpqalBXV4eGhgY0NTWhra2NatWqQUdHB/r6+qhdu7bQ/9wSQkSn3qQjNFr2RNbzGLkdQ7NlL6g37ii38X9XRcM1IyOjypuIFDp16oRbt25h5MiRUp/0PHnyJG7evIl9+/aVu74Uka1atWrB0NAQX758wfLly6Ueb8WKFWjUqBFWrFghg9lJR9Bu0dLSEvPmzRN7/5cv//fhBFHWxCtLrVq1kJiYyARrQOF6VdHR0UylVFFaWlowNDSEoaEhvn//jrdv36Jp06ZYtmwZDAwMYGBgAH19faEVTy9evMCoUaPA5XJx9uxZeHt7yz282b59OywtLVGrVi25Hkfejh07BqBwLcyKDNd+xQ+qaWpqwtHREY6OjgAk/1BBeHg4gML2hr16FW8xbWxsjHbt2uHMmTOYOnUqWrduLd2khfDz8wOPx8PIkSOFtgHNzc3Fvn37AAAjRowo9wMxw4cPx9WrVxEaGoo6deoAKGxxOWzYMJnPXUBJSQmWlpbYuXMn0tLScPr0aVhbW4u8f3h4ODZs2IAJEyaIHKwFBwfD2Ni4zBaahBBCCCGEEFLRquT/rpOTk8Hn88FisZhF3osqKChAQUEBcnJymJ9J21qqRo0a6NSpE/r3748BAwaAzWZLNR4hfyL9gdOQ++EFuJnlrzMiLkVNPVQfaC/zcf8ERdcoa9y4cSXORHLjx48Hm83G1KlTpRqHw+HA09MTa9asQceOFNRWBg8PD/D5fPTs2VOqcS5evAg+n4/w8HCxqyZk7datW1i8eDEGDBiANWvWSDwGABgYGKBGjRpSz8nFxaVEy9PGjRujZcuWaNq0KYyMjJhArehaacuWLcPbt29Ro0YNZo2tsrRo0QKTJk3C3r17oaGhIddgraCgACtWrEBOTs4vH6xxOBxm7cORI0dCU1NT4rHevn2Lq1evwt7+1/odeebMGbFCRS0tLeZyfn6+2MdLTk5mAs0ZM2YIfa6OGzcOjx49gre3N/bv3y/2Mcpy9+5dXLlyBZqamli0aJHQbQ4cOIDk5GQoKyvDycmp3DFNTU2hrq6OFy9e4MWLFwCAoUOHyn39wzFjxiAoKAh8Ph+hoaEih2tXrlzBypUrMWLECLE+YHH06FG4uLhIOFtCCCGEEEIIkY8qGa7Nnj0b1atXx8qVK9GzZ0+w2Wzk5+czX3l5eeBwOMxXXl4ecnNzkZeXh4KCAomO+fXrV0RFRSEqKgo6OjqYMWMGbG1tab03QsSgqK6F2uPckLjfHbzcTJmNq6Cqidrj3KCorlX+xqSEt2/fAihsBdaoUaNKno1k9PT0MH36dKnHycvLw7x582BhYSH9pIhEfq4WkVRubi5CQ0MrvdXp06dPMWvWLNjY2GDp0qWlhkuZmZmlBijx8fFMuztR1kETRa9evdC5c2ekp6fDxsYGgwYNgoGBATIyMvDo0SP07t1brPHi4+NRp04dobfP2dkZUVFRMDMzk8nchcnKyoKzszMSEhJw9OhRuR2nohw+fBjfv3+HsrIyHBwcpBrry5cveP36tcjbV5W/bTdt2iRWuJaZ+b+/K9LT06Gvry/W8QIDA5GXlwdjY+NS28gOGTIEnp6euHPnDk6fPi2zCjAejwdPT08AQKtWrRAfH49q1aoV+zDfly9fEBAQAACYOHEiDA0Nyx1XTU0NpqamOHnyJIDCij5Z/K4sj5GREbp164abN2/i+fPnePz4Mdq2bVvmPrGxsViwYAG6deuGdevWifw8/O+///Dff//9spX3hBBCCCGEkN9XlQzXgMJPjq5cuRLLli1DgwYNRN6Py+UiJycHOTk5yM7ORmZmJrKysoqtJ5KSkoKUlBR8/foVHz9+xMePH4uFcqmpqVi/fj1u3boFf39/qRd+J+RPwq5RH3UmrcLnsDUyqWBT1NRD7XFuYNeQbg2iP5lgTR8TE5M/vgWulpYW7OzsKnsaRAbMzc2LreEmD8+ePcP58+cBAAkJCXj16hWaN2/OXH/r1i0sWLAA8+fPx8SJE0sdJz8/H7a2tuBwOGjdujVatWqFJk2aMGurrVixAjweD4Bs10ry9/eHjo4OcxKbw+HAxcUFd+/eRXBwsNC2dKU5f/48QkNDMXDgQFhaWqJp06bMderq6tixY4fcTn5//foVjo6OeP/+PY4cOQINDQ25HKeiZGZmIjAwEAAwYcIE1K1bV6rx0tLSkJGRIfL2VSFc4/F4iI+PB4/HE7nasWib05SUFLE+LPLixQuEhYWBxWJh1apVpbbGVFFRgZ2dHTZu3Ah3d3e0atVK6jUQASA7OxujRo1Cs2bN8OzZM0yYMAGKiopo1aoV2rdvjw4dOmD37t3Izs5GvXr1MGfOHJHHLvp4GhkZSf18EtXo0aNx8+ZNAEBoaGiZ4VpmZibmzJmD2rVrY/PmzWL930qwjmO9evWkmzAhhBBCCCGEyFiVDdckpaioCE1NTbHa6+Tl5eHRo0c4ffo0jh8/Dg6HAz6fj2vXrmHDhg1YvHixHGdMyO+HXaM+DB388P38bmQ+vy7xOJote6H6QHuqWJPS8+fPAQB9+/at3IkQ8otIT0+Hn58fDh8+jMGDB8Pb2xvBwcGYPHkyAgIC0KlTJ0RGRuLgwYMIDg4ud400ZWVlRERE4MGDB/Dx8WHaAf6scePGMn2d6urqMpfz8/OxYMEC3LhxAwDg5OSEsLAwkQOKqVOnom/fvli2bBn27NmDNm3aYMyYMTA3N4eamhpatGghs3kX9ebNG0yfPh2JiYnw9PREkyZN5HKciuTr64vk5GTUqlVLrBClNKmpqcXW1iyPsJbrFS0tLQ0FBQVIS0uDnp6eSPsIqrCBwiovUeXm5sLV1RVcLheTJ09G165dy9x+8uTJOHjwIJKSkuDs7Izw8HCoqKiIfDxhNDU1MX78eOb71NRUXLt2DZcvX8ahQ4ewe/du5ro6deogJiYGffr0Kbfl7ZYtW3DixAnm+7i4OCxcuBAbN26UeF06UZmZmUFPTw8pKSk4c+YMFi9eXOpjuXLlSnz58gXHjh0T+f9oKSkp8Pb2xpUrV2BgYAB1dXXmQwiEEEIIIYQQUhXId8X5X4SKigq6du2KVatW4fjx46hduzaz3ltISAju3btX2VMk5JejqK6FGhYuqDVmKVTriXfSVbVeC9SyWYoaFi4UrEnp2bNn+Pz5MxQUFDBy5MjKng4hVRqfz8c///yDwYMH49ChQ5gxYwY2btwIExMTeHt7Y9CgQZgyZQoOHDgALS0tHDx4sNxgragOHTrg4MGDWLhwYYnrNDQ04OXlJZc1y3JycuDk5MRU4QGFgcOePXvEGqdhw4YIDQ2Fq6srXr16BTc3N/Tp0wcbNmzA9+/fZTJXLpfLXP73338xfvx4JCYmYuTIkaW28vuV3L9/H4cOHYKCggK8vLykWmtNQNzKtaoQriUnJwMorEoU1Zs3b5jLRdcSLY+7uztiY2PRvXt3/P333+Vur6amhrlz5wIAXr58iblz5yIvL0/k44lCV1cXFhYW8PDwQMuWLYtdd/v2bcydOxc9evTAxo0bweFwhI4RFhbGtJHs27cv03L33LlzWLZsWbHXkqiK7lPe/mw2G5aWlgAKq2IPHz4sdLuHDx/ixIkTYLFYcHJywuDBg8v9MjU1Rc+ePZk18qglJCGEEEIIIaQq+q3DtaCgILGDMWNjYzg7O4PP54PFYoHH42HDhg1ymiEhvz/1Jh1RZ9JqGDpshE53K6g1aAMF1eInExVUNaHWoA10ulvB0GEj6kxaDfXGHStpxr+XCxcuAAD69+8vk9ZWf5LHjx9jzZo1VeJENJHO5s2bcf162VW0r169wvjx47F06VKkpKTAwcEBzs7OxbZZsWIF+vbti9WrV+PQoUNMQCAOFosFBwcHuLi4MD9r1qwZDh48iFatWok9XnmSkpIwfvx4REdHAyj8O8fDwwM3btzA2rVrxR5PQUEBU6dOxeHDh1G/fn2kp6dj586dMDMzw86dOyU6oV9USkphO+Hc3FzY29sjIyMDxsbGWLFihVjjiBMSVJSMjAwsXboUfD4fLi4u+Ouvv2QyblpamljPRcF7Go/HY1qll/dVNLyTxf0pCGOTkpJE3uf9+/fMZVHDNV9fXxw/fhx//fUXAgICSm0H+TNra2t06dIFAHD16lU4OjoiJydH5LmK4sWLFxg1ahTu3LkDIyMjhIWFYe/evejcuTOA/7UPXblyZYl9Q0NDsWrVKgCFrfS3bdsGPz8/GBsbAwCOHTuGKVOm4Nu3b+XOg8fjISMjA3Fxcbhy5Qrz88jISLx79w4ZGRmlVoyNGTOGubxz505cu3YN6enpxQJBwWOdk5OD9+/fi/QVHx9f7HlGf78QQgghhBBCqqLfri1kUW3atMHff/+NEydOiPXJ4J//A/fo0SPcvXuX+c8uIUR87Br1oFdjAoDCE3t8Ti743HywFJXBYqtWiTVgfjc8Hg9nzpyBoqJisRP5pHyPHz+Gvb09fvz4gZycHKxZs+aPf45GRUUhMzOz2MnUX8GGDRuwc+dOqKioYNu2bejZs2eJbc6cOYMFCxYwJ3MtLCyEVpcpKirCz88P8+fPx4ULFzBw4EDY2Nhg9OjRYlWwAYCDgwOSk5PRvXt3mJqayuX5devWLSxcuBDfvn1D7dq1MXfuXFhYWJRZHSdqBZSJiQn++ecfuLi4ICYmBtnZ2diwYQNu376Nbdu2ldvOrjSPHj0CUPj+xePxoKysjA0bNoi9zlplhGu5ubmlXsflcjFv3jzExcXBxsYGjo6O5Y4neE6UVzX15csXJhwRhCtlEawzHBsbi27dupW7/c9Kq6QqGsAUXctYmA8fPgAAnj59ij59+oh03PT0dOayKKGRt7c3du/ejT59+sDf31+s1o4sFgteXl4YMWIEfvz4gVu3bmHChAnw8fERa603YfLz87Fnzx5s27YNubm5sLKywrJly5j/q3Tr1g1Xr17FypUrkZiYiOPHj2PFihVgs9ng8Xjw8vLC3r17oaCggEWLFmHatGkAAG1tbWzfvh0TJ07E9+/fcefOHVhYWGDVqlXo379/qfNJTEyEqalpiZ/fu3cPQ4YMAQBcunQJhoaGJbYxNjZGly5dcOfOHWRlZWH69OkAgOXLl5e5DqW4qHKNEEIIIYQQUhX91uFa165d0axZM6xduxaenp4i7/fzGgXm5uZC/0NJCJEMi8UCS0UNgFplT+W3FhUVhQ8fPmDy5MlVep0iPp8v8cnvolVlXC631BO6Rbcr76Tvs2fPMG3aNPz48QMAcPToUbBYLKxevfqPDdguXryIRYsWgcvlQllZmWkF9jMulyt1pR+fzy/3MRJ1u82bN2Pnzp0ACgMKJycnbN++HT169Ci23YABA2Bubo7jx4/DxMQEq1evLnVMNpsNf39/BAUFYevWrdi/fz/2798PIyMjdOzYESYmJjA2Noaenh6qV68ODQ0NqKqqQkVFpdjzR0lJCe7u7kKPweVykZeXh+zsbKSnp+PHjx9IT0+Hjo4O2rZtW+59w+Fw4O/vj127dkFZWRmzZs3C9OnTmcBrx44d0NfXh4GBAapXr45q1apBQ0MD8fHxuHXrFgCI1J5SS0sLO3bsgKurK06dOgUAuHHjBrZv34558+aVu7+w2/3y5ctiP5s9e7ZEFX1FnxuyDNdiYmKgra0NY2NjaGtrMz+/efMm8vPzARSusfczT09PxMTEwNraGh4eHiIdi81mAyh8PEsLzrhcLp48eQKg8HEV5e9dQQjWvHlzHD9+XKS5xMfHMwFMaWFf0UD17du3aN26danj3b59GwBw+PBhTJo0CdWqVSt3DsbGxnj9+jWAsltb5ubmYvny5Thx4gSsrKywatUqoY9JeerUqYNVq1Zh/vz54PP5eP78OaysrLBgwQJMnDhRohaud+/ehYeHB/777z80bdoUCxcuFBou9u3bF507d4anpyciIiLA5XKRlZWFv//+GxcvXoSRkRE8PT1LfPCvYcOGCA4Ohq2tLVPROHPmTHTs2BHz5s0T+kFBQ0ND5n6VxP79+8u83szMTKrxCSGEEEIIIaSq+q3DNQD4+++/MXz4cAwcOBD9+vUTaR/BCVUAsLOzg6urq7ymRwghcsHj8bB9+3YYGxtLdJK7In3+/BkTJ05EQkKCVOOI0mLtx48fJda3EcWRI0fAYrGwatWqCgvYcnNzkZ2dXe52mZmZzOWMjAymrd7PBNUmglZwonr06BFcXFyYsGLp0qVQVlaGubl5iW2PHz+OZcuWldpCTBSXL18W6TEShFriyMvLw8yZM0sEbEpKSvDy8oK+vj6sra2ZUKM0LBYL06dPx5AhQ+Dv74+oqCh8+vQJnz59QmRkZKn7sdlsqKioMOMXfS7x+Xzk5eUhLy+PCWmKHq9bt26wtbUt9zY+fPgQy5cvR1xcHEaPHg0nJyfUqlWr2DaNGzfGzZs3ceHChVLb8mlpibbepZKSEry9vfHjxw9cu3YNQOG6YpJQVFTE9u3bMWHCBGRlZaFNmzZwcHCQaKyi1VWyXC8rPT0dGzZswIsXL6CjowN9fX0oKyvjv//+Y7b5uYrRy8sL+/fvx/Tp0zF//nyR30MaN27MXF60aBHmzZuHmjVrMj/j8/k4cuQI8xhGRESgRo0a5b7n//z8EldpFXrNmzdn1ixev349fvz4ARMTkxJVh/fu3WPW/0tKSsLEiRNx+PBhqKmV/YGbGTNmYOnSpcjJySk1ZH7//j1cXFzw6dMn+Pj4YMSIERLcwv8ZOnQoPn36BD8/PwCFt33t2rU4ePAg7O3tMXLkyHLfL4DC2xwYGIjr16+jXr168PHxgbm5eZkBnYaGBtasWQMXFxc8fvwYS5cuRUJCAmxsbODq6lpqNWezZs2wb98+ODg4MGva3b9/HxMnTkTdunUxcOBATJ48GbVr15bgHiGEEEIIIYQQIvDbhGvZ2dm4e/cujI2Ni7V1bNCgAcaMGYPly5cjKiqq2KeMSyNoNdOyZUuRFj4nhJCqJjIyEvHx8QgPDy/3hGVlq1OnDvbv34/w8HA0b94cDRs2hJaWFjQ1NatcpZhgPc6KkJ6eDltbW8TFxYm8jyjhy+fPnyVqBSfA4/Hg6uoKZWVlDBo0qNh1VlZWUFVVRWJiIlq2bIkaNWpAS0tL4haB8vJzhTpQGGCJ+zvfyMgI3t7eWLBgAaKionD+/Hk8ffq01PCCw+GU2lKvNPXr18emTZvQokWLMrf79u0b/Pz8cPbsWVhZWWHHjh2oW7eu0G1NTU1hamqKpUuX4sKFC/D19cWnT5+KbSPOc0RRURG+vr6wsLBAQkKC2C0cizIxMcGWLVvg5OSENWvWCH2sRFG0ck2UkFpUw4YNw7BhwxATEwMvLy/ExsYWu15XV5epROLxeFixYgVOnToFb29vjBw5UqxjDRgwAJs3b0ZiYiKePHkCOzu7MrevVq2a0PZ+PxP3Ofiz0sLKGjVqwNzcHCdPnkRqairWrFkj0nhDhgwR6ffU0KFD0bdvX3z48AFNmzYtdh2Xy8W+ffuwefNmtGrVCseOHZPZOl2Ojo5ITk4uFuZ/+PAB+/fvx4cPH4S2kAUKn4PXrl1DcHAwHj58iF69emHz5s0wMzMTee237OxsBAQEICwsDG3btsX69euZteDK0rx5cxw+fBgODg548+YN8/O8vDykpqbK9DVBCCGEEEIIIX+q3yJcO3bsGLy8vJi1GIYPHw53d3dm7YLZs2cjMjIS69atw/r168sdLz4+HiwWCwsXLqxyJ3YJIaQ8nz9/hqenJzw9PdG8efPKno5I6tati/nz51f2NKqUmjVrIiQkBFFRUWjevDkaNGgATU1NaGhoVOnfTUOHDq3sKVS4mjVrws7ODnZ2dsjLy8OzZ8/w9u1bxMXFISkpCampqUhLS0N2djZycnKQm5sLDoeD/Pz8cquI1q9fX2awxuFwsHPnTly8eBHDhw/HlStXRGqxBxSGYoMHD0aPHj0wYcIEpnVb9+7dS239WRptbW0EBgZi2rRpsLa2Fmvfn/Xs2RPHjh2Tam2rogFQTk6OVPMRpmfPnujSpQvc3d1x7NgxAIVtEdeuXQsdHR18//4drq6uyMjIwD///IOGDRuKfQwNDQ0cOHAA27Ztw8uXL4WGYoqKitDS0kLHjh0xYcIE1KhRo9xxBZVn4qxHDBS2ZZw2bVqZIeG6detQs2ZNnD17Ft+/fy+1Jae6ujpatWqFSZMmoW/fviLPQV1dHSYmJsV+duPGDaxfvx58Ph9+fn4iBYzicnNzg66uLrZs2QIzMzN4eHjAwMBA6LavXr3CsWPHcOHCBRgZGcHU1BSbNm2Cvr6+yMfjcrk4duwYtm7dCm1tbQQEBIh9u2rXro3w8HC4u7vj1KlTcHZ2xrRp00SqtCOEEEIIIYQQUj4WX9rFUeSoefPmOHPmDBo0aFDqNhs2bMCuXbuKrb3AYrHQsmVLHD16lPnZ1q1bERAQgKCgIPTs2bPM4y5ZsgSvX79GRESE9DeC/JI4HA6ePn1a4uetW7eW+qREQUFBsU8RA0CTJk1E/hRzZeDxeCVOTqqpqUm03giRLz6fjylTpqB3796wt7ev7OkQQkTA5XKZLx6Px/xNw+fzy63g5PP5uHfvHjp16iRV6Hru3Dns2rULFhYWsLGxkfh3Eo/HqxK/G3bu3ImjR4/CxsYGVlZW0NXVlctxCgoKMGDAANSrVw+urq5o0aIFbty4gTVr1mD8+PGYMGFClbg/inJ3d0ePHj1gamoq8uNcUFAABQWFKnVbbt++jcDAQPD5fIwaNQpDhw6VuNJRVNevX0e3bt1Kvd8yMzMRGhqKzp07o3Xr1mKv9cbn83HmzBls27YNxsbGGD16NHr37i31ByouXbokl9Cxosnq79Ff8W9xQgghhBBSNeTn5zPrXgu0adNGonWef3XyPH/+q6jUcO348eNlfvq1vHAtNDQUq1evZv7DKbgpTZo0gYODQ7F1FrKysmBmZgZ1dXWcOnWqzPYzo0aNwogRI0Rqr0V+TxSuFUfh2q8jNzcXp06dwqhRoyp7KoQQUmlSUlKgq6tbIVWeKSkp0NPTY74PDw/HoEGDoKOjI/dj/6mysrJw4MABmJubl9r+9Ff05s0bREdHw8LCAtWrV6/s6VQ5FK4RQgghhJDKRuHa/1C4VoltIT98+IBly5aBxWJJtNj4q1ev4OnpySycrqamBhsbG1haWpZYSB4obK0zbdo0+Pj4YPPmzVi8eLHQcfl8Pt6+fVtiHRlCCPkVqKqqUrBGCPnjFQ27KvpYNjY2FXbsP5WGhgYcHR0rexoy16RJEzRp0qSyp0EIIYQQQgghRASVVnbi5+eHgoICrFq1Chs3bkRSUpLI+3K5XCxevJhpUTNp0iRcunQJixcvFhqsCUycOBH6+vrYv38/Hj16JHSb/Px8+Pj4oGbNmuLeJEIIIYQQQgghhBBCCCGEEPKbq5Rw7cmTJzh37hxYLBaysrKwc+dOmJqaYsGCBSXKKoU5cOAAXr16hRYtWuDIkSNYtmyZSJ9QVlFRwdSpU5lwruhi9wJsNhtmZmYS3S5CCCGEEEIIIYQQQgghhBDye6uUcC0wMBBAYQtGwVdBQQGioqJgY2ODsWPHIioqSug6GRwOB9u3b4ejoyMOHz6MFi1aiHXscePGQVdXFx8+fIC3t7dMbg8hhBBCCCGEEEIIIYQQQgj5M1RKuLZt2zbcuHEDwcHBmDNnDnr06AE2m80EbY8fP8aCBQsAAIcOHUJ2djazL5vNxqlTpzBv3jyJFp1WU1PDhAkTwOfzcfDgQZw7d05mt4sQQgghhBBCCCGEEEIIIYT83iptzbXq1aujW7dumDVrFnbv3o07d+4gMDAQo0aNgra2NhO0hYSEYODAgYiIiGD21dfXl+rYEydOhJqaGvh8PpYtW4b3799Le3MIIYQQQgghhBBCCCGEEELIH6DSwrWfqaqqom/fvlizZg1u3LiBHTt2YMCAAVBUVMS3b9+wbNkyjB07Fm/fvpX6WDo6Ohg+fDgAIDMzEw4ODvj+/bvU4xJCCCGEEEIIIYQQQgghhJDfW5UJ14pSUlJCnz594O/vjytXrmDu3LmoXr06Hj16BEtLS2zbtg1cLleqY0yYMAEAwGKxEB8fDzs7O3z9+lUW0yeEEEIIIYQQQgghhBBCCCG/qSoZrhVlYGAAJycnXL58GW5ubtDX18eWLVswceJEfPnyReJxmzVrhnbt2oHP54PFYiE2NhZ79uyR4cwJIYQQQgghhBBCCCGEEELI76bKh2sCbDYbEydOxLlz5+Dq6op3795h5MiRuHbtmkj7Dx8+HG/evCn2M0tLSwCAgoICFi1ahMWLF8t83oQQQgghhBBCCCGEEEIIIeT38cuEawLx8fEYMWIEzp8/D3Nzc8yePRsHDhwod79WrVohMDCw2M8GDRoEfX197NmzB/b29vKaMiGEEEIIIYQQQgghhBBCCPlN/FLh2tOnTzFhwgRkZGSgWrVqWL58OUJDQ3H48GGcPHmyzH0tLCxw7tw5fPr0ifmZjo4Ozpw5g65du8p76oQQQgghhBBCCCGEEEIIIeQ38MuEa7dv38bkyZORmppa7Odt2rRBREQE+vfvX+b+Xbt2RY0aNRAUFFTs51paWjKfKyGEEEIIIYQQQgghhBBCCPk9/RLhWk5ODhYuXIjs7Gyh1yspKUFDQ6PccUaOHIljx47hy5cvsp4iIYQQQgghhBBCCCGEEEII+QP8EuGampoagoKCoKOjI9U4VlZWyM/Px65du2QzMUIIIYQQQgghhBBCCCGEEPJH+SXCNQAwMTHB/v37pRrDyMgIHTp0wNGjR/Ht2zcZzYwQQgghhBBCCCGEEEIIIYT8KSo1XFuxYgU+ffok8vZNmjQBn8+X6piWlpbIzc0tsfYaIYQQQgghhBBCCCGEEEIIIeVRqqwDv3z5EuHh4bh9+zaWLVsGBQXRc74HDx4gISFBouNqamqCxWIhPDwcDg4O0NfXl2gcQgghhBBCCCGEEEIIIYQQ8ueptHBt69atAICPHz/C0dFRrH3d3NykPn5eXh6CgoKwZMkSqccihBBCCCGEEEIIIYQQQgghf4ZKaQv58uVLXLp0CQDA5/PF+pJkn9K+Dh06hKSkpMq4CwghhBBCCCGEEEIIIYQQQsgvqFIq1169eoWxY8dCRUUFSkpKzJeCggIUFRXBYrGYNpEsFgssFovZ18fHB9OmTYOenp7Ex8/Ly8PmzZvB4XAQGBiIFStWSH2bCCGEEEIIIYQQQgghhBBCyO+vUsI1S0tLWFpaSrSvj48PrK2t0aBBA6nmcOXKFTx58gRHjhzB5MmTYWxsLNV4hBBCCCGEEEIIIYQQQggh5PdXKW0hq4KhQ4cCALhcLnx8fCp5NoQQQgghhBBS6Pv370hLS6vsaRBCCCGEEEIIKcUfG66ZmZkxly9fvozLly9X4mwIIaRqOXfuHB4/flzZ0yAycuPGDURGRlb2NP5Y27Ztw4MHDyrl2LGxsXIZ99WrV3B0dMTXr1/lMr4o0tPTsXPnTmRkZFTaHEpTUFAg0X75+fkyngn5VcXGxsLMzAxr165FcnJyZU+HEEIIIYQQQshP/thwzdDQEE2aNAEA8Pl8eHh4ICUlpZJnRQghVQOLxYKNjQ1cXFzw+fPnyp5OqS5evIhPnz6Vu93x48crNQSobNu3b4erqyvs7e1Fur8qw8GDB8HhcMrcJi0tDZGRkRIHF+JITEwsdz6iio6Oxrhx42BhYYEjR44gJydHJuOKYsWKFZg8eTKePn0qszG5XC6WLVuGq1evYsSIEbh48aLMxhaHgoICNmzYgL59+2L9+vVV6r1q1qxZ2Llzp9jPVTs7O3h5eVXY+1VcXBxiYmLkMnZ2djaWLFlCwZCEunXrhhkzZiAkJAQDBgzAjh07wOVyK3tahBBCCCGEEEL+3y8ZrskqBOvTpw/4fD5YLBaSk5Ph5OSErKwsmYxNCCG/soEDB2Lq1Kk4c+YMhg4divDw8MqeklA3btzAkCFDsGzZsjJDo6ioKJiamsLDwwPx8fEVOMPK9/TpU9y9excAEBMTg+HDh+P06dOVPKuSvL29YWZmhgMHDpQaavF4PLi6umLgwIEICwuTWfglzJ07dzBkyBCcO3dO6rHYbDYA4OXLlwgJCUF6errUY4pKVVUVt2/fxqhRo9CsWTOZfLVo0QLPnj0DAKSmpmLfvn1yfSxKI7hfs7KyEBwcjFmzZlXKPH7248cP3LhxAxs2bICVlRWePHki8r5v377Fnj17mPcreX/wKzMzE/b29nBwcMDLly+RkpIik6+vX7/C0dERERERsLW1lTpge/PmDVavXo3v37/L6Jb/Guzt7dGjRw/k5OTAz88Pbm5ulT0lQgghhBBCCCH/T6myJyCJJ0+eoGPHjlKP07t3b+zatQtAYZVGjx49oK6uLvW4hBDyO3BxccGlS5cQFxcHd3d3JCQkYP78+ZU9rWLU1NSQn5+Po0eP4vjx4/Dz88PAgQNLbKeqqgoOh4OwsDAcOXIEmzZtwoABAyphxhVvz549xb4fNmwY+vXrV0mzKZ2amhqSkpKwevVq7Nu3D0ePHkW1atVKbAMACQkJ8PDwwN69e3Ho0CHo6urKfD4WFhZ4/fo15s6di/79+2Pt2rXQ09OTaCxFRUXm8r59+yQeRxIqKioAgNq1ayMiIkJm486YMQOPHz9G48aNsXv3biboqkjKysrM5dq1a2PXrl2VMo+fXb9+nWnv+Pr1a8yePRtHjhxBzZo1y9yPw+EgNTUVQGF7yGrVqkFHR0eucxU8P6KjoxEdHS2XY7x79w4uLi44cOAAWCyWRGM0atQIcXFxGDBgAGbOnIkpU6YUe/yF6devHw4dOlTu/S6q9PR0JCcno3HjxjIZTxQsFgsrVqzA0KFDUVBQwISVJiYmIo8hqMI1NjaW30QJIYQQQggh5A/0y4VrxsbGGD58uEzGat++PdTU1KChoQEfHx9069ZNJuMSQsjvgM1mw83NDdOmTQMA7NixA71790anTp0qeWb/U/QDEZMnTy41MBOcQFZSUoKPj88fE6y9fPkSZ8+eZb7X1tZG586dq+QHSQTBWbVq1eDp6VkiWAP+9zgCQLNmzRAQECCXYE1g/vz5+Pfff3H58mVYW1tjz549aNCggdjjKCgUNgpQUlKq0GAN+N99pqCgINNjCwJDPT29Sgu0FBQUwGKxwOfzYWRkVOH3bWmuXLnCXNbR0UFoaKhIAU9SUhL4fD6AwrWB582bJ7c5ChQNqJYsWYIpU6bIbOz+/fsjISEBbdu2xbZt2yQO1oD/tQAdOXIkfH19ceLECfj6+qJZs2ZCt09MTERiYiKSk5NlEq4lJSXB3t4eycnJCAoKQps2bUTe9+HDh8jIyECfPn0kOnb9+vVhZmaGs2fPQkVFBQYGBiLv+/nzZ9ja2iIvLw/79++ngI0QQgghhBBCZOiXawsZEhICfX19mYylrKyMmTNnIjIykoI1QggRolevXsU+pS9oBSeOkydPwsvLS5bTYigp/e8zIo6OjqWevBUEDGpqahg6dKhc5lIVeXl5gcfjAQCaNGmCw4cPw9fXFx8/fqzkmZUkOMnfqVOnUgNcBQUFZruuXbvCyMhI7nPy8PAAi8VCYmIiHBwckJmZKfY4gsBEQ0ND1lMslyDY+10VfQ+oCnJzc3Hp0iXm+yVLloj8PP3y5QtzuV27drKemlBFqyrlpV69ekLDcnHp6OjA29sbLBYLsbGxGDt2LF6+fCl023v37gEArK2tZdIKtXfv3njz5g3S0tJgZ2fHjC+K3NxcTJ8+HTNnzpR4zctBgwaBxWJh1apVIv8/6MuXL7C1tcWnT5/w9etX2Nra4sOHDxIdnxBCCCGEEEJISVXrjIQIatSoIdPxpk+fLtPxCCHl4/P5yCnIRQGPCyUFRagpqUr1iXYiXyNGjICfnx/U1dWFtlwsy6lTp+Dq6goulwsej4clS5bIdG6inhgur33Y7+jatWu4desWgMIqRF9fXzRo0AC2trZwcXFBaGgoUy1WFYjzWApa7lWENm3aoF+/frh8+TI+ffqEkJAQODk5iTWGIOD8HcM1QXBYWapaeHj58mVm/d6GDRtixIgRIu/77t075nLbtm1lPjdhKiKclOVzpGvXrrCyssI///yD7Oxs+Pv7Y9u2bSW2E7S43LJlCzp37iz1cQMDA7Fv3z4AhR8AyMjIEHlfbW1tAIXPjcuXL0s1D1dXV7i6ukq0b1JSEiZNmoT9+/ejfv36Us2DEEIIIYQQQsgvGK4RQn5NH9MSEPPxLt6mxOFd6idkcbKZ6zTY6mioa4RGesboWa8z6unUrcSZ/h5ycnKQk5Mjk7EMDQ3BYrGwdOlSqKqqIiUlRaT9rl+/jiVLloDL5QIA9u7dCxaLhcWLF8tkXoDooVlVOwEvb7m5uVi3bh3z/fz589G8eXMAgJ2dHU6ePIkFCxZg69atVea+EfUkf2UE8WPHjmVOir9+/Vrs/QXhQmWEmYLQMiEhodQWetKo7HCtqn0w4+TJk8zlmTNnivX6evPmDYDCx6xly5Yyn5swFfH6F4TLsuLs7IyTJ0+Cw+EIrcJNS0vDhQsXABS+5qRtF5qbm4sTJ05AVVUVa9euhbm5uVj7q6qqMpft7e2ZVsvCcLlcpt2pvBSdDwDEx8fD1NRUpH3ZbDa0tLRQt25dtG7dGkOGDEGnTp3Emi+fz0dMTAyioqLw6NEjJCUlgcPhQEdHB82aNUOfPn0watSoKtm+mBBCCCGEEEKKonCNECJXDxKf4vir83iZ/F+p22RxsvE06TWeJr1G5MtzMDFojJHNB6FDnVYVONPfy/fv32Fra4uEhASZjenm5gY3NzepxggODgaLxZL4k/eSqmon4OVt48aNiIuLA1DY2rPoOkrKysrw9vbG6NGjsWbNGri7u1fOJH8i6mNUGWFgt27dUK1aNaSnp4u11pKAoNKuMsI1wf1au3ZtREREyGzcGTNm4PHjxzIPTsRVVcJhAEhJScH169cBFK6TNWzYMLH2FwS3jRo1qrBgoSLaQhYUFMh0vJo1a2LQoEE4efIkbG1tARQGNo8fP0a7du3g5eWF3NxcACjWQjEtLQ3q6upirxEYGRmJvLw87N69W6I1R4u+7tlsdplhX1JSEuzs7ODs7Czy2qAeHh5o0qQJrK2tSwRnoqhTpw7u3r2Lb9++YeHChXj+/DmAwrWp161bx9yHOTk5SE1NxdOnT3H8+HGEhoYiNDSU2a5hw4blHuvVq1dwc3NDXl4erK2tMXnyZBgYGODHjx948uQJDh48iLVr12L79u3w8vJC7969xb49hBBCCCGEEFJRKFwjhMjFj7xM7HkQjhsfRV+XROBl8n94mfwfetbrDLsOY6CloimHGf7eDA0NERISgosXL8LExAT169eHpqYmNDQ0SoQYXC5XqhOs379/R/Xq1aWdMpGR+/fvIyQkBABgZGSEDRs2lHjMTUxM4OzsDF9fXygpKWHp0qWVMdVfBpvNxrJly/Dhw4diQaWoBOGCqOHa0aNHUa9ePXTp0kXsY/1MUDmqoKAgdQVPUYL3DKpc+5/w8HAmSHV0dBT7fTU2NhYA0KpV+R8suXfvHtavX4/AwECp1iKuiPtPHm1cFyxYgO7du8PKygpA4fteVFQUVFRUmBC5bdu2MDExYfbZtGkTXr58iS1btqBmzZolxjx79ixycnIwdOhQZp1OPp+P0NBQbN26VaJgDSgeYJYXRqurq+PNmzeYPXs2WrdujUWLFqFr165l7vPt2zeEhYXB398f48ePx5QpU5hWlKJQUFCAtrY2tLW10a9fPyZcs7KyKhaYaWtro2bNmmjevDlGjx6N48ePY9myZXj48CHGjx+P0NBQNGrUqNTj3Lt3Dw4ODmjbti3Cw8OL3S/Vq1eHsbExhg8fjtWrVyM0NBSOjo7w8/PDkCFDRL4thBBCCCGEEFKRKFwjhMjch7R4rIveitScdKnGifl4F8+TY7Gs9xxqFSkBQ0NDkYKA5cuXo0GDBrC3txe7CiQjIwP9+/fHqFGjMHPmTKlO8lYl3759+yVvy48fP7BkyRLweDyoqalh69atqFatmtBt7e3tcffuXezbtw88Hg/Lli2rUkGFLGRlZYHFYsmkCmjkyJES7ysI1wQn7Mty9OhRuLm5QU1NDTt27JA6YJN3+CXrqiRxyeo5u337drRs2VLiSpmCggIcOnQIAFC3bt1iz5esrCzk5eWVuX9qairS0tIAAA0aNCiz/e7r16/h5OSE7Oxs2NraIiQkROL3q6L3n6enJzw9PSUapyw/3/auXbvi33//lWrM2rVrM8EaUBiM9enTB/Xq1cP69evBYrEwaNAg5vZ9+fIFR48eRX5+PiwtLbFx48YSoVXXrl2xcOFCrF+/HmPGjMHEiRNhYGCA1atXo127dhLPVZxwrWjlGZvNRp06dcodX7CW448fP6CgoCDV+13RuZbXqnfkyJH49u0bvL29kZqaimXLljGvgZ9xOBzMnz8f2dnZGDx4cKnBM4vFYgK7Fy9e4O+//0azZs1EqoojhBBCCCGEkIpG4RohRKY+pMXD48rGYmuqSSM1Jx0rrvhhZb/5FLDJyfTp0zFy5EhER0fDz88PBgYGIu8bExOD3NxcHDhwABEREbC1tYWTk5NIIYIw/v7+mD17dqWGPBwOB8OGDYOjoyPs7Ox+mcCJz+dj0aJF+PDhA1gsFtatW8essyaMgoICfH19MXr0aOzfvx8pKSlYv3692C3ThPnvv/8QGxuLoUOHSj2WNK5cuQJ/f3/4+vqidevWMh07NjYWtra2SE1NFXmfmJgYkdc9y87OhqOjIw4fPowmTZpIOk2mck1e5D1+Rdi6dSv8/f3BZrMREBAgUcB28eJFfPnyBQAwbdq0YsFEQkICJk+eLPJ6lRs2bMCGDRtE2vbt27eYPHkyQkJCJKogLvr+5uLiAhsbG7HHKI2VlRU+f/6MrKws5mdcLheZmZkyOwZQGFhduXIFCxcuhKqqKiwtLUtss27dumIVdG/evCkRrunq6iIoKAh+fn7YuXMngoODYWlpCScnJ6nmJ86HVpSVlaGgoAAejwdLS0sYGRmVu4/gPbtNmzaYPXt2ietfvXpV5u8CaUyaNAl79+7F169f8fDhQzx58kRo69zo6GgkJSUBKHxvK4uioiKmT58OFxcXcDgcbNq0CVu2bJHL/AkhhBBCCCFEGhSuEUJk5kdeJtZFb5VZsCaQxcnG2mh/+A5yoxaRcmBsbAwHBwf4+/vD2toaQUFBIgcAx48fZy63bNkSpqamEgdrQGEl3K5du+Dg4CDxGNK6f/8+0tLS4OXlhejoaHh5eQltIVbVbN26FVeuXAEALF68WKRgS1tbG9u2bcPYsWNx+vRpfP78GQEBAVK3DzQyMoKTkxNatWqFevXqSTWWNKKjoxEXF4dx48Zhzpw5cHBwkNkaXU2bNkVwcDCOHTuG1q1bo0GDBqhWrRpTRVKUtbU1EhMTMWjQIHh4eJQ57ubNm5nqDzMzszLbrIlCUFnG4/FEDndEIQjV5NHyTxzSht/btm2Dv78/gMJgfdasWRIFbDt37gRQWJ34cxu7pk2bYv/+/bhz5w5atGiB2rVrQ0tLq1iFUVBQEHx9faGgoIC7d+9CU7NiftcVvf/U1NRk2jq0WrVqMDMzw4QJE5ifZWZmoqCgADk5OWW2Sc3IyICysrJIrVTv3bsHY2PjUtcbO3fuHM6dOwcAGDp0KDw8PEqt6FVQUMDChQtRp04drF69GocPH8aJEyfg7OyMqVOnljuX8ojy/qOoqCjWWoZltR8NDw/HihUrsGLFCowbN07kMUXFZrPRu3dvHD16FABw+/ZtoeHax48fmcuHDh3CpEmToKysXOq4vXr1YkLGK1euoKCgoNxKOkIIIYQQQgipaPS/FEKIzOx5EC51K8jSpOakI/jBYcztJv3JLVKSvb09Dh06hKSkJNjZ2eGff/5B7dq1y9zn69eviImJAQA0atQIgYGBUp8QtrOzw9ChQ9GtWzeR1h2Sh0uXLjGXb926hREjRmDfvn1y++S/LJw5cwYBAQEAAAcHB6HtQO/fv48OHTqUCCMaNWqEHTt2YOrUqXjw4AFGjRoFb29vidcXAgoDBmtrayxYsABhYWGVclK0oKAA165dA1AYAPn5+eHu3bvYvn17mSd1S8PhcEpU9ZmYmBRb00kYHo+H5ORkAEDDhg3LDS+KVvUsWLBA6jAwNzcXAPD582d069ZNqrGEKa/dYWl8fHywa9cumc3jzp07In8ooCySBGxXrlxh1qnKy8vD8OHD4ezsDCsrKyb4aNy4MRo3blzqGE+fPgVQ+HqsqGANkO+aa0eOHCnxmhFUen779q3MqqzExETMmTMHtra2GDt2bJmv2aioqFIfq+TkZKxatQosFgsLFiwQ+YMb48ePh6amJv7++2/k5ubCy8sLLBYLdnZ2Iu1fVNGgTNRwTZzQurT31xMnTsDDwwN8Ph8rV64Em82GtbW1yOOKqmhl7devX4VuU7Rt6YcPH5CZmQldXd1Sx9TU1ISuri6+f/8ODoeDlJQU1KhRQ3aTJoQQQgghhBAZoHCNECITDxKf4sbHe3I9RszHu+hZvzM61JFtezdSWLEwbtw4bNmyBd+/f4enp2e5bZhCQ0NRUFCAatWqYfv27TI5IVynTh0MGDAACxcuRGRkZKmVCPLC4/GYCgcAaN++PZYuXVqlg7Xr169j0aJF4PP5GD9+PBYuXCh0u3Xr1oHL5cLFxQV9+/Ytdl2HDh3g7++PmTNnIiEhAba2tpgxYwacnJwkDsbGjRuHwMBABAQEwNnZWaIxpHHz5k1mDSugsP3p1KlTJQrWfH198fz5c+zYsUPstpnJycnMiXJDQ8Nyt//27RuAwhPs4rRoLY0g/Kpbty4uX74s9XgC48aNw4MHD0pU6iUnJ4s070WLFqFVq1bQ0NCAsbGxxO8fvXv3Rn5+Ptq3b49t27ZJNIYw4jxPBMG2QHJyMs6ePYsBAwZAR0dHpDGePXsGAGjbtq3Ix63qhL1WBM/vuLi4MsO15s2bY/HixZg1axYOHjyIVatWoXPnziW243K5OHfuHA4ePFjiuoKCAri4uCAtLQ3r16+HhYVFsevz8vLg7u4OR0dHoWt6jRgxAikpKcw6dP7+/pgwYYLY7wFF1z1UUFCAq6srIiMjy93Pzc0Nbm5uIh/nwYMHpQbMfD4fbm5uYLPZGD58uMhjiqLoewCHwxG6zeDBg3H27FncvHkTgwYNKjNYEygaSgqrCCaEEEIIIYSQykbhGiFEJo6/Ol9Bx7lA4ZqcjBo1Cv7+/uDz+bh06VKZbbvy8vIQHh4ORUVF+Pn5oX79+jKbx9SpU2FhYQEvLy+sWLFCZuOK4t69e8wn75WVleHt7V2pbQ3Lc+/ePcyZMwf5+flwcHAoNVgDCm/Ps2fP4OjoiHbt2mHZsmXF2nf16tUL27Ztw5w5c5Cbm4uAgACcP38e8+bNg6mpqdhz09bWxqhRo7Bjxw706dMH7dq1k+QmSuzMmTPM5a5du2LBggWlbpuamlrsBHhRR44cQVBQEADA2dkZ/v7+YgWOnz59Yi6L8lz6/v07AKBGjRpltnsTVaNGjdCxY0eJ1hEry6BBg+Di4oIuXbowP4uKisKyZcswcuRILF++vNz5/9w+UVzp6elMcMnlcmXa0lBUFy9exNOnT6GsrAw+n4+CggK0bNkSu3btErkqLCUlBQkJCQAqN1yriBafgtfDgwcP0KtXrzK3NTU1hYODA3bu3AlbW1vMnj0bs2bNKrbN7du3oa6ujgYNGpTY38fHB0+ePMHmzZthZmZW4noVFRWcPHkSJ06cwKBBgzBjxowSH6SYMmUKbt++jStXriArKwuZmZliP88E1aMAoKqqCk9PT/Tp0we1atVCvXr1SlSz9e/fHzk5OViyZAlGjBhR7vibNm1CeHg42rZti8DAwDK3lcV7ys9+/PjBXC4tNGOz2WKF31lZWUhPL+yEUK9ePQrXCCGEEEIIIVWSbBYeIYT80T6mJeBl8n8VcqyXyW/wMS2hQo71p6lZsyYTtrDZ7DI/nX/06FGkpqZi4cKF6NmzZ5njZmRk4MSJEyLPw8TEBH/99RcOHjyI6OhokfeTBcG6MQBgY2NTpYO1O3fuwNHREbm5uVi0aFGZwRpQvBKnbt26MDY2LrFN7969sXv3bqaK6M2bN3BycsK4ceNw7ty5UqsSSjN58mQAhVVK2dmyXYuxLJmZmTh79iyAwrZ3ixYtKnP7iIgIdOvWTeiXn58fs93Vq1fFrv4SVCQpKCigRYsW5W4vaCFZq1YtsY5TGg8PD7i4uKBDhw4yGU9gypQp6Nq1K1gsFrhcLnx8fDBv3jxkZ2cjLCwM06dPL9biUh4EVVAA8Pz5c5muKSeK/Px8+Pj4AADmzJnDfBhBQ0NDrHaLgucIAJGeI8LmIamiYcurV68kHkdUDx8+BFAYWmdkZJS7vbOzM0xMTMDj8bBlyxbs37+/2PWnT58WGhyHhYXh8OHD2LFjh9BgTUBFRQU8Hg9nzpxBWFiY0G3c3d2hqqoKQ0NDiQLcnJwc5jKbzYaCggKGDh2KDh06QF9fH3p6esW+BEG/hoZGieuEfQnWOVVUVCx329LWmpNGXFwcc1mS568wN27cYCrXzM3NZTImIYQQQgghhMgaVa4R8hv4mvVd6M91VLXBVizZ2iqLk42s/JwSP2crKEFHTfiJl2/ZxU9aqvBUoaCgAB1VbcR8vCvBrCV34+M91NOpK9HtkMV9VUOjugSzrlyXLl1C//79yz3h279/fzx+/Bj29valfsI9Pz8fu3btwogRIzB1atlr4KWkpMDe3h4vXrzA169fMW3aNJHmO2nSJNy+fRtubm44ffo0tLS0RNpPGj9+/GBaQmpoaMDJyUnux5TU2bNnsWjRIqiqqiIwMLBEm0dhBOFanTp1igVGP+vUqRMOHz4MJycn5qTpgwcP8ODBA2hra2PAgAHo2LEjmjVrhiZNmjAndoUxNDREv379cPHiRfj6+sLd3V2s2ymp06dPM2He0KFD0bp12dWu9vb2MDAwAI/Hg4mJCfT19aGlpQU2mw1/f39s3boVWlpaCAgIQNeuXcWay5MnTwAAzZo1K7f1IYfDYVpZlrfmIVD4+rK1tcWbN2/EmlNFiImJwdixYxEYGChSO0xJfP78mbnM5XJx6dIljB49Wi7HEiY0NBRxcXFo3749pk2bxlQ4iksQrikrK6Np06Zi7RsVFYXNmzdjz549qFu3rtjH1tTUhI6ODtLS0hAVFYUOHTpg2LBhIrezFBWXy0VMTAzzQYvk5GRMnjwZR48eLbOaSklJCR4eHrCxsQFQ+NqeNGkSgMLXy4ULF+Dl5VVsnwsXLmD37t0ICwsrt6Uvm81GdnY2+vXrh5UrVwrdpk6dOli3bp3ELXKLfrCgtGrwooq2QxRHadW38iR4XAFAXV1dJus65ufnY/v27QAKP/RT3t8ZhBBCCCGEEFJZKFwj5Dcw+5TwNTlW9JuHljVKnqg7HXsZR5+fLvHzFgZN4NF/vtCxFl1eV+ox3qbEiT5ZGfjv/48nye2QxX112Ga7mDOufH///Tfq1q2LWbNmlVlpZm5uzpz0zMrKErrNiRMnoKmpiSVLlpS6DVDYsm369OnMiX8fHx/k5+dj5syZ5c63f//+MDQ0RHx8PNatW8eseSNPERERTPuuGTNmoHr1qhmi7t+/H+vWrUOTJk2wZcsWoRVowgjCNVEqaho1aoQjR45g0aJFuHr1KvPzjIwM/PPPP/jnn3+gp6eH+fPnlxtmTJo0CRcvXsTBgwcxcOBA/PXXXyLNVxqCChQVFZUy20EWVV77tVq1aokdrAHAo0ePABSGluURtIQERAvX9PT0sG/fPly7dg0NGzaEkZERWCwW4uPjmcdl+fLlGDp0aKljvH79GlOmTAEAHDt2TGYVcwLyXDfx/fv3zOX27dvj3LlzFRaupaSkYNu2bVBXV4ePj4/QgIjD4YhUVSYIYBs0aID8/HyRK9EuXLiApUuXgsvlYtKkSdi3b1+Z65iVxsLCAnv37gWPx8OqVauwatUqmbcP5PF4xcIfRUVFTJgwQaTjtGvXDt27d8fNmzeLVV5dv34dOTk5xd5TPn36hEOHDuHw4cMiVZkJArPywsRhw4aVO1Zpilauqaurl7s9l8uV6DgV0dbzZ//88w/T0tTW1hba2tpSjZeTk4OlS5fixYsXMDAwwM6dOyvkwzWEEEIIIYQQIgkK1wghUuHz+XiX+qn8DWXoXerHSvmE9q9MTU0Nr1+/xty5c0Xa/sCBA+VuI8kn1Ddt2oSCggLMmTOnzO0UFBRgY2ODDRs2ICIiAubm5ujRo4fYxxMVj8dDSEgIgMJqK0HYUJVkZ2fD3d0dp0+fhp2dHVxcXMps3fkzcasutLW1sWPHDhw6dAheXl5M9YWRkRHc3d1FXsPrr7/+QqNGjfD27Vu4u7vjxIkTcg1cbt++jZcvXwIA7OzsJKrmkZX//vuPOfEsyvM3KSmJuVynTh2RjlG9enVYWVkV+1nRNZDU1dXLDBmKVtNpa2tLvG5ZTk4Ovn//LrcqNWGKtqObOXMmZs6ciYSEhAp5zL28vJCeng4/P79SA624uDhMnjxZ5HaVsbGxErfvTEhIwKRJkxASEiJ2O9sFCxZAS0sLJ0+eRGJiIjgcjsQBT1lUVVVRvXp1dOzYERMmTBBrHUYbGxvcvn272HP99OnT6NixY7HAytDQEIGBgcXa4JZF3PfFW7duoVmzZmK9Top+CKW88InP5/8y4drly5exevVqAIXv87Nnz5ZonPz8fLx9+xY3b95EaGgokpKSYGNjAxcXl0pZR5EQQgghhBBCREXhGiFEKhwuB1mciltLCShs1ZhbkFehx/zVCUKYOnXqMGtRVXXW1tbYsmUL8vPzsXz5ckRFRcktlDl//jzi4+MBAIsXLxYrtKoIb968wdy5c8FisbB///5iVVD5+fkinUgW9WTzz8aOHYs+ffpg48aNiI6ORlhYGAwMDMQaY8yYMfD09MSHDx+wdevWcteHk0ZwcDAAoEaNGpg+fbrcjiOK8+fPAwB0dXXLXZsQAL5+/cpcFqVyrSpZsWIFbt68iaCgIJiYmFTIMV+8eAGgMEDs3bs36tevjwMHDsDV1VWux7179y4iIyMxYcKEMiuamjZtipCQEFy/fh0mJiYwNDSEpqZmsTAoNTUVffr0AVD43jN+/Hip5iZJxRmbzcbs2bMlDkcEbty4gTp16qBBgwZSjSPM4MGD0axZM2bsrKwsXL58ucScWSyWWO914qyN9+DBA8ycORMtW7ZEcHCwyL8nilaulReuFQ3I3Nzc4OYmvNpeGEHltTzl5OTg0aNHCA8Px5kzZwAAo0ePhru7u0S/YwYOHIgPHz4w32tpacHHxwdDhgyR2ZwJIYQQQgghRF4oXKvisrKyMHz4cISEhFTop8EJERVXwrVBpJXPK6iU4/7qWCxWmWtkVSXVq1dH//79ce7cOSQkJGDHjh1wdnaW+XH4fD4CAwMBAL1798aAAQNE2m/48OHF1nyS1r1790r8TLC+3YEDB+Dg4ICJEyeWqLQIDg7Gy5cv4ejoWOb6QpKuFwQUBj3e3t7IzMwsd90wYSwsLODr64v8/Hzs3bsXVlZWaNiwocTzKc2LFy+YNpZLliyBhoaGzI8hjgsXLgAobCknyonn5ORk5rKolWuyxOPxsH37dpErrQRSUlJw6tQpAMDEiROxdetWmay9VJaCggImXGvTpg1YLBYGDRqE0NBQzJkzR6T2e5LIzs7GsmXL0LZtWyxevLjc7Zs0aYImTZqUer1gvTUA6Nq16y/z/izMo0ePMG3aNAwYMADTp09Hq1atZDp+0dDuwoULyMnJKRFaZ2ZmgsPhiDymoAqew+GU+bxPSEjAjBkzkJOTg3v37sHd3R3r168X6RhFw7Xy2g3n5f3vg0NLliwpt10tUFgRHh4eDl1dXZHmI6qVK1di3br/tQTncrlMBXONGjVgYWGB8ePHo23bthIfY/fu3UhNTcXXr1/x7NkzXLhwAS4uLggMDMTSpUslasVLCCGEEEIIIRWFwrUqLC8vD0uWLGFaSlUl/fv3l3heISEh9J/l34iigkKlHFdZgd6+JPGrtdMcOXIkzp07B6DwJJyNjY3M14S6dOkSXr58CRUVFSxfvlzk/YKCglBQIL+Q9969e1i/fj06d+6MkydPltoei81mIyoqCmfOnEHfvn0xc+ZMqU52AkBiYqLQcEeSYA0oXM+ob9++uHDhAvLz8+Hl5YUdO3ZINUdhAgICAADdu3cvc52xivDkyRMm/Pm5bWNpvnz5wlyWVbi2ZMkSLFmyRKRtFRQU0K9fPyxevBg1a9YEl8tFSkoKunfvXuZ+vCIfsmCz2fj06ZPcw7VXr14xlTqCVoqjR4/Gjh07EBYWBnt7e7kc18fHB1wuF9u3b5dJheu///4LoLBip6xw/Fegrq4OHo+Hc+fO4cKFC9i5cyd69eoll2OdPHkSBgYGaNasWbGff/r0CVOmTEFaWppY450+fRqnT5dcw7U0x44dQ8OGDUWqji0amuvr65e5bXZ2Nho2bIhJkybB0tISampq5Y7fsWNHmJqaityqV1Rz587FoEGDmO/v3r3LBMq2trZwcHCQ+hhGRkZMW1UzMzM4Ozvj1KlTWL58OWxtbTFjxgy4uLiIVWFICCGEEEIIIRWFzk5XMVwuF9++fUN0dDRCQkIQGxtb2VOSuYpqF0UqBluRDQ22eoW2htRgq0NV6df9dH9lEIRqCpUUhkqqd+/e0NbWRkZGBvLy8hAQEMCs8SILPB4PW7ZsAQDMmjVLrLWKZB3y/ezbt2/YsWNHuZUOgkoXPp+PmJgY9O3bV6pwLTIyEm5ubjJvzWVubs5Ucl29ehUPHjyQeH0pYZ48eYJLly5BVVUVK1eulNm4ktq7dy8AoF+/fmjZsqVI+wjCNS0tLVSrVk0m81i+fHmZQePr16+LrTHYvHlzREZGAgA+fPgAJyenctt4Cu5vRUVFhIeHi73mlyRu3rzJXBZ8YKdu3bro06cPdu7cidGjR5fbgk9cV65cQVRUFA4ePFju61JUMTExAAoDwl/t/flnRcNGHx8focEal8vF8uXLYWlpic6dO0t0nK9fv+LWrVswNzcvEbqYmJhg7969uHPnDlq2bAkjIyNoamoWq2Ldvn07Nm3aVGw/S0tLvH79Gt7e3mVWGkpCsJaijo5OuRWV+vr6iIqKEitMGjlyJHM5PDwcw4YNk/iDEEXp6uoW65pRu3Zt+Pn54evXrzh06BDs7e1l/pxlsVgYPnw4NDU1MWPGDAQGBoLFYsHFxUWmxyGEEEIIIYQQWaBwrYpwdHTE/fv3kZ2dLZdF5OVBR0dH7BY0BgYGMj/ZRYCt5muE/lxHVfh9Paxpf/RtULKqgF1GNZhP/6XFvldRVYWCggJ0VLXRUNcIT5NeizFj6TTUrQcWiyXR7ZDVffWrEbRyklerNHlRVlZG//79mZP9kZGRWLBgAXR0dGQy/vHjx/H69Ws0b95cbpUukho8eLBI2xWtbNi0aRPMzMwkPmZkZCSWLFkCHo+HhQsXQkFBoVjlgjT69OkDVVVVptpo7969Mg3XfH19wefzMXfu3AoJd8qSkJDAVFzOmTNH5P0EbUZl2RJSXV291KpHoOxqxPr16yM7OxuvXr0qtaoqMzOTeX0OGTKkwu7769evAygMIjt27Mj8fMKECbhy5Qp27NiBRYsWyex48fHxWLt2LXbt2oVGjRrJZMyPHz/i3bt3AFBsHcXyxMTE4NGjR1KvkSZrRVvPtmvXrsT1XC4Xf//9N06dOoUzZ84gKChIrNstcOLECXC5XPTo0UPo9SYmJqV+kOv79+8ICgoCADRs2BA/fvxgKsusra2xfv167N69W+w5lUUQmovy2pBkzTyBwMBAbNy4EcePH8euXbtk/vteUVER1tbW2L59O+Lj4xEdHY2+ffvK9BgC/fr1g6mpKS5duoTAwECYmpqidevWcjkWIYQQQgghhEiKwrUqYs2aNcw6CwUFBbhx4wZWr15dpVu4TZw4UayThkR+amiI9wl6DbY6NNjinXTRVy9+clZNTY35xHIjPeMKDdca6xkDkOx2VMR9VRUJAo1fLVwDCkMZwcl7DoeD27dvixw8lSU3NxdbtmyBsrIy1q9fL9WaZJWp6MnYunXrlrt9QkJCiVZqwhQUFGD+/PnYtGmTyOvQlUVNTQ2dO3dmQhHBv7Jw7do1/Pvvv2jXrl2xKqzK4uPjg4KCApibm4tctQYUtuMEUKXWWO3evTvCw8OxYsUKodeHhoYiOzsbLBYLjo6OFTKn9PR0PHz4EEBhdWvR9ex69eqFNm3aYP/+/Rg/frxIrwlRpKSkYOPGjTI7wZ+TkwM/Pz/me1FDpnv37mH27NnIycnBt2/f4O7uLlL1UFZWlsRzFVV+fj5zOTc3t9gx+Xw+VqxYwazNl52dDQcHB4kCtn/++QcsFqvUcK0smzdvZubl7u6OpUv/98GhESNGwMfHBxcvXpTqQwo/E4RrjRs3LnHdtWvXMHv2bLHWiSvP/fv3MXPmTOzYsQOqqqoyGxf4X+tVHo+H0NBQuYVrAGBjY4NLly6Bz+dj27Zt2L59u9yORQghhBBCCCGS+DXPJP6GDAwMin1vbGyM48eP4/Hjx5U0I0JE17NeZ0S+PFdhx+tRT/xPuv/JeDweE679ipWjP6/RGB8fL5Nxd+7cicTERCxYsOCXblcrbqVD7dq1ERERIfL2sgwdu3btyoRq2dnZSElJKbOqShQcDgfr1q2Duro6fHx8pKr8kIV79+7hzJkzMDAwgJubm8j75efnMyfhq1K4NnDgQDg7O2Pu3LklqtVzcnKY9peDBw9G06ZNK2ROJ06cYIKcoi3xBJydnWFvbw93d3eZVSG1adNGJuMIBAYG4syZM8z348aNE3uMsLAwpKenw8vLq9z133bt2oVt27aJfQxJDRs2rNxtJAnY7t69i3fv3qFp06blrl/2s4cPH+Lw4cMACtvU/rwuoLa2NgYPHoyVK1eia9eu0NLSEmv80ghe1w0bNixxXZ8+fbBv3z5wOBw0adIELBYLy5cvx4ABA8RaQ23//v3M46upqYmOHTvKZU3QunXronv37oiJicH169fx8eNHuVWrduzYESwWC3w+H9euXUN6errMHhNCCCGEEEIIkQUK16qwX7HChPyZ6unUhYlBY7xM/k/uxzIxaIJ6OrKpRPhTpKWlMZdr1qxZeRORUPXq1VGzZk1m3RpZrPHy6dMn7Nq1C127dsW0adOkHq8yiRsmKSgoSB1oSernKi5x1hUqTXBwMOLi4uDp6Vnp7SDz8vKY9cdWr14tVuvk+Ph4pi20LG/HkiVLsGTJEon37969O1RUVBASEgJnZ+di1x04cAApKSlQUlKq0DWRjh49CqDw/axnz54lru/Zsyc6d+6MmJgYREREwMrKqsLmJipnZ2ekpKTg8OHD6NWrF7y9vcvdJyMjAzY2Nsx7ev369dGyZUt8+/at3Faizs7OqFOnDlgsFkxMTKCnpwctLa1iVX9v3rxB7dq1JV6vKyIiAh4eHgCAM2fOiFw1KM57uiAs/TkYK09+fj7c3d3B5/NRs2ZNLF++XOh2kyZNQmRkJNauXYv169eLdQxhcnJykJ6eDqDk+5/Az+1xly5digkTJqBz584i3YevX7/Grl27ABT+3yEsLEyuQbeNjQ1iYmLA5/MRFhYGV1dXuRxHU1MT2traSE9PB5fLxbNnz8R+3AkhhBBCCCFEnihcI4TIxMjmAyskXBvZfKDcj/G7+dXDNaDwE/+CcE3YWj7iWr16NbS1tbFhwwaZhHWVqbIrtcRRtHLD2NhY7HU7fxYfH4/AwEBYWVlViQBl7dq1iI2NxYwZM9CvXz+x9hWsvQUUhiaysnz5cgwdOrTU61+/fl1mK01lZWUMGzYMwcHBGD16NBPipKWlYefOnQCAMWPGwNjYWGZzLktMTAxevXoFAJg8eXKpz/8lS5Zg9OjRWL9+PXr27IkaNWpUyPxEpaCggNWrV6NJkyawtraGhoZGufusXr2aeT+fPn065syZU27FWlGjR48u8/pr164hIiICGzdulKhSr2iVK5vNhoqKithjlOXYsWO4cuUKAKBLly5i7evn54fY2FiwWCx4eXkx63b+3H69VatWGDRoEI4dO4a//voLFhYWUs1ZULWmoKAg8n1at25dTJgwAbNnz8aBAwfKfG5kZmbC2dkZHA4HLBYLPj4+cq8g7d+/PwwMDJCcnIyIiAg4OzuL1H7y/fv3iI+PR9euXUV+3qqqqjLhZEpKilTzJoQQQgghhBBZ+7XPKBJCqowOdVrLvV1jz3qd0aFOK7ke43dUNFwzMjKqvIlIQdAybOTIkSU+5S+ukydP4ubNm9i8eXOJlrxEvmrVqgVDQ0MoKSmVWjkijhUrVqBRo0alrgdWkU6fPo3w8HBYWlpi3rx5Yu//8uVL5rIoa+KJSl1dHXp6eqV+iVKlNHnyZOTl5RWr5PH390dGRgZ0dXVLVLTJC5/Ph6+vLwBAR0cHNjY2pW7bsmVLTJo0Cenp6Vi0aBFTFVjV2NraihSsnTp1ClFRUVBUVISnpycWLFggVrAmimnTpoHP52P8+PE4cuSITMcW16NHj2BtbY3p06fj77//houLC5YtW8ZcL87vgRs3biA4OBgAMHv27HKrn+bNmwclJSV4eHgwQa6k3rx5A6BwvTVxKgKnTp0KFRUVuLi4lNrekcfjYeHChXj//j0AYO7cuTJdK640SkpKsLS0BFD498Xp06fL3efy5csYPnw4pk2bJlYlbWZmJnP5V2xrTQghhBBCCPm9UbhGCJGZqR1soKtWTS5j66pVg12HMXIZ+3dXdI2yxo0bV+JMJDd+/HgsWLAA69atk2ocDocDT09PrFmzBh07dpTR7Ig4PDw8sH37dqHt/MRx8eJFvH37FgEBASJVTcjTrVu3sHjxYgwYMABr1qyReAygcA3WqlJllZmZiYyMDNSvXx+mpqY4d+4cwsPD8eTJExw8eBAAsGjRIqYKSN7++ecfJoScO3duuWHF3LlzUbt2bdy+fRsbNmyoiCnKRVJSElatWgUFBQV4enrKrUpTRUUFTk5OyM/Ph5ubG/z9/eVyHFG0a9cOu3btgpmZGV6+fIkzZ84wAamOjo7IrW3j4+OxYMEC8Pl8DBw4ELNmzSp2/c+VawDQoEEDWFtbIycnB9OmTZNqnc/nz58DgNjvd4qKivDx8cH9+/exePFioeHwunXrmEo+KysrODk5STxPcY0ZM4Zp6xsaGlru9t7e3sw6iadPn2aq0cqSmZmJ7Oxs5vtf9e8XQgghhBBCyO+LwjVCiMxoqWhiWe850GDLdr1ADbY6lvWeAy0VydaB+dO9ffsWQGFbqkaNGlXybCSjp6eH6dOnF2s7Jom8vDw4ODhI3eqLSK5Xr17o3bu31OPk5uZiz549ld7q9OnTp5g1axZsbGywZcuWUp+jRSswfhYfH48HDx4AKFzjrLI8f/4cBw4cgKurK4YNG4bJkycz4YCgmmfNmjVYtGgReDweevbsWWHtOD9+/MiE661atcLYsWPL3UdDQwOenp5QUFDA7t27sXfvXjnPUvb4fD4WL16MjIwMrFy5EiNHjpTr8SwsLJjWn1u3bkVISIhcj1cWXV1djBkzBpGRkcVam4q6JmFWVhacnJyQmpqK9u3bw8vLq8Q6j8LCNaAwmNXR0UFycjKmTJmCT58+SXQbBGGwJO95RkZG8PLywqlTp7Bw4cJiFWzbt2/H/v37ARQGd6tWrZJofpIyMjJiKgCfP3+Ox48fl7m9oD0mUNhqVpSWoc+ePWMenxYtWoi8hh8hhBBCCCGEVBQK14hM3L59G+7u7hg+fDg6d+6M1q1bo3///li4cCHu3LlT2dMjFaieTl2s7DdfZhVsumrVsLLffNTToZMqkvrvv8K18ExMTKCmplbJs6lcWlpasLOzq+xpEBkwNzcvtoabPDx79gznz58HACQkJJRoEXfr1i04Ojpi/vz5cHNzK3X9vvz8fNja2sLc3BxLlixBaGgo7ty5g6SkJCQlJWHFihXg8XgAgMGDB8v1NgFAdnY2njx5gqNHj+LAgQPMzxctWoRr166hXbt22LNnD/755x/mBHqjRo0wYcIEcDgcxMXFQUdHB+vWrSsRVsgDh8PBggULkJWVBXV1dWzYsEHktQa7devGVPR4enoy68RVtg8fPpQZuAocOHAAN2/exJIlSzBmjPyrt5WUlIodx9vbG1+/fpX7ccuiqKhYrNWqoaFhuftwOBzMnj0br1+/RsuWLREUFAR19ZIf/BG87n6mr6+P1atXAwA+ffqE8ePHM0GzqLhcLh4/fgw9PT2mtbG4BgwYAEdHR0RFRcHOzg6pqanYuXMnNm3aBADo2LEjtm7dCmVlZYnGl0bRNfzKq15r3rw5c9nc3FykauMzZ84wlyuyKo8QQgghhBBCRCVdCQD543G5XLi7u+Po0aMAADU1Nejp6YHH4yEhIQEJCQk4efIkxowZAw8PD5FPhlVVBQUFUp9ILCgoKPFJaR6PV+oJnqqstDkbateG94Cl2PvoCG58vCfx+D3qdcKUdqOhpaL5S94/VYXghGCfPn1+u/ux6GuprNfRz9v9ToreHlHeS/h8fpW/D0SZnzxvR3p6OjZu3IgjR45g0KBBWL9+Pfbu3QtbW1sEBASgY8eOiIyMRFhYGPbs2YOmTZuWORdFRUUcPXoUDx8+hK+vLyIiIoRu16hRI/Tu3Vvq21V0/0+fPuHkyZN48+YNYmNj8ebNGyQkJDCviaKBw7Fjx9CgQQOh4wBAmzZtmMtqamrIzs6W+3OJx+Nh0aJFePLkCRQVFeHr64t69eqJddyZM2fi3r17THvI+Ph4LF26VOZrlgmI8tx8/vw5vL290bhxYwwcOBCmpqbQ1dUtts27d+/g6+uLGTNmYNKkSRX2uh0yZAgT3uTn5+P169fQ19cvd7+i85P167Po8TU0NMocOz8/H/PmzcPNmzfRsWNHBAQElLqP4HUgbL5mZmawtLTEsWPH8PXrV4wdOxaLFi3ChAkTRPpb8MGDB0hLS8OkSZOgqKgo8f0xd+5cJCUl4dixYxgyZAhSU1MBAO3bt0dgYCBUVFRkdl8X/V1Z3mPYv39/6OnpISUlBWfOnIGrq2uJ57CAvb09Zs+eDaBwPcTy5vv+/XtERkYCKHw+mpqalrmPuLefx+OV+Fs8Pz+/1EpGQgghhBBCBIStiVzaOsm/uz/1dhdF4RqRyvLly3Hq1Ck4OjrCwsKiWBXBvXv3sGrVKrx+/RqHDx9GTk4OfH19K3G20hO095EVwSd3c3Nzf7ngMScnp8zrlaCIaW3GonPNNjjz9ipep7wTeexmeg0xpFE/tK1pAvDKPxYp3YsXL/D582coKChg4MCBv919KVjDBSh8HZX26X3BL3w+n//b3QccDoe5nJeXV+rtq+r3geDkKJfLLXN+gpOfBQUFMr8dfD4fJ06cwJYtW5CamgoHBwfMnDkTQOFacWvXroWdnR3mzZuH2rVrY9euXVBSUhJ5Hs2bN0dQUBD27duHLVu2FLtOQ0MDK1euRF5enkRz//btG+Li4hAXF4dnz54xP9+2bVux7XR0dNCvXz906tQJnTp1Ql5eHiZNmgSg8DEo7bY8e/YMy5YtA4vFgpaWFj5//oxx48bB19cX7du3l2jOoli7di3Onj0LJSUlLF++HH/99ZdEj/vatWthb2+PuLg4hIeH4/Hjx1i9erVMW+UKnsP5+fnlzrFPnz7o0qULDh8+DF9fX6xcuRKdOnVigjY1NTUsWrQIw4YNw/Tp0yv0NWtgYIBGjRoxLYV1dHREOv7P78eynHNcXBxzWVlZudSxc3JysGjRIty8eRMDBgzA6tWrwWazS91esJZZae878+fPx927dxEfHw8Oh4O1a9fi2LFjcHFxKbca7dKlSwAKK7WkvS/mzJmD6OhofP/+HUBh9Z6/vz8UFRVlej/n5uYyl3Nycsod29zcHCEhIeBwOAgNDYW9vb3Q7bp37w4HBwcEBQXh2LFjGDx4cKmV9ImJiZg9ezZyc3PRpUsXLF++vMx5SHL7uVwu87tEcJtfvHgh9jiEEEIIIYQA9Lfkn4zCNSKx06dPIykpCSEhIWjXrl2J6zt16oSwsDCMHj0ab9++xcmTJ9GrVy+5rxdCqpa2NVugbc0WiM/4jH8TH+Fd2kd8SI9HVv7/ToZoKKuhfjVDNNSph6512sFQu3Ylzvj3cvnyZQCFJ3JFXaeGFHr69CnOnj2LhQsXVkjrOyI/27dvR9u2bctczyw2Nhbr16/Ho0ePAABTpkxhgjWBxYsXIyUlBd7e3ujRoweaNWuGWrVqiTUXFouFKVOmoKCggAm+mjRpgtWrV6Np06bi3TAArq6uuHnzJrKysoRer6qqivbt26Nr167o0qULmjVrVuz5LMp/At6+fYu5c+eioKAAq1evRvPmzTF9+nSkpKTA0dERc+fOFbmaR1T5+flYs2YNTp48CXV1dXh5eaFHjx4Sj6erq4tt27Zh6tSp+PLlC169eoVx48Zh4sSJsLOzg5aWltRzFgQ1on56T01NDZMnT4aVlRX27t2L0NBQ3L59G+vXr0fDhg3BYrHg6uoq9bwkYW5ujs2bN2Pw4MFyb78qCsHrEkCpLQW/ffuGBQsW4NWrV5g/f75Iz0lBwFI0GCxKQ0MDW7ZswdSpU5GWlgag8DUzffp0NG7cGCEhIaXO5+rVq+jduzeaNGlSzq0r2+nTp7Fp0yYmWAMK12mcNm0aZs6ciV69ekk1Po/HQ1ZWFlJTU3H9+nXm56dOnUL79u1RvXp1aGhoCG17a2VlxazLt3fvXjRr1gxt2rSBmppaiQ+7zJw5E/Xq1cOGDRswatQoWFtbo1u3bqhRowby8vLw6dMnREdH49ixY+Dz+bCzs8PMmTOlXmuVEEIIIYQQQuSF/rdCJPb+/XusW7dOaLAmoKGhAQ8PD+ZT8Zs2bYK5ufkvV6VFpGeoXZsJzfh8PnK5eSjgcaGkoAhVRRUKL+SAx+PhwoULUFRUpPVKxPT06VPMmjULmZmZyMnJwfLly//45+i5c+eQlZUFKyuryp6KWPz9/REcHAwVFRX4+fkxa4gVdeHCBSxdupQJR8zNzTF37twS2ykqKmL9+vVYvHgxrly5AgsLC1hZWcHS0lLsE+hTpkzBt2/f8Ndff6FPnz4SP79GjhyJCxcuFPuZkZER+vTpgx49eqB9+/ZStT989eoVnJycUFBQgI0bNzIBV2BgIBwdHZGamgo/Pz9cu3YNCxcuRLNmzSQ+lsCPHz+waNEi3LlzB/r6+ti0aRNatGgh9bi1atXC9u3bMW3aNHz//h0FBQXYu3cvjh49inHjxmHUqFEwMDCQeHxBqCZuawwtLS3MmTMHlpaW2LBhA65du8as7zdmzBiMHTsWFhYWcmtjKcyECRPQtGlT/PXXXyLvk5KSIrf53L9/n7ks7LXy5MkTLFq0CNWrV8fevXthYmIi0riCasOyKkaNjY2xZcsWODo6IicnB7169cLo0aNhZGRU6mPy+PFjvH//HitXrhRpHsK8ffsW69evZ257/fr1merLffv24dWrV3B2dkarVq1gbm4OU1NTVK9eXezjfPnyBebm5iV+/vDhQ+b9/tSpU6hTp06JberVq4dOnTrh3r17yMrKYt43//77b4wdO7bE9sOGDUPfvn0RFRWF6OhoHDp0COnp6VBSUkK1atXQsGFDODo6wtzcXKLbQgghhBBCCCEVicI1IhE7Ozt8+/YN1tbW5W7bpUsXNG/eHK9evUJiYiJiYmLQp0+fCpglqapYLBbUlMpfzJ5I5/z58/j06RPGjx8v09Znssbn85lQQ5J9Bbhcrkgntcvb5sWLF5g9ezYyMzMBAJGRkWCxWHBzc/tjA7arV69i+fLl4HK5UFZWxvDhw4VuV7TVlqT4fL7I4UR5223fvh3BwcEACk+ez58/Hxs3biwRGPTr1w+DBw/G6dOn0axZM7i5uZU6prKyMnx9fbFv3z7s2LEDhw4dwqFDh2BoaIh27dqhadOmqF+/PvT09KCrqwsNDQ2oqKhARaX4hwiUlJSwePFiocfgcrngcDjIzs5GRkYGfvz4gR8/fkBbWxutW7cutm337t0xZMgQnDt3DoMGDcL48ePRsmXLMu8XUT148ADz5s2Dvr4+NmzYAGNjY+a6xo0bIzg4GM7Ozvjw4QPu37+PcePGoVWrVhg6dCg6duwIIyOjUqt6SvP48WO4ubkhISEBAwcOxOLFi6GjoyOT2wMUBhR79+7FnDlzmFaDmZmZCAoKQnBwMLp37w5TU1P07t0b1apVE3ncos9bSfvOGxoaYuPGjbh06RK8vb2RnJyMuLg4rF+/Hrt378b8+fMxaNAgicYWl5KSUokges6cOcjKykKNGjVgYGAAfX19VK9eHTo6OkhJScHBgweZbUtr0Supe/eEr99aUFCAPXv24OjRo7C3t8eoUaPE+gCXIFwr2l5XmFatWsHHxwdPnjzBjBkzyh03IiICf/31F1q1aiXyXARevXqF4OBgXLp0CTweD0ZGRnBwcMCQIUOY29a3b1+sWLEC79+/x7Nnz/Ds2TN4e3ujQ4cO6NWrF0xMTNCsWTORKjLr1KmDBw8eiD1PgZ07d4q1vYaGBkaPHo3Ro0dLfExCCCGEEEIIqQooXCMSEVSiiapv377Mp7D//fffXzZcMzExkfqEUUFBAd6/f1/sZ6qqqlW+7c3Pa1qUtlYGqRp4PB52794NY2NjLFy4sEo/XomJibC1tUVCQoJU4/Tv37/cbTIzM9GlSxexxz527BiUlJSwcuXKCgvYRF2zqGg7Mw6HU2zNnKIEJ5EFY4vq0aNHcHV1ZQKDlStXQkNDA8OGDSux7bFjx+Dm5lbsWOK6du2aSI9RWFgYwsLCxBo7Ly8P8+bNQ0BAQIkWgz4+PqhZsyasrKxEClVmzpwJc3NzbN26FWfOnEF8fDzi4+PL3IfNZkNFRYWpdin6XOLz+cjLy0NeXl6JFnUsFgt//fUXJk2aJPS17ObmBktLS4naw6moqDCXVVVVmfEjIyPh7u6OoUOHYvny5dDQ0Cixb7NmzXDo0CHMnz8ft27dAgDmRL9g3n379i2x7pswHA4HO3fuRGBgIKpVq4bNmzdj4MCBYt8eUTRu3BiHDh3C7Nmzi4U2BQUFiI6OxvPnz/H9+/cSbUHLUjScKSgokOo919zcHL169cLq1atx+vRpAEBycjKWLFmCuLg4uLi4SDy2NNatW4erV6/izJkzuHDhQqlBur6+PoyMjGT2Xpmbm4ukpCTmeyUlJeb+PXPmDKpVq4bz589LdJ8L3te4XG65+5uamsLU1LTcMdPT03Hp0iXs2rVLrDnduXMHQUFBiImJAVDYXn3MmDEYOnRoicCwc+fOOH78OCIjIxEUFIRPnz6Bx+Ph3r17zHOaxWLByMgIjRo1QvXq1VG9enXo6enB2NgYvXv3FnleVY0s/h4tKChgnp+C/Rs0aFDl/xYnhBBCCCGVr6CgoMTyCi1atPgj/5bMz8/Hy5cvK3salerPe9RJpSjazulXftEpKSlJHa6xWKwSJ5wUFBSErmVRVZR2orwqz/lPFxkZiYSEBISHhws9KV6VGBoaYv/+/QgPD0fz5s3RsGFDaGlpQVNTs8pVirFYrAp73v/48QO2trZMdY0oJk+eXO42nz9/LnPtsfLweDwsXrwYbDa7RBWNtbU11NTUkJiYiJYtW6JGjRrQ0tISu3pJ3hQVFYU+juKub1W/fn34+Phg4cKFiIqKwvnz5/H06dNS12/icDjlVsgIO0Z5bRH19fUl/tBK0dcYi8UCl8vFhg0bcPz4cWzcuBEDBgwoc389PT0EBwdj37598PPzY9rrjRw5Evb29mjatGm5r+OrV6/C09MTycnJmD59OqZOnQptbW2Jbo+odHV1ERwcjC1btmD37t3g8XjQ09PDypUrYWZmJvbrvGj1bX5+vtTvE7q6uvDz80Pv3r2xcuVKZGdnAyisEurTpw86duwo1fiSqFWrFsaOHYuxY8fi/fv32LZtG06cOFFiu5kzZ8q0/be6ujoMDAyQnJwMoPjrV1jILw7B45aXlyez9/Z9+/ZhxIgRIj1GgnQzq48AAQAASURBVDWJT58+jY8fP6JGjRpwdHSEtbU16tevX+a+KioqsLGxgbW1NU6dOoXDhw/jwYMHTOjJ5/Px8eNHfPz4kdmnQ4cOsLS0/GX/fpPV36MKCgol3peUlZX/yBMihBBCCCFEerI4X/wrkrZz0e+A/gdBKoShoSFzOTU1tRJnQsjv7/Pnz/D09ISnpyeaN29e2dMRSd26dTF//vzKnkaVUrNmTYSEhCAqKgrNmzdHgwYNoKmpCQ0NjSoXOhY1dOjQyp5ChatZsybs7OxgZ2eHvLw8PHv2DG/fvkVcXBySkpKQmpqKtLQ0ZGdnIycnB7m5ueBwOMjPzy81iBNYv369TNYbKw+LxcLnz58xf/58NGzYEKdPn4aenp7I+06ZMgVmZmbYvHkzdHR0sGzZsnL3e/jwIQICAnD//n1MnDgR9vb2Mm0BWR42m42FCxeiX79+WLlyJTZu3ChxC92ia3aJG6CWxcLCAi1atICTkxM+ffoEPp+P6OjoSgnXimrQoAF8fHwwaNAgzJs3j7nN06ZNw8SJE2V+vH379mHPnj34+PGjzLof8Pl85vUnq1AlNTUVFy5cQHh4uNDrc3Nz8fjxY9y9excXL17E69ev0aJFCwwdOhS9evVC+/btxQ4mlZSUYGFhAQsLC3z+/BmnTp3C+fPn8fz5cyY8VFNTw8aNG9GvXz+pbyMhhBBCCCGEkEIUrpEKUbRyRvDpa0KI7PH5fCxevBgzZsz4I0OO340gtCG/DhUVFXTs2FGs8IPL5TJfPB6vWOWJpqamvKbKaNq0KVasWIGnT5/i77//RqdOnSQax9DQED4+PuVu9+TJE/j6+qKgoABWVlbYvHlzpVbYduzYUWgFljjy8/Ohra2NAQMGYOTIkTKaWaGmTZvi0KFDmDp1Kl6/fo0GDRrIdHxpmJmZYe7cuTh58iT+/vtv9OzZUy7HadSoEdauXSvTMXNzc9GyZUvY2trK7Pfl3r17sWjRohKv25CQEJw5cwZfv36FsbExmjZtiunTp6Nbt27Q1dWVybEBoHbt2nBwcICDgwOysrLw6NEj3Lt3D3Xq1KFgjRBCCCGEEEJkjMI1UiGKtnFRV1evxJkQ8nvLy8vD8OHDMWrUqMqeCiFERIqKijJtoyeORo0aISIiAsrKyhKHauJSUlLCqlWrYGxsXCHHqwh6enq4ceMGs56erOnr62Pfvn04fvx4lfvgxJQpU+Dg4FDZ0xCbmpoaIiIiZDrmlClThIZlvXv3hoWFhdzbnRaloaGBHj16lFhfkhBCCCGEEEKIbPyaDfdJpQoMDET37t1haWnJrH9RnqLVahV5YoGQP42qqioFa4QQkamrq1d4b/gWLVr8VsEaULhek7yCNQFdXV1MmTJF7scR15+4tkBpSqtCMzY2pr9/CSGEEEIIIeQ3Q+EaEcv9+/exceNGfP/+HS9evMA///wj0n5JSUnM5caNG8treoQQQgghhBBCCCGEEEIIIXJF4RoRy8OHD4t9n5eXJ9J+b9++ZS63adNGpnMihBBCCCGEEEIIIYQQQgipKBSuEbEUbWmjrKyMkSNHirTfzZs3AQBsNht9+/aVx9QIIYQQQgghhBBCCCGEEELkjsK131RiYiKmT5+O9u3bY/jw4Uy4Ja3u3btDQaHwaTNs2DCR1kz5/Pkz7t69CwAYOnQodHR0ZDIXQgghhBBCCCGEEEIIIYSQikbhWhWWkZHBXE5LSxNrX1dXV1y7dg3Z2dmIjY2Fk5MTPn/+LPWcDA0NMXbsWACAurq6SPts3rwZBQUF0NLSwoIFC6SeAyGEEEIIIYQQQgghhBBCSGWhcK2KSkpKwn///cd8f/nyZbH2f/bsWbHvc3Jy8PTpU5nMbfHixejXrx+OHz+Ox48fl7nt3r17cezYMbDZbPj4+KBGjRoymQMhhBBCCCGEEEIIIYQQQkhlUKrsCZBCWVlZ4HK5SE9Px6tXr7Blyxbk5eUx1wcGBiIvLw/Dhg1DrVq1oKSkBFVVVbDZbKHjtWrVCnfu3GG+V1ZWRvPmzWUyVxUVFQQEBGD37t2YMmUKunbtClNTU7Ro0QL6+vooKCjAmzdvcOjQIVy5cgX16tXDqlWr0K1bN5kcnxBCCCGEEEIIIYQQQgghpLKw+Hw+v7InQYBJkyYVC8NE4enpCSsrK6HXJSYmwsPDA3fv3kXNmjWxYMECDBgwQBZTLSYlJQXHjh1DTEwMYmNjkZ6eDlVVVejp6aFly5bo06cPhg4dWmoIWFVxOByhlX6tW7eW+rYIwseimjRpAiWlqpt183g85OTkFPuZmpoas/4eIYQQQggh8iSrv0d/xb/FCSGEEEJI1ZCfn48nT54U+1mbNm2grKxcSTOqPPI8f/6roP9BVBH79++X6Xh16tTBzp07ZTqmMHp6erC3t4e9vb3cj0UIIYQQQgghhBBCCCGEEFLZqOyEEEIIIYQQQgghhBBCCCGEEBFRuEYIIYQQQgghhBBCCCGEEEKIiChcI4QQQgghhBBCCCGEEEIIIUREFK4RQgghhBBCCCGEEEIIIYQQIiIK1wghhBBCCCGEEEIIIYQQQggREYVrhBBCCCGEEEIIIYQQQgghhIiIwjVCCCGEEEIIIYQQQgghhBBCREThGiGEEEIIIYQQQgghhBBCCCEionCNEEIIIYQQQgghhBBCCCGEEBFRuEYIIYQQQgghhBBCCCGEEEKIiChcI4QQQgghhBBCCCGEEEIIIUREFK4RQgghhBBCCCGEEEIIIYQQIiIK1wghhJRw7tw5PH78uLKnQWTkxo0biIyMrOxp/LG2bduGBw8eVPY0fmtcLhc8Hk/s/VxdXfHy5Us5zAjg8/kS7ffu3Tu4ubkhJydHxjMS7diS3I8AkJmZia9fv8p4RoQQQgghhBBCSNVE4RohhJASWCwWbGxs4OLigs+fP1f2dEp18eJFfPr0qdztjh8//kef9N2+fTtcXV1hb28v0v1VGQ4ePAgOh1PmNmlpaYiMjERBQYHc55OYmFjufEQVHR2NcePGwcLCAkeOHKmU0ERg165duHDhgsTBT3ny8vLkMm5ZMjMzMWPGDKxZs0bsfc+fPw8LCwvY2dnh5s2bMptTVlYWzM3NceXKFYnmdOTIEVhZWckt+CvNrl27MHjwYBw8eBC5ubli7Xvv3j0MGjQIAQEBYu9LCCGEEEIIIYT8aihcI4QQUsLAgQMxdepUnDlzBkOHDkV4eHhlT0moGzduYMiQIVi2bFmZoVFUVBRMTU3h4eGB+Pj4Cpxh5Xv69Cnu3r0LAIiJicHw4cNx+vTpSp5VSd7e3jAzM8OBAwdKDbV4PB5cXV0xcOBAhIWFySz8EubOnTsYMmQIzp07J/VYbDYbAPDy5UuEhIQgPT1d6jF/dvLkSZGCrbi4OMyePRuWlpa4ePGiTEO2a9euYeDAgfjnn39kNmZ5Pn/+jPHjxyM6OhqhoaHYuXOnWPurq6sDAB48eIDs7GyZzUtDQwPm5uaYMWMGVq5cKdZz9fz58wAKq8jGjBmDa9euyWxe5VFRUcGHDx+wcuVKjBgxApmZmcWuf/v2ban7Pnz4ENnZ2diyZQsGDx6MEydOyHu6hBBCCCGEEEJIpaFwjRBCiFAuLi4wNjZGdnY23N3d4efnV9lTKkFNTQ35+fk4evQohgwZwpyU/pmqqio4HA7CwsIwaNAgXLhwoYJnWnn27NlT7Pthw4ahX79+lTSb0qmpqSEpKQmrV6/GsGHDhAZQampqAICEhAR4eHhg+PDhSE1Nlct8LCwsMHDgQMydOxczZ85ESkqKxGMpKioyl/ft24datWrJYorFbNy4EWZmZggJCSkzZFNWVgZQGPTt3r0bP378kOq4BQUFuHTpEmxtbTF9+nR8+fIFy5cvx9mzZ8vd9+3bt+jZsyeaNWsm8Vffvn3x+vVrZkw/Pz8cP34cAHD79m28evWqzDmoqKgAACZNmgQzMzMp7omS7O3tUadOHRw8eBBTpkxBRkZGufu8evUKz58/BwC0bdsWFy5cQJ8+fWQ2pxs3bmD9+vWlVpYJ7g91dXUEBARAU1OTuY7P58POzg5z5szB9+/fS+z76NEj5nJBQQEMDQ1lNm9CCCGEEEIIIaSqUarsCRBCCKma2Gw23NzcMG3aNADAjh070Lt3b3Tq1KmSZ/Y/gqoTAJg8eTIGDBggdDvBCWMlJSX4+PiUut3v5uXLl8VCDm1tbXTu3LnY/VZVCIKzatWqwdPTE9WqVSuxjeBxBIBmzZohICAAurq6cpvT/Pnz8e+//+Ly5cuwtrbGnj170KBBA7HHUVAo/CyTkpIS9PT0ZD1NAIWhWUJCAtauXYubN28iMDBQ6HaCKrrq1atj7969xe5TYTZv3gxHR0eoqqoyP0tJScHt27dx/fp1XLlypUTAyePxsGLFCtSuXRtt27YtdexGjRph3759OHHiBJo3bw5jY2NoampCQ0MDABAbG4vJkycDAFq3bi1yVZqSUuGft8+fP4e3tzd69eqFadOm4a+//ip126IBqKyw2Ww4OTnBzc0N9+/fh729PQ4cOFDmfX7kyBEAhe9tvr6+Mg9iO3fujDVr1uDKlSvYuHEjWrRoUex6wf2hq6uLJk2aFLvu5s2bSEpKwvnz53Hv3j3s3r2b2b+goABPnjwBUNhWeMuWLejQoYNM504IIYQQQgghhFQlFK4RQggpVa9evdC4cWP8999/AIBnz56JHa6dPHkSL168gKurq8znJzgRDACOjo5gsVhCtxOczFZTU8PQoUNlPo+qysvLCzweDwDQpEkT+Pv7Y9KkSejQoQPq1atXybMrTlBR1alTp1KfYwoKClBWVkZ+fj66du0KIyMjuc/Jw8MDY8aMQWJiIhwcHBAZGVmsmkcUgtaLgtBIHgSvhdatW5dZZSoI+lRVVYuFPHw+HxwOp0Tws2vXLpw8eRJjxozBu3fv8PjxY7x79w6qqqowMjJChw4dUL9+fTRs2BANGzZE48aNoaWlxRynPI0aNcK8efOEXrd3714Aha9fLy8vsYNJQSB4/fp13Lx5E76+viVe//II1YoaOXIkNm3ahG/fvuHJkyfYvXs3nJychG6blZXFtFJcvny5XF6jbDYbS5YsgYODA2xsbODl5VXsPhG8DoURVMEaGBhg06ZNxYK5Z8+eMW01e/fuTcEaIYQQQgghhJDfHrWFJIRUOD6fj4LsbORnZKAgO1uma/4Q2RsxYgSAwkqKgQMHirXvqVOn4Orqij179sDT01PmcxP1xHhZJ4x/V9euXcOtW7cAFJ5Q9/X1RYMGDWBrawsXFxfk5ORU8gyLq6qPZZs2bZg2mp8+fUJISIjYYwgCzooI1xo3bgx1dXV8+fKlzO1+9u7dO4wePbrEmlrKysr49OkTbt68ia5du2L16tWIjo7Go0ePcOrUKWzbtg2urq4YPXo0OnbsiGrVqokcrJXl8uXLuHLlCoDCCsJGjRqVuu2pU6eErmkmqIYEgJCQEKHBurzDNTabDUtLS+Z7wWtSmCNHjiAjIwODBw+GlZWV3OYkqEDmcDhYtGgRHj9+zFxX2v3x4MEDxMTEQENDA7t27SoRgN+5c4e5PGHCBPlMnBBCCCGEEEIIqUKoco0QUiGy4j7g2/UY/Ih9g6x371GQmclcp6SpCY2GDaDVtAn0e/eCRv2qVVHzK8rJyZFZeGJoaAgWi4WlS5dCVVVV5LWnrl+/jiVLloDL5QIorEJhsVhYvHixTOYFiB60yOJk/68kNzcX69atY76fP38+mjdvDgCws7PDyZMnsWDBAmzdurXK3DelhT4/K606UZ7Gjh2Ly5cvA0Cx9b1EJfgAQdGwR9Z+DkXCwsLw9etXeHh4FKtGKys8ef36NaytrbF06VKMGTMGwP9eYx07diwWEslTWloaVq5cCQDo2rUr0xpSmP3792PNmjXo0qULAgICoK2tzVxX9LZWZjvbvn37IigoCABQv359odsUFBQgJCQEtWvXxqpVq+Q+pylTpuDevXsoKCjAli1bsHv3bgClPz98fX3BZrMREBDAvJcU9e+//wIA6tSpg169ekk9v/79+yMhIaHc7RQVFaGlpQUDAwOYmJigX79+MDMzY9qfykJKSgqGDRuGlJQUXLp0idaSI4QQQgghhBACgMI1Qoicpdy7j4R/jiHjxctStynIzET6k6dIf/IU8UcjoN3CBHWtLaHXqWMFzvT38v37d9ja2op0clJUbm5ucHNzk2qM4OBgsFgsubSILEtlBDKVaePGjYiLiwNQ2NpzypQpzHXKysrw9vbG6NGjsWbNGri7u1fOJH8i6mNUGWFgt27dUK1aNaSnp6NNmzZi75+fnw9AvuHaz0Gzg4MDzMzM8PbtW+zYsYNZm660EFNQJZaTk4P379+Dw+GAzWaLHHrKCp/Px+LFi5nKu3///VdomPOzO3fuYNy4cQgKCkKdOnUAyL8qTVRt2rSBqqoquFwuxo0bJ3SbkydP4suXLwgJCRG63qCs9enTh3lOF/0ghrD7LCoqCo8fP8bmzZvRrVu3Etfn5ubi7t27AABra2uZvEZPnDiBrKwsbNq0CREREQAK14HbunUrmjRpAhaLhYKCAqSlpSE2NhaXLl1CVFQUTpw4gdq1a2PlypXo06eP1PMAgHXr1on8oRJCCCGEEEIIIX8OCtcIIXKRn/ED74J24Vt0jNj7Zrx4iYwXL6HfuxcaOthDWVtLDjP8vRkaGiIkJAQXL16EiYkJ6tevD01NTWhoaJQIMbhcrlQnob9//47q1atLO2UiI/fv32daFxoZGWHDhg0lHnMTExM4OzvD19cXSkpKWLp0aWVM9ZfBZrOxbNkyfPjwoVhQKaqCggIAoodrR48eRb169dClSxeRj/FzoKGpqYnJkydj06ZNmDx5Mg4ePAhNTU2hIWZubi5u3rwJAHBxccHMmTOZ6yo6XNuwYQOuXLkCZWVlbNy4ER07lv4hi3fv3sHW1hZcLhfq6uro27dvsTbDRe8TPp8v95D948ePqFGjBrPWmwCbzcbs2bPRtGlTtGzZssR++fn5CAgIwIwZM0qtsDt9+jQ0NDTQt29fmcyVzWZjwIABOHr0KEaPHs38/Of7KCsrCz4+PvDx8YGZmZnQse7cuYO8vDwoKCiI1c4yMzOz1PULNTU1oampCQsLCyZc69GjR4n7R09PDw0bNsTgwYMxZcoUzJgxA58/f8aMGTOwYcMGqdfYvHbtGk6ePCnVGIQQQgghhBBCfk9VoxcUIeS3khUXh0fO8yUK1or6Fn0dj5znIyvug4xm9mcxNDTElClT0LVrV9SqVavUE+vLly9HUFAQsy6UODIyMtC/f3+sXr0a3759k8W0q4Rf9bb8+PEDS5YsAY/Hg5qaGrZu3VpqFYy9vT369OmDffv2Yc2aNb/l2odZWVnIzs6WyVgjR47E3LlzJQqiBeFa0faMpTl69Cjc3Nzg6OhYbB2r8ggLwcaPHw8VFRW8fv0a3t7eAIRX/l24cAE5OTno1atXsWANqNjqr7179yIoKAjKysrYtGkTBgwYAD09vVK//P39mbaza9aswaJFi1C3bl1mvKLvd5K8v4nrxo0bGDBgAMLDw5nHXMDBwaHUSqqIiAjo6+tj1qxZQq+Pj4/H8uXLMWPGDPj6+pYYW1IzZszAvHnzirX7/Pl3xIMHD+Di4sKEVFevXsWkSZOQWaS1c3R0NIDCCk9B1WB57t+/D1NTU1y6dKnM7Yo+/8prA9yyZUum1S2Px4O7u7tUFWeZmZlYsWKFxPsTQgghhBBCCPm9UeUaIUSmsuLi8GzZimJrqkmDk5KCZ8vc0WrtKmgYC1+rhkhn+vTpGDlyJKKjo+Hn5wcDAwOR942JiUFubi4OHDiAiIgI2NrawsnJSaQQQRh/f3/Mnj27Uts4cjgcDBs2DI6OjrCzs/tlWkry+XwsWrQIHz58AIvFwrp168psp6egoABfX1+MHj0a+/fvR0pKCtavXy+TtYr+++8/xMbGSl01Iq0rV67A398fvr6+aN26tUzHjo2Nha2tLVJTU0XeJyYmBs2aNRNp2+zsbDg6OuLw4cNo0qRJudsLC8GqVauGAQMG4NSpU4iIiICbm5vQcC0yMhK6urrw9PQscV1FteEMDg7G+vXrUb16ddjb25dbtXfy5Encvn0bAGBubo5hw4aV2KZooCZtha4oVFRU8PXrV7i7u0vUbrVFixblbhMUFIRGjRqJvf5dampqiQBdQ0MDY8aMKRZA5ebmAii871JSUphKu5SUFDx48ADz5s0Dh8PBtGnTsGvXLmhqajIBmZmZmUhhVmxsLJycnJCVlQVnZ2ds3rwZpqamYt2e0rRt2xYDBgzAuXPn8OPHD0RERGDatGkSjbVhwwZwOBw0btwY//33n0zmRwghhBBCCCHk90HhGiFEZvIzfuDFyrUyC9YECjIz8WLlGrTb7EctIuXA2NgYDg4O8Pf3h7W1NYKCgkQOAI4fP85cbtmyJUxNTSUO1oDCSrhdu3bBwcFB4jGkdf/+faSlpcHLywvR0dHw8vJCzZo1K20+otq6dSuzbtbixYtFCra0tbWxbds2jB07FqdPn8bnz58REBAAPT09qeZiZGQEJycntGrVCvXq1ZNqLGlER0cjLi4O48aNw5w5c+Dg4CCzsKhp06YIDg7GsWPH0Lp1azRo0ADVqlWDhoZGiW2tra2RmJiIQYMGwcPDo8xxN2/ejEOHDgEoDCsaNWok0nxKu12mpqY4deoUtLW1wWazS4TFiYmJuHXrFjZv3iw0WJd3IMXj8eDt7Y3g4GC0b98emzZtwokTJ2Bqaoq5c+di4sSJJeb88eNHrFy5EgBQv379UquLBFVtQGHrRVkEx2Up+t4XEBCADh06yGTcBw8eMFVtc+bMETtYA4Bjx47By8tL5O0/f/4sdH01gYcPH2LatGlwcXFBYmIiAGDlypXM4yKq/Px8mQdspqamOHfuHADg9u3bEoVr9+7dQ1hYGPz8/HD48GEK1wghhBBCCCGElEDhGiFEZt4F7QJHihZMZeGkpOBd0G40W+Ail/H/dPb29jh06BCSkpJgZ2eHf/75B7Vr1y5zn69fvyImprD1Z6NGjRAYGFjq+jmisrOzw9ChQ9GtWze0atVKqrEkVbRN2a1btzBixAjs27evzCqwynbmzBkEBAQAKGw/J2xdsPv376NDhw4lgopGjRphx44dmDp1Kh48eIBRo0bB29u71LWfRKGiogJra2ssWLAAYWFhFb5uF1DYivHatWsACk/g+/n54e7du9i+fXu57eWE4XA4JcIZExMTmJiYlLkfj8dDcnIyAKBhw4blBpdF2+0tWLBA5DCwtO26d+8OZWVlzPg/9u47vMnybeP4mdXdUlr2LHuIIAiCAwQEFERF3P4Q8VUBFURFHKigIjhQGQ5cuHChiIgKKKioqAiIMkS2QFugjEILLW3SJO8fpaEhSUnStCnw/RwHh0+e3M/93GlLLTl7XffQoZI82/598skn6tevn3r27BnQvKFw8OBBjRw5Un///bceffRR/e9//5PRaNStt96qOXPm6KmnntI///yjZ555xnVNXl6ehg8frkOHDik2NlavvvqqEhISvM5fvHLNZrOV2esoUjxci4+PL3VIXXyuIsEGdv/3f/+nGjVqqKCgQC1atFBycrLi4uJcX9O//fabbr31VtfHrHbt2vrhhx9OOO+LL77oOl66dKkqV67sddx///2nSy65RFJhtfTIkSODeh3+KF7pmZGREfD1VqtVjz76qC666CL16dNHn376aSiXBwAAAAA4RbDnGoCQyFzxZ6n3WDuRfT//oswVf5bpPU5X0dHRuuGGGyRJ+/fv99oe7ngffvihCgoKVKlSJU2bNq3UwZok1apVSz179tT999/vak9WnhwOh6viQZLatm2rN998s0IHa7/88otGjRolp9OpG2+8Uffff7/XcRMmTNCVV16pxYsXezzXrl07vfTSS7JYLEpPT9fAgQM1derUUu3tdMMNN2jz5s2u0K+8/fbbbzp48KDr8eDBgzVx4sSggrXnn39eQ4YMkdVqDfjavXv3uoKdOnXqnHB80X5/JpMpoBatvkKwxMREzZgxQzfddJPX5//66y+NHj3a9fjwcZXHwbRFtVqtJ/xY/fbbb7r66qtVq1YtzZs3TzfddJPrNZhMJg0ePFhSYcVV0Z5eNptNI0eO1Pr162U2m/Xiiy+qcePGPu9R/HtIeXw/Ka8WmsHq06ePLr/8cjVp0kRJSUmuYG3fvn0aNWqUWxiZk5OjJUuWeHw9FOd0OvXVV1/JbDbrzDPPdFWweVO8XWQgX9fBKF49Gkyo+sorr2j//v3stwYAAAAAKFHFfhcAwEkj/fMvyuc+s+eUy31OR1dffbXrjfTvv/9eR44c8Tk2Pz9fM2fOlMlk0osvvqj69UO3H97//d//6b///guohVmorFixQnv27JEkWSwWPffcc2rdunW5r8NfK1as0PDhw2Wz2XT77beX+GawxWLRv//+qyFDhui6667T6tWr3Z7v3LmzXn31VUVFRclut+uVV15Rv3793Cr5ApGQkKCrr75ar7/+uv7++++g5iiN+fPnu447duyokSNH+qyqOXDggDIzM73+ef311/Xmm2/qt99+04gRIwIOHFNTU13H/rTI3L9/vySpWrVqAbVkLCkEa9u2rev548e98847rmD81VdfVb9+/bRr1y6/73s8q9WqESNG6K677vIZsOXn5+vnn3/W+++/r3Hjxnltu9qrVy9XEJqdna2CggLdd999WrRokUwmk55//nl17dq1xLUUBWpVq1Yt8/aWUtm30CwLBQUFGjVqlPbt26e6detq4MCBkgp/0WDcuHE655xzdOONN+rdd991C6sladmyZdq5c6cuueQSNWnSROnp6T7vU3xvwrIO1w4dOuQ69vV33pf169frrbfe0gMPPKBq1aqFemkAAAAAgFMIbSEBlFrOtu3KXvdvudwr+591ytm+Q7H1w7eP06mqevXqat26tVatWqWIiIgS9yeaNWuWDhw4oAcffFAXXHBBifNmZ2dr8eLFuvzyy/1aR4sWLdSpUyd99NFH6tatm7p06RLQ6yiNWbNmuY6vu+66sO4XdiLLli3THXfcoby8PI0aNeqE+woVr9iqXbu2UlJSPMZ06dJF06dP15AhQ3T48GFt2rRJd955p9q1a6dBgwapW7duAe1bdfPNN+vDDz/UqFGj9OWXXyomJsbva0vj8OHDWrBggaTCMGnUqFEljp89e7aee+65E867ePFi/fDDD+rVq5ffa1m7dq2kwqqmli1bnnB8UQvJGjVq+H2P43399deuCrjj/flnYfXv4cOH9e6777rOb9u2TR9//LEkaeDAgZoxY4Zq1KjhCuOOHDniVn3kjdPp1KOPPupqJ3jXXXfplVde8fiaiYyM1EMPPVTiXDExMWrVqpWMRqM6deqkIUOGaMmSJbJYLHr22WfVu3dvbd++XbVq1fJZjWixWHTvvffq5ptvVnR0tNcxoWwXWR6Va06nM6RzPfzww/rtt99Uu3Ztvffee66/N/Hx8friiy80ZswYffXVV/rzzz81bdo0vfvuu65WqJ9++qmMRqNuv/12zZ49u8TKteLtGUvzte2Pbdu2uY79+TtXxG63a/To0TrnnHN0zTXXlMHKAAAAAACnEsI14BSQl7EnJPMYIyyKCPC3vK0HDmj3gm9PPDCEds//VrWvvMLn88G+DofV801Wc2yszHGxXq44uXz//ffq3r37CVu8de/eXatWrdKtt97qswrDZrPprbfe0uWXX67/+7//K3G+zMxM3XrrrVq3bp327NlzwgCoyE033aSlS5fq0Ucf1TfffOO251BZOXTokKslZGxsrO68884yv2ewFixYoFGjRikqKkqvvfbaCSt4pGPhWq1atdz2STpe+/bt9emnn+rOO+90vUm9cuVKrVy5UgkJCerZs6fOPvtsNWvWTE2aNHHbZ+p4derUUbdu3bRo0SI9//zzGjNmTECvM1jffPONcnNzJRW2wjvzzDNLHH/rrbeqatWqcjgcatGihapUqaL4+HhFRETopZde0ssvv6z4+Hi98sor6tixY0BrKaoQbNas2Qlbp1qtVld10In2PCxJ8+bNNXjwYNWsWdNtnjVr1rg+pzabzRX8Fbnssstcx7NmzdJdd93l+p4xffp0TZ8+PaB1/Pzzzxo2bJhefvnlgELZIk899ZT27t2ra6+9Vunp6apcubJefvll136AX375pWbPnq1bb71V1113ncc9/ve//0kq/LhmZWUpNjbWbf+/1atXKy0tTZJ87tsWiJMtXBs/frzmzp2revXq6e2331bt2rXdno+JidHzzz+v2rVr67XXXtPBgwc1YcIEzZgxQ5mZmfr22291xRVXqHnz5oqPj3d9LL0pHq750x61NIr2WpSkiy66yO/r3n77bf3333+aO3duWSwLAAAAAHCKIVwDTgF/Dr4jJPMktDpDZ45/MqBrNjw/Sdlr/wnJ/f21e/4C7Z6/wOfzoXwdda+/VvVuuC7gNVY0DzzwgGrXrq277rqrxEqzvn37au/evbr55puVk5PjdczcuXMVFxenhx9+2OcYScrKytLgwYO1adMmSdLEiRNls9l0xx0n/nrt3r276tSpo7S0NE2YMMGvPeBKa/bs2a42ckOHDlVycnKZ3zMYM2bM0IQJE9SkSRNNnTrVawWaN0Xhmj97aDVq1EifffaZRo0a5bZHW3Z2tj7//HN9/vnnSkpK0n333XfCCo+bbrpJixYt0kcffaRevXqpU6dOfq23NIoqsCIjIzVy5Ei/rjlRZWWNGjUCDtYkuVpiFgVCJSlqCSmVLlxr3Lixq3qsuKuvvtp1XLlyZT3//PMnnMtut0uS7rzzTo0YMcLrmL59+2rTpk0aNmyYhg8fHuSq3R05ckSzZ8/We++9p4KCAp1zzjl69tlnVatWLdeYYcOG6d9//9VTTz2ld955R0899ZTOO+88j7m2b9+uBx98UOvWrZPFYlFkZKQMBoOys7NdY5o0aVLqNRd9rMpS8X3RSjNHUUjWoUMHvfTSSyW2T7z33nuVm5ur999/X9u3b5dU+HfMYrHonnvukVRY6bZixQqfcxS1jIyOji7TtpCbN2/WvHnzJBW2Q/X29eDN9u3b9fLLL+vee+9V3bp1y2x9AAAAAIBTB+EagFIJ5W/Ro+xER0drw4YNuvvuu/0a/8EHH5xwzLnnnhvwOiZPnqyCgoITvgFvNBp13XXX6YUXXtDs2bPVt29fnX/++QHfz18Oh0Pvv/++pMKqikGDBpXZvYKVm5urMWPG6JtvvtEtt9yie+65J6BqoOIVO/5ISEjQ66+/rk8++UTPPvusqxKsbt26GjNmjN/tOjt16qRGjRppy5YtGjNmjObOnauoqKiA1hKIpUuX6t9/C9vU3nLLLR7VOOVp8+bNrlDBn6/f4tU9xUOkUMjMzNQ//xz7BYLc3FxlZmYqKSmpxOuKwpySgqNQ/39gwYIFmjhxotLS0lSrVi2NGDFCV1xxhUcwbDQa9fTTT+uKK65Qenq6brvtNr399tseAW6TJk00e/ZsZWRk6LPPPtPbb7/t9osBjRs3PmF7W3+EssWkL6UN16xWq+6//359++23uvrqqzV27Fi/vo889NBD2rRpkwwGgytoGzVqlKvFY40aNfTPP//I6XR6DfCLKibLstXu1q1bNXToUBUUFKh69eqaNGmSX9cVtTNt3ry5a885AAAAAABOhHANQOmE4LfoUfaK3jytVauWa0+diu6qq67S1KlTZbPZ9Nhjj2nevHllFsp89913rpZmDz30UFAt7MrSpk2bdPfdd8tgMGjGjBluVVA2m83nnlPF+TPGm+uvv14XXnihJk2apJ9//lkff/xxwJUn1157rZ5++mlXdcj9998f1Fr88c4770iSqlWrpsGDB5fZffzx3XffSSqsEvMnvNmz51iL39JUrnnz66+/ugUzNptN119/vQYPHuxW0XY8f8K1UFm2bJlefPFF/fXXX2rYsKHGjRunfv36lfj3MTExUQ899JDuuece2e12zZgxw2d1ZPXq1TVs2DBdccUV+t///qeMjAxVrlxZL7zwQkhaOhYUFJR6jrK8x759+3T33Xdr69atmjJlii655BKfY4//eJhMJr3yyivKyMjQRx99pJYtW+qGG25wPZ+SkqJDhw5p27ZtatCggcd8RRVvoagQLM5ut2vDhg36+uuv9dFHH+nIkSNq27atJk+e7Pfebp9++qn++usvffnll+XS2hMAAAAAcGogXANQKlSunVwMBkOJe2RVJMnJyerevbu+/fZbpaen6/XXX/fZlq40nE6nXnvtNUlSly5d1LNnT7+uu+yyy7Rr166QrcNbS7Wi/e0++OAD3X777RowYIBHBdo777yjf//9V0OGDFHz5s19zh9o5VpxNWvW1HPPPafDhw+fcN8wb/r166fnn39eNptN7777rvr376+GDRsGvR5f1q1b52pj+fDDDys2Nrz7JS5cuFCSdOmll/oVbu7du9d1HOrKteLtPSWpUqVKeu6553TLLbfom2++0fjx473es6gaq6TQoTT/H3A6nfrpp5/02muvacOGDerevbtGjBihTp06+dXCVJJ69+6tV199VRs3btSRI0dOOL5u3boaNmyYvvrqKz355JNq0KCB7Ha7li9fXqq2peURrgVbufb7779r1KhRatu2raZOnaoqVap4jDl48KDsdrvatm2r8ePHezwfGxurypUr68svv9R7773n9vlJSUmRwWDQ2rVrPcK1nTt36tChQ5IKqwSD9fXXX2vRokWux06nUzk5OXI6nYqNjVWHDh3Uv39/XXLJJX5/7WRkZGjixIm644471KhRo6DXBgAAAAA4/RCuASgVf9/AQsVwsoWhV1xxhb799ltJ0vTp03Xdddf5XY3gr++//17//vuvIiMj9dhjj/l93Ztvvlmmb6avWLFCzzzzjDp06KCvvvrKZ/u+iIgIzZs3T/Pnz1fXrl11xx13qE2bNqW6986dO70GLcEEa1JhdVHXrl21cOFC2Ww2Pfvss3r99ddLtUZvXnnlFUnSeeedpz59+oR8/kCsXr1a69atkyT179/fr2t2797tOg5luJadne0KJS644AItWbJEknTWWWdp8uTJGjp0qC6//HI99dRTHtVMVqtVUmHlUigdOHBAs2bN0rx581S/fn0NHDhQXbt2VUxMTFDz9e/fX88884wuvvhiv8ZfccUVuvbaayUVtsi855579Msvv2jKlCnq1atXUGso3hby0KFDyszMDGqe4xUFU8ffw1+vvfaafvrpJ02ePLnEvf+mTJmiuXPn6oEHHvD59ZeZmanx48d7fD+KiopSzZo19ffff+uyyy5ze279+vWu4zPOOCPg9Rfp3r27HnjgAdfjnJwcXXXVVbLZbDr77LP15ptvBjznE088oVq1aoW9yhUAAAAAcPIhXANQOrRQOikUhWonW8urLl26KCEhQdnZ2crPz9crr7yicePGhWx+h8OhqVOnSpLuuuuugPYDCnXId7x9+/bp9ddfV3JyconjiioRnU6nlixZoq5du5YqXJszZ44effRRTZw4Ub179w56nuP17dvXVcm1ePFirVy5Uu3atQvZ/KtXr9b333+vqKgoPfHEEyGbN1jvvvuuJKlbt25+BwpF4Vp8fLwqVaoUsrV89dVXysvLU6tWrXTuuee6wjVJuvDCCzVixAhNmjRJI0aM0MiRI92ChqJwraTWjMGE9gcOHFCbNm00aNCgoFuWFte/f3/l5ua6ArMTKfp7s3fvXg0ZMsS1H93999+vt99+u8QQypfiYftdd90V8PX+yM/PD/ianj17aujQoSWOcTqd+v7773X48GGNGTNGX3/9taZPn+7xeS+puqthw4ZavHixxy8p/P33367j1q1bB7z+IjExMapTp47buR49emj+/PlasmSJUlNTVbduXb/nmzdvnhYvXqxPP/00JF+DAAAAAIDTC+EacAo4+41pIZnHGBH4m0vNR92n9c9M1KH1G0KyBn/Et2iupvf6bg8YzOtodv+9clg9KwLMYW4rFyq5ubmSFHRVSLhYLBZ1795dc+bMkVQY/IwcOVKJiYkhmf/LL7/Uhg0b1Lx5c916660hmTNUStoPqbjo6GjX8eTJk9WjR4+g7zlnzhw9/PDDcjgcuv/++2U0Gv2uBDqRCy+8UFFRUcrLy5NUGD6FMlx7/vnn5XQ6dffddwcUkpaF9PR0V8Xl8OHD/b6uqM1oqFtCzpo1S1Jh4LNlyxZJ7oHY4MGDtXjxYv3111964YUX1KBBA1d71KLvHcFWLXrzxx9/aMiQIX61cAxUUVgerPz8fN1xxx366KOPAt4frHi49v7776tjx44eY3bt2hXwfnp//PGHBg4c6HEPf/nT7vDPP/9URkaGpMK2mS+99JJHsLZo0SJ16NDBZ/Dbrl07LVmyRBs3blTTpk3d5pakBg0ahOx7d5HrrrtO8+fPl8Ph0EcffaQHH3zQr+sOHjyop556SoMGDVKrVq1CuiYAAAAAwOmBcA04BURVrxa2e0dUrqxKrc4o13Ct0hktQ/6aIypXDul8FU1RoHGyhWtSYShTFK5ZrVYtXbrU7+CpJHl5eZo6daosFoueeeaZUu1JFk7F2/XVrl37hOPT09PVrFmzE44rKCjQfffdp8mTJ/u9D11JoqOj1aFDB/3yyy+S5PpvKPz000/6448/dNZZZ2nQoEEhmzdYEydOVEFBgfr27RtQG7ydO3dKkkd1Tmn88ccfWrdunbp27aru3btr8+bNktz37jIajXr88cfVr18/OZ1Ovf322+rZs6dyc3Nlt9slSQkJCT7vEWjlWseOHfX6669r6dKlOuOMM1SnTh3Fx8e7BcXe2Gw23Xbbbdq4caMk6ZZbbtHgwYM1c+ZMZWRk6O677/Z7DScKuaKiovyeq0hBQYESEhLUrVs3paSkeDyfm5urW265Re3bt9ewYcP8rn6NjIzUFVdcoWuuuUYdOnQIal0nqnj7/PPPJRW2en7yySdlsViUk5Pjen716tW65557VLduXb3xxhteK8TOOeccSdIPP/zgCtcOHz6sVatWSSps1xpqnTp1Ur169bRjxw7Nnj1bI0aM8Otz9/TTTys+Pj6grxkAAAAAAIo7Od9JBFChVOl8gdJmzS6/+3XpXG73OhU4HA5XuFbSG+QV1fHVH2lpaSGZ94033tDOnTs1cuRItWjRIiRzhkOge2HVrFlTs2f7//c1lKFjx44dXaFabm6uMjMzfe4l5y+r1aoJEyYoJiZGEydODPneYIFasWKF5s+fr6pVq+rRRx/1+zqbzeZqCxnKcG3q1KmKiopyraUoCDs+EGvevLkuvvhiLViwwLWvV/G9vqpUqeLzHsG0hezYsaPXyq6SPPLII65grV+/fnrwwQdlMBh0/fXXq0ePHho0aJDXUOt4zz77rL766it9/PHHAbURPJE+ffro2muv9dlCMyYmRq+//rpuuOEGffXVV/q///s/DRky5IRh0FlnnaWzzjor6HUdOXJEt956qyvkKonT6dQtt9zi8/mtW7fqhhtu0Ny5cz3+7rZp00aRkZH67rvvXG0oly5d6vp6KotwzWAw6JprrtELL7yggwcP6ptvvtFVV11V4jW//PKLvvzyS82YMSOoEBUAAAAAAIlwDUAIxKbUV0LLFspe92+Z3yvhjJaKrR/elm8nm4MHD7qOq1evHr6FBCk5OVnVq1d3tSwLxb5xqampeuutt9SxY0fddtttpZ4vnAINk4xGY6kDrWAdX8VlMBhKPec777yjbdu26emnnw57O8j8/HzXfm/jxo1T5QAqYtPS0lxVYqF6Hb/++qtWrFihxx9/3BUiFd2jeOVakRtuuEELFixQ165dJRXu+1ekpO8dwYRrgZoyZYqrvWW3bt00fvx419dP5cqV1aNHD40fP15vvvlmifO8/PLLevvttyVJgwYN0kcffRSy74vF/159+OGHioqKUr9+/dz+jtavX19Tp07VoEGD9Oqrr+qrr77S888/X6rw7ETi4+M1ffp0vffee2rSpIkaN26shIQExcXFyWg0atasWXryyScVHR2tuXPnev143HjjjVq7dq0aN26sKVOmeP0eEhERoTZt2mjZsmVavXq1Wrdurfnz50sqDBYvuOCCMnl9/fv319SpU2Wz2fThhx+eMFwr+vzfcccdfs1fvH3p5Zdf7vH/oBUrVgS4YgAAAADAqYBwDUBI1L7qynIJ12r371fm9zjVnOzhmiQ1bNjQFa6F4k3ocePGKSEhQS+88EJIwrpwCnelViAaNmzoOk5JSQkofPImLS1Nr732mvr376/+/fuXdnmlNn78eG3cuFFDhw5Vt27dArp269atruP69euXei0Oh0OTJk3SJZdcohtuuMHtvHQsZCuuU6dOGjVqlGv83r17JRUGsiUFft6CulB644039Oqrr0oqbD04ZcoUj4rKW265RVdeeaW+++479erVy2MOm82mcePGaebMma5z+fn5evzxxzV58mRFRkYGvb4vvvhCV155pdu5gwcPaurUqXrrrbd0991365JLLnGFge3bt9eoUaM0YcIEpaam6qabbnKF/b688847Wr16tW655Ra1bt064DXGx8dr2LBhHuetVqsrkBwyZIjXz/Pq1au1du1aSdLdd9+txo0b+7xP165dtWzZMn3wwQcaM2aMvv/+e0nH9lwsC1WqVFH37t317bff6p9//tGqVavUpk0bn+Ofe+65E7bJLG7UqFFauXKlpMKvRX/beQIAAAAATm0n9zuKACqMpPZnq0qXsvmt9CJVunRWUvuzy/Qep6Li4VooW6CVp/bt20uSrrjiCrVr165Uc3311Vf67bffNGXKFFWtWjUUy4OfatSooTp16shsNuuxxx4r9Xxjx45Vo0aNNHbs2BCsrnS++eYbzZw5U1deeaXuvffegK//999jv5zgz554JzJjxgzl5ORo/PjxbueLgjBfgdhtt92m2NhYSdJ///0nqbCSrqTwqSwr1yZOnKgXXnhBktSkSRNNmzbN61qaN2+uK664Qo899phH69hdu3bppptu0syZM2UwGHTBBRfozTff1C+//OJzPn9t2bJFL7/8ssf5ojm3bt2q8ePHu31+JWngwIGuPcqsVqvH5+l4N910k6sF4s0336w///wz6DUX98EHH2jXrl2qX7++br31Vq9jXnvtNUmFgfiJ9l/s27evjEaj5s+frzfffNNV9XWiarLSuuaaa1zHH330UYljq1atqjp16vj9p/jXR9H3sOJ/AAAAAACnJ8I1ACHT8PbbFFFG7eYikpLU8Hbvb/yhZMXfaC6p4qAiu/HGGzVy5EhNmDChVPNYrVY9/fTTeuqpp3T22QS14fD4449r2rRppW4Rt2jRIm3ZskWvvPJK2PdN+v333/XQQw+pZ8+eeuqpp4KeQyp8479atWqlWk9aWpo++OADvfnmm4qLi3N7rqCgQJJc+2CVpGh/sxNVSpVFuHbkyBHdc889euutt1znWrVq5fF6irv33nuVn5+vu+++W1arVZI0Z84c9evXT+vWrdN1112nb775RtOnT1eXLl1C0pb0448/9toi0WKxuI7ffvtttWzZ0u15g8GgMWPGuCpnDxw44HX+ZcuW6YcffpDZbNbzzz+vvn37aunSpbrxxht11113lWoPyuzsbL3++uuSCvez87ZX3OrVq13VZ3fdddcJK32rV6+u9u3by2q1ukK5evXqlVlLyCIXXHCBateuLUmaN2+eMjMzy/R+AAAAAAAQrgEIGUtCvFqOfVTmEt78DIY5Lk4txz4qS0J8SOc9XWzZskVSYWu3Ro0ahXk1wUlKStLgwYM9WsEFKj8/X7fffrv69esXmoUhYJ07d1aXLl1KPU9eXp7efvvtsLc6XbNmje666y5dd911mjp1qs+v0cOHD/ucIy0tzdV27rzzziv1mmbMmKFJkyZ5raopCtX8Cdf++OMPSSqxXaEU+raQ27Zt0/XXX6/58+crNjbW789x9erVdf/99+uff/7Rww8/rFtvvVXjx4/X9ddfrx9//FFPPvlkSL8H7t69WzNnzvQahhZv1xoTE+P1+iZNmqh3796SpPPPP9/rmO3bt7tahhqNRj3zzDOusYsWLdLll1+uRYsWBbX+l19+WQcPHlS1atVkNBp16NAht+ftdrsef/xxSdKZZ56pvn37+jXv5Zdf7vZ48ODBIQkyS1JU1ScV/hJF0f58AAAAAACUFfZcAxBSsSn11Wr8k1r3xFOyhuA3xyOSktRy7KOKTSn9HkSnq82bN0uSWrRooejo6DCvJrzi4+N1yy23hHsZCIG+ffu67eFWFtauXavvvvtOkpSenq7169erefPmrud///13jRw5Uvfdd58GDBjgcx6bzaaBAwfKarXqzDPPVKtWrdSkSRPX3mpjx451BVSXXHJJqdf98MMP+3zO38q1zZs3Kz09XWazWd27dy9xrLf924I1a9YsjR8/Xrm5uerevbvGjh2rsWPHuvZcPJEBAwZo6dKl+vrrr9WgQQMtWrRIlSpVCtn6ihs/frysVqvXENPfvRzvvfde/fvvv7rrrru8Pn/w4EFlZ2e7HlssFk2dOlX9+/fX9u3blZOTo3vuuUeffvqpR3XciZx11llyOp1as2aNhg0bJqvVqsaNG6tt27Zq166d1qxZo3/++UcREREaN25cUPtTGo1GderUKeDrgtG/f3+9/PLLKigo0CeffKLbbrvtpN9TEwAAAABQcRGuAQi52JT6OmvKi9r65nTt+/mXoOep0qWzGt5+KxVrpfTPP/9Ikrp27RrehQAniaysLL344ov69NNPdckll+i5557TO++8o5tvvlmvvPKK2rdvrzlz5uijjz7SO++8c8I90iwWi2bPnq2VK1dq4sSJmj17ttdxjRs3LvO/p/n5+ZIKWzlarVavrQAl6YsvvpAk9ejRw2vbw+JC0RYyLS1Njz/+uH755RelpKRo1KhR6tGjR1BzTZgwQVu2bNHWrVs1dOhQTZ48OeQVju+//74rePVWDedvqFO3bl3Nnz/f5/P79+/3qCiLi4vTpEmTdM0118hut8tms2nWrFkaM2ZMAK9A6tOnj/r06SOp8Ovi999/1w8//KAff/xRM2fOdI2rUaOGVq9erapVq6pKlSolzrlkyRI98cQTrscOh0O33367PvzwQyUnJwe0vkBVr15dF154ob7//nulp6dr8eLFJwyG/VH867ss9xcEAAAAAJxc+HVOAGXCkhCvZiPvUYvHRivhjMB+mz7hjJZq8dhoNRt5D8FaKa1du1a7du2S0WjUFVdcEe7lABWa0+nU559/rksuuUSffPKJhg4dqkmTJqlFixZ67rnndPHFF2vQoEH64IMPFB8fr48++uiEwVpx7dq100cffaT777/f47nY2Fg9++yzZV5pExkZqRtvvFFffvmlIiIi9PXXX+uOO+5w7VEmSTk5OZo1a5bMZrPPiqriiirXgqlgy8vL02uvvaa+fftq/fr1Gjt2rL755puggzVJSkhI0HvvvacGDRpo5cqVuuyyy/Txxx+HrH3lxx9/7Lb/o7c96UL1eUxLS9OePXs8zp9xxhm67rrrXI+LV7cFIzIyUl27dtWYMWN08cUXuz23Y8cOjRkzRl26dNF9993ndT1SYYvU4cOHy2azqX79+rrpppskSf/995/+7//+L6h90Ip/TRVVXZak+Mfkgw8+CPh+3qSnp7uOt2/fHpI5AQAAAAAnPyrXAJSppPZnK6n92crZvkP7fv5FhzZtVs6WrSootv+QOS5OsY0aKr5JY1Xp0lmx9euFccWnloULF0qSunfv7mpBB/+sWrVKX331lR555JEy3y8IZWvKlClq166dOnfu7HPM+vXr9cQTT7j2Prv99ts1YsQItzFjx45VZmamxo0bpy5duqhly5aqWbNmQGsxGAy6/fbbVVBQoMmTJ0uSmjVrpueee86t5WQgfvrpJy1btkxS4X5TJYUYd9xxh+t4zpw5Gj16tOx2u4YNG6aXX35ZERER+uCDD3Tw4EENGjRITZs2PeH9i0Irf8KP4td89dVXmjx5siIiIvTQQw/pyiuvVGRkpN9zFLHb7Vq2bJnmz5+vv//+W7NmzVK1atX03nvv6bbbbtPGjRv1+OOP67PPPtMdd9yhiy66KOjwa86cOa59yCSpQYMGQX/e/LFp0yafoeCdd96pzz//XPn5+SHZx/DAgQO65557tHTpUsXExGj06NFq3769pk2bpq+//lp2u13ffPONNm3apFmzZrl9rv7880/deeedys3NVdu2bfXqq68qKSlJe/fu1YIFC7R+/XpdddVVmjJlitcw8niHDx9WTk6O5s6d6zr322+/6c8//1Tjxo0VHR3tteqyc+fOqlmzpnbt2qVff/1Vb731lvr376/o6Gi/2iJbrVbl5eXJ4XBo//79mjlzplJTU13Pjxs3TqNHj1bz5s0VHR2tyMjIoL5mAQAAAAAnP8I1AOUitn49xd70P0mF1SH2I3lyFthkMFtkio4ivCgDDodD8+fPl8lk0j333BPu5ZxUVq1apVtvvVWHDh3SkSNH9NRTT532X6Pz5s3T4cOHde2114Z7KQF54YUX9MYbbygyMlKvvvqqLrjgAo8x8+fP18iRI11VMv369fNaXWYymfTiiy/qvvvu08KFC9WrVy9dd911uuaaawKqYJMKw7u9e/fqvPPO00UXXRT011dBQYGmTJmi3NxcSdI333yjb775JuB5fvrpJ911110aPXq0XnvtNdWtW1fDhw/369qi8OdE+7hJhd//582bp2nTpql69eoaPXp0UGFX8UBt4cKFMplMGjRokB544AFX6FK9enV9+umnevzxxzVnzhz9888/GjZsmFJSUtS7d29ddNFFatmypUwmk9/3LQpEpcKvh0ceecTruOKfz71793rdl+1EduzYoW3btkkq3N/v3HPPdXu+atWqGjNmjFatWqW+ffsGPH9x3333nZ566illZGTorLPO0nPPPef6hYznnntOQ4YM0WOPPaY///xTGzdu1N9//62OHTtKKvyae+ihh2S1WtW7d289++yzrsDpmWeeUUZGhv766y/t3LlTN954o+69917ddNNNPluSStLll1/uVjEmFbbIvPHGGyVJTz/9tPr37+9xndFo1NVXX62XXnpJkjRx4kRNnDhRF198saZOnXrCj8PXX39d4p6F27Zt0+DBg12Phw0b5vffEwAAAADAqYVwDUC5MxgMMsdESzrxb5EjePPmzdP27dt18803q0mTJuFejk9OpzOodnJF1xax2+0+K2eKjztRdc3atWt12223ufY5mjVrlgwGg8aNG3faBmyLFi3SqFGjZLfbZbFYdOWVV3odZ7fbS70nkdPp9KsCyp9xU6ZM0RtvvCGpcE+pO++8U9OmTdP555/vNq5nz57q27evvvzyS7Vo0ULjxo3zOWdERIReeuklvfnmm3r55Zc1Y8YMzZgxQ3Xr1tXZZ5+tFi1aKCUlRUlJSUpOTlZsbKyioqIUGRnp9vVjNpt97pFlt9uVn5+v3NxcZWVl6dChQ8rKylJiYqLatGnjNtZsNuvdd9/V4MGD9ddff+mZZ57x+fk5EYfDoVtvvVWS9MorryguLs6v64o+D8VbS5YkNTVVb7zxhmrVquXX+KysLEmFH5fff//dFahlZmaqevXqGjZsmK655hqvYU10dLSeffZZnXvuuXruuee0f/9+bdu2TdOmTdO0adMUExOjli1bqlGjRqpbt66SkpIUFxcnp9OpSy65xGO+p59+WrNmzVJMTIyuvPJKtWvXzuuai3+up0yZomeeeUY1atTw6/VKhSHOQw895Ho8ZMgQjR07VldddZXbuKuvvlpXX3213/MeLy0tTePGjdPixYuVlJSk0aNHa8CAAR6BY6NGjfThhx/qww8/1DPPPCObzSan06lp06Zp6tSpio6O1ujRo3XDDTe4XRcdHa0333xTgwYN0tq1a2Wz2fTcc8/pww8/1N13361LL71UFovFY10//PBD0K9p2LBhGjZsWFDX9u/f32toBwAAAADA8QxOduYGPFitVq1Zs8bj/Jlnnlnib1r7o6CgQJs2bXI716RJE5nNFTfrdjgcOnLkiNu56OjoMt8bCMFzOBy67LLLVFBQoDlz5vjVDitcdu7cqQEDBnhUKVQ01157rZ588slyC9jy8vJc1Ugl+fnnn/Xggw9Kkt5//32fQerjjz+ub7/9VjVr1tTs2bP9Xsfff/+tu+++21WVZDQaNXHiRK+VMrNnz9YjjzwSsr2tykJkZKTXgM3pdGrixIm66qqr1KhRI7/mSk1N1UsvvaR58+b5VbUVERGhyMhI1/9Hin8tOZ1O5efnKz8/32Mug8Ggc889VwMHDlS3bt28zn3kyBH9+OOP6tOnj19r92bq1KmaNm2aJk+e7LHvVklat26t/Px8XXXVVW57kYXCnj17dPHFF3v8XYiPj9fQoUN10003+d2W79ChQ3r55Zf14Ycflvj5atu2rc4///xSVSRt2bJFffv2DdnfhUqVKmn27NlBVcB5k5qaqjfffFNffPGFIiMjdcstt2jQoEGKjY094bWZmZk6fPiwHnnkES1btkznnHOOJkyYoLp16/q8JisrS3feeadWrFjhdj4hIUHdu3fX9ddfr7Zt25b6deHUFqqfR0/Gn8UBAABQMdhsNq1evdrtXOvWrb3+0uCprizfPz9Z8C8IADgFzZkzR2lpaZo5c2aFDtYkqVatWpoxY4Zmzpyp5s2bq2HDhoqPj1dcXFyFqxRzOp3ltqasrCwNHDjQ1RbOHwMHDjzhmF27dnm0lwuEw+HQgw8+KIvF4hHA9O/fX1FRUdq5c6fOOOMMVatWTfHx8YqKigr6fmXBWxtAg8GgBx54IKB56tatq+eee04jR47UvHnz9N1332nNmjU+gxur1ep3dVeR+vXra/LkyWrZsmWJ46Kjo0sVrM2ZM0dvvPGGnn/++YCCNUmuytP8/Pyg7+/LTz/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cayq6T6yXaiQpxCFhMFV+vgKjgEPCELaeDLDyMJh1hfJ1+K7A8/WxDSLsDOXnoxy+rkL1OorfoygctNptkiSjj2tSs3bq372bPM5X8hHUZ+Vla9Y/33h9rkPtNl7DtU/Xfu11H8OejTrr9vY3epxPy96lUQueOtYGtFgL0Wd6Pew1kJu9br427Nsik6u1Z2G42Lp6C3VJ6egxPjv/sH7c+ptbOFgULnas29brG8q7D+/VEVvesUrFo+FitCVKMRbv37MAoKLavH+b5m/6UUtTV3r9RTCL0axOddupd5NuapycUv4LBAAAAOCXmjVr6txzz9Vvv/0mSZo/f75f4dqPP/4op9MpSWrUqJHOOuusslym3wjXAEiSejfpFtJwrXeTbkFdZzAYZDaZZZZZUqRf1zRMCmz/jSqxSRp41lUBXfN/7a4LaHyjpPp6+8rn3cLBogDPbPL+rbd/y97q2uC8Y6GiszDES0ms63V8QmScejXq4hZEFoV+voKTylGVVCOuqmtc0ZqifASXwQRfvgI8X687qJAw0Oq4ULaeDGEoZQ+0hWYQFZGhbKEZcNvUYCoJy+F1hKwi0m53hYM6Gg4WMcp7u8utmTv0165/PM7HWKK9hmsHjhzUh6u/8DrXWTVbeg3XPvh7tpal/+1x/uLGF+rWs6/3OJ+WtUuPfj/RLYgrCubGXXS/4iI89z388t/vtCVzuyscLNoHsFX1ZupUt53H+Oz8w/ptx4pibUaPVQe2rXmGLF5ex77cTFntNo81RZoivI4HcGo5YsvTjFWfa9GWJSWOszkK9Mv2Zfpl+zL1aHSBbmpzlaK9VFkDAAAAJ7N58+bppZde0t69e3X++edr7NixSkry3pWoIrv33ntd4dq3336rMWPGKCYmpsRrFixY4DoePny4z+5N5Y1wDYAkqXFyino0uuCEb2D4o2ejzqf9bw4XtZILRPOqjQMaXy2uim5rf0NA1ww956aAxjdOqq9pl01wBXEF9oLCMMFh9xk4XNasp86r294VDhYFeU18fE3ERcaqS0pHtyCy6FpfwUlsRIwqR1VyhYNF4aLZZ8tG36FUwCFhSFuBhi4kLI8qv0BbewbTbjTgvfx8hLahbQUautDW5+soh5DQ13ir3aZc2xGvzxnl/e/Uhn1btGLnao/zZqPJa7i2P/eA3l450+tcb1/5vNew7J2Vn2p5+iqP872bdNMt7a71OJ+atVNPLZ56rJKwWFg4pts9Xqv2vt7wvbYdTHWFg0XXtqzaRO1rt/YYfyj/sFakr3bbk7Ao9DujWjOvn/esvOzC72fHVRKajCYZDRXjHwRARZOZe1DjFk9R+iHPvU9LsmjLEv27Z7Me6zrC656fAAAAQDgUVV0df+yvlStXauTIkXI4CrsmLViwQPv379eMGTPKbS/70r6GIq1bt1bv3r01f/585ebm6t1339Wdd97pc/yWLVv0008/SZLOOOMMXXLJJUHfO9QI1wC43NTmKv27Z3PAb2QUVzuhhga06R/CVSGczCazkmMqB3RN6xotAhpfK766hnUcFNA195x7q9fzvv7n3qhyfU3u83ixVqBHQz+n3eeb272bdFeH2me5tRotcBSoRdUmXsfHRcSqU512RwO/Ardg0VeoEWGyKNoS5VpXUWvJkFbHhXJ/ukCrykIYEvpsaRpEu9HAW4GGLiQMtLIzlCFhoO1ipSA+HyFsYxtoMGy123QgL8vrcwYflYRrM9Zr5a61nk84nV7Dtb05+zVt+Qyvc73b/0WZjZ4B3mvLP9CfO9d4nO/TtLsGtb3G43xq1k5NXPKaR6tRk9Gkhzvf6XXfwwWbFista5dbhZ/ZaFKzKo3UpkZLj/GH83O0OmO929jCikKTmiQ38PoxzrUekUMOtyCyvP4Rh9PLEVteUMFakfRDuzXupyma0ONBKtgAAAAQdlarVXv27HE9Tk1NVadOnQKaY9GiRa5grcjy5cu1bds2NWjQICTrPJG0tDTXcWpqaqnmGjt2rFatWqWdO3fqrbfeUr9+/VSrVi2vY1988UU5HA7FxMRo4sSJFerfoYRrAFyiLVF6rOsIjftpitKzA39Do3ZCDT124QjeyEDY+PofbIQ5QrXiqwc0l7c31ktSp1JN3Xf+7QFd82Bn99/McTqdhSGEj9fRsHI9PdtrdLGAsLDKz+7wHRL2bNRFZ9U4wxUOFgV+zao08jo+xhKttjXPONY6tFi46Kvs3mAwyGgwynHcvnNBhYQ+XkeBw/uedqGs8iuX/el8BUYhDAl9v47AKzsDrsALY2gbzOsI6z6UPu6RV5Cv3Yf3en3O4OPvx1+71nptN3pZsx5ew7Xdh/dq8u9veZ3r/asme339U5a+rb+OCyJNBqMua95TN7bu5zF+x8F0vbT0nWKVhGaZjUaZDCbdf/4Qr/uoLtqyRDsPZRyrCDwaLjZJbqCW1Tx/sSHHmqv1+7a4gkHXnoRGk+pXqu31e5bVbpNRBsLBCmzGqs9L9YtekpSevVsfrJrtdb9MAAAAoDzNmTNHBQXH/r06c+ZM9evXTxZL6bc7OD5wKyu//vqrdu3a5Xo8d+5cDRgwIOj5KleurLfeeksDBw7Uvn37NGTIEH3wwQeqVKmS27hXXnlFixYtUkREhCZPnqxGjby/lxUuhGsA3CTFJGpCjwf92uOiuJ6NOmtAm/4Ea0ApFO056EuUJUoNKnvfg8+X8+qdHdD4lMp19HCXYQFd81jXEZIkh9Mhh8Ph2gfQYvT+g2LDyvX1ZPf7XcFgUejncDp8vtndrcG5OqNak2Nh39Hgr7GPPRejzZFqWbXJsSDy6Jrk9B3C+mwLGWDwVRQ2BnSPAMMfg8HgM+z0FTL5eh0lhYS+XkegYWdIW4GWR9gZTLgW8J6EZf86yjrstDt9/0PuSEGetmele3/Sx9/BP9JWatXufz3OX9G8l9dwLT17t5795VWvc31w1RRFGD0DvIlLXtOq3eskFX59F1XuXdr0Il3bqq/H+NSsnXp9+YdHxxkLw7ujlXsjOv2f1+/Zi//7XXty9nnsL9iwcj01rdLQY3yu9Yi2HthRrJLQLJPBKLPRpBrx1bz+PXQ6nadkOLh5/7aQtCiXpIVbflG3Bued9q3KAQAAUH6ys7O1e/duORwOHThwQEuWLNH777/vNmbNmjW69tprNWDAADVo0EBxcXGyWCwlVqD16NFD77zzjluY1rRpU6WkpIT8NeTm5iotLU1Op1PZ2dlasWKFpk+f7jYmPT1d119/vXr06KE6deooLi5OklS3rv/vWTVq1EgzZ87U/fffr7/++kuXXnqp7rjjDrVs2VJ79+7VZ599pp9//lk1a9bU888/r/bt24f0dYYC4RoAD9GWKA1u/z91b3C+5m/6UUtTV8rm5U1Oi9GsTnXbqXeTbrxxAUBGg1FGk1FmmSVF+hwXExGt5lUD+22jrg3ODWh8w6T6erz7fQFd8+RF97v2Fywe4EWZvb+WBpXr6pELh7u3Gj0aEvpyQf0OapLcwCP0S0ms7XV8pClCjSrXd40rWldJb6oH2haypKAs0CAy0Aoxo8FY5mGn0WAsISQMzeswyHfYGXhL0xKCyABfR2j3bwxd2BnOfSiLr8vhdMhhd8hmt8nhY64ca6427t/q/SY+vnZ/2b5MazLWe5zv1+Jir+HajqydenLxZK9zfXT1SzKaPD/vE35+SWszNhyrDDwa5PVu2k39WlzsMT41a6fe/etTt3CwKMwb2uEmr5/HJduXa3/uAY/WoSmV63r9RY8jtjylZ+92b0969F5J0Yl+hYHzN/14wjGBmL/pRw1PviWkcwIAAAC+LFq0SA8//PAJx61bt06jR492Pa5du7Z++OEHn+PbtWunF154QVOnTlVGRoY6dOigMWPGyGTy/u+e0lizZo0GDhx4wnGpqal655133M599NFHAd2rTp06+vjjj7Vw4ULNmTNH06ZN04EDBxQdHa2mTZtq9OjRuvbaaxUd7bkFQ0VAuAbAp8bJKRqefIuGdBigHQfTtSMrXfl2qyJNEapXqbbqJdZWhKn0JcwAUBEUVQ6a/fzxKD4yzmvLvZL0anxhQOObVmmop3s9FNA1Y7rdK5vDdjSIcxRWBjrtiouI9To+pXId3X/+EI/9BUvan7hjnbZKSazjCiILgz+H6iTU9Do+wmRRnYSaroCwMCz03c5UCl3Q4mu8FMT+dAGuSQqmqqzsw07f1Y1lH3aWVHUZeDBcQtjpq3o0hPs3Bhp2hnLfw4Kj+3Ta7Q7JbnOdzy+weh1/KD9HazI2eH3uzg7e/+H8/dYl+mfPRo/z/Vv29hqu/XcgVY//+KLXuT6+5mWvH+N3Vn6qW9pdK6mwZefS1JVerw/W0tSVGtJhgOvn1W0H0+RwOBRhsighKl4JkXEhvR8AAABOb/3791f//v3LZO4+ffqoT58+ZTJ3cR07dtSGDd7/7VCc1WrVmjWe+4sHymAwqFevXurVq1ep5ypvhGsATijCZFHj5BSq0wDgJBBpjlCkPNvh+ZIYlaBz6pwV0D0ua94joPEtqjbRi73HBHTNIxcOl9VuOy7AsysxKsHr+PqV6mh4x1s89hcsydm1zlTthBpulYR2R4FqxFX1Ot5kNKlqTJIrHCyqJPQVYkklBFkBhoS+qr1KvEeArUBLDAlDtD9dya8jNC00SwoiC+yBfT5KCnlDVtlZQtWlz9cRokA1uLAziMpOH0FklZjKruPtB9O8dkooDZujQDsOprt+hv1u80+utpP9Wlzsdb/ALZnb9czPr8hisshiMivCFKEIo1kWk0WPXni311agP279TXtzMxVhsijCZJHFWHhtSmJdpVSu4zHearcpKy9blqPjI4wW9gEEAAAATjKEawAAAKhw4iK9V9r5khSTqM4p5wR0zVVnBPZbf2dWb65XLhsf0DUPXnCn8uz5bq1GCxx2VY1N8jq+XqVaGtz+f8eq/I5WExrk+0331jVaqFpsFY8g0tc9DAaD4iPj3MbanY7QVvn5Cgl9jC/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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_name = '额外产能分布参数P6'\n",
+    "y_choose = [0, 1, 2, 3]\n",
+    "y_prop = pd.DataFrame({'y_name': ['系统恢复用时R1', '产业-企业边累计扰乱次数R2', '产业-企业边最大传导深度R3', '产业-企业边断裂总数R4'],\n",
+    "                       'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
+    "                       'palette': sns.color_palette(\"deep\")[0:4]})\n",
+    "df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
+    ").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
+    "df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
+    "sns.set_theme(style=\"whitegrid\", rc=config)\n",
+    "ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
+    "                  markers=['o'],\n",
+    "                  dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
+    "                  palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
+    "                  legend='brief')\n",
+    "ax.set_title(x_name)\n",
+    "for item in df_x.groupby('响应变量'):\n",
+    "    for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
+    "        ax.text(x, y+0.05, f'{m:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_name = '新供应关系构成概率P7'\n",
+    "y_choose =[1]\n",
+    "y_prop = pd.DataFrame({'y_name': ['系统恢复用时R1', '产业-企业边累计扰乱次数R2', '产业-企业边最大传导深度R3', '产业-企业边断裂总数R4'],\n",
+    "                       'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
+    "                       'palette': sns.color_palette(\"deep\")[0:4]})\n",
+    "df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
+    ").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
+    "df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
+    "sns.set_theme(style=\"whitegrid\", rc=config)\n",
+    "ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
+    "                  markers=['o'],\n",
+    "                  dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
+    "                  palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
+    "                  legend='brief')\n",
+    "ax.set_title(x_name)\n",
+    "for item in df_x.groupby('响应变量'):\n",
+    "    for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
+    "        ax.text(x, y+0.0005, f'{m:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_27216\\1838672856.py:10: UserWarning: \n",
+      "The markers list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
+      "  ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_name = '最大尝试时间步P8'\n",
+    "y_choose=[0,1,2]\n",
+    "y_prop = pd.DataFrame({'y_name': ['系统恢复用时R1', '产业-企业边累计扰乱次数R2', '产业-企业边最大传导深度R3', '产业-企业边断裂总数R4'],\n",
+    "                       'line_style': [(1, 0),(3, 1), (1,1), (3,2,1,2)],\n",
+    "                       'palette': sns.color_palette(\"deep\")[0:4]})\n",
+    "df_x = df.loc[df['自变量'] == x_name, 'level':].set_index('level').stack(\n",
+    ").reset_index().rename(columns={'level': '水平', 'level_1': '响应变量', 0: '均值'})\n",
+    "df_x = df_x.loc[df_x['响应变量'].isin(y_prop.loc[y_choose]['y_name'])]\n",
+    "sns.set_theme(style=\"whitegrid\", rc=config)\n",
+    "ax = sns.lineplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\", style=\"响应变量\",\n",
+    "                  markers=['o'],\n",
+    "                  dashes=y_prop.loc[y_choose]['line_style'].to_list(),\n",
+    "                  palette=y_prop.loc[y_choose]['palette'].to_list(),\n",
+    "                  legend='brief')\n",
+    "ax.set_title(x_name)\n",
+    "for item in df_x.groupby('响应变量'):\n",
+    "    for x, y, m in item[1][['水平', '均值', '均值']].values:\n",
+    "        ax.text(x, y+0.05, f'{m:.2f}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "iiabm_py3.8.8",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/anova_visualization.py b/anova_visualization.py
new file mode 100644
index 0000000..80baf13
--- /dev/null
+++ b/anova_visualization.py
@@ -0,0 +1,14 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+df_anova = pd.read_csv('analysis/anova.csv', index_col=0)
+df_anova = df_anova.stack().reset_index()
+df_anova.rename(columns={'level_0': 'x',
+                         'level_1': 'y type',
+                         0: 'p value'}, inplace=True)
+print(df_anova)
+sns.set_theme(style="whitegrid")
+g = sns.catplot(data=df_anova, kind="bar", x="x", y="p value", hue="y type")
+g.set_xticklabels(rotation=30)
+plt.show()
diff --git a/conf_db_prefix.yaml b/conf_db_prefix.yaml
index 42cedea..1391188 100644
--- a/conf_db_prefix.yaml
+++ b/conf_db_prefix.yaml
@@ -1 +1 @@
-db_name_prefix: test
+db_name_prefix: with_exp
diff --git a/conf_experiment.yaml b/conf_experiment.yaml
index 5566629..83d2795 100644
--- a/conf_experiment.yaml
+++ b/conf_experiment.yaml
@@ -1,7 +1,7 @@
 # read by ControllerDB
 
 # run settings
-meta_seed: 0
+meta_seed: 1
 
 test:  # only for test scenarios
   n_sample: 1
diff --git a/controller_db.py b/controller_db.py
index bb1b520..5e4ab77 100644
--- a/controller_db.py
+++ b/controller_db.py
@@ -54,24 +54,13 @@ class ControllerDB:
         # fill dct_lst_init_disrupt_firm_prod
         list_dct = []
         if self.is_with_exp:
-            str_sql = "select e_id, count, max_max_ts, " \
-                "dct_lst_init_disrupt_firm_prod from " \
-                "iiabmdb.without_exp_experiment as a " \
-                "inner join " \
-                "(select e_id, count(id) as count, max(max_ts) as max_max_ts "\
-                "from iiabmdb.without_exp_sample as a " \
-                "inner join (select s_id, max(ts) as max_ts from " \
-                "iiabmdb.without_exp_result where ts > 0 group by s_id) as b "\
-                "on a.id = b.s_id " \
-                "group by e_id) as b " \
-                "on a.id = b.e_id " \
-                "order by count desc;"
+            with open('SQL_export_high_risk_setting.sql', 'r') as f:
+                str_sql = f.read()
             result = pd.read_sql(sql=str_sql, con=engine)
             result['dct_lst_init_disrupt_firm_prod'] = \
                 result['dct_lst_init_disrupt_firm_prod'].apply(
                 lambda x: pickle.loads(x))
-            list_dct = result.loc[result['count'] > 10,
-                                  'dct_lst_init_disrupt_firm_prod'].to_list()
+            list_dct = result['dct_lst_init_disrupt_firm_prod'].to_list()
         else:
             for _, row in Firm.iterrows():
                 code = row['Code']
@@ -82,10 +71,11 @@ class ControllerDB:
         # list_dct = [{'140': ['1.4.5.1']}]
         # list_dct = [{'133': ['1.4.4.1']}]
         # list_dct = [{'2': ['1.1.3']}]
-        list_dct = [{'135': ['1.3.2.1']}]
+        # list_dct = [{'135': ['1.3.2.1']}]
         # list_dct = [{'79': ['2.1.3.4']}]
         # list_dct = [{'99': ['1.3.3']}]
         # list_dct = [{'41': ['1.4.5']}]
+        # list_dct = [{'168': ['1.1.2']}]
 
         # fill g_bom
         BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
@@ -128,8 +118,7 @@ class ControllerDB:
                          dct_lst_init_disrupt_firm_prod, g_bom,
                          n_max_trial, prf_size, prf_conn,
                          cap_limit_prob_type, cap_limit_level,
-                         diff_new_conn, crit_supplier,
-                         proactive_ratio, remove_t, netw_prf_n):
+                         diff_new_conn, remove_t, netw_prf_n):
         e = Experiment(
             idx_scenario=idx_scenario,
             idx_init_removal=idx_init_removal,
@@ -143,8 +132,6 @@ class ControllerDB:
             cap_limit_prob_type=cap_limit_prob_type,
             cap_limit_level=cap_limit_level,
             diff_new_conn=diff_new_conn,
-            crit_supplier=crit_supplier,
-            proactive_ratio=proactive_ratio,
             remove_t=remove_t,
             netw_prf_n=netw_prf_n
         )
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diff --git a/firm.py b/firm.py
index 10c1166..e322b84 100644
--- a/firm.py
+++ b/firm.py
@@ -1,5 +1,4 @@
 import agentpy as ap
-import math
 
 
 class FirmAgent(ap.Agent):
@@ -7,7 +6,7 @@ class FirmAgent(ap.Agent):
         self.firm_network = self.model.firm_network
         self.product_network = self.model.product_network
 
-        # self para
+        # self parameter
         self.code = code
         self.name = name
         self.type_region = type_region
@@ -15,7 +14,7 @@ class FirmAgent(ap.Agent):
         self.dct_prod_up_prod_stat = {}
         self.dct_prod_capacity = {}
 
-        # para in trial
+        # parameter in trial
         self.dct_n_trial_up_prod_disrupted = {}
         self.dct_cand_alt_supp_up_prod_disrupted = {}
         self.dct_request_prod_from_firm = {}
@@ -26,24 +25,27 @@ class FirmAgent(ap.Agent):
         self.str_cap_limit_prob_type = str(self.p.cap_limit_prob_type)
         self.flt_cap_limit_level = float(self.p.cap_limit_level)
         self.flt_diff_new_conn = float(self.p.diff_new_conn)
-        self.flt_crit_supplier = float(self.p.crit_supplier)
 
-        # init size_stat (self para)
+        # initialize size_stat (self parameter)
         # (size, time step)
         self.size_stat.append((revenue_log, 0))
 
-        # init dct_prod_up_prod_stat (self para)
+        # init dct_prod_up_prod_stat (self parameter)
         for prod in a_lst_product:
             self.dct_prod_up_prod_stat[prod] = {
-                # (Normal / Disrupted / Removed, time step)
-                'status': [('N', 0)],
-                # have or have no supply
-                'supply': dict.fromkeys(prod.a_predecessors(), True)
+                # status: (Normal / Disrupted / Removed, time step)
+                'p_stat': [('N', 0)],
+                # supply for each component and respective disrupted supplier
+                # set_disrupt_firm is refreshed to empty at each update
+                's_stat': {up_prod: {'stat': True,
+                                     'set_disrupt_firm': set()}
+                           for up_prod in prod.a_predecessors()}
+                # Note: do not use fromkeys as it's a shallow copy
             }
 
-        # init extra capacity (self para)
+        # initialize extra capacity (self parameter)
         for product in a_lst_product:
-            # init extra capacity based on discrete uniform distribution
+            # initialize extra capacity based on discrete uniform distribution
             assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
                 "cap_limit_prob_type other than uniform, normal"
             if self.str_cap_limit_prob_type == 'uniform':
@@ -52,59 +54,73 @@ class FirmAgent(ap.Agent):
                 extra_cap = self.model.nprandom.integers(extra_cap_mean-2,
                                                          extra_cap_mean+2)
                 extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
-                # print(firm_agent.name, extra_cap)
                 self.dct_prod_capacity[product] = extra_cap
             elif self.str_cap_limit_prob_type == 'normal':
                 extra_cap_mean = \
                     self.size_stat[0][0] / self.flt_cap_limit_level
                 extra_cap = self.model.nprandom.normal(extra_cap_mean, 1)
                 extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
-                # print(firm_agent.name, extra_cap)
                 self.dct_prod_capacity[product] = extra_cap
 
-    def remove_edge_to_cus_disrupt_cus_up_prod(self, disrupted_prod):
-        # para disrupted_prod is the product that self got disrupted
+    def remove_edge_to_cus(self, disrupted_prod):
+        # parameter disrupted_prod is the product that self got disrupted
         lst_out_edge = list(
             self.firm_network.graph.out_edges(
                 self.firm_network.positions[self], keys=True, data='Product'))
         for n1, n2, key, product_code in lst_out_edge:
             if product_code == disrupted_prod.code:
+                # update customer up product supplier status
+                customer = ap.AgentIter(self.model, n2).to_list()[0]
+                for prod in customer.dct_prod_up_prod_stat.keys():
+                    if disrupted_prod in \
+                            customer.dct_prod_up_prod_stat[
+                                prod]['s_stat'].keys():
+                        customer.dct_prod_up_prod_stat[
+                            prod]['s_stat'][disrupted_prod][
+                            'set_disrupt_firm'].add(self)
+                        # print(f"{self.name} disrupt {customer.name}'s "
+                        #       f"{prod.code} due to {disrupted_prod.code}")
                 # remove edge to customer
                 self.firm_network.graph.remove_edge(n1, n2, key)
 
-                # customer up product affected conditionally
-                customer = ap.AgentIter(self.model, n2).to_list()[0]
-                lst_in_edge = list(
-                    self.firm_network.graph.in_edges(n2,
-                                                     keys=True,
-                                                     data='Product'))
-                lst_select_in_edge = [
-                    edge for edge in lst_in_edge
-                    if edge[-1] == disrupted_prod.code
-                ]
-                prob_lost_supp = math.exp(-1 * self.flt_crit_supplier *
-                                          len(lst_select_in_edge))
-                if self.model.nprandom.choice([True, False],
-                                              p=[prob_lost_supp,
-                                                 1 - prob_lost_supp]):
-                    customer.dct_n_trial_up_prod_disrupted[disrupted_prod] = 0
-                    for prod in customer.dct_prod_up_prod_stat.keys():
-                        if disrupted_prod in \
-                                customer.dct_prod_up_prod_stat[
-                                    prod]['supply'].keys():
-                            customer.dct_prod_up_prod_stat[
-                                prod]['supply'][disrupted_prod] = False
-                            status, _ = customer.dct_prod_up_prod_stat[
-                                prod]['status'][-1]
-                            if status != 'D':
-                                customer.dct_prod_up_prod_stat[
-                                    prod]['status'].append(('D', self.model.t))
-                            print(self.name, disrupted_prod.code, 'disrupt',
-                                  customer.name, prod.code)
+    def disrupt_cus_prod(self, prod, disrupted_up_prod):
+        # parameter prod is the product that has disrupted_up_prod
+        # parameter disrupted_up_prod is the product that
+        # self's component exists disrupted supplier
+        num_lost = \
+            len(self.dct_prod_up_prod_stat[prod]['s_stat']
+                [disrupted_up_prod]['set_disrupt_firm'])
+        num_remain = \
+            len([u for u, _, _, d in
+                 self.firm_network.graph.in_edges(self.get_firm_network_node(),
+                                                  keys=True,
+                                                  data='Product')
+                 if d == disrupted_up_prod.code])
+        lost_percent = num_lost / (num_lost + num_remain)
+        lst_size = \
+            [firm.size_stat[-1][0] for firm in self.model.a_lst_total_firms]
+        std_size = (self.size_stat[-1][0] - min(lst_size) + 1) \
+            / (max(lst_size) - min(lst_size) + 1)
+
+        # calculate probability of disruption
+        prob_disrupt = 1 - std_size * (1 - lost_percent)
+        if self.model.nprandom.choice([True, False],
+                                      p=[prob_disrupt,
+                                         1 - prob_disrupt]):
+            self.dct_n_trial_up_prod_disrupted[disrupted_up_prod] = 0
+            self.dct_prod_up_prod_stat[
+                prod]['s_stat'][disrupted_up_prod]['stat'] = False
+            status, _ = self.dct_prod_up_prod_stat[
+                prod]['p_stat'][-1]
+            if status != 'D':
+                self.dct_prod_up_prod_stat[
+                    prod]['p_stat'].append(('D', self.model.t))
+                # print(f"{self.name}'s {prod.code} turn to D status due to "
+                #       f"disrupted supplier of {disrupted_up_prod.code}")
 
     def seek_alt_supply(self, product):
-        # para product is the product that self is seeking
-        print(f"{self.name} seek alt supply for {product.code}")
+        # parameter product is the product that self is seeking
+        # print(f"{self.name} seek alt supply for {product.code}")
         if self.dct_n_trial_up_prod_disrupted[
                 product] <= self.model.int_n_max_trial:
             if self.dct_n_trial_up_prod_disrupted[product] == 0:
@@ -120,18 +136,12 @@ class FirmAgent(ap.Agent):
                 if self.is_prf_conn:
                     for firm in \
                             self.dct_cand_alt_supp_up_prod_disrupted[product]:
-                        out_edges = self.model.firm_network.graph.out_edges(
-                            self.model.firm_network.positions[firm], keys=True)
-                        in_edges = self.model.firm_network.graph.in_edges(
-                            self.model.firm_network.positions[firm], keys=True)
-                        lst_adj_firm = []
-                        lst_adj_firm += \
-                            [ap.AgentIter(self.model, edge[1]).to_list()[
-                                0].code for edge in out_edges]
-                        lst_adj_firm += \
-                            [ap.AgentIter(self.model, edge[0]).to_list()[
-                                0].code for edge in in_edges]
-                        if self.code in lst_adj_firm:
+                        node_self = self.get_firm_network_node()
+                        node_firm = firm.get_firm_network_node()
+                        if self.model.firm_network.graph.\
+                                has_edge(node_self, node_firm) or \
+                                self.model.firm_network.graph.\
+                                has_edge(node_firm, node_self):
                             lst_firm_connect.append(firm)
                 if len(lst_firm_connect) == 0:
                     # select based on size or not
@@ -163,10 +173,10 @@ class FirmAgent(ap.Agent):
                     else:
                         select_alt_supply = \
                             self.model.nprandom.choice(lst_firm_connect)
-                print(
-                    f"{self.name} selct alt supply for {product.code} "
-                    f"from {select_alt_supply.name}"
-                )
+                # print(
+                #     f"{self.name} selct alt supply for {product.code} "
+                #     f"from {select_alt_supply.name}"
+                # )
                 assert select_alt_supply.is_prod_in_current_normal(product), \
                     f"{select_alt_supply} \
                         does not produce requested product {product}"
@@ -179,17 +189,17 @@ class FirmAgent(ap.Agent):
                     select_alt_supply.dct_request_prod_from_firm[product] = [
                         self
                     ]
-                print(
-                    select_alt_supply.name, 'dct_request_prod_from_firm', {
-                        key.code: [v.name for v in value]
-                        for key, value in
-                        select_alt_supply.dct_request_prod_from_firm.items()
-                    })
+                # print(
+                #     select_alt_supply.name, 'dct_request_prod_from_firm', {
+                #         key.code: [v.name for v in value]
+                #         for key, value in
+                #         select_alt_supply.dct_request_prod_from_firm.items()
+                #     })
 
                 self.dct_n_trial_up_prod_disrupted[product] += 1
 
     def handle_request(self):
-        print(self.name, 'handle_request')
+        # print(self.name, 'handle_request')
         for product, lst_firm in self.dct_request_prod_from_firm.items():
             if self.dct_prod_capacity[product] > 0:
                 if len(lst_firm) == 0:
@@ -201,22 +211,12 @@ class FirmAgent(ap.Agent):
                     lst_firm_connect = []
                     if self.is_prf_conn:
                         for firm in lst_firm:
-                            out_edges = \
-                                self.model.firm_network.graph.out_edges(
-                                    self.model.firm_network.positions[firm],
-                                    keys=True)
-                            in_edges = \
-                                self.model.firm_network.graph.in_edges(
-                                    self.model.firm_network.positions[firm],
-                                    keys=True)
-                            lst_adj_firm = []
-                            lst_adj_firm += \
-                                [ap.AgentIter(self.model, edge[1]).to_list()[
-                                    0].code for edge in out_edges]
-                            lst_adj_firm += \
-                                [ap.AgentIter(self.model, edge[0]).to_list()[
-                                    0].code for edge in in_edges]
-                            if self.code in lst_adj_firm:
+                            node_self = self.get_firm_network_node()
+                            node_firm = firm.get_firm_network_node()
+                            if self.model.firm_network.graph.\
+                                    has_edge(node_self, node_firm) or \
+                                    self.model.firm_network.graph.\
+                                    has_edge(node_firm, node_self):
                                 lst_firm_connect.append(firm)
                     if len(lst_firm_connect) == 0:
                         # handling based on size or not
@@ -254,14 +254,21 @@ class FirmAgent(ap.Agent):
                     down_firm.dct_cand_alt_supp_up_prod_disrupted[
                         product].remove(self)
 
-                    print(
-                        f"{self.name} denied {product.code} request "
-                        f"from {down_firm.name} for lack of capacity"
-                    )
+                    # print(
+                    #     f"{self.name} denied {product.code} request "
+                    #     f"from {down_firm.name} for lack of capacity"
+                    # )
 
     def accept_request(self, down_firm, product):
-        # para product is the product that self is selling
-        prod_accept = self.flt_diff_new_conn
+        # parameter product is the product that self is selling
+        # connected firm has no probability for accepting request
+        node_self = self.get_firm_network_node()
+        node_d_firm = down_firm.get_firm_network_node()
+        if self.model.firm_network.graph.has_edge(node_self, node_d_firm) or \
+                self.model.firm_network.graph.has_edge(node_d_firm, node_self):
+            prod_accept = 1.0
+        else:
+            prod_accept = self.flt_diff_new_conn
         if self.model.nprandom.choice([True, False],
                                       p=[prod_accept, 1 - prod_accept]):
             self.firm_network.graph.add_edges_from([
@@ -275,25 +282,25 @@ class FirmAgent(ap.Agent):
 
             for prod in down_firm.dct_prod_up_prod_stat.keys():
                 if product in down_firm.dct_prod_up_prod_stat[
-                        prod]['supply'].keys():
+                        prod]['s_stat'].keys():
                     down_firm.dct_prod_up_prod_stat[
-                        prod]['supply'][product] = True
+                        prod]['s_stat'][product]['stat'] = True
                     down_firm.dct_prod_up_prod_stat[
-                        prod]['status'].append(('N', self.model.t))
+                        prod]['p_stat'].append(('N', self.model.t))
             del down_firm.dct_n_trial_up_prod_disrupted[product]
             del down_firm.dct_cand_alt_supp_up_prod_disrupted[product]
 
-            print(
-                f"{self.name} accept {product.code} request "
-                f"from {down_firm.name}"
-            )
+            # print(
+            #     f"{self.name} accept {product.code} request "
+            #     f"from {down_firm.name}"
+            # )
         else:
             down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
 
-            print(
-                f"{self.name} denied {product.code} request "
-                f"from {down_firm.name}"
-            )
+            # print(
+            #     f"{self.name} denied {product.code} request "
+            #     f"from {down_firm.name}"
+            # )
 
     def clean_before_trial(self):
         self.dct_request_prod_from_firm = {}
@@ -305,17 +312,21 @@ class FirmAgent(ap.Agent):
 
         # update the status of firm
         for prod in self.dct_prod_up_prod_stat.keys():
-            status, ts = self.dct_prod_up_prod_stat[prod]['status'][-1]
+            status, ts = self.dct_prod_up_prod_stat[prod]['p_stat'][-1]
             if ts != self.model.t:
-                self.dct_prod_up_prod_stat[prod]['status'].append(
+                self.dct_prod_up_prod_stat[prod]['p_stat'].append(
                     (status, self.model.t))
+            # refresh set_disrupt_firm
+            for up_prod in self.dct_prod_up_prod_stat[prod]['s_stat'].keys():
+                self.dct_prod_up_prod_stat[prod][
+                    's_stat'][up_prod]['set_disrupt_firm'] = set()
 
     def get_firm_network_node(self):
         return self.firm_network.positions[self]
 
     def is_prod_in_current_normal(self, prod):
         if prod in self.dct_prod_up_prod_stat.keys():
-            if self.dct_prod_up_prod_stat[prod]['status'][-1][0] == 'N':
+            if self.dct_prod_up_prod_stat[prod]['p_stat'][-1][0] == 'N':
                 return True
             else:
                 return False
diff --git a/firm_n_prod.csv b/firm_n_prod.csv
new file mode 100644
index 0000000..2775e46
--- /dev/null
+++ b/firm_n_prod.csv
@@ -0,0 +1,172 @@
+code,n_prod
+0,1
+1,1
+2,1
+3,4
+4,1
+5,4
+6,5
+7,1
+8,1
+9,2
+10,1
+11,1
+12,1
+13,17
+14,2
+15,1
+16,4
+17,1
+18,1
+19,1
+20,1
+21,1
+22,24
+23,10
+24,1
+25,1
+26,7
+27,1
+28,1
+29,1
+30,1
+31,7
+32,1
+33,4
+34,1
+35,1
+36,1
+37,6
+38,5
+39,1
+40,4
+41,7
+42,3
+43,2
+44,1
+45,9
+46,1
+47,9
+48,1
+49,8
+50,1
+51,1
+52,1
+53,15
+54,3
+55,6
+56,2
+57,4
+58,7
+59,1
+60,5
+61,1
+62,5
+63,3
+64,1
+65,1
+66,1
+67,1
+68,3
+69,1
+70,2
+71,1
+72,1
+73,1
+74,2
+75,1
+76,1
+77,2
+78,5
+79,16
+80,2
+81,4
+82,4
+83,1
+84,3
+85,2
+86,1
+87,1
+88,1
+89,3
+90,1
+91,1
+92,1
+93,1
+94,1
+95,2
+96,2
+97,3
+98,1
+99,6
+100,1
+101,1
+102,2
+103,1
+104,1
+105,1
+106,6
+107,1
+108,2
+109,1
+110,1
+111,3
+112,1
+113,1
+114,1
+115,2
+116,1
+117,11
+118,1
+119,1
+120,1
+121,1
+122,1
+123,1
+124,2
+125,1
+126,7
+127,2
+128,1
+129,2
+130,5
+131,5
+132,1
+133,2
+134,1
+135,11
+136,1
+137,6
+138,1
+139,1
+140,7
+141,1
+142,3
+143,5
+144,4
+145,1
+146,1
+147,1
+148,3
+149,4
+150,1
+151,1
+152,1
+153,2
+154,6
+155,1
+156,1
+157,1
+158,1
+159,1
+160,1
+161,3
+162,2
+163,6
+164,1
+165,4
+166,1
+167,1
+168,7
+169,1
+170,1
diff --git a/model.py b/model.py
index 034487c..0d41cd6 100644
--- a/model.py
+++ b/model.py
@@ -10,7 +10,7 @@ import json
 
 class Model(ap.Model):
     def setup(self):
-        # self para
+        # self parameter
         self.sample = self.p.sample
         self.int_stop_ts = 0
         self.int_n_iter = int(self.p.n_iter)
@@ -23,15 +23,15 @@ class Model(ap.Model):
         # external variable
         self.int_n_max_trial = int(self.p.n_max_trial)
         self.is_prf_size = bool(self.p.prf_size)
-        self.proactive_ratio = float(self.p.proactive_ratio)
+        # self.proactive_ratio = float(self.p.proactive_ratio) # dropped
         self.remove_t = int(self.p.remove_t)
         self.int_netw_prf_n = int(self.p.netw_prf_n)
 
-        # init graph bom
+        # initialize graph bom
         G_bom = nx.adjacency_graph(json.loads(self.p.g_bom))
         self.product_network = ap.Network(self, G_bom)
 
-        # init graph firm
+        # initialize graph firm
         Firm = pd.read_csv("Firm_amended.csv")
         Firm['Code'] = Firm['Code'].astype('string')
         Firm.fillna(0, inplace=True)
@@ -49,7 +49,7 @@ class Model(ap.Model):
             firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
         nx.set_node_attributes(G_Firm, firm_labels_dict)
 
-        # init graph firm prod
+        # initialize graph firm prod
         Firm_Prod = pd.read_csv("Firm_amended.csv")
         Firm_Prod.fillna(0, inplace=True)
         firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
@@ -122,11 +122,12 @@ class Model(ap.Model):
         # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
         # nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
 
-        # unconnected node
+        # connect unconnected nodes
         for node in nx.nodes(G_Firm):
             if G_Firm.degree(node) == 0:
                 for product_code in G_Firm.nodes[node]['Product_Code']:
-                    # unconnect node does not have possible suppliers
+                    # unconnected node does not have possible suppliers,
+                    # therefore find possible customer instead
                     # current node in graph firm prod
                     current_node = \
                         [n for n, v in G_FirmProd.nodes(data=True)
@@ -135,10 +136,10 @@ class Model(ap.Model):
 
                     lst_succ_product_code = list(
                         G_bom.successors(product_code))
-                    # different from for different types of product,
+                    # different from: for different types of product,
                     # finding a common supplier (the logic above),
-                    # for different types of product,
-                    # finding a custormer for each product
+                    # instead: for different types of product,
+                    # finding a customer for each product
                     for succ_product_code in lst_succ_product_code:
                         # for each product successor (finished product)
                         # the firm sells to,
@@ -187,14 +188,14 @@ class Model(ap.Model):
         # nx.draw(G_FirmProd)
         # plt.show()
 
-        # init product
+        # initialize product
         for ag_node, attr in self.product_network.graph.nodes(data=True):
             product = ProductAgent(self, code=ag_node.label, name=attr['Name'])
             self.product_network.add_agents([product], [ag_node])
         self.a_lst_total_products = ap.AgentList(self,
                                                  self.product_network.agents)
 
-        # init firm
+        # initialize firm
         for ag_node, attr in self.firm_network.graph.nodes(data=True):
             firm_agent = FirmAgent(
                 self,
@@ -210,7 +211,7 @@ class Model(ap.Model):
             self.firm_network.add_agents([firm_agent], [ag_node])
         self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents)
 
-        # init dct_lst_init_disrupt_firm_prod (from string to agent)
+        # initialize dct_lst_init_disrupt_firm_prod (from string to agent)
         t_dct = {}
         for firm_code, lst_product in \
                 self.dct_lst_init_disrupt_firm_prod.items():
@@ -222,123 +223,123 @@ class Model(ap.Model):
         self.dct_lst_init_disrupt_firm_prod = t_dct
 
         # set the initial firm product that are disrupted
-        print('\n', '=' * 20, 'step', self.t, '=' * 20)
+        # print('\n', '=' * 20, 'step', self.t, '=' * 20)
         for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
             for product in a_lst_product:
                 assert product in firm.dct_prod_up_prod_stat.keys(), \
                     f"product {product.code} not in firm {firm.code}"
                 firm.dct_prod_up_prod_stat[
-                    product]['status'].append(('D', self.t))
-                print(f"initial disruption {firm.name} {product.code}")
+                    product]['p_stat'].append(('D', self.t))
+                # print(f"initial disruption {firm.name} {product.code}")
 
-        # proactive strategy
-        # get all the firm prod affected
-        for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
-            for product in a_lst_product:
-                init_node = \
-                    [n for n, v in
-                     self.firm_prod_network.nodes(data=True)
-                     if v['Firm_Code'] == firm.code and
-                     v['Product_Code'] == product.code][0]
-                dct_affected = \
-                    nx.dfs_successors(self.firm_prod_network,
-                                      init_node)
-                lst_affected = set()
-                for i, (u, vs) in enumerate(dct_affected.items()):
-                    # at least 2 hops away
-                    if i > 0:
-                        pred_node = self.firm_prod_network.nodes[u]
-                        for v in vs:
-                            succ_node = self.firm_prod_network.nodes[v]
-                            lst_affected.add((succ_node['Firm_Code'],
-                                              succ_node['Product_Code']))
-                lst_affected = list(lst_affected)
-                lst_firm_proactive = \
-                    [lst_affected[i] for i in
-                     self.nprandom.choice(range(len(lst_affected)),
-                                          round(len(lst_affected) *
-                                                self.proactive_ratio),
-                                          replace=False)]
+        # # proactive strategy (dropped)
+        # # get all the firm prod affected
+        # for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
+        #     for product in a_lst_product:
+        #         init_node = \
+        #             [n for n, v in
+        #              self.firm_prod_network.nodes(data=True)
+        #              if v['Firm_Code'] == firm.code and
+        #              v['Product_Code'] == product.code][0]
+        #         dct_affected = \
+        #             nx.dfs_successors(self.firm_prod_network,
+        #                               init_node)
+        #         lst_affected = set()
+        #         for i, (u, vs) in enumerate(dct_affected.items()):
+        #             # at least 2 hops away
+        #             if i > 0:
+        #                 pred_node = self.firm_prod_network.nodes[u]
+        #                 for v in vs:
+        #                     succ_node = self.firm_prod_network.nodes[v]
+        #                     lst_affected.add((succ_node['Firm_Code'],
+        #                                       succ_node['Product_Code']))
+        #         lst_affected = list(lst_affected)
+        #         lst_firm_proactive = \
+        #             [lst_affected[i] for i in
+        #              self.nprandom.choice(range(len(lst_affected)),
+        #                                   round(len(lst_affected) *
+        #                                         self.proactive_ratio),
+        #                                   replace=False)]
 
-                for firm_code, prod_code in lst_firm_proactive:
-                    pro_firm_prod_code = \
-                        [n for n, v in
-                         self.firm_prod_network.nodes(data=True)
-                         if v['Firm_Code'] == firm_code and
-                         v['Product_Code'] == prod_code][0]
-                    pro_firm_prod_node = \
-                        self.firm_prod_network.nodes[pro_firm_prod_code]
-                    pro_firm = \
-                        self.a_lst_total_firms.select(
-                            [firm.code == pro_firm_prod_node['Firm_Code']
-                             for firm in self.a_lst_total_firms])[0]
-                    lst_shortest_path = \
-                        list(nx.all_shortest_paths(self.firm_prod_network,
-                                                   source=init_node,
-                                                   target=pro_firm_prod_code))
+        #         for firm_code, prod_code in lst_firm_proactive:
+        #             pro_firm_prod_code = \
+        #                 [n for n, v in
+        #                  self.firm_prod_network.nodes(data=True)
+        #                  if v['Firm_Code'] == firm_code and
+        #                  v['Product_Code'] == prod_code][0]
+        #             pro_firm_prod_node = \
+        #                 self.firm_prod_network.nodes[pro_firm_prod_code]
+        #             pro_firm = \
+        #                 self.a_lst_total_firms.select(
+        #                     [firm.code == pro_firm_prod_node['Firm_Code']
+        #                      for firm in self.a_lst_total_firms])[0]
+        #             lst_shortest_path = \
+        #                 list(nx.all_shortest_paths(self.firm_prod_network,
+        #                                            source=init_node,
+        #                                            target=pro_firm_prod_code))
 
-                    dct_drs = {}
-                    for di_supp_code in self.firm_prod_network.predecessors(
-                            pro_firm_prod_code):
-                        di_supp_node = \
-                            self.firm_prod_network.nodes[di_supp_code]
-                        di_supp_prod = \
-                            self.a_lst_total_products.select(
-                                [product.code == di_supp_node['Product_Code']
-                                 for product in self.a_lst_total_products])[0]
-                        di_supp_firm = \
-                            self.a_lst_total_firms.select(
-                                [firm.code == di_supp_node['Firm_Code']
-                                 for firm in self.a_lst_total_firms])[0]
-                        lst_cand = self.a_lst_total_firms.select([
-                            firm.is_prod_in_current_normal(di_supp_prod)
-                            for firm in self.a_lst_total_firms
-                        ])
-                        n2n_betweenness = \
-                            sum([True if di_supp_code in path else False
-                                 for path in lst_shortest_path]) \
-                            / len(lst_shortest_path)
-                        drs = n2n_betweenness / \
-                            (len(lst_cand) * di_supp_firm.size_stat[-1][0])
-                        dct_drs[di_supp_code] = drs
-                    dct_drs = dict(sorted(
-                        dct_drs.items(), key=lambda kv: kv[1], reverse=True))
-                    for di_supp_code in dct_drs.keys():
-                        di_supp_node = \
-                            self.firm_prod_network.nodes[di_supp_code]
-                        di_supp_prod = \
-                            self.a_lst_total_products.select(
-                                [product.code == di_supp_node['Product_Code']
-                                 for product in self.a_lst_total_products])[0]
-                        # find a dfferent firm can produce the same product
-                        # and is not a current supplier for the same product
-                        lst_current_supp_code = \
-                            [self.firm_prod_network.nodes[code]['Firm_Code']
-                             for code in self.firm_prod_network.predecessors(
-                                pro_firm_prod_code)
-                             if self.firm_prod_network.nodes[code][
-                                 'Product_Code'] == di_supp_prod.code]
-                        lst_cand = self.model.a_lst_total_firms.select([
-                            firm.is_prod_in_current_normal(di_supp_prod)
-                            and firm.code not in lst_current_supp_code
-                            for firm in self.model.a_lst_total_firms
-                        ])
-                        if len(lst_cand) > 0:
-                            select_cand = self.nprandom.choice(lst_cand)
-                            self.firm_network.graph.add_edges_from([
-                                (self.firm_network.positions[select_cand],
-                                 self.firm_network.positions[pro_firm], {
-                                    'Product': di_supp_prod.code
-                                })
-                            ])
-                            print(f"proactive add {select_cand.name} to "
-                                  f"{pro_firm.name} "
-                                  f"for {di_supp_node['Firm_Code']} "
-                                  f"{di_supp_node['Product_Code']}")
-                            # change capacity
-                            select_cand.dct_prod_capacity[di_supp_prod] -= 1
-                            break
-        # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm_proactive.csv')
+        #             dct_drs = {}
+        #             for di_supp_code in self.firm_prod_network.predecessors(
+        #                     pro_firm_prod_code):
+        #                 di_supp_node = \
+        #                     self.firm_prod_network.nodes[di_supp_code]
+        #                 di_supp_prod = \
+        #                     self.a_lst_total_products.select(
+        #                         [product.code == di_supp_node['Product_Code']
+        #                          for product in self.a_lst_total_products])[0]
+        #                 di_supp_firm = \
+        #                     self.a_lst_total_firms.select(
+        #                         [firm.code == di_supp_node['Firm_Code']
+        #                          for firm in self.a_lst_total_firms])[0]
+        #                 lst_cand = self.a_lst_total_firms.select([
+        #                     firm.is_prod_in_current_normal(di_supp_prod)
+        #                     for firm in self.a_lst_total_firms
+        #                 ])
+        #                 n2n_betweenness = \
+        #                     sum([True if di_supp_code in path else False
+        #                          for path in lst_shortest_path]) \
+        #                     / len(lst_shortest_path)
+        #                 drs = n2n_betweenness / \
+        #                     (len(lst_cand) * di_supp_firm.size_stat[-1][0])
+        #                 dct_drs[di_supp_code] = drs
+        #             dct_drs = dict(sorted(
+        #                 dct_drs.items(), key=lambda kv: kv[1], reverse=True))
+        #             for di_supp_code in dct_drs.keys():
+        #                 di_supp_node = \
+        #                     self.firm_prod_network.nodes[di_supp_code]
+        #                 di_supp_prod = \
+        #                     self.a_lst_total_products.select(
+        #                         [product.code == di_supp_node['Product_Code']
+        #                          for product in self.a_lst_total_products])[0]
+        #                 # find a dfferent firm can produce the same product
+        #                 # and is not a current supplier for the same product
+        #                 lst_current_supp_code = \
+        #                     [self.firm_prod_network.nodes[code]['Firm_Code']
+        #                      for code in self.firm_prod_network.predecessors(
+        #                         pro_firm_prod_code)
+        #                      if self.firm_prod_network.nodes[code][
+        #                          'Product_Code'] == di_supp_prod.code]
+        #                 lst_cand = self.model.a_lst_total_firms.select([
+        #                     firm.is_prod_in_current_normal(di_supp_prod)
+        #                     and firm.code not in lst_current_supp_code
+        #                     for firm in self.model.a_lst_total_firms
+        #                 ])
+        #                 if len(lst_cand) > 0:
+        #                     select_cand = self.nprandom.choice(lst_cand)
+        #                     self.firm_network.graph.add_edges_from([
+        #                         (self.firm_network.positions[select_cand],
+        #                          self.firm_network.positions[pro_firm], {
+        #                             'Product': di_supp_prod.code
+        #                         })
+        #                     ])
+        #                     # print(f"proactive add {select_cand.name} to "
+        #                     #       f"{pro_firm.name} "
+        #                     #       f"for {di_supp_node['Firm_Code']} "
+        #                     #       f"{di_supp_node['Product_Code']}")
+        #                     # change capacity
+        #                     select_cand.dct_prod_capacity[di_supp_prod] -= 1
+        #                     break
+        # # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm_proactive.csv')
 
         # draw network
         # self.draw_network()
@@ -349,35 +350,35 @@ class Model(ap.Model):
         # reduce the size of disrupted firm
         for firm in self.a_lst_total_firms:
             for prod in firm.dct_prod_up_prod_stat.keys():
-                status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                 if status == 'D':
                     size = firm.size_stat[-1][0] - \
                         firm.size_stat[0][0] \
                         / len(firm.dct_prod_up_prod_stat.keys()) \
                         / self.remove_t
                     firm.size_stat.append((size, self.t))
-                    print(f'in ts {self.t}, reduce {firm.name} size '
-                          f'to {firm.size_stat[-1][0]} due to {prod.code}')
+                    # print(f'in ts {self.t}, reduce {firm.name} size '
+                    #       f'to {firm.size_stat[-1][0]} due to {prod.code}')
                     lst_is_disrupt = \
                         [stat == 'D' for stat, _ in
-                         firm.dct_prod_up_prod_stat[prod]['status']
+                         firm.dct_prod_up_prod_stat[prod]['p_stat']
                          [-1 * self.remove_t:]]
                     if all(lst_is_disrupt):
                         # turn disrupted firm into removed firm
                         # when last self.remove_t times status is all disrupted
                         firm.dct_prod_up_prod_stat[
-                            prod]['status'].append(('R', self.t))
+                            prod]['p_stat'].append(('R', self.t))
 
-        # stop simulation if any firm still in disrupted except inital removal
+        # stop simulation if any firm still in disrupted except initial removal
         if self.t > 0:
             for firm in self.a_lst_total_firms:
                 for prod in firm.dct_prod_up_prod_stat.keys():
-                    status, _ = firm.dct_prod_up_prod_stat[prod]['status'][-1]
+                    status, _ = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                     is_init = \
                         firm in self.dct_lst_init_disrupt_firm_prod.keys() \
                         and prod in self.dct_lst_init_disrupt_firm_prod[firm]
                     if status == 'D' and not is_init:
-                        print("not stop because", firm.name, prod.code)
+                        # print("not stop because", firm.name, prod.code)
                         break
                 else:
                     continue
@@ -390,19 +391,27 @@ class Model(ap.Model):
             self.stop()
 
     def step(self):
-        print('\n', '=' * 20, 'step', self.t, '=' * 20)
+        # print('\n', '=' * 20, 'step', self.t, '=' * 20)
 
         # remove edge to customer and disrupt customer up product
         for firm in self.a_lst_total_firms:
             for prod in firm.dct_prod_up_prod_stat.keys():
                 # repetition of disrupted firm that last for multiple ts is ok,
                 # as their edge has already been removed
-                status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                 if status == 'D' and ts == self.t-1:
-                    firm.remove_edge_to_cus_disrupt_cus_up_prod(prod)
+                    firm.remove_edge_to_cus(prod)
+
+        for firm in self.a_lst_total_firms:
+            for prod in firm.dct_prod_up_prod_stat.keys():
+                for up_prod in firm.dct_prod_up_prod_stat[prod][
+                        's_stat'].keys():
+                    if firm.dct_prod_up_prod_stat[prod][
+                            's_stat'][up_prod]['set_disrupt_firm']:
+                        firm.disrupt_cus_prod(prod, up_prod)
 
         for n_trial in range(self.int_n_max_trial):
-            print('=' * 10, 'trial', n_trial, '=' * 10)
+            # print('=' * 10, 'trial', n_trial, '=' * 10)
             # seek_alt_supply
             # shuffle self.a_lst_total_firms
             self.a_lst_total_firms = self.a_lst_total_firms.shuffle()
@@ -410,12 +419,12 @@ class Model(ap.Model):
             for firm in self.a_lst_total_firms:
                 lst_seek_prod = []
                 for prod in firm.dct_prod_up_prod_stat.keys():
-                    status = firm.dct_prod_up_prod_stat[prod]['status'][-1][0]
+                    status = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1][0]
                     if status == 'D':
                         for supply in firm.dct_prod_up_prod_stat[
-                                prod]['supply'].keys():
+                                prod]['s_stat'].keys():
                             if not firm.dct_prod_up_prod_stat[
-                                    prod]['supply'][supply]:
+                                    prod]['s_stat'][supply]['stat']:
                                 lst_seek_prod.append(supply)
                 # commmon supply only seek once
                 lst_seek_prod = list(set(lst_seek_prod))
@@ -435,11 +444,9 @@ class Model(ap.Model):
 
             # reset dct_request_prod_from_firm
             self.a_lst_total_firms.clean_before_trial()
-            # do not use:
-            # self.a_lst_total_firms.dct_request_prod_from_firm = {} why?
 
     def end(self):
-        print('/' * 20, 'output', '/' * 20)
+        # print('/' * 20, 'output', '/' * 20)
 
         qry_result = db_session.query(Result).filter_by(s_id=self.sample.id)
         if qry_result.count() == 0:
@@ -448,11 +455,11 @@ class Model(ap.Model):
                 for prod, dct_status_supply in \
                         firm.dct_prod_up_prod_stat.items():
                     lst_is_normal = [stat == 'N' for stat, _
-                                     in dct_status_supply['status']]
+                                     in dct_status_supply['p_stat']]
                     if not all(lst_is_normal):
-                        print(f"{firm.name} {prod.code}:")
-                        print(dct_status_supply['status'])
-                        for status, ts in dct_status_supply['status']:
+                        # print(f"{firm.name} {prod.code}:")
+                        # print(dct_status_supply['p_stat'])
+                        for status, ts in dct_status_supply['p_stat']:
                             db_r = Result(s_id=self.sample.id,
                                           id_firm=firm.code,
                                           id_product=prod.code,
diff --git a/oa_with_exp.csv b/oa_with_exp.csv
index 5ee736e..9bbc6a7 100644
--- a/oa_with_exp.csv
+++ b/oa_with_exp.csv
@@ -1,37 +1,37 @@
-X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18,X19,X20,X21,X22,X23
+X12,X1,X2,X3,X13,X14,X15,X16,X4,X5,X6,X7,X8,X9,X10,X11,X17,X18,X19,X20,X21,X22,X23
 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-1,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1
-2,0,0,0,2,2,2,2,2,2,0,0,0,0,0,0,0,0,2,2,2,2,2
-0,0,0,0,0,0,0,1,1,1,0,0,1,1,1,1,1,1,1,2,2,2,2
-1,0,0,0,1,1,1,2,2,2,0,0,1,1,1,1,1,1,2,0,0,0,0
-2,0,0,0,2,2,2,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,1
-0,0,0,1,0,1,2,0,1,2,1,1,0,0,0,1,1,1,2,0,1,1,2
-1,0,0,1,1,2,0,1,2,0,1,1,0,0,0,1,1,1,0,1,2,2,0
-2,0,0,1,2,0,1,2,0,1,1,1,0,0,0,1,1,1,1,2,0,0,1
-0,0,1,0,0,2,1,0,2,1,1,1,0,1,1,0,0,1,2,1,0,2,1
-1,0,1,0,1,0,2,1,0,2,1,1,0,1,1,0,0,1,0,2,1,0,2
-2,0,1,0,2,1,0,2,1,0,1,1,0,1,1,0,0,1,1,0,2,1,0
-0,0,1,1,1,2,0,2,1,0,0,1,1,0,1,0,1,0,2,2,1,0,1
-1,0,1,1,2,0,1,0,2,1,0,1,1,0,1,0,1,0,0,0,2,1,2
-2,0,1,1,0,1,2,1,0,2,0,1,1,0,1,0,1,0,1,1,0,2,0
-0,0,1,1,1,2,1,0,0,2,1,0,1,1,0,1,0,0,1,2,2,1,0
-1,0,1,1,2,0,2,1,1,0,1,0,1,1,0,1,0,0,2,0,0,2,1
-2,0,1,1,0,1,0,2,2,1,1,0,1,1,0,1,0,0,0,1,1,0,2
-0,1,0,1,1,0,2,2,2,0,1,0,0,1,1,0,1,0,1,1,0,1,2
-1,1,0,1,2,1,0,0,0,1,1,0,0,1,1,0,1,0,2,2,1,2,0
-2,1,0,1,0,2,1,1,1,2,1,0,0,1,1,0,1,0,0,0,2,0,1
-0,1,0,1,1,1,2,2,0,1,0,1,1,1,0,0,0,1,0,0,2,2,1
-1,1,0,1,2,2,0,0,1,2,0,1,1,1,0,0,0,1,1,1,0,0,2
-2,1,0,1,0,0,1,1,2,0,0,1,1,1,0,0,0,1,2,2,1,1,0
-0,1,0,0,2,1,0,1,2,2,1,1,1,0,1,1,0,0,0,2,0,1,1
-1,1,0,0,0,2,1,2,0,0,1,1,1,0,1,1,0,0,1,0,1,2,2
-2,1,0,0,1,0,2,0,1,1,1,1,1,0,1,1,0,0,2,1,2,0,0
-0,1,1,1,2,1,1,1,0,0,0,0,0,0,1,1,0,1,2,1,2,0,2
-1,1,1,1,0,2,2,2,1,1,0,0,0,0,1,1,0,1,0,2,0,1,0
-2,1,1,1,1,0,0,0,2,2,0,0,0,0,1,1,0,1,1,0,1,2,1
-0,1,1,0,2,2,2,1,2,1,1,0,1,0,0,0,1,1,1,0,1,0,0
-1,1,1,0,0,0,0,2,0,2,1,0,1,0,0,0,1,1,2,1,2,1,1
-2,1,1,0,1,1,1,0,1,0,1,0,1,0,0,0,1,1,0,2,0,2,2
-0,1,1,0,2,0,1,2,1,2,0,1,0,1,0,1,1,0,0,1,1,2,0
-1,1,1,0,0,1,2,0,2,0,0,1,0,1,0,1,1,0,1,2,2,0,1
-2,1,1,0,1,2,0,1,0,1,0,1,0,1,0,1,1,0,2,0,0,1,2
+1,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1
+2,0,0,0,2,2,2,2,0,0,0,0,0,0,0,0,2,2,2,2,2,2,2
+0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,2,2,2,2
+1,0,0,0,1,1,1,2,0,0,1,1,1,1,1,1,2,2,2,0,0,0,0
+2,0,0,0,2,2,2,0,0,0,1,1,1,1,1,1,0,0,0,1,1,1,1
+0,0,0,1,0,1,2,0,1,1,0,0,0,1,1,1,1,2,2,0,1,1,2
+1,0,0,1,1,2,0,1,1,1,0,0,0,1,1,1,2,0,0,1,2,2,0
+2,0,0,1,2,0,1,2,1,1,0,0,0,1,1,1,0,1,1,2,0,0,1
+0,0,1,0,0,2,1,0,1,1,0,1,1,0,0,1,2,1,2,1,0,2,1
+1,0,1,0,1,0,2,1,1,1,0,1,1,0,0,1,0,2,0,2,1,0,2
+2,0,1,0,2,1,0,2,1,1,0,1,1,0,0,1,1,0,1,0,2,1,0
+0,0,1,1,1,2,0,2,0,1,1,0,1,0,1,0,1,0,2,2,1,0,1
+1,0,1,1,2,0,1,0,0,1,1,0,1,0,1,0,2,1,0,0,2,1,2
+2,0,1,1,0,1,2,1,0,1,1,0,1,0,1,0,0,2,1,1,0,2,0
+0,0,1,1,1,2,1,0,1,0,1,1,0,1,0,0,0,2,1,2,2,1,0
+1,0,1,1,2,0,2,1,1,0,1,1,0,1,0,0,1,0,2,0,0,2,1
+2,0,1,1,0,1,0,2,1,0,1,1,0,1,0,0,2,1,0,1,1,0,2
+0,1,0,1,1,0,2,2,1,0,0,1,1,0,1,0,2,0,1,1,0,1,2
+1,1,0,1,2,1,0,0,1,0,0,1,1,0,1,0,0,1,2,2,1,2,0
+2,1,0,1,0,2,1,1,1,0,0,1,1,0,1,0,1,2,0,0,2,0,1
+0,1,0,1,1,1,2,2,0,1,1,1,0,0,0,1,0,1,0,0,2,2,1
+1,1,0,1,2,2,0,0,0,1,1,1,0,0,0,1,1,2,1,1,0,0,2
+2,1,0,1,0,0,1,1,0,1,1,1,0,0,0,1,2,0,2,2,1,1,0
+0,1,0,0,2,1,0,1,1,1,1,0,1,1,0,0,2,2,0,2,0,1,1
+1,1,0,0,0,2,1,2,1,1,1,0,1,1,0,0,0,0,1,0,1,2,2
+2,1,0,0,1,0,2,0,1,1,1,0,1,1,0,0,1,1,2,1,2,0,0
+0,1,1,1,2,1,1,1,0,0,0,0,1,1,0,1,0,0,2,1,2,0,2
+1,1,1,1,0,2,2,2,0,0,0,0,1,1,0,1,1,1,0,2,0,1,0
+2,1,1,1,1,0,0,0,0,0,0,0,1,1,0,1,2,2,1,0,1,2,1
+0,1,1,0,2,2,2,1,1,0,1,0,0,0,1,1,2,1,1,0,1,0,0
+1,1,1,0,0,0,0,2,1,0,1,0,0,0,1,1,0,2,2,1,2,1,1
+2,1,1,0,1,1,1,0,1,0,1,0,0,0,1,1,1,0,0,2,0,2,2
+0,1,1,0,2,0,1,2,0,1,0,1,0,1,1,0,1,2,0,1,1,2,0
+1,1,1,0,0,1,2,0,0,1,0,1,0,1,1,0,2,0,1,2,2,0,1
+2,1,1,0,1,2,0,1,0,1,0,1,0,1,1,0,0,1,2,0,0,1,2
diff --git a/oa_with_exp.xlsx b/oa_with_exp.xlsx
index a880df4..4deb8cb 100644
Binary files a/oa_with_exp.xlsx and b/oa_with_exp.xlsx differ
diff --git a/oa_without_exp.csv b/oa_without_exp.csv
index 66c1886..216d380 100644
--- a/oa_without_exp.csv
+++ b/oa_without_exp.csv
@@ -1,2 +1,2 @@
-X1,X2,X3,X4,X5,X6,X7,X8,X9,X10
-0,0,0,0,0,0,0,0,0,0
+X1,X2,X3,X4,X5,X6,X7,X8
+0,0,0,0,0,0,0,0
diff --git a/orm.py b/orm.py
index 649bff0..f497b0e 100644
--- a/orm.py
+++ b/orm.py
@@ -60,8 +60,6 @@ class Experiment(Base):
     cap_limit_prob_type = Column(String(16), nullable=False)
     cap_limit_level = Column(DECIMAL(8, 4), nullable=False)
     diff_new_conn = Column(DECIMAL(8, 4), nullable=False)
-    crit_supplier = Column(DECIMAL(8, 4), nullable=False)
-    proactive_ratio = Column(DECIMAL(8, 4), nullable=False)
     remove_t = Column(Integer, nullable=False)
     netw_prf_n = Column(Integer, nullable=False)
 
diff --git a/product.py b/product.py
index 09238f9..78d33fa 100644
--- a/product.py
+++ b/product.py
@@ -9,6 +9,7 @@ class ProductAgent(ap.Agent):
         self.name = name
 
     def a_successors(self):
+        # find successors of a product, return in AgentList (ProductAgent)
         nodes = self.product_network.graph.successors(
             self.product_network.positions[self])
         return ap.AgentList(
@@ -16,6 +17,7 @@ class ProductAgent(ap.Agent):
             [ap.AgentIter(self.model, node).to_list()[0] for node in nodes])
 
     def a_predecessors(self):
+        # find predecessors of a product, return in AgentList (ProductAgent)
         nodes = self.product_network.graph.predecessors(
             self.product_network.positions[self])
         return ap.AgentList(
diff --git a/test.ipynb b/test.ipynb
index e52427d..5015e92 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -357,6 +357,231 @@
     "\n",
     "lst[-5:]"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>e_id</th>\n",
+       "      <th>n_disrupt_sample</th>\n",
+       "      <th>total_n_disrupt_firm_prod_experiment</th>\n",
+       "      <th>dct_lst_init_disrupt_firm_prod</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>383</td>\n",
+       "      <td>50</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>227</td>\n",
+       "      <td>50</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>83</td>\n",
+       "      <td>50</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>135</td>\n",
+       "      <td>50</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>179</td>\n",
+       "      <td>50</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>76</td>\n",
+       "      <td>24</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x14\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>89</td>\n",
+       "      <td>24</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>90</td>\n",
+       "      <td>24</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>335</td>\n",
+       "      <td>24</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>449</td>\n",
+       "      <td>24</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>b'\\x80\\x05\\x95\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>95 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    e_id  n_disrupt_sample  total_n_disrupt_firm_prod_experiment  \\\n",
+       "0    383                50                                 300.0   \n",
+       "1    227                50                                 250.0   \n",
+       "2     83                50                                 200.0   \n",
+       "3    135                50                                 200.0   \n",
+       "4    179                50                                 200.0   \n",
+       "..   ...               ...                                   ...   \n",
+       "90    76                24                                  56.0   \n",
+       "91    89                24                                  54.0   \n",
+       "92    90                24                                  54.0   \n",
+       "93   335                24                                  54.0   \n",
+       "94   449                24                                  53.0   \n",
+       "\n",
+       "                       dct_lst_init_disrupt_firm_prod  \n",
+       "0   b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "1   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "2   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "3   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "4   b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "..                                                ...  \n",
+       "90  b'\\x80\\x05\\x95\\x14\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "91  b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "92  b'\\x80\\x05\\x95\\x16\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "93  b'\\x80\\x05\\x95\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "94  b'\\x80\\x05\\x95\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00...  \n",
+       "\n",
+       "[95 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('SQL_export_high_risk_setting.sql', 'r') as f:\n",
+    "    contents = f.read()\n",
+    "\n",
+    "import pandas as pd\n",
+    "from orm import engine\n",
+    "result = pd.read_sql(sql=contents, con=engine)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "\n",
+    "G = nx.MultiDiGraph()\n",
+    "G.add_edge(1, 2)\n",
+    "nx.draw(G)\n",
+    "G.has_edge(1, 2, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = set()\n",
+    "s.add(1)\n",
+    "s.add(2)\n",
+    "s.add(1)\n",
+    "len(s)\n",
+    "if s:\n",
+    "    print(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/xv_with_exp.csv b/xv_with_exp.csv
index e002363..ce2bf23 100644
--- a/xv_with_exp.csv
+++ b/xv_with_exp.csv
@@ -1,4 +1,4 @@
-n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,crit_supplier,diff_disrupt,proactive_ratio,remove_t,netw_prf_n
-15,TRUE,TRUE,uniform,5,0.3,2,0.5,0.3,3,3
-10,FALSE,FALSE,normal,10,0.5,1,1,0.5,5,2
-5,,,,15,0.7,0.5,2,0.7,7,1
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n
+7,TRUE,TRUE,uniform,5,0.3,3,3
+5,FALSE,FALSE,normal,10,0.5,5,2
+3,,,,15,0.7,7,1
diff --git a/xv_without_exp.csv b/xv_without_exp.csv
index 027114d..caa6fef 100644
--- a/xv_without_exp.csv
+++ b/xv_without_exp.csv
@@ -1,2 +1,2 @@
-n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,crit_supplier,proactive_ratio,remove_t,netw_prf_n
-10,TRUE,TRUE,uniform,10,0.5,0.01,1,5,2
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n
+5,TRUE,TRUE,uniform,10,0.5,5,2