0209review: firm risk component + 2way interation on n_max_trial and cap_limit_level

.gitignore

@@ -0,0 +1,4 @@
+.idea/
+venv
+# intermediate file for analysis_firm_risk_component.ipynb (2GB)
+count_with_gfirm.csv

.vscode/launch.json

@@ -8,9 +8,14 @@
             "name": "Python: Current File",
             "type": "python",
             "request": "launch",
-            "program": "C:\\Users\\ASUS\\OneDrive\\Project\\ScrAbm\\Dissertation\\IIabm\\main.py",
+            "program": "${workspaceFolder}\\main.py",
             "console": "integratedTerminal",
-            "justMyCode": true
+            "justMyCode": true,
+            "args": [
+                "--exp", "with_exp",
+                "--reset_db", "True",
+                "--job", "24"
+            ]
         }
     ]
 }

InitRemovalHighRisk.csv

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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;

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;

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;

SQL_check_num_dis_firm_in_ts.sql

@@ -0,0 +1,21 @@
+select * from 
+(select s_id, ts, count(id) as num_dis_firm from
+iiabmdb.with_exp_result
+where ts > 0
+group by s_id, ts) as a
+where a.num_dis_firm > 3;
+
+select * from 
+iiabmdb.with_exp_result
+where s_id = '54170';
+
+SELECT * FROM iiabmdb.with_exp_sample where id='54170';
+SELECT * FROM iiabmdb.with_exp_experiment where id='1084';
+SELECT max(id) FROM iiabmdb.with_exp_sample;
+
+select * from 
+(select s_id, id_firm, id_product, ts, count(id) as num_dup from
+iiabmdb.with_exp_result
+where ts > 0
+group by s_id, id_firm, id_product, ts) as a
+where a.num_dup > 1;

SQL_duplicate_without_exp_tables.sql

@@ -0,0 +1,43 @@
+-- SQL script to duplicate without_exp tables from iiabmdb20260127 to iiabmdb
+-- This allows reusing previous experiment results as the baseline for new experiments
+
+-- Drop existing tables if they exist (optional - comment out if you want to keep existing data)
+DROP TABLE IF EXISTS iiabmdb.without_exp_result;
+
+DROP TABLE IF EXISTS iiabmdb.without_exp_sample;
+
+DROP TABLE IF EXISTS iiabmdb.without_exp_experiment;
+
+-- Create and copy without_exp_experiment table
+CREATE TABLE iiabmdb.without_exp_experiment LIKE iiabmdb20260127.without_exp_experiment;
+
+INSERT INTO
+    iiabmdb.without_exp_experiment
+SELECT *
+FROM iiabmdb20260127.without_exp_experiment;
+
+-- Create and copy without_exp_sample table
+CREATE TABLE iiabmdb.without_exp_sample LIKE iiabmdb20260127.without_exp_sample;
+
+INSERT INTO
+    iiabmdb.without_exp_sample
+SELECT *
+FROM iiabmdb20260127.without_exp_sample;
+
+-- Create and copy without_exp_result table
+CREATE TABLE iiabmdb.without_exp_result LIKE iiabmdb20260127.without_exp_result;
+
+INSERT INTO
+    iiabmdb.without_exp_result
+SELECT *
+FROM iiabmdb20260127.without_exp_result;
+
+-- Verify the copy
+SELECT 'without_exp_experiment' as table_name, COUNT(*) as row_count
+FROM iiabmdb.without_exp_experiment
+UNION ALL
+SELECT 'without_exp_sample', COUNT(*)
+FROM iiabmdb.without_exp_sample
+UNION ALL
+SELECT 'without_exp_result', COUNT(*)
+FROM iiabmdb.without_exp_result;

SQL_export_high_risk_setting.sql

@@ -0,0 +1,15 @@
+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;

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

SQL_migrate_db.sql

@@ -0,0 +1,13 @@
+CREATE DATABASE iiabmdb20230821;
+RENAME TABLE iiabmdb.not_test_experiment TO iiabmdb20230821.not_test_experiment,
+iiabmdb.not_test_result TO iiabmdb20230821.not_test_result,
+iiabmdb.not_test_sample TO iiabmdb20230821.not_test_sample,
+iiabmdb.test_experiment TO iiabmdb20230821.test_experiment,
+iiabmdb.test_result TO iiabmdb20230821.test_result,
+iiabmdb.test_sample TO iiabmdb20230821.test_sample;
+RENAME TABLE iiabmdb.with_exp_experiment TO iiabmdb20230821.with_exp_experiment,
+iiabmdb.with_exp_result TO iiabmdb20230821.with_exp_result,
+iiabmdb.with_exp_sample TO iiabmdb20230821.with_exp_sample,
+iiabmdb.without_exp_experiment TO iiabmdb20230821.without_exp_experiment,
+iiabmdb.without_exp_result TO iiabmdb20230821.without_exp_result,
+iiabmdb.without_exp_sample TO iiabmdb20230821.without_exp_sample;

SQL_running_check.sql

@@ -0,0 +1,35 @@
+select id, e_id, idx_sample, seed, ts_done from iiabmdb.without_exp_sample where is_done_flag != -1 order by ts_done;
+select count(id) from iiabmdb.without_exp_sample where is_done_flag != -1;
+select count(id) from iiabmdb.without_exp_sample‘;
+select count(id) from iiabmdb.with_exp_sample where is_done_flag != -1;
+select * from iiabmdb.with_exp_sample where is_done_flag != 1;
+
+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;
+

adj_g_firm.csv

@@ -0,0 +1,172 @@
+,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170
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analysis/20230818anova_visualization.csv

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+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

analysis/20230818experiment_result.csv

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analysis/20230821experiment_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
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analysis/20260128experiment_result.csv

@@ -0,0 +1,10 @@
+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,5.0000,0.5000,5,2,2.6036,2.6036,2.0808,1.0808,1.0808,1.0808,0.6044,0.2131,1.8726
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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

analysis/count_dcp_prod.csv

@@ -0,0 +1,227 @@
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+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
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+1.4.5.1,恶意代码检测系统,1,供给,50
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+1.4.3.2,流量检测,1.4,工业互联网安全,50
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+1.4.5.8,数据加密,1.4,工业互联网安全,50
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+1.4.5.1,恶意代码检测系统,1.4.5,数据安全,50
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+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
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+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
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+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
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+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
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+2.1.4.2,工业大数据管理,2.1,PaaS,18
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+1.4.4.4,工业应用行为监控,1,供给,17
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+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
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+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
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+2.1.3.1,物联网服务,2.1,PaaS,4
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+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
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+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

analysis/count_firm.csv

@@ -0,0 +1,172 @@
+id_firm,Name,count
+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

analysis/count_firm_prod.csv

@@ -0,0 +1,476 @@
+id_firm,name_firm,id_product,name_product,count
+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
+53,天融信,1.4.3.6,沙箱类设备,50
+53,天融信,1.4.2.3,工控漏洞扫描,50
+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
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+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
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+37,绿盟,1.4.1.2,下一代防火墙,21
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+44,圣邦微电子,1.1.1,工业计算芯片,21
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+38,牛刀,2.1.1.2,低代码开发工具,20
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+54,网御星云,1.4.2.4,安全隔离与信息交换系统,13
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+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
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+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
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+63,长扬科技,1.4.2.4,安全隔离与信息交换系统,8
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+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

analysis/count_prod.csv

@@ -0,0 +1,108 @@
+id_product,Name,count
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+2.1.1,开发工具,1159
+2.3,边缘层,1073
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+1.1,工业自动化,904
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+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
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+2.3.1,工业数据接入,237
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+2.1.2.1,数据算法模型,230
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+2.3.3,协议转换,227
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+2.1.3.5,容器服务,211
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+2.1.3.1,物联网服务,203
+1.3.1.7,电子设计自动化EDA,201
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+1.3.1.6,产品生命周期管理PLM,200
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+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
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+1.3.3.4,可编程逻揖控制系统PLC,147
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+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
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+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
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+1.3.5,仓储物流,120
+1.4.4.1,身份鉴别与访问控制,119
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+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
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+1.4.1.3,防毒墙,83
+1.4.1.1,工业防火墙,81
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+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
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+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
+1.4.5.1,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
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+1.4.2.7,工控原生安全,50
+1.4.2.3,工控漏洞扫描,50
+1.4.5.8,数据加密,50

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
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+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
+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
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+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,80,Salesforce,2.1.1,开发工具,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,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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+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
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+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
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+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,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
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+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,2.1.2.3,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
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+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
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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,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
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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,1
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+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,1.2.3,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
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+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
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+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,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
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analysis/dissertation_count_dcp_prod.csv

@@ -0,0 +1,98 @@
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analysis/dissertation_count_firm.csv

@@ -0,0 +1,144 @@
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analysis/dissertation_count_firm_prod.csv

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+41,启明星辰,1.4.3.2,流量检测,50
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+63,长扬科技,1.4.4.5,安全态势感知,50
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+79,PTC,2.1.3.7,制造类API,7
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+81,SAP,2.1.4.2,工业大数据管理,7
+79,PTC,2.1.3.6,微服务,7
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+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
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+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
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+2.3.1,工业数据接入,33
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+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

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

analysis/experiment_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,5.0000,0.3000,3,3,2.7027,2.7027,2.0928,1.0928,1.0928,1.0928,0.6051,0.2131,1.5524
+1,5,1,1,uniform,10.0000,0.5000,5,2,2.7023,2.7004,2.2211,1.2211,1.2211,1.2211,0.9634,0.3309,2.4335
+2,3,1,1,uniform,15.0000,0.7000,7,1,2.5387,2.5360,2.1756,1.1756,1.1756,1.1756,1.0019,0.3293,3.1926
+3,7,1,1,uniform,5.0000,0.3000,3,2,2.5589,2.5589,2.0884,1.0884,1.0884,1.0884,0.6059,0.2139,1.5533
+4,5,1,1,uniform,10.0000,0.5000,5,1,2.5387,2.5356,2.1756,1.1756,1.1756,1.1756,0.9143,0.3143,2.4840
+5,3,1,1,uniform,15.0000,0.7000,7,3,2.9109,2.9074,2.2773,1.2773,1.2773,1.2773,1.1680,0.3872,3.4848
+6,7,1,1,normal,5.0000,0.5000,7,3,2.6356,2.6356,2.1055,1.1055,1.1055,1.1055,0.6008,0.2107,2.1806
+7,5,1,1,normal,10.0000,0.7000,3,2,2.5914,2.5909,2.0857,1.0857,1.0857,1.0857,0.6829,0.2354,1.6272
+8,3,1,1,normal,15.0000,0.3000,5,1,2.5219,2.5189,2.1617,1.1617,1.1617,1.1617,0.8895,0.3088,2.5105
+9,7,1,0,uniform,5.0000,0.7000,5,3,2.7091,2.7091,2.0952,1.0952,1.0952,1.0952,0.6051,0.2131,1.8733
+10,5,1,0,uniform,10.0000,0.3000,7,2,2.7107,2.7086,2.2280,1.2280,1.2280,1.2280,0.9674,0.3320,3.0187
+11,3,1,0,uniform,15.0000,0.5000,3,1,2.5771,2.5731,2.2095,1.2095,1.2095,1.2095,1.1646,0.3939,2.1248
+12,7,1,0,normal,10.0000,0.7000,3,1,2.4051,2.4042,2.0539,1.0539,1.0539,1.0539,0.6888,0.2347,1.6442
+13,5,1,0,normal,15.0000,0.3000,5,3,2.7368,2.7364,2.2015,1.2015,1.2015,1.2015,0.8714,0.3021,2.3029
+14,3,1,0,normal,5.0000,0.5000,7,2,2.5665,2.5665,2.0600,1.0600,1.0600,1.0600,0.6000,0.2105,2.1863
+15,7,1,0,normal,10.0000,0.7000,5,3,2.6705,2.6705,2.1389,1.1389,1.1389,1.1389,0.6943,0.2406,2.0160
+16,5,1,0,normal,15.0000,0.3000,7,2,2.6707,2.6680,2.1577,1.1577,1.1577,1.1577,0.8642,0.2973,2.8053
+17,3,1,0,normal,5.0000,0.5000,3,1,2.4105,2.4103,2.0594,1.0594,1.0594,1.0594,0.6903,0.2394,1.6615
+18,7,0,1,normal,10.0000,0.3000,7,1,2.4520,2.4493,2.1034,1.1034,1.1034,1.1034,0.7008,0.2423,2.6644
+19,5,0,1,normal,15.0000,0.5000,3,3,2.8086,2.8059,2.2133,1.2133,1.2133,1.2133,0.8821,0.3017,1.7964
+20,3,0,1,normal,5.0000,0.7000,5,2,2.5808,2.5808,2.0684,1.0684,1.0684,1.0684,0.6004,0.2105,1.8642
+21,7,0,1,normal,10.0000,0.5000,7,1,2.4149,2.4141,2.0697,1.0697,1.0697,1.0697,0.6594,0.2274,2.4549
+22,5,0,1,normal,15.0000,0.7000,3,3,2.8084,2.8057,2.2131,1.2131,1.2131,1.2131,0.8811,0.3013,1.7956
+23,3,0,1,normal,5.0000,0.3000,5,2,2.5808,2.5808,2.0684,1.0684,1.0684,1.0684,0.6006,0.2107,1.8648
+24,7,0,1,uniform,15.0000,0.5000,3,2,2.6992,2.6935,2.2269,1.2269,1.2269,1.2269,1.1223,0.3851,2.0112
+25,5,0,1,uniform,5.0000,0.7000,5,1,2.3648,2.3646,2.0248,1.0248,1.0248,1.0248,0.6084,0.2118,1.9465
+26,3,0,1,uniform,10.0000,0.3000,7,3,2.8187,2.8160,2.2122,1.2122,1.2122,1.2122,0.9806,0.3446,3.0491
+27,7,0,0,normal,15.0000,0.5000,5,2,2.6756,2.6731,2.1558,1.1558,1.1558,1.1558,0.8455,0.2893,2.2659
+28,5,0,0,normal,5.0000,0.7000,7,1,2.3678,2.3678,2.0261,1.0261,1.0261,1.0261,0.6008,0.2112,2.2375
+29,3,0,0,normal,10.0000,0.3000,3,3,2.7467,2.7463,2.1556,1.1556,1.1556,1.1556,0.7137,0.2482,1.6533
+30,7,0,0,uniform,15.0000,0.7000,7,2,2.6956,2.6899,2.2232,1.2232,1.2232,1.2232,1.1025,0.3741,3.2589
+31,5,0,0,uniform,5.0000,0.3000,3,1,2.4206,2.4183,2.0752,1.0752,1.0752,1.0752,0.8027,0.2905,1.7909
+32,3,0,0,uniform,10.0000,0.5000,5,3,2.8238,2.8211,2.2158,1.2158,1.2158,1.2158,0.9766,0.3423,2.4798
+33,7,0,0,uniform,15.0000,0.3000,5,1,2.5533,2.5451,2.1931,1.1931,1.1931,1.1931,1.0897,0.3749,2.7922
+34,5,0,0,uniform,5.0000,0.5000,7,3,2.6741,2.6741,2.0787,1.0787,1.0787,1.0787,0.6034,0.2114,2.1891
+35,3,0,0,uniform,10.0000,0.7000,3,2,2.6552,2.6518,2.1880,1.1880,1.1880,1.1880,0.9922,0.3379,1.9097

analysis_count.py

@@ -0,0 +1,8 @@
+import pandas as pd
+
+count = pd.read_csv("analysis\\count.csv", dtype={'s_id': str, 'id_firm': str})
+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)

analysis_firm_network.py

@@ -0,0 +1,102 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import networkx as nx
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+# count firm category
+count_firm = pd.read_csv("analysis\\count_firm.csv")
+print(count_firm.describe())
+
+count_dcp = pd.read_csv("analysis\\count_dcp.csv",
+                        dtype={
+                            'up_id_firm': str,
+                            'down_id_firm': str
+                        })
+# print(count_dcp)
+count_dcp = count_dcp[count_dcp['count'] > 35]
+
+list_firm = count_dcp['up_id_firm'].tolist(
+) + count_dcp['down_id_firm'].tolist()
+list_firm = list(set(list_firm))
+
+# init graph firm
+Firm = pd.read_csv("Firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
+firm_product = []
+for _, row in Firm.loc[:, '1':].iterrows():
+    firm_product.append(row[row == 1].index.to_list())
+Firm_attr.loc[:, 'Product_Code'] = firm_product
+Firm_attr.set_index('Code', inplace=True)
+
+G_firm = nx.MultiDiGraph()
+G_firm.add_nodes_from(list_firm)
+
+firm_labels_dict = {}
+for code in G_firm.nodes:
+    firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
+nx.set_node_attributes(G_firm, firm_labels_dict)
+
+count_max = count_dcp['count'].max()
+count_min = count_dcp['count'].min()
+k = 5 / (count_max - count_min)
+for _, row in count_dcp.iterrows():
+    # print(row)
+    lst_add_edge = [(
+        row['up_id_firm'],
+        row['down_id_firm'],
+        {
+            'up_id_product': row['up_id_product'],
+            '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['down_id_product']}",
+            'edge_width': k * (row['count'] - count_min),
+            'count': row['count']
+        })]
+    G_firm.add_edges_from(lst_add_edge)
+
+# dcp_networkx
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="dot", args="")
+node_label = nx.get_node_attributes(G_firm, 'Name')
+# node_degree = dict(G_firm.out_degree())
+# desensitize
+node_label = {
+    # key: f"{node_label[key]} {node_degree[key]}"
+    # key: f"{node_label[key]}"
+    key: key
+    for key in node_label.keys()
+}
+node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
+node_size = list(map(lambda x: x**2, node_size))
+edge_label = nx.get_edge_attributes(G_firm, "edge_label")
+edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+edge_width = nx.get_edge_attributes(G_firm, "edge_width")
+edge_width = [w for (n1, n2, _), w in edge_width.items()]
+colors = nx.get_edge_attributes(G_firm, "count")
+colors = [w for (n1, n2, _), w in colors.items()]
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+fig = plt.figure(figsize=(10, 8), dpi=300)
+nx.draw(G_firm,
+        pos,
+        node_size=node_size,
+        labels=node_label,
+        font_size=8,
+        width=3,
+        edge_color=colors,
+        edge_cmap=cmap,
+        edge_vmin=vmin,
+        edge_vmax=vmax)
+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([0.95, 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()

analysis_prod.py

@@ -0,0 +1,22 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import networkx as nx
+import math
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+Firm = pd.read_csv("Firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+count_prod = pd.read_csv("analysis\\count_prod.csv")
+for index, row in count_prod.iterrows():
+    count_prod.loc[index, 'num_firm'] = sum(Firm[row['id_product']]==1)
+    count_prod.loc[index, 'avg_size'] = Firm.loc[Firm[row['id_product']]==1, 'Revenue_Log'].median()
+
+
+print(count_prod)
+# sns.scatterplot(x='count', y='avg_size',data=count_prod)
+ax = plt.subplot(projection = '3d')  # 创建一个三维的绘图工程
+ax.scatter(count_prod['avg_size'], count_prod['num_firm'], count_prod['count'], c = 'r')
+plt.show()

analysis_prod_network.py

@@ -0,0 +1,163 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import networkx as nx
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+count_prod = pd.read_csv("analysis\\count_prod.csv")
+print(count_prod)
+
+# category
+print(count_prod.describe())
+
+# pie
+count_prod_trim = count_prod[count_prod['count'] > 50]
+plt.pie(count_prod_trim['count'], labels=count_prod_trim['Name'])
+plt.savefig("analysis\\count_prod_pie")
+plt.close()
+
+# prod_networkx
+BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
+BomNodes.set_index('Code', inplace=True)
+BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
+BomCateNet.fillna(0, inplace=True)
+
+G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph())
+
+labels_dict = {}
+for code in G.nodes:
+    node_attr = BomNodes.loc[code].to_dict()
+    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'] = 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)
+# print(labels_dict)
+
+pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
+dict_node_name = nx.get_node_attributes(G, 'Name')
+node_labels = {}
+for node in nx.nodes(G):
+    node_labels[node] = f"{node} {str(dict_node_name[node])}"
+    # node_labels[node] = f"{str(dict_node_name[node])}"
+colors = list(nx.get_node_attributes(G, 'node_color').values())
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+fig = plt.figure(figsize=(10, 10), dpi=300)
+nx.draw(G,
+        pos,
+        node_size=list(nx.get_node_attributes(G, 'node_size').values()),
+        labels=node_labels,
+        font_size=6,
+        node_color=colors,
+        cmap=cmap,
+        vmin=vmin,
+        vmax=vmax,
+        edge_color='grey')
+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])
+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
+count_dcp = pd.read_csv("analysis\\count_dcp.csv",
+                        dtype={
+                            '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_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'] > 50]
+# print(count_dcp_prod)
+
+list_prod = count_dcp_prod['up_id_product'].tolist(
+) + count_dcp['down_id_product'].tolist()
+list_prod = list(set(list_prod))
+
+# init graph bom
+
+BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
+BomNodes.set_index('Code', inplace=True)
+
+g_bom = nx.MultiDiGraph()
+g_bom.add_nodes_from(list_prod)
+
+bom_labels_dict = {}
+for code in list_prod:
+    dct_attr = BomNodes.loc[code].to_dict()
+    bom_labels_dict[code] = dct_attr
+nx.set_node_attributes(g_bom, bom_labels_dict)
+
+
+count_max = count_dcp_prod['count'].max()
+count_min = count_dcp_prod['count'].min()
+k = 5 / (count_max - count_min)
+for _, row in count_dcp_prod.iterrows():
+    # print(row)
+    lst_add_edge = [(
+        row['up_id_product'],
+        row['down_id_product'],
+        {
+            'count': row['count']
+        })]
+    g_bom.add_edges_from(lst_add_edge)
+
+# 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
+node_labels = temp
+colors = nx.get_edge_attributes(g_bom, "count")
+colors = [w for (n1, n2, _), w in colors.items()]
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+
+pos_new = {}
+for node, p in pos.items():
+    pos_new[node] = (p[1], p[0])
+
+fig = plt.figure(figsize=(6, 10), dpi=300)
+# plt.subplots_adjust(right=0.7)
+nx.draw(g_bom,
+        pos_new,
+        node_size=50,
+        labels=node_labels,
+        font_size=5,
+        width=1.5,
+        edge_color=colors,
+        edge_cmap=cmap,
+        edge_vmin=vmin,
+        edge_vmax=vmax)
+plt.axis('off')
+axis = plt.gca()
+axis.set_xlim([1.2*x for x in axis.get_xlim()])
+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([0.75, 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()

analysis_sum_result.py

@@ -0,0 +1,190 @@
+from orm import engine
+from sqlalchemy import text
+import pandas as pd
+import networkx as nx
+import json
+import matplotlib.pyplot as plt
+
+# prep data
+Firm = pd.read_csv("Firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
+
+with open('SQL_analysis_risk.sql', 'r') as f:
+    str_sql = f.read()
+with engine.connect() as conn:
+    result = pd.read_sql(sql=text(str_sql), con=conn)
+result.to_csv('analysis\\count.csv',
+              index=False,
+              encoding='utf-8-sig')
+print(result)
+
+# G bom
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+exp_id = 1
+with engine.connect() as conn:
+    G_bom_str = pd.read_sql(
+        sql=text(f'select g_bom from iiabmdb.without_exp_experiment '
+                 f'where id = {exp_id};'),
+        con=conn)['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.close()
+
+# G firm
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+sample_id = 1
+with engine.connect() as conn:
+    G_firm_str = pd.read_sql(
+        sql=text(f'select g_firm from iiabmdb.without_exp_sample where id = {exp_id};'),
+        con=conn)['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="")
+node_label = nx.get_node_attributes(G_firm, 'Name')
+# desensitize
+node_label = {
+    key: key
+    for key in node_label.keys()
+}
+node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
+node_size = list(map(lambda x: x**2, node_size))
+edge_label = nx.get_edge_attributes(G_firm, "Product")
+edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+plt.figure(figsize=(12, 12), dpi=300)
+nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=6)
+nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4)
+# plt.show()
+plt.savefig(f"analysis\\g_firm_sample_id_{exp_id}_de.png")
+plt.close()
+
+# count firm product
+count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
+count_firm_prod.name = 'count'
+count_firm_prod = count_firm_prod.to_frame().reset_index()
+count_firm_prod = pd.merge(count_firm_prod,
+                           Firm[['Code', 'Name']],
+                           how='left',
+                           left_on='id_firm',
+                           right_on='Code')
+count_firm_prod.drop('Code', axis=1, inplace=True)
+count_firm_prod.rename(columns={'Name': 'name_firm'}, inplace=True)
+count_firm_prod = pd.merge(count_firm_prod,
+                           BomNodes[['Code', 'Name']],
+                           how='left',
+                           left_on='id_product',
+                           right_on='Code')
+count_firm_prod.drop('Code', axis=1, inplace=True)
+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')
+print(count_firm_prod)
+
+# count firm
+count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
+count_firm = count_firm.to_frame().reset_index()
+count_firm = pd.merge(count_firm,
+                      Firm[['Code', 'Name']],
+                      how='left',
+                      left_on='id_firm',
+                      right_on='Code')
+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')
+print(count_firm)
+
+# count product
+count_prod = count_firm_prod.groupby('id_product')['count'].sum()
+count_prod = count_prod.to_frame().reset_index()
+count_prod = pd.merge(count_prod,
+                      BomNodes[['Code', 'Name']],
+                      how='left',
+                      left_on='id_product',
+                      right_on='Code')
+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')
+print(count_prod)
+
+# DCP disruption causing probability
+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'
+])
+for sid, group in result.groupby('s_id'):
+    ts_start = max(group['ts'])
+    while ts_start >= 1:
+        ts_end = ts_start - 1
+        while ts_end >= 0:
+            up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
+            down = group.loc[group['ts'] == ts_start,
+                             ['id_firm', 'id_product']]
+            for _, up_row in up.iterrows():
+                for _, down_row in down.iterrows():
+                    row = [sid]
+                    row += up_row.tolist()
+                    row += down_row.tolist()
+                    result_dcp.loc[len(result_dcp.index)] = row
+            ts_end -= 1
+        ts_start -= 1
+count_dcp = result_dcp.value_counts(
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'])
+count_dcp.name = 'count'
+count_dcp = count_dcp.to_frame().reset_index()
+count_dcp = pd.merge(count_dcp,
+                     Firm[['Code', 'Name']],
+                     how='left',
+                     left_on='up_id_firm',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'up_name_firm'}, inplace=True)
+count_dcp = pd.merge(count_dcp,
+                     BomNodes[['Code', 'Name']],
+                     how='left',
+                     left_on='up_id_product',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'up_name_product'}, inplace=True)
+count_dcp = pd.merge(count_dcp,
+                     Firm[['Code', 'Name']],
+                     how='left',
+                     left_on='down_id_firm',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'down_name_firm'}, inplace=True)
+count_dcp = pd.merge(count_dcp,
+                     BomNodes[['Code', 'Name']],
+                     how='left',
+                     left_on='down_id_product',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'down_name_product'}, inplace=True)
+count_dcp = count_dcp[[
+    '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'
+]]
+count_dcp.to_csv('analysis\\count_dcp.csv', index=False, encoding='utf-8-sig')
+print(count_dcp)

anova.py

@@ -0,0 +1,125 @@
+import numpy as np
+import pandas as pd
+from orm import engine
+from scipy.stats import f
+
+
+"""
+    This file needs to define the info in the *main* block,
+    and then run the anova function.
+"""
+
+
+def do_print(lst_value, str_col):
+    """
+    Just for friendly-looking printing
+
+    :param lst_value:
+    :param str_col:
+    :return:
+    """
+    str_data = '\t'.join(
+        [str(round(e, 4 if 'P value' in str_col else 3)) for e in lst_value])
+    print(f'{str_col}\t{str_data}')
+
+
+def anova(lst_col_seg, n_level, oa_file, result_file, alpha=0.1):
+    """
+    Give the files and info, compute the significance of each X for each Y
+
+    :param lst_col_seg: record the number of X, E, and Y.
+    :param n_level:
+    :param oa_file:
+    :param result_file:
+    :param alpha: significance level, usually 0.1, 0.05, 0.01
+    :return:
+    """
+    # read and check the files
+    df_oa = pd.read_csv("oa_with_exp.csv", index_col=None)
+    df_res = result_file
+    assert df_res.shape[1] == sum(lst_col_seg), 'the column number is wrong'
+    assert df_oa.shape[1] == lst_col_seg[0] + \
+        lst_col_seg[1], 'the column number is wrong'
+    lst_head = [f"{idx+1}_{ind_name}" for idx,
+                ind_name in enumerate(df_res.columns)]
+
+    # The three lines below define some coefficients for further computation
+    n_col_input = lst_col_seg[0] + lst_col_seg[1]
+    n_exp_row = df_res.shape[0]
+    n_degree_error = n_exp_row - 1 - (n_level - 1) * lst_col_seg[0]
+
+    df_output = df_res.iloc[:, n_col_input:]
+
+    print("Source\tSource\t" + '\t'.join(lst_head[:lst_col_seg[0]]) + "\te")
+    print("DOF\tDOF\t" + '\t'.join([str(n_level-1)]
+          * lst_col_seg[0]) + f"\t{n_degree_error}")
+
+    lst_report = []
+
+    # start to loop each Y
+    for idx_col in range(lst_col_seg[2]):
+        str_ind_name = lst_head[idx_col+n_col_input]
+
+        df_y_col = df_output.iloc[:, idx_col]  # the y column
+        df_y_col_repeated = np.tile(
+            df_y_col, (n_col_input, 1)).T  # repeat the y column
+        big_t = df_y_col.sum()  # the big T
+
+        # generate T1, ..., T(n_levels)
+        lst_2d_big_t = []  # Table 1, row 10, 11, 12
+        for level in range(n_level):
+            arr_big_t = np.sum(df_y_col_repeated *
+                               np.where(df_oa == level, 1, 0), axis=0)
+            lst_2d_big_t.append(arr_big_t.tolist())
+        arr_big_t_2 = np.power(np.array(lst_2d_big_t), 2)
+        arr_s = np.sum(arr_big_t_2, axis=0) / (n_exp_row / n_level) - \
+            big_t * big_t / n_exp_row  # Table 1, last row
+        assert arr_s.size == n_col_input, 'wrong arr_s size'
+
+        # so far, the first table is computed. Now, compute the second table
+        df_s = pd.DataFrame(arr_s.reshape((1, n_col_input)),
+                            columns=lst_head[:n_col_input])
+        do_print(arr_s.tolist(), f'{str_ind_name}\tS')  # Table 2, col 2
+
+        df_s_non_error = df_s.iloc[:, :lst_col_seg[0]] / (n_level - 1)
+        ms_of_error = \
+            df_s.iloc[:, lst_col_seg[0]:].sum().sum() / n_degree_error
+
+        do_print(df_s_non_error.values.tolist()[
+                 0] + [ms_of_error], f'{str_ind_name}\tMS')  # Table 2, col 4
+
+        arr_f = df_s_non_error / ms_of_error
+        # Table 2, col 5
+        do_print(arr_f.values.tolist()[0], f'{str_ind_name}\tF ratio')
+
+        # from scipy.stats import f
+        arr_p_value = f.sf(arr_f, n_level - 1, n_degree_error)
+        # Table 2, col 6
+        do_print(arr_p_value.tolist()[0], f'{str_ind_name}\tP value')
+
+        lst_sig = [c for c, p in zip(
+            lst_head[:lst_col_seg[0]], arr_p_value[0].tolist()) if p < alpha]
+
+        if len(lst_sig) > 0:
+            lst_report.append(
+                f"For indicator {str_ind_name}, the sig factors are {lst_sig}")
+
+    for s in lst_report:
+        print(s)
+
+
+if __name__ == '__main__':
+    # prep data
+    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)
+    scenario_result = pd.concat(
+        [result.iloc[:, 0:10],
+         df_oa.iloc[:, -4:],
+         result.iloc[:, -2:]], axis=1)
+    result.to_csv('analysis\\experiment_result.csv')
+
+    # 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)

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()

check_db.py

@@ -0,0 +1,19 @@
+from orm import engine
+from sqlalchemy import text
+import pandas as pd
+import networkx as nx
+import json
+import matplotlib.pyplot as plt
+
+# prep data
+Firm = pd.read_csv("Firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
+
+with open('SQL_analysis_risk.sql', 'r') as f:
+    str_sql = f.read()
+str_sql = str_sql.replace('iiabmdb', 'iiabmdb20260127')
+with engine.connect() as conn:
+    result = pd.read_sql(sql=text(str_sql), con=conn)
+print(result)

computation.py

@@ -1,12 +1,13 @@
 import os
 import datetime
+import time
 
 from model import Model
 
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from controller_db import ControllerDB
-    
+
 
 class Computation:
 
@@ -21,7 +22,12 @@ class Computation:
 
         # lock this row by update is_done_flag to 0
         self.c_db.lock_the_sample(sample_random)
-        print(f"Pid {self.pid} ({str_code}) is running sample {sample_random.id} at {datetime.datetime.now()}")
+        
+        # Record start time
+        start_time = time.time()
+        print(
+            f"Pid {self.pid} ({str_code}) is running "
+            f"sample {sample_random.id} at {datetime.datetime.now()}")
 
         dct_exp = {column: getattr(sample_random.experiment, column)
                    for column in sample_random.experiment.__table__.c.keys()}
@@ -32,6 +38,14 @@ class Computation:
                            **dct_exp}
         model = Model(dct_sample_para)
         results = model.run(display=False)
+        
+        # Calculate elapsed time
+        elapsed_time = time.time() - start_time
+        print(
+            f"Pid {self.pid} ({str_code}) completed "
+            f"sample {sample_random.id} in {elapsed_time:.2f} seconds "
+            f"(stopped at t={sample_random.stop_t})")
+        
         return False
 
 

conf_db_prefix.yaml

@@ -1 +1 @@
-db_name_prefix: test
+db_name_prefix: with_exp

conf_experiment.yaml

@@ -1,38 +1,12 @@
 # read by ControllerDB
 
 # run settings
-meta_seed: 0
+meta_seed: 2
-
-fixed:  # unchanged all the time
-  int_n_country: 2
-  max_int_n_supplier: 3  # make firms heterogeneous
-  flt_bm_price_ratio: 20.0
-  flt_beta_developing: 0.5
 
 test:  # only for test scenarios
-  int_n_product: 12
+  n_sample: 1
-  int_n_firm_per_product_per_country: 2
-  flt_demand_total: 1000.0
-  n_sample: 5
   n_iter: 100
 
 not_test:  # normal scenarios
-  int_n_product: 50
-  int_n_firm_per_product_per_country: 10
-  flt_demand_total: 10000.0
   n_sample: 50
-  n_iter: 10000
+  n_iter: 100
-
-default:
-  is_eliminated: 0  # add when all positive profits and keep max n supplier; remove the worst when all negative wealth
-  flt_beta_developed: 0.5  # benchmarks flt_beta_developed
-  tariff_percentage: 0
-
-experiment:
-  1:
-    range_lambda_tier: 0, 1, 0.1  # describe the network. 0: chain 1: one iter
-  2:
-    is_eliminated: 1
-  3:
-    range_flt_beta_developed: 0.5, 0.9, 0.1
-    range_tariff_percentage: 0, 1, 0.1

controller_db.py

@@ -1,11 +1,15 @@
 # -*- coding: utf-8 -*-
 from orm import db_session, engine, Base, ins
-from orm import Experiment, Sample, Product, Firm
+from orm import Experiment, Sample, Result
 from sqlalchemy.exc import OperationalError
+from sqlalchemy import text
 import yaml
 import random
-import numpy as np
+import pandas as pd
 import platform
+import networkx as nx
+import json
+import pickle
 
 
 class ControllerDB:
@@ -14,97 +18,139 @@ class ControllerDB:
     db_name_prefix: str = None
     reset_flag: int
 
-    lst_saved_s_id_3: list
+    lst_saved_s_id: list
-    lst_saved_s_id_1_2: list
-    # n_sample_1_2: int
 
     def __init__(self, prefix, reset_flag=0):
         with open('conf_experiment.yaml') as yaml_file:
             dct_conf_experiment = yaml.full_load(yaml_file)
+
+        assert prefix in ['test', 'without_exp', 'with_exp'], \
+            "db name not in test, without_exp, with_exp"
+
         self.is_test = prefix == 'test'
+        self.is_with_exp = \
+            False if prefix == 'test' or prefix == 'without_exp' else True
         self.db_name_prefix = prefix
-        dct_para_in_test = dct_conf_experiment['test'] if self.is_test else dct_conf_experiment['not_test']
+        dct_para_in_test = dct_conf_experiment[
-        self.dct_parameter = {'meta_seed': dct_conf_experiment['meta_seed'],
+            'test'] if self.is_test else dct_conf_experiment['not_test']
-                              'experiment': dct_conf_experiment['experiment'],
+        self.dct_parameter = {
-                              **dct_conf_experiment['fixed'], **dct_conf_experiment['default'], **dct_para_in_test}
+            'meta_seed': dct_conf_experiment['meta_seed'],
-        self.reset_flag = reset_flag  # 0, not reset; 1, reset self; 2, reset all
+            **dct_para_in_test
-        self.lst_saved_s_id_1_2, self.lst_saved_s_id_3 = [], []
+        }
+        print(self.dct_parameter)
+        # 0, not reset; 1, reset self; 2, reset all
+        self.reset_flag = reset_flag
+        self.lst_saved_s_id = []
 
     def init_tables(self):
         self.fill_experiment_table()
         self.fill_sample_table()
 
-    @staticmethod
-    def get_lst_of_range(str_range: str):
-        s1, s2, s3 = tuple(str_range.split(','))
-        return list(np.linspace(float(s1), float(s2), num=int((float(s2) - float(s1)) / float(s3)) + 1))
-
     def fill_experiment_table(self):
-        # prepare the list of lambda tier
+        Firm = pd.read_csv("Firm_amended.csv")
-        lst_lambda = self.get_lst_of_range(self.dct_parameter['experiment'][1]['range_lambda_tier'])
+        Firm['Code'] = Firm['Code'].astype('string')
-        # prepare the list of alpha_2nd_country
+        Firm.fillna(0, inplace=True)
-        lst_beta_developed = self.get_lst_of_range(self.dct_parameter['experiment'][3]['range_flt_beta_developed'])
-        # prepare the list of tariff_percentage
-        lst_tariff = self.get_lst_of_range(self.dct_parameter['experiment'][3]['range_tariff_percentage'])
-        # prepare the default values
-        is_eliminated = int(self.dct_parameter['is_eliminated'])
-        beta_developed = float(self.dct_parameter['flt_beta_developed'])
-        tariff_percentage_1 = tariff_percentage_2 = float(self.dct_parameter['tariff_percentage'])
-        for idx_scenario in self.dct_parameter['experiment'].keys():
-            n_exp = 0
-            # if idx_scenario == 1:  # add S1 experiments
-            #     n_exp = self.add_experiment_1(idx_scenario, lst_lambda, is_eliminated,
-            #                                   beta_developed, tariff_percentage_1, tariff_percentage_2)
-            # if idx_scenario == 2:  # add S2 experiments
-            #     n_exp = self.add_experiment_1(idx_scenario, lst_lambda,
-            #                                   int(self.dct_parameter['experiment'][idx_scenario]['is_eliminated']),
-            #                                   beta_developed, tariff_percentage_1, tariff_percentage_2)
-            if idx_scenario == 3:
-                # int_eliminated = int(self.dct_parameter['experiment'][idx_scenario-1]['is_eliminated'])
-                int_eliminated = is_eliminated  # is_eliminated is 0 at default, so stop eliminating under S3
-                for beta_developed in lst_beta_developed:
-                # for beta_developed in [0.5]:  # fix beta as 0.5
-                    for tariff_percentage_1 in lst_tariff:
-                        for tariff_percentage_2 in lst_tariff:
-                            # fix lambda as 0.5
-                            n_exp += self.add_experiment_1(idx_scenario, [0.5], int_eliminated,
-                                                           beta_developed, tariff_percentage_1, tariff_percentage_2)
-            print(f'Inserted {n_exp} experiments for exp {idx_scenario}!')
 
-    def add_experiment_1(self, idx_exp, lst_lambda, is_eliminated, flt_beta_developed,
+        # fill dct_lst_init_disrupt_firm_prod
-                         tariff_percentage_1: float, tariff_percentage_2: float):
+        list_dct = []
-        lst_exp = []
+        if self.is_with_exp:
-        for lambda_tier in lst_lambda:
+            with open('SQL_export_high_risk_setting.sql', 'r') as f:
-            e = Experiment(idx_exp=idx_exp,
+                str_sql = f.read()
-                           int_n_country=int(self.dct_parameter['int_n_country']),
+            with engine.connect() as conn:
-                           max_int_n_supplier=int(self.dct_parameter['max_int_n_supplier']),
+                result = pd.read_sql(sql=text(str_sql), con=conn)
-                           int_n_product=int(self.dct_parameter['int_n_product']),
+            result['dct_lst_init_disrupt_firm_prod'] = \
-                           int_n_firm_per_product_per_country=int(
+                result['dct_lst_init_disrupt_firm_prod'].apply(
-                               self.dct_parameter['int_n_firm_per_product_per_country']),
+                lambda x: pickle.loads(x))
-                           flt_demand_total=float(self.dct_parameter['flt_demand_total']),
+            list_dct = result['dct_lst_init_disrupt_firm_prod'].to_list()
-                           flt_bm_price_ratio=float(self.dct_parameter['flt_bm_price_ratio']),
+        else:
-                           flt_beta_developing=float(self.dct_parameter['flt_beta_developing']),
+            for _, row in Firm.iterrows():
-                           n_sample=int(self.dct_parameter['n_sample']),
+                code = row['Code']
-                           n_iter=int(self.dct_parameter['n_iter']),
+                row = row['1':]
-                           is_eliminated=is_eliminated,
+                for product_code in row.index[row == 1].to_list():
-                           flt_beta_developed=flt_beta_developed,
+                    dct = {code: [product_code]}
-                           tariff_percentage_1=tariff_percentage_1,
+                    list_dct.append(dct)
-                           tariff_percentage_2=tariff_percentage_2,
+        # list_dct = [{'140': ['1.4.5.1']}]
-                           lambda_tier=float(lambda_tier))
+        # list_dct = [{'133': ['1.4.4.1']}]
-            lst_exp.append(e)
+        # list_dct = [{'2': ['1.1.3']}]
-        db_session.bulk_save_objects(lst_exp)
+        # 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)
+        BomNodes.set_index('Code', inplace=True)
+        BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
+        BomCateNet.fillna(0, inplace=True)
+        g_bom = nx.from_pandas_adjacency(BomCateNet.T,
+                                         create_using=nx.MultiDiGraph())
+        bom_labels_dict = {}
+        for code in g_bom.nodes:
+            bom_labels_dict[code] = BomNodes.loc[code].to_dict()
+        nx.set_node_attributes(g_bom, bom_labels_dict)
+        g_product_js = json.dumps(nx.adjacency_data(g_bom))
+
+        # insert exp
+        df_xv = pd.read_csv(
+            f"xv_{'with_exp' if self.is_with_exp else 'without_exp'}.csv",
+            index_col=None)
+        # read the OA table
+        df_oa = pd.read_csv(
+            f"oa_{'with_exp' if self.is_with_exp else 'without_exp'}.csv",
+            index_col=None)
+        df_oa = df_oa.iloc[:, 0:df_xv.shape[1]]
+        for idx_scenario, row in df_oa.iterrows():
+            dct_exp_para = {}
+            for idx_col, para_level in enumerate(row):
+                dct_exp_para[df_xv.columns[idx_col]] = \
+                    df_xv.iloc[para_level, idx_col]
+            # different initial removal
+            for idx_init_removal, dct_init_removal in enumerate(list_dct):
+                self.add_experiment_1(idx_scenario,
+                                      idx_init_removal,
+                                      dct_init_removal,
+                                      g_product_js,
+                                      **dct_exp_para)
+                print(f"Inserted experiment for scenario {idx_scenario}, "
+                      f"init_removal {idx_init_removal}!")
+
+    def add_experiment_1(self, idx_scenario, idx_init_removal,
+                         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, remove_t, netw_prf_n):
+        e = Experiment(
+            idx_scenario=idx_scenario,
+            idx_init_removal=idx_init_removal,
+            n_sample=int(self.dct_parameter['n_sample']),
+            n_iter=int(self.dct_parameter['n_iter']),
+            dct_lst_init_disrupt_firm_prod=dct_lst_init_disrupt_firm_prod,
+            g_bom=g_bom,
+            n_max_trial=n_max_trial,
+            prf_size=prf_size,
+            prf_conn=prf_conn,
+            cap_limit_prob_type=cap_limit_prob_type,
+            cap_limit_level=cap_limit_level,
+            diff_new_conn=diff_new_conn,
+            remove_t=remove_t,
+            netw_prf_n=netw_prf_n
+        )
+        db_session.add(e)
         db_session.commit()
-        return len(lst_exp)
 
     def fill_sample_table(self):
         rng = random.Random(self.dct_parameter['meta_seed'])
-        lst_seed = [rng.getrandbits(32) for _ in range(int(self.dct_parameter['n_sample']))]
+        lst_seed = [
+            rng.getrandbits(32)
+            for _ in range(int(self.dct_parameter['n_sample']))
+        ]
         lst_exp = db_session.query(Experiment).all()
         lst_sample = []
         for experiment in lst_exp:
             for idx_sample in range(int(experiment.n_sample)):
                 s = Sample(e_id=experiment.id,
-                           idx_sample=idx_sample+1,
+                           idx_sample=idx_sample + 1,
                            seed=lst_seed[idx_sample],
                            is_done_flag=-1)
                 lst_sample.append(s)
@@ -114,8 +160,11 @@ class ControllerDB:
 
     def reset_db(self, force_drop=False):
         # first, check if tables exist
-        lst_table_obj = [Base.metadata.tables[str_table] for str_table in ins.get_table_names()
+        lst_table_obj = [
-                         if str_table.startswith(self.db_name_prefix)]
+            Base.metadata.tables[str_table]
+            for str_table in ins.get_table_names()
+            if str_table.startswith(self.db_name_prefix)
+        ]
         is_exist = len(lst_table_obj) > 0
         if force_drop:
             while is_exist:
@@ -128,51 +177,62 @@ class ControllerDB:
                     pass
                 else:
                     lst_table_obj.remove(a_table)
-                    print(f"Table {a_table.name} is dropped for exp: {self.db_name_prefix}!!!")
+                    print(
+                        f"Table {a_table.name} is dropped "
+                        f"for exp: {self.db_name_prefix}!!!"
+                    )
                 finally:
                     is_exist = len(lst_table_obj) > 0
 
         if is_exist:
-            print(f"All tables exist. No need to reset for exp: {self.db_name_prefix}.")
+            print(
-            # change the is_done_flag from 0 to -1, to rerun the in-finished tasks
+                f"All tables exist. No need to reset "
+                f"for exp: {self.db_name_prefix}."
+            )
+            # change the is_done_flag from 0 to -1
+            # rerun the in-finished tasks
             if self.reset_flag > 0:
                 if self.reset_flag == 2:
-                    result = db_session.query(Sample).filter(Sample.is_done_flag == 0)
+                    sample = db_session.query(Sample).filter(
+                        Sample.is_done_flag == 0)
                 elif self.reset_flag == 1:
-                    result = db_session.query(Sample).filter(Sample.is_done_flag == 0,
+                    sample = db_session.query(Sample).filter(
-                                                             Sample.computer_name == platform.node())
+                        Sample.is_done_flag == 0,
+                        Sample.computer_name == platform.node())
                 else:
                     raise ValueError('Wrong reset flag')
-                if result.count() > 0:
+                if sample.count() > 0:
-                    for res in result:
+                    for s in sample:
-                        qry_product = db_session.query(Product).filter_by(s_id=res.id)
+                        qry_result = db_session.query(Result).filter_by(
-                        if qry_product.count() > 0:
+                            s_id=s.id)
-                            for p in qry_product:
+                        if qry_result.count() > 0:
-                                db_session.query(Firm).filter(Firm.p_id == p.id).delete()
+                            db_session.query(Result).filter(s_id=s.id).delete()
-                                db_session.commit()
+                            db_session.commit()
-                                db_session.query(Product).filter(Product.id == p.id).delete()
+                        s.is_done_flag = -1
-                                db_session.commit()
-                        res.is_done_flag = -1
                         db_session.commit()
-                        print(f"Reset the task id {res.id} flag from 0 to -1")
+                        print(f"Reset the sample id {s.id} flag from 0 to -1")
         else:
-            Base.metadata.create_all()
+            Base.metadata.create_all(bind=engine)
             self.init_tables()
-            print(f"All tables are just created and initialized for exp: {self.db_name_prefix}.")
+            print(
+                f"All tables are just created and initialized "
+                f"for exp: {self.db_name_prefix}."
+            )
 
     def prepare_list_sample(self):
-        res = db_session.execute(f'''SELECT count(*) FROM {self.db_name_prefix}_sample s, 
+        res = db_session.execute(
-        {self.db_name_prefix}_experiment e WHERE s.e_id=e.id and e.idx_exp < 3''').scalar()
+            text(f"SELECT count(*) FROM {self.db_name_prefix}_sample s, "
-        n_sample_1_2 = 0 if res is None else res
+                 f"{self.db_name_prefix}_experiment e WHERE s.e_id=e.id"
-        print(f'There are {n_sample_1_2} sample for exp 1 and 2.')
+                 )).scalar()
-        res = db_session.execute(f'SELECT id FROM {self.db_name_prefix}_sample WHERE is_done_flag = -1')
+        n_sample = 0 if res is None else res
+        print(f'There are a total of {n_sample} samples.')
+        res = db_session.execute(
+            text(f"SELECT id FROM {self.db_name_prefix}_sample "
+                 f"WHERE is_done_flag = -1"
+                 ))
         for row in res:
             s_id = row[0]
-            if s_id <= n_sample_1_2:
+            self.lst_saved_s_id.append(s_id)
-                self.lst_saved_s_id_1_2.append(s_id)
-            else:
-                self.lst_saved_s_id_3.append(s_id)
-        print(f'Left: {len(self.lst_saved_s_id_1_2)} for exp 1 and 2; {len(self.lst_saved_s_id_3)} for exp 3')
 
     @staticmethod
     def select_random_sample(lst_s_id):
@@ -181,7 +241,8 @@ class ControllerDB:
                 return None
             s_id = random.choice(lst_s_id)
             lst_s_id.remove(s_id)
-            res = db_session.query(Sample).filter(Sample.id == int(s_id), Sample.is_done_flag == -1)
+            res = db_session.query(Sample).filter(Sample.id == int(s_id),
+                                                  Sample.is_done_flag == -1)
             if res.count() == 1:
                 return res[0]
 
@@ -193,11 +254,7 @@ class ControllerDB:
             else:
                 return res[0]
 
-        sample = self.select_random_sample(self.lst_saved_s_id_1_2)
+        sample = self.select_random_sample(self.lst_saved_s_id)
-        if sample is not None:
-            return sample
-
-        sample = self.select_random_sample(self.lst_saved_s_id_3)
         if sample is not None:
             return sample
 
@@ -205,33 +262,8 @@ class ControllerDB:
 
     @staticmethod
     def lock_the_sample(sample: Sample):
-        sample.is_done_flag, sample.computer_name = 0, platform.node()
+        from datetime import datetime
+        sample.is_done_flag = 0
+        sample.computer_name = platform.node()
+        sample.ts_start = datetime.now()
         db_session.commit()
-
-
-if __name__ == '__main__':
-    # pprint.pprint(dct_exp_config)
-    # pprint.pprint(dct_conf_problem)
-    db = ControllerDB('first')
-    ratio = db_session.execute('SELECT COUNT(*) / 332750 FROM first_sample s WHERE s.is_done_flag = 1').scalar()
-    print(ratio)
-    # db.fill_experiment_table()
-    # print(db.dct_parameter)
-    # db.init_tables()
-    # db.fill_sample_table()
-    # pprint.pprint(dct_conf_exp)
-    # db.update_bi()
-    # db.reset_db(force_drop=True)
-    # db.prepare_list_sample()
-    #
-    # for i in range(1000):
-    #     if i % 10 == 0:
-    #         print(i)
-    #         print(len(db.lst_saved_s_id_1_2), len(db.lst_saved_s_id_3))
-    #     r = db.fetch_a_sample()
-    #     if i % 10 == 0:
-    #         print(len(db.lst_saved_s_id_1_2), len(db.lst_saved_s_id_3))
-    #         print(r, r.experiment.idx_exp)
-    #     if i == 400:
-    #         print()
-    #         pass