增加绘图以及图片属性转换(中英)

增加企业规模描述统计表
增加绘制供应链以及产业链度图

.idea/csv-editor.xml

@@ -143,6 +143,27 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\output_result\resilience\anova.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\output_result\resilience\anova_visualization.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\output_result\resilience\experiment_result.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\output_result\risk\count.csv">
           <value>
             <Attribute>

.idea/dataSources.local.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="dataSourceStorageLocal" created-in="PY-242.23726.102">
+    <data-source name="iiabmdb@localhost" uuid="8145438e-516b-4005-a581-b91b5eedc759">
+      <database-info product="MySQL" version="8.0.36" jdbc-version="4.2" driver-name="MySQL Connector/J" driver-version="mysql-connector-j-8.2.0 (Revision: 06a1f724497fd81c6a659131fda822c9e5085b6c)" dbms="MYSQL" exact-version="8.0.36" exact-driver-version="8.2">
+        <extra-name-characters>#@</extra-name-characters>
+        <identifier-quote-string>`</identifier-quote-string>
+      </database-info>
+      <case-sensitivity plain-identifiers="lower" quoted-identifiers="lower" />
+      <secret-storage>master_key</secret-storage>
+      <user-name>iiabm_user</user-name>
+      <schema-mapping>
+        <introspection-scope>
+          <node kind="schema">
+            <name qname="@" />
+            <name qname="information_schema" />
+          </node>
+        </introspection-scope>
+      </schema-mapping>
+    </data-source>
+  </component>
+</project>

.idea/dataSources.xml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="iiabmdb@localhost" uuid="8145438e-516b-4005-a581-b91b5eedc759">
+      <driver-ref>mysql.8</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
+      <jdbc-url>jdbc:mysql://localhost:3306/iiabmdb</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </component>
+</project>

.idea/dataSources/8145438e-516b-4005-a581-b91b5eedc759/storage_v2/_src_/schema/iiabmdb.ykAgaw.meta

@@ -0,0 +1,2 @@
+#n:iiabmdb
+!<md> [0, 0, null, null, -2147483648, -2147483648]

.idea/dataSources/8145438e-516b-4005-a581-b91b5eedc759/storage_v2/_src_/schema/information_schema.FNRwLQ.meta

@@ -0,0 +1,2 @@
+#n:information_schema
+!<md> [0, 0, null, null, -2147483648, -2147483648]

.idea/developer-tools.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DeveloperToolsToolWindowSettingsV1" lastSelectedContentNodeId="base64-encoder-decoder">
+    <developerToolsConfigurations />
+  </component>
+</project>

.idea/encodings.xml

@@ -1,6 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="Encoding">
-    <file url="file://$PROJECT_DIR$/input_data/input_firm_data/Firm_amended.csv" charset="UTF-8" />
+    <file url="file://$PROJECT_DIR$/input_data/input_firm_data/firm_amended.csv" charset="UTF-8" />
+    <file url="file://$PROJECT_DIR$/output_result/resilience/anova_visualization.csv" charset="UTF-8" />
+    <file url="file://$PROJECT_DIR$/查看进度.py" charset="GBK" />
   </component>
 </project>

.idea/sqldialects.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="SqlDialectMappings">
+    <file url="file://$PROJECT_DIR$/SQL_analysis_experiment.sql" dialect="MySQL" />
+    <file url="file://$PROJECT_DIR$/SQL_analysis_risk.sql" dialect="MySQL" />
+    <file url="file://$PROJECT_DIR$/SQL_db_user_create.sql" dialect="MySQL" />
+    <file url="file://$PROJECT_DIR$/SQL_export_high_risk_setting.sql" dialect="MySQL" />
+    <file url="file://$PROJECT_DIR$/SQL_migrate_db.sql" dialect="MySQL" />
+    <file url="file://$PROJECT_DIR$/iiabmdb_basic_info.sql" dialect="MySQL" />
+  </component>
+</project>

README.md

@@ -13,13 +13,13 @@
 1. 将*conf_db_prefix.yaml*文件中的*db_prefix*改为*db_name_prefix: without_exp*
 2. 打开命令行，进入代码所在目录，运行
 ```shell
-python main.py --exp without_exp --reset_db True
+python main.py --exp without_exp --job 6 --reset_db True
 ```
-3. 等待运行完成（77.5个样本）。结束后，将*db_name_prefix: without_exp*改为*db_name_prefix: with_exp*，并运行
+3. 等待运行完成（1.2万个样本）。结束后，将*db_name_prefix: without_exp*改为*db_name_prefix: with_exp*，并运行
 ```shell
-python main.py --exp with_exp --reset_db True
+python main.py --exp with_exp --job 6 --reset_db True
 ```
-4. 漫长的等待（万个样本），直到运行完成
+4. 漫长的等待（3.4万个样本），直到运行完成
 
 ## 获得结果，绘制图表
 

SQL_analysis_experiment.sql

@@ -74,7 +74,7 @@ left join
 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
+left join iiabmdb.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

SQL_migrate_db.sql

@@ -1,13 +1,16 @@
-CREATE DATABASE iiabmdb20230829;
-RENAME TABLE iiabmdb.not_test_experiment TO iiabmdb20230829.not_test_experiment,
-iiabmdb.not_test_result TO iiabmdb20230829.not_test_result,
-iiabmdb.not_test_sample TO iiabmdb20230829.not_test_sample,
-iiabmdb.test_experiment TO iiabmdb20230829.test_experiment,
-iiabmdb.test_result TO iiabmdb20230829.test_result,
-iiabmdb.test_sample TO iiabmdb20230829.test_sample;
-RENAME TABLE iiabmdb.with_exp_experiment TO iiabmdb20230829.with_exp_experiment,
-iiabmdb.with_exp_result TO iiabmdb20230829.with_exp_result,
-iiabmdb.with_exp_sample TO iiabmdb20230829.with_exp_sample,
-iiabmdb.without_exp_experiment TO iiabmdb20230829.without_exp_experiment,
-iiabmdb.without_exp_result TO iiabmdb20230829.without_exp_result,
-iiabmdb.without_exp_sample TO iiabmdb20230829.without_exp_sample;
+-- 创建新的数据库
+CREATE DATABASE iiabmdb2025211;
+
+-- 重命名表到新数据库
+RENAME TABLE 
+    iiabmdb.test_experiment TO iiabmdb2025211.test_experiment,
+    iiabmdb.test_result TO iiabmdb2025211.test_result,
+    iiabmdb.test_sample TO iiabmdb2025211.test_sample;
+
+RENAME TABLE 
+    iiabmdb.with_exp_experiment TO iiabmdb2025211.with_exp_experiment,
+    iiabmdb.with_exp_result TO iiabmdb2025211.with_exp_result,
+    iiabmdb.with_exp_sample TO iiabmdb2025211.with_exp_sample,
+    iiabmdb.without_exp_experiment TO iiabmdb2025211.without_exp_experiment,
+    iiabmdb.without_exp_result TO iiabmdb2025211.without_exp_result,
+    iiabmdb.without_exp_sample TO iiabmdb2025211.without_exp_sample;

ToDo.md

@@ -1,5 +1,4 @@
 ## 下一步计划
-~~1. 加快运行速度，优化属性值计算。~~
-~~2. 优化mesa架构，确保合乎规范。~~
-3.等待运行结果（77.5万）
-4.进行方差分析
+1. 运行with_exp
+2. 方差分析
+3. 优化资源消耗逻辑

computation.py

@@ -1,6 +1,7 @@
 import json
 import os
 import datetime
+import time
 
 import networkx as nx
 import pandas as pd
@@ -45,5 +46,3 @@ class Computation:
 
         model.step()  # 运行仿真一步
         model.end()  # 汇总结果
-
-        return False

conf_db_prefix.yaml

@@ -1 +1 @@
-db_name_prefix: with_exp
+db_name_prefix: without_exp

conf_experiment.yaml

@@ -8,5 +8,5 @@ test:  # only for test scenarios
   n_iter: 100
 
 not_test:  # normal scenarios
-  n_sample: 10
-  n_iter: 50
+  n_sample: 50
+  n_iter: 100

input_data/input_firm_data/firm_amended.csv

@@ -1,241 +1,241 @@
-Code,固定资产原值（万元人民币）,固定资产净值（万元人民币）,资产总和（万元人民币）,存货（万元人民币）,企业名称,Type_Region,Revenue_Log,是否自我供给
-7,738304.9775,521745.74,1562279.24,20629.145,中船（邯郸）派瑞特种气体股份有限公司,河北省,6.2,0
-9,118435.7825,68257.45,459657.555,99540.5725,河北华通线缆集团股份有限公司,河北省,5.7,0
-640320,586321.240445073,335029.670514021,1031071.55115557,206268.473938857,安泰科技股份有限公司,北京市,6.0,0
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-2010673,1275971.70452992,934279.359402009,2021526.974885,133209.227714714,北京京运通科技股份有限公司,北京市,6.3,0
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-26162741,80762.2185454289,38320.2320816127,280927.450768857,46986.1612615714,中核苏阀科技实业股份有限公司,江苏省,5.4,0
-26516263,9770657.164643,6267233.74672667,16031243.6918968,1389136.97893083,万华化学集团股份有限公司,山东省,7.2,0
-27075840,240383.397345603,138907.79119652,345197.977003382,21818.458064,上海生物制品研究所有限责任公司,上海市,5.5,0
-27731896,1608736.72383966,1100882.85217291,6303473.97195071,225975.029698857,东旭光电科技股份有限公司,河北省,6.8,0
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-31654817,737.674091690682,426.271863328108,1059.32276085326,66.95517,中燕能源集团有限公司,北京市,3.0,0
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-43407343,34670.682309462,20034.7775764211,49788.1697601031,3146.89299,宁波荣申精密铸造有限公司,浙江省,4.7,0
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-70634828,4412979.01454591,2550081.13819878,6337174.05278817,380474.996249,红狮控股集团有限公司,浙江省,6.8,0
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-144312602,8114.4150085975,4688.99049660919,11652.5503693858,736.50687,成都湛艺电子科技有限公司,四川省,4.1,0
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-151606446,1626783.46002289,1021353.15789806,4041634.8112335,705886.797687276,阿特斯阳光电力集团股份有限公司,江苏省,6.6,0
-152008168,280316.154842459,161983.308064681,402542.649124238,25442.9646,乐山无线电股份有限公司,四川省,5.6,0
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-194210021,308062.028854586,178016.520492827,442386.5804645,469.804659,海润光伏科技股份有限公司,江苏省,5.6,0
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-249316393,5409.61000573167,3125.99366440613,7768.36691292389,491.00458,大连奥首科技有限公司,辽宁省,3.9,0
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-278221281,491.782727793788,284.181242218739,706.215173902172,44.63678,邢台先腾光电科技有限公司,河北省,2.8,0
-301209792,51111.2877957612,39283.408485579,372200.412021167,111866.716299773,华海清科股份有限公司,天津市,5.6,0
-314284469,93478.8051045442,56792.855184085,190929.033459167,18539.5011983333,四川华丰科技股份有限公司,四川省,5.3,0
-343012684,737.674091690682,426.271863328108,1059.32276085326,66.95517,沈阳市鑫红峰高压硅堆有限公司,辽宁省,3.0,0
-354897041,2704.80500286583,1562.99683220306,3884.18345646195,245.50229,宁夏鑫昊缘特种合金有限公司,宁夏回族自治区,3.6,0
-359737460,3118.597955,2478.276413,22273.339243,12656.693555,长春光华微电子设备工程中心有限公司,吉林省,4.3,0
-400488703,3196.58773065962,1847.1780744218,4590.39863036412,290.13907,厦门桥南工贸有限公司,福建省,3.7,0
-400692942,178271.238825248,103015.700304293,256003.000539537,16180.83275,湘能华磊光电股份有限公司,湖南省,5.4,0
-413274977,20643.9674441109,11929.3093901634,29645.3743383333,6146.1294835,厦门三优光电股份有限公司,福建省,4.5,0
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-499022815,39256.6909747101,26249.6701913886,74787.2919863333,3738.7602482029,深圳市迅捷兴科技股份有限公司,广东省,4.9,0
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-525126393,1721.23954727826,994.634347765586,2471.7531086576,156.22873,安徽华晶微电子材料科技有限公司,安徽省,3.4,0
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-591452402,16597.6670630403,9591.11692488244,23834.7621191983,1506.491325,武汉光电工业技术研究院有限公司,湖北省,4.4,0
-593312758,16597.6670630403,9591.11692488244,23834.7621191983,1506.491325,湖北永绍科技股份有限公司,湖北省,4.4,0
-594378026,1024511.57056233,636647.240723,886208.347894167,24898.1373601667,安徽华塑股份有限公司,安徽省,5.9,0
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-615763365,4917.82727793788,2841.81242218739,7062.15173902172,446.3678,欧瑞康美科表面技术（上海）有限公司长春分公司,吉林省,3.8,0
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-631449822,122868.272332646,71000.5786852338,176442.630878333,49014.7605131667,武汉日新科技股份有限公司,湖北省,5.2,0
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-653528340,245.891363896894,142.090621109369,353.107586951086,22.31839,合肥沁泉轩生物科技有限公司,安徽省,2.5,0
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-677887241,378695.182400345,254183.885856668,994951.0107498,148933.6095836,昆山丘钛微电子科技股份有限公司,江苏省,6.0,0
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-888478182,491.782727793788,284.181242218739,706.215173902172,44.63678,东莞市道滘博尔日涂料助剂厂,广东省,2.8,0
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-2311541114,87906.1625931395,50797.3970465996,126235.962335013,7978.824425,一诠科技（中国）有限公司,广东省,5.1,0
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-2312693498,47740.0144863412,27587.0132347507,68556.1340956667,13557.3351533333,深圳市夏瑞科技股份有限公司,广东省,4.8,0
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-2316990095,69894.7882595,56812.003379,167801.3590105,9765.7389965,广东中图半导体科技股份有限公司,广东省,5.2,0
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-2321857672,172615.737455619,99747.6160187774,247881.526039662,15667.50978,福建福顺半导体制造有限公司,福建省,5.4,0
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-2324844174,7868.52364470058,4546.89987549982,11299.4427824348,714.18848,珠海市分板自动化有限公司,广东省,4.1,0
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-2327031723,135066.901471,105068.760506333,324604.3707355,21706.8537546667,聚灿光电科技股份有限公司,江苏省,5.5,0
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-2329375731,245.891363896894,142.090621109369,353.107586951086,22.31839,无锡兴锋光伏科技有限公司,江苏省,2.5,0
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-2352036411,491.782727793788,284.181242218739,706.215173902172,44.63678,甘肃中瀚制冷空调设备有限公司,甘肃省,2.8,0
-2352538239,16223.5357741212,9374.92166541306,23297.4980423333,2700.333564,广东信力科技股份有限公司,广东省,4.4,0
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-2481687500,2458.91363896894,1420.90621109369,3531.07586951086,223.1839,深圳市鑫创鑫自动化设备销售部,广东省,3.5,0
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-2965658107,33627.9499162838,22617.886222157,57037.7426061667,8422.16001716667,安徽晶赛科技股份有限公司,安徽省,4.8,0
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-3025036704,1475.34818338136,852.543726656217,2118.64552170652,133.91034,泗县博尚装饰工程有限公司,安徽省,3.3,0
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-3029702382,245.891363896894,142.090621109369,353.107586951086,22.31839,河南地天泰车业有限公司,河南省,2.5,0
-3045721313,2458.91363896894,1420.90621109369,3531.07586951086,223.1839,传化智联股份有限公司石家庄分公司,河北省,3.5,0
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+2349656760,16976.6475090839,9263.96039459785,57224.236615,14903.7379424235,苏州鸿安机械股份有限公司,江苏省,48.0,0
+2350111843,92789.448255777,62286.1122935528,131188.586355714,1480.14299457143,广东利扬芯片测试股份有限公司,广东省,51.0,0
+2352036411,491.782727793788,284.181242218739,706.215173902172,44.63678,甘肃中瀚制冷空调设备有限公司,甘肃省,28.0,0
+2352538239,16223.5357741212,9374.92166541306,23297.4980423333,2700.333564,广东信力科技股份有限公司,广东省,44.0,0
+2354145351,2950.69636676273,1705.08745331243,4237.29104341303,267.82068,江苏协鑫特种材料科技有限公司,江苏省,36.0,0
+2379638202,5409.61000573167,3125.99366440613,7768.36691292389,491.00458,延安新兴东风汽车技术服务站,陕西省,39.0,0
+2424229017,114276.893374667,77926.1367106667,500826.828284333,41132.540103,湖北鼎龙控股股份有限公司,湖北省,57.0,0
+2481687500,2458.91363896894,1420.90621109369,3531.07586951086,223.1839,深圳市鑫创鑫自动化设备销售部,广东省,35.0,0
+2545430247,368.837045845341,213.135931664054,529.661380426629,33.477585,广州顺安高新材料有限公司,广东省,27.0,0
+2791956547,245.891363896894,142.090621109369,353.107586951086,22.31839,广州市粤盛工程材料有限公司,广东省,25.0,0
+2820140348,6759.83660375,6057.056538,441817.1010005,215119.38793925,拉普拉斯新能源科技股份有限公司,广东省,56.0,0
+2944404352,4381.18779540463,2531.71028530659,6291.520881,938.8492245,安徽先捷电子股份有限公司,安徽省,38.0,0
+2944892892,18401.38999975,13587.32820175,41068.08841875,7301.21620425,安徽安芯电子科技股份有限公司,安徽省,46.0,0
+2965658107,33627.9499162838,22617.886222157,57037.7426061667,8422.16001716667,安徽晶赛科技股份有限公司,安徽省,48.0,0
+3006753238,2041970.43921619,1162815.70513596,2626399.81194,236376.363264714,通富微电子股份有限公司,江苏省,64.0,0
+3025036704,1475.34818338136,852.543726656217,2118.64552170652,133.91034,泗县博尚装饰工程有限公司,安徽省,33.0,0
+3026382513,1721.23954727826,994.634347765588,2471.7531086576,156.22873,绍兴上虞鑫丰物资有限公司,浙江省,34.0,0
+3029702382,245.891363896894,142.090621109369,353.107586951086,22.31839,河南地天泰车业有限公司,河南省,25.0,0
+3045721313,2458.91363896894,1420.90621109369,3531.07586951086,223.1839,传化智联股份有限公司石家庄分公司,河北省,35.0,0
+3047163873,2430149.11286677,1339451.5244595,4326866.03200717,278136.343715333,巨化集团有限公司,浙江省,66.0,0
+3048263744,20654.8745673391,11935.612173187,29661.0373038912,1874.74476,东莞市康柏工业陶瓷有限公司,广东省,45.0,0
+3069206426,1229.45681948447,710.453105546847,1765.53793475543,111.59195,东莞万兴鸿自动化有限公司上海分公司,上海市,32.0,0
+3070859372,491.782727793788,284.181242218739,706.215173902172,44.63678,江苏丰禾兰环保科技有限公司,江苏省,28.0,0
+3072715478,21638.4400229267,12503.9746576245,31073.4676516956,1964.01832,江西宁和达新材料有限公司,江西省,45.0,0
+3103797386,37788.230163,25727.5525585,162597.015751167,11040.109561,江西晨光新材料股份有限公司,江西省,52.0,0
+3111033905,3393.30082177713,1960.8505713093,4872.88469992499,307.993782,安徽开华散热器制造科技有限公司,安徽省,37.0,0
+3113895788,3688.37045845341,2131.35931664054,5296.61380426629,334.77585,山西华晶恒基新材料有限公司,山西省,37.0,0
+3120341363,177544.316546708,129292.795388716,626033.402033857,102806.403100857,武汉精测电子集团股份有限公司,湖北省,58.0,0
+3122923980,1229.45681948447,710.453105546847,1765.53793475543,111.59195,贵州鼎辉农旅商务实业有限责任公司,贵州省,32.0,0
+3127420424,2458.91363896894,1420.90621109369,3531.07586951086,223.1839,西安唐晶量子科技有限公司,陕西省,35.0,0
+3133307899,243008.157268805,176966.316004727,498232.321410136,48875.2580525,九江德福科技股份有限公司,江西省,57.0,0
+3147511625,21761.3857048751,12575.0199681792,31250.0214451711,1975.177515,安徽东昇新能源有限公司,安徽省,45.0,0
+3177507356,245.891363896894,142.090621109369,353.107586951086,22.31839,东莞市易亮电子科技有限公司,广东省,25.0,0
+3188903709,1721.23954727826,994.634347765586,2471.7531086576,156.22873,上海江丰平芯电子科技有限公司,上海市,34.0,0
+3195502499,5901.39273352545,3410.17490662487,8474.58208682606,535.64136,福建天甫电子材料有限公司,福建省,39.0,0
+3203777710,980459.123449225,526964.137174853,1085867.46938081,45238.3255234,和舰芯片制造（苏州）股份有限公司,江苏省,60.0,0
+3211956484,14261.6991060198,8241.25602434343,20480.240043163,1294.46662,广州利诺士电子科技有限公司,广东省,43.0,0
+3215814536,737.674091690682,426.271863328108,1059.32276085326,66.95517,甘肃省晶科新能源电力工程有限公司,甘肃省,30.0,0
+3221190269,1475.34818338136,852.543726656217,2118.64552170652,133.91034,天津浩纳自动化设备有限公司,天津市,33.0,0
+3226664625,8790.61625931396,5079.73970465996,12623.5962335013,797.8824425,珠海市优邦新材料有限公司,广东省,41.0,0
+3267688490,3442.47909455651,1989.26869553117,4943.5062173152,312.45746,科毅科技（东莞）有限公司,广东省,37.0,0
+3269039233,737.674091690682,426.271863328108,1059.32276085326,66.95517,合肥烯盛芯业集成电路有限公司,安徽省,30.0,0
+3269940677,30244.6377593179,17477.1463964524,43432.2331949836,2745.16197,吉和昌新材料（荆门）有限公司,湖北省,46.0,0
+3271705843,491.782727793788,284.181242218739,706.215173902172,44.63678,辽宁卫重起重设备有限公司,辽宁省,28.0,0
+3299144127,3606.40667048778,2083.99577627075,5178.9112752826,327.336386666667,重庆睿科半导体有限公司,重庆市,37.0,0
+3312358902,66438.2226519953,41878.7735649207,237082.540978857,87569.4877398571,沈阳芯源微电子设备股份有限公司,辽宁省,54.0,0
+3331430009,737.674091690682,426.271863328108,1059.32276085326,66.95517,福州康丽佳商贸有限公司,福建省,30.0,0
+3344297292,688.495818911302,397.853739106235,988.701243463041,62.491492,合肥市鑫山新型建材有限公司,安徽省,30.0,0
+3372913783,824403.673322169,516140.824501698,1699364.11085187,187067.647483167,贝特瑞新材料集团股份有限公司,广东省,62.0,0
+3373311444,1314.60015,1097.90318233333,73687.6299513333,21062.3211216667,北京晶亦精微科技股份有限公司,北京市,49.0,0
+3384021594,24097.3536618956,13924.8808687182,34604.5435212064,2187.20222,中电化合物半导体有限公司,浙江省,45.0,0
+3391580446,3442.47909455651,1989.26869553117,4943.5062173152,312.45746,芯珉微电子（上海）有限公司,上海市,37.0,0
+3395900897,737.674091690682,426.271863328108,1059.32276085326,66.95517,安徽林泰新材料有限公司,安徽省,30.0,0
+3398677646,36420.8435164999,21053.0034803726,78385.10020775,33245.58495675,矽电半导体设备（深圳）股份有限公司,广东省,49.0,0
+3407754893,232859.633511805,168326.909166578,411069.08997625,40891.55866,比亚迪半导体股份有限公司,广东省,56.0,0
+3409328588,3073.64204871117,1776.13276386712,4413.84483688857,278.979875,苏州矽利康测试系统有限公司深圳分公司,广东省,36.0,0
+3433628561,245.891363896894,142.090621109369,353.107586951086,22.31839,深圳市景辉新科技有限公司,广东省,25.0,0
+3442780535,396622.76996569,229192.171849413,569562.537752102,35999.56307,武汉光网信息技术有限公司,湖北省,58.0,0
+3444191691,115814.832395437,66924.682542513,166313.673453961,10511.96169,无锡伟测半导体科技有限公司,江苏省,52.0,0
+3445244192,11065.1113753602,6394.07794992163,15889.8414127989,1004.32755,上海彤程电子材料有限公司,上海市,42.0,0
+3445928818,26556.2673008645,15345.7870798119,38135.6193907173,2410.38612,吉光半导体科技有限公司,吉林省,46.0,0
+4037576402,9835.65455587576,5683.62484437478,14124.3034780434,892.7356,强一半导体（上海）有限公司,上海市,41.0,0
+4144746564,3442.47909455651,1989.26869553117,4943.5062173152,312.45746,重庆摩尔精英速芯半导体有限公司,重庆市,37.0,0
+4208851809,71851.6551423333,52930.2237671667,445347.011300833,49697.6881215,西陇科学股份有限公司,广东省,56.0,0
+4518234098,614.728409742235,355.226552773424,882.768967377716,55.795975,武汉跃鹏新材料有限公司,湖北省,29.0,0
+5007015990,305520.01964189,176547.596728392,438736.176786724,27730.599575,深圳市鹏芯微集成电路制造有限公司,广东省,56.0,0
+11164476478,20163.0918395453,11651.4309309683,28954.8221299891,1830.10798,江苏芯诺半导体科技有限公司,江苏省,45.0,0
+11212932825,491.782727793788,284.181242218739,706.215173902172,44.63678,河南雷电光源技术研究院,河南省,28.0,0
+11220388001,737.674091690682,426.271863328108,1059.32276085326,66.95517,新疆蚂蚁网约出租汽车服务有限公司,新疆维吾尔自治区,30.0,0
+397847929,4794.881596,2770.767112,6885.597946,435.208605,厦门科塔电子有限公司,福建省,38.0,0
+517717050,40080.29232,23160.77124,57556.53667,3637.89757,深圳市实锐泰科技有限公司,广东省,48.0,0
+737770776,5962.865574,3445.697562,8562.858984,541.2209575,福建省南安市物资有限公司,福建省,39.0,0
+872394725,25724.57103,15462.17311,55978.44597,7283.825438,北京凯德石英股份有限公司,北京市,47.0,0
+2311581270,32088.82299,18542.82605,46080.5401,2912.549895,安徽亚格盛电子新材料股份有限公司,安徽省,47.0,0
+2313209417,15122.31888,8738.573198,21716.1166,1372.580985,武汉拓材科技有限公司,湖北省,43.0,0
+2347013470,23006.64933,17588.56669,81317.91108,12851.94016,江苏先锋精密科技股份有限公司,江苏省,49.0,0
+2350418059,73931.33674,42721.91341,106167.6811,6710.395927,江苏纳沛斯半导体有限公司,江苏省,50.0,0
+2352951203,29261.0723,16908.78391,42019.80285,2655.88841,中山市江波龙电子有限公司,广东省,46.0,0
+2359644835,232613.2302,134417.7276,334039.7773,21113.19694,广东气派科技有限公司,广东省,55.0,0
+2388955897,2458.913639,1420.906211,3531.07587,223.1839,上海鑫匀源科技有限公司,上海市,35.0,0
+2977767486,9790.429458,5657.491099,14059.35884,1748.344959,深圳市三联盛科技股份有限公司,广东省,41.0,0
+3031009366,43768.66277,25292.13056,62853.15048,3972.67342,天通瑞宏科技有限公司,浙江省,48.0,0
+3089095447,245.8913639,142.0906211,353.107587,22.31839,常州金坛沸腾商贸有限公司,江苏省,25.0,0
+3100891962,14801.53049,8553.202633,21255.45458,3889.830944,北京中超伟业信息安全技术股份有限公司,北京市,43.0,0
+3151203276,341884.6624,201430.569,477385.6924,28574.49401,合肥颀中科技股份有限公司,安徽省,57.0,0
+3188352290,13032.24229,7530.802919,18714.70211,1182.87467,湖北深紫科技有限公司,湖北省,43.0,0
+3288105727,6639.066825,3836.44677,9533.904848,602.59653,奥趋光电技术（杭州）有限公司,浙江省,40.0,0
+3462551351,5901.392734,3410.174907,8474.582087,535.64136,无锡博加电子新材料有限公司,江苏省,39.0,0

main.py

@@ -10,7 +10,6 @@ from computation import Computation
 from sqlalchemy.orm import close_all_sessions
 import yaml
 from controller_db import ControllerDB
-from 查看进度 import visualize_progress
 
 
 def controll_db_and_process(exp_argument, reset_sample_argument, reset_db_argument):
@@ -36,16 +35,14 @@ def do_process(target: object, controller_db: ControllerDB, ):
         i.join()
 
     # 所有子进程完成后刷新最终进度
-    visualize_progress()
 
     # 显示最终进度后关闭图表
-    plt.show()
 
 def do_computation(c_db):
     exp = Computation(c_db)
 
     while 1:
-        time.sleep(random.uniform(0, 1))
+        # time.sleep(random.uniform(0, 1))
         is_all_done = exp.run()
         if is_all_done:
             break
@@ -54,10 +51,10 @@ def do_computation(c_db):
 if __name__ == '__main__':
     # 输入参数
     parser = argparse.ArgumentParser(description='setting')
-    parser.add_argument('--exp', type=str, default='with_exp')
+    parser.add_argument('--exp', type=str, default='without_exp')
     parser.add_argument('--job', type=int, default='4')
     parser.add_argument('--reset_sample', type=int, default='0')
-    parser.add_argument('--reset_db', type=bool, default=False)
+    parser.add_argument('--reset_db', type=bool, default=True)
 
     args = parser.parse_args()
     # 几核参与进程

my_model.py

@@ -515,7 +515,6 @@ class MyModel(Model):
                         f"{firm.unique_id}'s production status. Skipping."
                     )
                     continue
-
                 # 更新产品状态为干扰状态，并记录干扰时间
                 firm.dct_prod_up_prod_stat[product]['p_stat'].append(('D', self.t))
 
@@ -528,7 +527,6 @@ class MyModel(Model):
     def resource_integration(self):
         """
         整合企业资源，包括材料、设备和产品数据。
-
         功能:
         - 加载并处理企业的材料、设备和产品数据。
         - 合并设备数据与设备残值数据。
@@ -638,7 +636,8 @@ class MyModel(Model):
 
             # 4. 资源消耗和产品生产
             self._consume_resources_and_produce()
-
+            # 5. 刷新企业干扰字典
+            self._process_firms_step()
             # 增加时间步
             self.t += 1
 
@@ -813,6 +812,46 @@ class MyModel(Model):
         for product in firm.P:
             product[1] *= production_increase_ratio
 
+    def _process_firms_step(self):
+        """
+        处理企业的状态更新，包括：
+        1. 刷新企业字典（清理前置步骤）。
+        2. 减少中断企业的规模。
+        3. 判断企业是否需要从中断状态转为移除状态。
+        4. 判断是否停止模拟。
+        """
+
+        # 减少中断企业的规模
+        # 刷新企业字典
+        for firm in self.company_agents:
+            firm.clean_before_time_step()
+            for prod in firm.dct_prod_up_prod_stat.keys():
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
+                if status == 'D':
+                    size = firm.size_stat[-1][0] - \
+                           firm.size_stat[0][0] / len(firm.dct_prod_up_prod_stat.keys()) / self.remove_t
+                    firm.size_stat.append((size, self.t))
+                    lst_is_disrupt = [stat == 'D' for stat, _ in
+                                      firm.dct_prod_up_prod_stat[prod]['p_stat'][-self.remove_t:]]
+                    if all(lst_is_disrupt):
+                        # 转换中断企业为已移除企业
+                        firm.dct_prod_up_prod_stat[prod]['p_stat'].append(('R', self.t))
+
+        # 判断是否需要停止模拟
+        if self.t > 0:
+            for firm in self.company_agents:
+                for prod in firm.dct_prod_up_prod_stat.keys():
+                    status, _ = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
+                    is_init = firm in self.dct_lst_init_disrupt_firm_prod.keys() and prod in \
+                              self.dct_lst_init_disrupt_firm_prod[firm]
+                    if status == 'D' and not is_init:
+                        break
+                else:
+                    continue
+                break
+            else:
+                self.int_stop_ts = self.t
+
     def end(self):
         """
         结束模型运行并保存结果。

output_result/resilience/anova.csv

@@ -0,0 +1,9 @@
+Unnamed: 0.1,Unnamed: 0,mean_end_ts,mean_n_remove_firm_prod,mean_max_ts_firm_prod,mean_count_firm_prod
+p1,n_sourcing,0.319,0.145,0.043,0.186
+p2,is_prf_size,0.607,0.608,0.005,0.111
+p3,n_max_trial,0.003,0.135,0.0,0.0
+p4,is_prf_conn,0.504,0.567,0.001,0.0
+p5,ex_cap_type,0.403,0.667,0.329,0.444
+p6,ex_cap_para,0.0,0.0,0.0,0.0
+p7,prob_new_conn,0.017,0.334,0.01,0.007
+p8,t_max_trial,0.0,0.014,0.939,0.1

output_result/resilience/anova_visualization.csv

@@ -0,0 +1,22 @@
+自变量,level,系统恢复用时R1,产业-企业边累计扰乱次数R2,产业-企业边最大传导深度R3,产业-企业边断裂总数R4
+采购策略P1,三供应商,3.549,62.11,1.715,22.14
+采购策略P1,双供应商,3.743,62.43,1.759,21.71
+采购策略P1,单供应商,3.668,62.21,1.736,22.17
+是否规模偏好P2,倾向,3.681,62.13,1.715,22.06
+是否规模偏好P2,不倾向,3.627,62.37,1.758,21.96
+最大尝试次数P3,高,3.47,61.08,1.636,21.85
+最大尝试次数P3,中,3.552,62.08,1.742,21.86
+最大尝试次数P3,低,3.939,63.58,1.832,22.31
+是否已有连接偏好P4,倾向,3.619,61.95,1.711,21.95
+是否已有连接偏好P4,不倾向,3.689,62.55,1.762,22.07
+额外产能分布P5,均匀分布,3.698,62.19,1.73,21.96
+额外产能分布P5,正态分布,3.61,62.3,1.743,22.05
+额外产能分布参数P6,高,2.949,61.48,1.808,12.41
+额外产能分布参数P6,中,3.787,62.2,1.661,22.87
+额外产能分布参数P6,低,4.224,63.06,1.741,30.75
+新供应关系构成概率P7,低,3.882,62.41,1.749,22.2
+新供应关系构成概率P7,中,3.543,62.44,1.756,22.01
+新供应关系构成概率P7,高,3.535,61.9,1.705,21.82
+最大尝试时间步P8,低,2.601,62.03,1.738,22.47
+最大尝试时间步P8,中,3.656,62.31,1.733,21.78
+最大尝试时间步P8,高,4.704,62.4,1.738,21.78

output_result/resilience/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,
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output_result/risk/count_dcp_prod.csv

@@ -1,23 +1,710 @@
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output_result/risk/count_firm.csv

@@ -1,46 +1,175 @@
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output_result/risk/count_firm_prod.csv

@@ -1,58 +1,370 @@
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+654825436,61,6
+1452048,30,6
+3147511625,7,6
+3226664625,28,6
+549184982,69,6
+301209792,7,6
+2545430247,25,6
+2329375731,31,6
+27075840,65,6
+857978527,60,6
+2424229017,26,6
+3048263744,7,6
+3433628561,29,5
+517717050,36,5
+495782506,19,5
+483081978,36,5
+27731896,20,5
+3089095447,22,5
+2310825263,15,4

output_result/risk/count_prod.csv

@@ -1,35 +1,73 @@
 id_product,count
-2.1.3,14
-1.3.1,10
-1.3.4,4
-1.3.3,4
-1.1,3
-2.1.3.7,3
-1.3.1.3,3
-2.1.1,3
-2.3,2
-2.1.3.4,2
-2.1.1.5,2
-2.3.1,2
-1.3.3.3,2
-1.3.3.4,1
-2.1.2.2,1
-1.1.3,1
-2.2,1
-2.1.4.1.1,1
-2.1.4.1,1
-1.3.1.1,1
-1.3.1.4,1
-2.1.3.2,1
-1.3.1.5,1
-2.1.2,1
-1.3.4.2,1
-1.3.1.7,1
-2.1.1.4,1
-2,1
-1.4.5.6,1
-1.1.1,1
-1.4.3.4,1
-1.4.3,1
-1.3.4.3,1
-1.4.5,1
+52,5571
+55,5513
+51,4711
+53,4687
+50,4642
+54,4622
+95,3416
+49,3272
+46,3254
+48,3233
+44,3196
+45,3195
+47,3164
+99,1384
+94,1280
+93,1174
+91,1122
+90,911
+92,768
+41,474
+42,458
+43,301
+39,267
+38,257
+40,250
+7,138
+23,54
+11,50
+68,47
+20,45
+67,45
+34,41
+61,41
+31,40
+8,39
+62,38
+32,38
+27,38
+74,37
+64,37
+63,36
+15,35
+69,35
+79,34
+24,33
+60,32
+35,31
+37,30
+33,29
+72,28
+18,28
+9,27
+73,27
+36,27
+28,27
+66,26
+30,26
+26,26
+12,25
+10,25
+19,25
+97,24
+71,23
+59,23
+16,22
+13,20
+70,18
+17,18
+22,18
+25,17
+29,17
+65,16

requirements_manual_selected_20230304.txt

@@ -1,5 +1,5 @@
 agentpy==0.1.5
-matplotlib==3.3.4
+matplotlib==3.7.5
 matplotlib-inline==0.1.6
 networkx==2.5
 numpy==1.20.3

risk_analysis_firm_network.py

@@ -13,8 +13,7 @@ count_dcp = pd.read_csv("output_result/risk/count_dcp.csv",
                             'up_id_firm': str,
                             'down_id_firm': str
                         })
-# print(count_dcp)
-count_dcp = count_dcp[count_dcp['count'] > 35]
+count_dcp = count_dcp[count_dcp['count'] > 130]
 
 list_firm = count_dcp['up_id_firm'].tolist(
 ) + count_dcp['down_id_firm'].tolist()
@@ -24,7 +23,7 @@ list_firm = list(set(list_firm))
 Firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
 Firm['Code'] = Firm['Code'].astype('string')
 Firm.fillna(0, inplace=True)
-Firm_attr = Firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
+Firm_attr = Firm.loc[:, ["Code", "企业名称", "Type_Region", "Revenue_Log"]]
 firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv")
 firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string')
 firm_product = []
@@ -43,7 +42,7 @@ 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)
+k = 15 / (count_max - count_min)
 for _, row in count_dcp.iterrows():
     # print(row)
     lst_add_edge = [(
@@ -54,20 +53,43 @@ for _, row in count_dcp.iterrows():
             'down_id_product': row['down_id_product'],
             'edge_label': f"{row['up_id_product']} - {row['down_id_product']}",
             'edge_width': k * (row['count'] - count_min),
-            'count': row['count']
+            'count': (row['count'])*18
         })]
     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, 'Revenue_Log')
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
+node_label = nx.get_node_attributes(G_firm, '企业名称')
 # desensitize
+node_label = {key: f"{key} " for key, value in node_label.items()}
 node_label = {
-    key: key
-    for key in node_label.keys()
+  '343012684': '59',
+  '2944892892': '165',
+  '3269039233': '194',
+  '503176785': '73',
+  '3111033905': '178',
+  '3215814536': '190',
+  '413274977': '64',
+  '2317841563': '131',
+  '2354145351': '157',
+  '653528340': '88',
+  '888395016': '104',
+  '3069206426': '174',
+  '3299144127': '197',
+  '2624175': '8',
+  '25685135': '24',
+  '2348941764': '151',
+  '750610681': '95',
+  '2320475044': '133',
+  '571058167': '78',
+  '152008168': '44',
+  '448033045': '66',
+  '2321109759': '134',
+  '3445928818': '213'
 }
+
 node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
-node_size = list(map(lambda x: x**2, node_size))
+node_size = list(map(lambda x: x * 10, 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")
@@ -77,23 +99,30 @@ 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)
+fig = plt.figure(figsize=(10, 8), dpi=500)
 nx.draw(G_firm,
         pos,
         node_size=node_size,
         labels=node_label,
         font_size=8,
-        width=3,
+        width=2,
         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)
+# nx.draw_networkx_edge_labels(G_firm, pos, font_size=6)
+nx.draw_networkx_edge_labels(
+    G_firm,
+    pos,
+    edge_labels=edge_label,
+    font_size=5
+)
+
 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.ax.tick_params(labelsize=4)
 cb.outline.set_visible(False)
 plt.savefig("output_result\\risk\\count_dcp_network")
 plt.close()

risk_analysis_prod_network.py

@@ -11,53 +11,82 @@ print(count_prod)
 print(count_prod.describe())
 
 # prod_networkx
-BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
-BomNodes.set_index('Code', inplace=True)
-BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
-BomCateNet.fillna(0, inplace=True)
+# BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
+# BomNodes.set_index('Code', inplace=True)
+# BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
+# BomCateNet.fillna(0, inplace=True)
 
-G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph())
+bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+bom_nodes['Code'] = bom_nodes['Code'].astype(str)
+bom_nodes.set_index('Index', inplace=True)
+
+bom_cate_net = pd.read_csv('input_data/input_product_data/合成结点.csv')
+g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())
 
 labels_dict = {}
-for code in G.nodes:
-    node_attr = BomNodes.loc[code].to_dict()
+for code in g_bom.nodes:
+    node_attr = bom_nodes.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_size'] = (count_prod['count'].get(index, 0))/10
     node_attr['node_color'] = count_prod['count'].get(index, 0)
     labels_dict[code] = node_attr
-nx.set_node_attributes(G, labels_dict)
+nx.set_node_attributes(g_bom, labels_dict)
 # print(labels_dict)
 
-pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
-dict_node_name = nx.get_node_attributes(G, 'Name')
+pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
+dict_node_name = nx.get_node_attributes(g_bom, 'Name')
 node_labels = {}
-for node in nx.nodes(G):
+for node in nx.nodes(g_bom):
     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())
+colors = list(nx.get_node_attributes(g_bom, 'node_color').values())
 vmin = min(colors)
 vmax = max(colors)
 cmap = plt.cm.Blues
+# 创建绘图对象
 fig = plt.figure(figsize=(10, 10), dpi=300)
-nx.draw(G,
-        pos,
-        node_size=list(nx.get_node_attributes(G, 'node_size').values()),
+ax = fig.add_subplot(111)
+
+# 绘制网络图（优化样式参数）
+nx.draw(g_bom, pos,
+        node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()),
         labels=node_labels,
-        font_size=6,
+        font_size=3,
         node_color=colors,
         cmap=cmap,
         vmin=vmin,
         vmax=vmax,
-        edge_color='grey')
+        edge_color='#808080',  # 中性灰
+        width=0.3,
+        edgecolors='#404040',
+        linewidths=0.2)
+
+# 创建颜色条（修正实现方式）
 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("output_result\\risk\\count_prod_network")
+sm.set_array([])
+
+# 设置颜色条位置和样式
+cax = fig.add_axes([0.88, 0.3, 0.015, 0.4])  # 右侧垂直对齐
+cb = plt.colorbar(sm, cax=cax)
+cb.ax.tick_params(labelsize=4, width=0.5, colors='#333333')
+cb.outline.set_linewidth(0.5)
+cb.set_label('Risk Level', fontsize=5, labelpad=2)
+
+# 添加图元信息
+ax.set_title("Production Risk Network", fontsize=6, pad=8, color='#2F2F2F')
+plt.text(0.5, 0.02, 'Data: USTB Production System | Viz: DeepSeek-R1',
+         ha='center', fontsize=3, color='#666666',
+         transform=fig.transFigure)
+
+# 调整边界和保存
+plt.subplots_adjust(left=0.05, right=0.85, top=0.95, bottom=0.1)  # 适应颜色条
+plt.savefig(r"output_result/risk/count_prod_network.png",  # 规范路径格式
+           dpi=600,
+           bbox_inches='tight',
+           pad_inches=0.05,
+           transparent=False)
 plt.close()
 
 # dcp_prod
@@ -74,7 +103,7 @@ count_dcp_prod.sort_values('count', inplace=True, ascending=False)
 count_dcp_prod.to_csv('output_result\\risk\\count_dcp_prod.csv',
                       index=False,
                       encoding='utf-8-sig')
-count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 50]
+count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 1000]
 # print(count_dcp_prod)
 
 list_prod = count_dcp_prod['up_id_product'].tolist(
@@ -83,8 +112,8 @@ list_prod = list(set(list_prod))
 
 # init graph bom
 
-BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
-BomNodes.set_index('Code', inplace=True)
+BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+BomNodes.set_index('Index', inplace=True)
 
 g_bom = nx.MultiDiGraph()
 g_bom.add_nodes_from(list_prod)
@@ -95,7 +124,6 @@ for code in list_prod:
     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)
@@ -110,28 +138,55 @@ for _, row in count_dcp_prod.iterrows():
     g_bom.add_edges_from(lst_add_edge)
 
 # dcp_networkx
-pos = nx.nx_agraph.graphviz_layout(g_bom, prog="dot", args="")
+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'
+
 temp = {}
 for key, value in node_labels.items():
-    temp[key] = key + " " + value
+    temp[key] = str(key) + " " + value
 node_labels = temp
+node_labels ={
+    38: 'SiC Substrate',
+    39: 'GaN Substrate',
+    40: 'Si Substrate',
+    41: 'AlN Substrate',
+    42: 'DUV LED Substrate',
+    43: 'InP Substrate',
+    44: 'Mono-Si Wafer',
+    45: 'Poly-Si Wafer',
+    46: 'InP Cryst./Wafer',
+    47: 'SiC Cryst./Wafer',
+    48: 'GaAs Wafer',
+    49: 'GaN Cryst./Wafer',
+    50: 'Si Epi Wafer',
+    51: 'SiC Epi Wafer',
+    52: 'AlN Epi',
+    53: 'GaN Epi',
+    54: 'InP Epi',
+    55: 'LED Epi Wafer',
+    90: 'Power Devices',
+    91: 'Diode',
+    92: 'Transistor',
+    93: 'Thyristor',
+    94: 'Rectifier',
+    95: 'IC Fab',
+    99: 'Wafer Test'
+}
 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])
+pos_new = {node: (p[1], p[0]) for node, p in pos.items()}  # 字典推导式优化
 
-fig = plt.figure(figsize=(6, 10), dpi=300)
-# plt.subplots_adjust(right=0.7)
-nx.draw(g_bom,
-        pos_new,
+fig = plt.figure(figsize=(8, 8), dpi=300)
+plt.subplots_adjust(right=0.85)  # 关键调整：右侧保留15%空白
+
+# 使用Axes对象精准控制
+main_ax = fig.add_axes([0.1, 0.1, 0.75, 0.8])  # 主图占左75%宽，上下各留10%边距
+nx.draw(g_bom, pos_new,
+        ax=main_ax,
         node_size=50,
         labels=node_labels,
         font_size=5,
@@ -139,17 +194,30 @@ nx.draw(g_bom,
         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()])
+        edge_vmax=vmax,
+        )
+main_ax.axis('off')
 
+# 颜色条定位系统
+cbar_ax = fig.add_axes([0.86, 0.15, 0.015, 0.3])  # 右边缘86%位置，底部15%起，占30%高度
 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("output_result\\risk\\count_dcp_prod_network")
+sm._A = []  # 必需的空数组
+
+# 微调颜色条样式
+cbar = fig.colorbar(sm, cax=cbar_ax, orientation='vertical')
+cbar.ax.tick_params(labelsize=4,
+                    width=0.3,    # 刻度线粗细
+                    length=1.5,   # 刻度线长度
+                    pad=0.8)      # 标签与条间距
+cbar.outline.set_linewidth(0.5)   # 边框线宽
+
+# 输出前验证边界
+print(f"Colorbar position: {cbar_ax.get_position().bounds}")  # 应输出(0.86,0.15,0.015,0.3)
+
+# 专业级保存参数
+plt.savefig("output_result/risk/count_dcp_prod_network.png",
+            dpi=900,
+            bbox_inches='tight',  # 自动裁剪白边
+            pad_inches=0.05,      # 保留0.05英寸边距
+            metadata={'CreationDate': None})  # 避免时间戳污染元数据
 plt.close()

risk_analysis_sum_result.py

@@ -1,3 +1,5 @@
+import pickle
+
 from sqlalchemy import text
 from orm import engine, connection
 import pandas as pd
@@ -5,107 +7,450 @@ import networkx as nx
 import json
 import matplotlib.pyplot as plt
 
-# prep data
+# Prepare data
 Firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
 Firm['Code'] = Firm['Code'].astype('string')
 Firm.fillna(0, inplace=True)
 BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
 
+# SQL query
 with open('SQL_analysis_risk.sql', 'r') as f:
     str_sql = text(f.read())
 
-result = pd.read_sql(sql=str_sql,
-                     con=connection)
-result.to_csv('output_result\\risk\\count.csv',
-              index=False,
-              encoding='utf-8-sig')
+result = pd.read_sql(sql=str_sql, con=connection)
+result.to_csv('output_result/risk/count.csv', index=False, encoding='utf-8-sig')
 print(result)
 
-# G bom
+# G_bom
 plt.rcParams['font.sans-serif'] = 'SimHei'
 
 exp_id = 1
-G_bom_str = pd.read_sql(
-    sql=text(f'select g_bom from iiabmdb.without_exp_experiment '
-        f'where id = {exp_id};'),
-    con=connection)['g_bom'].tolist()[0]
+G_bom_df = pd.read_sql(
+    sql=text(f'select g_bom from iiabmdb.without_exp_experiment where id = {exp_id};'),
+    con=connection
+)
+
+if G_bom_df.empty:
+    raise ValueError(f"No g_bom found for exp_id = {exp_id}")
+
+G_bom_str = G_bom_df['g_bom'].tolist()[0]
+if G_bom_str is None:
+    raise ValueError(f"g_bom data is None for exp_id = {exp_id}")
+
 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'] = '工业互联网'
-# node_labels['1.1'] = '工业自动化硬件'
-# node_labels['1.4'] = '工业互联网安全管理'
-# node_labels['1.2.1'] = '网络互联服务'
-# node_labels['1.2.2'] = '标识解析服务'
-# node_labels['1.2.3'] = '数据互通服务'
-# node_labels['1.3.1'] = '设计研发软件'
-# node_labels['1.3.2'] = '采购供应软件'
-# node_labels['1.3.3'] = '生产制造软件'
-# node_labels['1.3.4'] = '企业运营软件'
-# node_labels['1.3.5'] = '仓储物流软件'
-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"output_result\\risk\\g_bom_exp_id_{exp_id}.png")
+node_labels = {
+    7: 'Si Raw Mtl.',
+    8: 'Photoresist & Reagents',
+    9: 'Etch Solution',
+    10: 'SiF4',
+    11: 'Developer',
+    12: 'PCE Superplasticizer',
+    13: 'Metal Protectant',
+    14: 'Deep Hole Cu Plating',
+    15: 'Thinner',
+    16: 'HP Boric Acid (Nuc.)',
+    17: 'E-Grade Epoxy',
+    18: 'Stripper',
+    19: 'HP-MOC',
+    20: 'CMP Slurry & Consumables',
+    21: 'PR Remover',
+    22: 'Poly-Si Cutting Fluid',
+    23: 'Passivation',
+    24: 'E-Grade Phenolic',
+    25: 'Surfactant',
+    26: 'Mag. Carrier',
+    27: 'Wet Chems.',
+    28: 'Plating Chems.',
+    29: 'E-FR Materials',
+    30: 'LC Alignment Agent',
+    31: 'Func. Wet Chems.',
+    32: 'InP',
+    33: 'SiC',
+    34: 'GaAs',
+    35: 'GaN',
+    36: 'AlN',
+    37: 'Si3N4',
+    38: 'SiC Substrate',
+    39: 'GaN Substrate',
+    40: 'Si Wafer',
+    41: 'AlN Substrate',
+    42: 'DUV LED Substrate',
+    43: 'InP Substrate',
+    44: 'Mono-Si Wafer',
+    45: 'Poly-Si Wafer',
+    46: 'InP Cryst./Wafer',
+    47: 'SiC Cryst./Wafer',
+    48: 'GaAs Wafer',
+    49: 'GaN Cryst./Wafer',
+    50: 'Si Epi Wafer',
+    51: 'SiC Epi Wafer',
+    52: 'AlN Epi',
+    53: 'GaN Epi',
+    54: 'InP Epi',
+    55: 'LED Epi',
+    56: 'EDA/IP',
+    57: 'MPW Service',
+    58: 'IC Design',
+    59: 'Track System',
+    60: 'Wafer Grinder',
+    61: 'Etcher',
+    62: 'Ox/Diff Furnace',
+    63: 'Wafer Metrology',
+    64: 'Crystal Grower',
+    65: 'CMP Tool',
+    66: 'Stepper',
+    67: 'Wafer Dicer',
+    68: 'Deposition System',
+    69: 'Edge Profiler',
+    70: 'Descum Tool',
+    71: 'Clean System',
+    72: 'SAF',
+    73: 'Plating Eqpt.',
+    74: 'Implanter',
+    75: 'Trim/Form',
+    76: 'Probe Card',
+    77: 'ATE',
+    78: 'PCM Eqpt.',
+    79: 'Inspection Sys.',
+    80: 'Prober',
+    81: 'Dicing Saw',
+    82: 'Handler',
+    83: 'Backgrinder',
+    84: 'Die Bonder',
+    85: 'Reflow Oven',
+    86: 'FT Tester',
+    87: 'Wire Bonder',
+    88: 'BGA Mounter',
+    89: 'Molding Press',
+    90: 'Power Devices',
+    91: 'Diode',
+    92: 'Transistor',
+    93: 'Thyristor',
+    94: 'Rectifier',
+    95: 'IC Fab',
+    96: 'IC PKG',
+    97: 'DV',
+    98: 'IPM',
+    99: 'CP Test',
+    100: 'FT Test',
+    101: 'Bumping',
+    102: 'DA Materials',
+    103: 'Leadframe',
+    104: 'Solder Ball',
+    105: 'Substrate',
+    106: 'EMC',
+    107: 'Bond Wire',
+    108: 'Underfill',
+    109: 'Dicing Tape'
+}
+plt.figure(figsize=(12, 12), dpi=500)
+plt.axis('off')  # 关闭坐标轴边框
+
+# 优化节点绘制参数
+nx.draw_networkx_nodes(
+    G_bom, pos,
+    node_size=100,               # 优化节点尺寸
+    linewidths=0.0              # 去除节点边框
+)
+# 优化边绘制参数
+nx.draw_networkx_edges(
+    G_bom, pos,
+    width=0.3,                  # 更细的边宽
+    alpha=0.5                   # 半透明边
+)
+# 优化标签参数
+nx.draw_networkx_labels(
+    G_bom, pos,
+    labels=node_labels,
+    font_size=3,                # 适当增大字号
+    font_family='sans-serif',   # 使用无衬线字体
+    font_weight='bold',         # 增强可读性
+)
+
+# 专业级保存参数设置
+plt.savefig(
+    f"output_result/risk/g_bom_exp_id_{exp_id}.png",
+    bbox_inches='tight',        # 去除图像白边
+    pad_inches=0.1,             # 适当内边距
+    facecolor='white'           # 保证背景纯白
+)
 plt.close()
 
-# G firm
+# G_firm
 plt.rcParams['font.sans-serif'] = 'SimHei'
 
 sample_id = 1
+# G_firm_df = pd.read_sql(
+#     sql=text(f'select g_firm from iiabmdb.without_exp_sample where id = {sample_id};'),
+#     con=connection
+# )
+#
+# if G_firm_df.empty:
+#     raise ValueError(f"No g_firm found for sample_id = {sample_id}")
+#
+# G_firm_str = G_firm_df['g_firm'].tolist()[0]
+# if G_firm_str is None:
+#     raise ValueError(f"g_firm data is None for sample_id = {sample_id}")
+#
+# G_firm = nx.adjacency_graph(json.loads(G_firm_str))
 
-G_firm_str = pd.read_sql(
-    sql=text(f'select g_firm from iiabmdb.without_exp_sample ' 
-             f'where id = {exp_id};'),
-    con=connection)['g_firm'].tolist()[0]
+with open("firm_network.pkl", 'rb') as f:
+    G_firm = pickle.load(f)
+print(f"Successfully loaded cached data from firm_network.pkl")
 
-G_firm = nx.adjacency_graph(json.loads(G_firm_str))
+# 1. 移除孤立节点
+isolated_nodes = list(nx.isolates(G_firm))  # 找出所有没有连接的孤立节点
+G_firm.remove_nodes_from(isolated_nodes)   # 从图中移除这些节点
+
+# 2. 重新布局和绘图
 pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
-# desensitize
-node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
+node_label = {key: key for key in nx.get_node_attributes(G_firm, 'Revenue_Log').keys()}
 node_label = {
-    key: key
-    for key in node_label.keys()
+    "7": "1",
+    "9": "2",
+    "829768": "4",
+    "863079": "5",
+    "1452048": "6",
+    "2010673": "7",
+    "2624175": "8",
+    "2728939": "9",
+    "5278074": "10",
+    "5849940": "11",
+    "7299120": "12",
+    "9746245": "13",
+    "11807506": "14",
+    "15613202": "15",
+    "24284343": "19",
+    "24673506": "20",
+    "25036634": "21",
+    "25685135": "24",
+    "25945288": "25",
+    "26162741": "26",
+    "26516263": "27",
+    "27075840": "28",
+    "27731896": "29",
+    "29954548": "30",
+    "43407343": "33",
+    "70634828": "36",
+    "71271700": "37",
+    "80158773": "39",
+    "118882692": "40",
+    "145511905": "42",
+    "151606446": "43",
+    "152008168": "44",
+    "159511306": "45",
+    "191912252": "46",
+    "194210021": "47",
+    "203314437": "48",
+    "213386023": "49",
+    "218633337": "50",
+    "251189644": "53",
+    "271860868": "55",
+    "278221281": "56",
+    "301209792": "57",
+    "343012684": "59",
+    "354897041": "60",
+    "400488703": "62",
+    "400692942": "63",
+    "413274977": "64",
+    "420984285": "65",
+    "448033045": "66",
+    "453289520": "67",
+    "474279224": "68",
+    "483081978": "69",
+    "495782506": "70",
+    "503176785": "73",
+    "549184982": "75",
+    "560866402": "76",
+    "561545339": "77",
+    "571058167": "78",
+    "581407487": "79",
+    "591452402": "80",
+    "593312758": "81",
+    "594378026": "82",
+    "607512171": "83",
+    "615763365": "84",
+    "620220747": "85",
+    "631449822": "86",
+    "644292599": "87",
+    "653528340": "88",
+    "654825436": "89",
+    "688155470": "92",
+    "695995052": "93",
+    "750610681": "95",
+    "762985858": "96",
+    "771821595": "97",
+    "857978527": "100",
+    "868012326": "101",
+    "887840774": "102",
+    "888356483": "103",
+    "888395016": "104",
+    "888478182": "105",
+    "930767828": "107",
+    "996174506": "108",
+    "1033972427": "110",
+    "1128343125": "111",
+    "1217957486": "113",
+    "1307012237": "115",
+    "1375606900": "116",
+    "1549474227": "118",
+    "1606833003": "120",
+    "1679596339": "121",
+    "2310825263": "122",
+    "2311838590": "124",
+    "2312490120": "125",
+    "2316990095": "128",
+    "2317245827": "129",
+    "2317841563": "131",
+    "2320102626": "132",
+    "2320475044": "133",
+    "2321109759": "134",
+    "2324787028": "137",
+    "2324844174": "138",
+    "2326478786": "139",
+    "2327031723": "140",
+    "2327979389": "141",
+    "2329375731": "142",
+    "2333843479": "143",
+    "2337952436": "146",
+    "2339188563": "147",
+    "2339684065": "148",
+    "2341555098": "149",
+    "2343704209": "150",
+    "2348941764": "151",
+    "2352036411": "155",
+    "2354145351": "157",
+    "2424229017": "159",
+    "2545430247": "161",
+    "2820140348": "163",
+    "2944892892": "165",
+    "3025036704": "168",
+    "3026382513": "169",
+    "3045721313": "171",
+    "3047163873": "172",
+    "3048263744": "173",
+    "3069206426": "174",
+    "3070859372": "175",
+    "3072715478": "176",
+    "3103797386": "177",
+    "3111033905": "178",
+    "3113895788": "179",
+    "3120341363": "180",
+    "3122923980": "181",
+    "3127420424": "182",
+    "3133307899": "183",
+    "3147511625": "184",
+    "3177507356": "185",
+    "3188903709": "186",
+    "3195502499": "187",
+    "3203777710": "188",
+    "3211956484": "189",
+    "3215814536": "190",
+    "3221190269": "191",
+    "3226664625": "192",
+    "3267688490": "193",
+    "3269039233": "194",
+    "3269940677": "195",
+    "3271705843": "196",
+    "3299144127": "197",
+    "3312358902": "198",
+    "3344297292": "200",
+    "3372913783": "201",
+    "3373311444": "202",
+    "3384021594": "203",
+    "3395900897": "205",
+    "3398677646": "206",
+    "3407754893": "207",
+    "3433628561": "209",
+    "3445244192": "212",
+    "3445928818": "213",
+    "4208851809": "216",
+    "5007015990": "218",
+    "11164476478": "219",
+    "517717050": "223",
+    "737770776": "224",
+    "872394725": "225",
+    "2311581270": "226",
+    "2313209417": "227",
+    "2347013470": "228",
+    "2350418059": "229",
+    "3031009366": "234",
+    "3089095447": "235",
+    "3100891962": "236",
+    "3188352290": "238",
+    "3288105727": "239",
+    "3462551351": "240"
 }
-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"output_result\\risk\\g_firm_sample_id_{exp_id}_de.png")
+
+node_size = [value * 5 for value in nx.get_node_attributes(G_firm, 'Revenue_Log').values()]
+edge_label = {(n1, n2): label for (n1, n2, _), label in nx.get_edge_attributes(G_firm, "Product").items()}
+
+plt.figure(figsize=(15, 15), dpi=500)
+plt.axis('off')  # 完全关闭坐标轴系统
+
+# 分层绘制网络组件
+nodes = nx.draw_networkx_nodes(
+    G_firm, pos,
+    node_size=node_size,        # 保持原始尺寸设置
+)
+
+edges = nx.draw_networkx_edges(
+    G_firm, pos,
+    width=0.3,                  # 保持原始线宽设置
+)
+
+# 优化节点标签
+labels = nx.draw_networkx_labels(
+    G_firm, pos,
+    labels=node_label,
+    font_size=6,                # 保持原始字号
+)
+
+# 增强边标签可读性
+edge_labels = nx.draw_networkx_edge_labels(
+    G_firm, pos,
+    edge_labels=edge_label,
+    font_size=2,
+    label_pos=0.5,              # 标签沿边偏移量
+    rotate=False,              # 禁止自动旋转
+)
+
+# 专业级输出配置
+plt.savefig(
+    f"output_result/risk/g_firm_sample_id_{sample_id}_de.png",
+    bbox_inches='tight',
+    pad_inches=0.05,            # 更紧凑的边距
+    facecolor='white',          # 强制白色背景
+    metadata={
+        'Title': f"Supply Chain Risk Map - Sample {sample_id}",
+        'Author': 'USTB Risk Analytics',
+        'Copyright': 'Confidential'
+    }
+)
 plt.close()
 
-# count firm product
+
+# 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.to_csv('output_result\\risk\\count_firm_prod.csv',
-                       index=False,
-                       encoding='utf-8-sig')
+count_firm_prod.to_csv('output_result/risk/count_firm_prod.csv', index=False, encoding='utf-8-sig')
 print(count_firm_prod)
 
-# count firm
+# Count firm
 count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
 count_firm = count_firm.to_frame().reset_index()
 count_firm.sort_values('count', inplace=True, ascending=False)
-count_firm.to_csv('output_result\\risk\\count_firm.csv',
-                  index=False,
-                  encoding='utf-8-sig')
+count_firm.to_csv('output_result/risk/count_firm.csv', index=False, encoding='utf-8-sig')
 print(count_firm)
 
-# count product
+# Count product
 count_prod = count_firm_prod.groupby('id_product')['count'].sum()
 count_prod = count_prod.to_frame().reset_index()
 count_prod.sort_values('count', inplace=True, ascending=False)
-count_prod.to_csv('output_result\\risk\\count_prod.csv',
-                  index=False,
-                  encoding='utf-8-sig')
+count_prod.to_csv('output_result/risk/count_prod.csv', index=False, encoding='utf-8-sig')
 print(count_prod)
 
 # DCP disruption causing probability
@@ -114,26 +459,33 @@ 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'
 ])
+
+result_dcp_list = []  # 用列表收集数据，避免DataFrame逐行增长的问题
 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']]
+            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
+                    result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
             ts_end -= 1
         ts_start -= 1
+
+# 转换为DataFrame
+result_dcp = pd.DataFrame(result_dcp_list, columns=[
+    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
+])
+
+# 统计
 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.to_csv('output_result\\risk\\count_dcp.csv',
-                 index=False, encoding='utf-8-sig')
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
+).reset_index(name='count')
+
+# 保存文件
+count_dcp.to_csv('output_result/risk/count_dcp.csv', index=False, encoding='utf-8-sig')
+
+# 输出结果
 print(count_dcp)

企业描述性数据分析.py

@@ -0,0 +1,50 @@
+import pandas as pd
+
+# 读取数据
+df = pd.read_csv('input_data/input_firm_data/firm_amended.csv')  # 替换为你的 CSV 文件路径
+
+# 要分析的列
+columns = [
+    "固定资产原值（万元人民币）",
+    "固定资产净值（万元人民币）",
+    "资产总和（万元人民币）",
+    "存货（万元人民币）"
+]
+
+# 字段类型定义（可人工定义，也可自动判断）
+column_types = {
+    "固定资产原值（万元人民币）": "连续型",
+    "固定资产净值（万元人民币）": "连续型",
+    "资产总和（万元人民币）": "连续型",
+    "存货（万元人民币）": "连续型"
+}
+
+# 统计分析
+summary = []
+
+for col in columns:
+    data = df[col].dropna()
+    summary.append({
+        "字段名": col,
+        "类型": column_types[col],
+        "计数（非空）": data.count(),
+        "均值": data.mean(),
+        "标准差": data.std(),
+        "最小值": data.min(),
+        "中位数": data.median(),
+        "最大值": data.max()
+    })
+
+# 转为 DataFrame 展示
+summary_df = pd.DataFrame(summary)
+
+# 设置列顺序
+summary_df = summary_df[["字段名", "类型", "计数（非空）", "均值", "标准差", "最小值", "中位数", "最大值"]]
+
+# 打印结果
+print(summary_df)
+# 保存为 Excel 文件
+output_path = "企业规模数据描述性统计表.xlsx"
+summary_df.to_excel(output_path, index=False)
+
+print(f"统计结果已保存为 Excel 文件：{output_path}")

查看进度.py

@@ -12,7 +12,7 @@ def visualize_progress():
     """
 
     # 设置全局字体
-    rcParams['font.family'] = 'SimHei'  # 黑体，适用于中文
+    rcParams['font.family'] = 'Microsoft YaHei'  # 黑体，适用于中文
     rcParams['font.size'] = 12
 
     # 查询数据库中各 is_done_flag 的数量
@@ -46,7 +46,8 @@ def visualize_progress():
         ax.text(i, v + 0.5, str(v), ha='center', fontsize=12)
 
     # 刷新绘图
-    plt.pause(1)  # 暂停一段时间以更新图表
+    plt.pause(0)  # 暂停一段时间以更新图表
 
 # 关闭窗口时，停止交互模式
 # plt.ioff()
+visualize_progress()

绘制度.py

@@ -0,0 +1,115 @@
+import pickle
+
+import pandas as pd
+import networkx as nx
+import matplotlib.pyplot as plt
+
+# 1. 读取并处理数据
+bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+bom_nodes['Code'] = bom_nodes['Code'].astype(str)
+bom_nodes.set_index('Index', inplace=True)
+
+bom_cate_net = pd.read_csv('input_data/input_product_data/合成结点.csv')
+
+# 2. 构建图结构
+g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())
+
+# 填充每一个结点的具体内容
+bom_labels_dict = {}
+for index in g_bom.nodes:
+    try:
+        bom_labels_dict[index] = bom_nodes.loc[index].to_dict()
+    except KeyError:
+        print(f"节点 {index} 不存在于 bom_nodes 中")
+
+# 分配属性给每一个结点
+nx.set_node_attributes(g_bom, bom_labels_dict)
+
+# 3. 计算每个节点的度数
+degrees = dict(g_bom.degree())  # 总度数（适用于有向图）
+
+# 4. 统计每个度数的节点数量
+degree_counts = {}
+for degree in degrees.values():
+    if degree in degree_counts:
+        degree_counts[degree] += 1
+    else:
+        degree_counts[degree] = 1
+
+# 转换为排序后的列表（横坐标：度数，纵坐标：节点数）
+sorted_degrees = sorted(degree_counts.keys())
+sorted_counts = [degree_counts[d] for d in sorted_degrees]
+
+# 5. 绘制度分布图
+plt.figure(figsize=(12, 8))  # 增大画布尺寸
+bars = plt.bar(sorted_degrees, sorted_counts, width=0.8)
+plt.title('Degree Distribution In Industrial Chain', fontsize=16)
+plt.xlabel('Degree', fontsize=14)
+plt.ylabel('Number of Nodes', fontsize=14)
+plt.grid(True, linestyle='--', alpha=0.5)
+plt.xticks(rotation=45)  # 如果度数较多，可以旋转x轴标签
+plt.tight_layout()  # 防止标签重叠
+
+# 6. 在每个柱子上方标注数值
+for bar in bars:
+    height = bar.get_height()
+    plt.text(
+        bar.get_x() + bar.get_width() / 2,  # x坐标：柱子中心
+        height + max(sorted_counts) * 0.02,  # y坐标：柱子顶部上方（留出空间）
+        f'{int(height)}',  # 显示数值（转换为整数）
+        ha='center',  # 水平居中
+        va='bottom',  # 垂直底部对齐
+        fontsize=10,  # 字体大小
+        color='black'  # 字体颜色
+    )
+
+# 7. 保存超高清图片（300 DPI）
+output_path = "degree_distribution_with_labels.png"  # 输出文件名
+plt.savefig(output_path, dpi=500, bbox_inches='tight')  # dpi=300 确保高分辨率
+print(f"图片已保存至: {output_path}")
+
+# 1. 加载企业网络数据
+with open("firm_network.pkl", 'rb') as f:
+    G_firm = pickle.load(f)
+print(f"Successfully loaded cached data from firm_network.pkl")
+
+# 2. 计算企业网络的度分布
+degrees_firm = dict(G_firm.degree())  # 总度数
+degree_counts_firm = {}
+for degree in degrees_firm.values():
+    if degree in degree_counts_firm:
+        degree_counts_firm[degree] += 1
+    else:
+        degree_counts_firm[degree] = 1
+
+# 转换为排序后的列表
+sorted_degrees_firm = sorted(degree_counts_firm.keys())
+sorted_counts_firm = [degree_counts_firm[d] for d in sorted_degrees_firm]
+
+# 3. 绘制企业网络的度分布图
+plt.figure(figsize=(12, 6))  # 单独画布尺寸
+plt.bar(sorted_degrees_firm, sorted_counts_firm, width=0.8)
+plt.title('Degree Distribution of Firm Network', fontsize=16)
+plt.xlabel('Degree (Number of Connections)', fontsize=14)
+plt.ylabel('Number of Firms', fontsize=14)
+plt.grid(True, linestyle='--', alpha=0.5)
+plt.xticks(rotation=45)
+plt.tight_layout()
+
+# 在柱子上方标注数值
+for bar in plt.gca().containers[0]:  # 获取当前图中的柱子对象
+    height = bar.get_height()
+    plt.text(
+        bar.get_x() + bar.get_width() / 2,
+        height + max(sorted_counts_firm) * 0.02,
+        f'{int(height)}',
+        ha='center',
+        va='bottom',
+        fontsize=10,
+        color='black'
+    )
+
+# 保存图片
+plt.savefig("degree_distribution_firm.png", dpi=500, bbox_inches='tight')
+print("企业度分布图已保存至: degree_distribution_firm.png")
+