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/dataSources/8145438e-516b-4005-a581-b91b5eedc759.xml

@@ -926,11 +926,11 @@ iiabmdb|schema||iiabm_user|localhost|UPDATE|G</Grants>
     </collation>
     <schema id="288" parent="1" name="iiabmdb">
       <Current>1</Current>
-      <LastIntrospectionLocalTimestamp>2025-02-22.02:31:45</LastIntrospectionLocalTimestamp>
+      <LastIntrospectionLocalTimestamp>2025-02-22.13:47:26</LastIntrospectionLocalTimestamp>
       <CollationName>utf8mb4_0900_ai_ci</CollationName>
     </schema>
     <schema id="289" parent="1" name="information_schema">
-      <LastIntrospectionLocalTimestamp>2025-02-22.02:31:45</LastIntrospectionLocalTimestamp>
+      <LastIntrospectionLocalTimestamp>2025-02-22.13:47:27</LastIntrospectionLocalTimestamp>
       <CollationName>utf8mb3_general_ci</CollationName>
     </schema>
     <schema id="290" parent="1" name="performance_schema">

.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>

computation.py

@@ -10,7 +10,6 @@ from mesa import Model
 from typing import TYPE_CHECKING
 
 from my_model import MyModel
-from 查看进度 import visualize_progress
 
 if TYPE_CHECKING:
     from controller_db import ControllerDB

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

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):
@@ -36,10 +35,8 @@ 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)
@@ -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

@@ -527,7 +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。

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,
+0,7,1,1,uniform,5.0000,0.3000,3,3,59.6916,15.7589,13.3347,1.5063,1.5032,1.3842,12.3074,1.5379,2.0400
+1,5,1,1,uniform,10.0000,0.5000,5,2,61.8937,17.1126,13.8095,1.7284,1.7263,1.6042,22.7779,2.9611,3.7432
+2,3,1,1,uniform,15.0000,0.7000,7,1,63.9568,18.2253,14.2779,1.8263,1.8221,1.7347,30.9263,3.7842,5.6253
+3,7,1,1,uniform,5.0000,0.3000,3,2,59.5811,15.7474,13.3168,1.4958,1.4937,1.3884,12.8358,1.4621,2.0221
+4,5,1,1,uniform,10.0000,0.5000,5,1,61.8200,17.0116,13.8053,1.7095,1.7084,1.6032,22.5474,2.9579,3.6811
+5,3,1,1,uniform,15.0000,0.7000,7,3,63.8821,18.2547,14.2432,1.8421,1.8305,1.7295,30.9474,3.7411,5.6632
+6,7,1,1,normal,5.0000,0.5000,7,3,59.9116,15.7516,13.3316,1.4905,1.4884,1.3674,12.2463,1.3326,3.1600
+7,5,1,1,normal,10.0000,0.7000,3,2,61.3095,16.8326,13.7716,1.7011,1.7011,1.6011,22.4779,2.9642,2.4916
+8,3,1,1,normal,15.0000,0.3000,5,1,63.6568,18.1316,14.2358,1.8253,1.8232,1.7242,31.1253,3.7400,4.3474
+9,7,1,0,uniform,5.0000,0.7000,5,3,59.7158,15.6811,13.3000,1.4600,1.4568,1.3537,12.4063,1.3400,2.5316
+10,5,1,0,uniform,10.0000,0.3000,7,2,63.0063,17.6695,14.0432,1.8063,1.8053,1.6747,22.6916,3.0042,5.1126
+11,3,1,0,uniform,15.0000,0.5000,3,1,63.6779,18.3842,14.3547,1.8621,1.8600,1.7621,31.3663,4.0253,2.9632
+12,7,1,0,normal,10.0000,0.7000,3,1,60.6295,16.3884,13.5811,1.6179,1.6147,1.5147,22.5221,2.7800,2.3495
+13,5,1,0,normal,15.0000,0.3000,5,3,63.3484,18.0042,14.2074,1.8316,1.8263,1.7232,30.6379,3.7747,4.2979
+14,3,1,0,normal,5.0000,0.5000,7,2,64.0737,18.3684,14.3000,1.8505,1.8484,1.7589,11.4789,1.1663,4.1400
+15,7,1,0,normal,10.0000,0.7000,5,3,61.0337,16.5684,13.6053,1.6358,1.6347,1.5074,22.7474,2.8937,3.5147
+16,5,1,0,normal,15.0000,0.3000,7,2,63.4747,18.0568,14.1989,1.8347,1.8305,1.7263,30.4063,3.7989,5.7295
+17,3,1,0,normal,5.0000,0.5000,3,1,63.7158,18.2863,14.2958,1.8547,1.8537,1.7579,14.6568,2.1432,2.8368
+18,7,0,1,normal,10.0000,0.3000,7,1,61.2326,16.6442,13.6789,1.6705,1.6684,1.5495,22.5453,2.8379,4.7474
+19,5,0,1,normal,15.0000,0.5000,3,3,62.3863,17.4684,13.9905,1.7874,1.7853,1.6705,31.1558,3.8189,2.7926
+20,3,0,1,normal,5.0000,0.7000,5,2,62.8305,17.6074,14.0811,1.7705,1.7695,1.6768,11.7621,1.2474,3.2684
+21,7,0,1,normal,10.0000,0.5000,7,1,61.1832,16.5389,13.6874,1.6505,1.6484,1.5337,22.8484,2.8147,4.7326
+22,5,0,1,normal,15.0000,0.7000,3,3,62.3305,17.5337,14.0011,1.7768,1.7747,1.6495,30.6705,3.7832,2.7316
+23,3,0,1,normal,5.0000,0.3000,5,2,62.8821,17.6916,14.0905,1.7821,1.7821,1.6895,12.2158,1.3442,3.3484
+24,7,0,1,uniform,15.0000,0.5000,3,2,62.2463,17.4084,13.9789,1.7979,1.7958,1.6674,30.6842,3.7126,2.7589
+25,5,0,1,uniform,5.0000,0.7000,5,1,60.9453,16.4442,13.6316,1.6274,1.6263,1.5032,12.2347,1.2663,2.7368
+26,3,0,1,uniform,10.0000,0.3000,7,3,63.3400,17.8968,14.1147,1.8084,1.8074,1.6937,22.7768,3.0442,5.2442
+27,7,0,0,normal,15.0000,0.5000,5,2,62.6505,17.5074,14.0032,1.7811,1.7800,1.6758,30.0211,3.6116,4.1263
+28,5,0,0,normal,5.0000,0.7000,7,1,60.9200,16.5126,13.6168,1.6368,1.6358,1.5168,11.9432,1.2495,3.3305
+29,3,0,0,normal,10.0000,0.3000,3,3,63.9074,18.4432,14.3916,1.8811,1.8779,1.7789,25.4905,3.4789,3.0295
+30,7,0,0,uniform,15.0000,0.7000,7,2,62.2442,17.2747,13.9400,1.7400,1.7358,1.6253,30.5084,3.6589,5.4421
+31,5,0,0,uniform,5.0000,0.3000,3,1,61.9147,17.2211,13.9347,1.7558,1.7526,1.6453,12.8168,1.6895,2.4516
+32,3,0,0,uniform,10.0000,0.5000,5,3,64.1074,18.3579,14.3558,1.8579,1.8568,1.7505,22.2800,3.0684,4.0642
+33,7,0,0,uniform,15.0000,0.3000,5,1,62.8737,17.5600,14.0642,1.7895,1.7874,1.6684,30.5453,3.6695,4.2147
+34,5,0,0,uniform,5.0000,0.5000,7,3,61.6337,16.9042,13.7632,1.7032,1.7011,1.5716,12.0011,1.2263,3.5221
+35,3,0,0,uniform,10.0000,0.7000,3,2,62.9663,17.9737,14.2221,1.8221,1.8211,1.7211,22.6979,3.1600,2.7389

output_result/risk/count_firm.csv

@@ -1,164 +1,175 @@
 id_firm,count
-70634828,5066
-3147511625,5066
-653528340,5052
-3103797386,5046
-888395016,5046
-301209792,5046
-2327031723,5042
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-194210021,5022
-191912252,5012
-3048263744,5002
-3215814536,4996
-2354145351,4992
-2312490120,1910
-503176785,1850
-25685135,1566
-2944892892,1556
-2624175,1546
-3269039233,1546
-3445928818,1476
-2321109759,1466
-3299144127,1456
-930767828,726
-3407754893,716
-571058167,350
-343012684,350
-750610681,350
-2317841563,350
-888356483,350
-2320475044,350
-43407343,350
-152008168,350
-413274977,350
-3111033905,350
-3069206426,350
-25945288,350
-607512171,340
-448033045,340
+653528340,4007
+2348941764,3957
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+888395016,3750
+2354145351,3619
+3147511625,3346
+3048263744,3326
+2317245827,3230
+631449822,3149
+3103797386,3123
+194210021,3077
+2327031723,3004
+301209792,2997
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+3299144127,1130
+3445928818,1120
+2312490120,1077
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+930767828,467
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output_result/risk/count_firm_prod.csv

@@ -1,359 +1,370 @@
 id_firm,id_product,count
-25685135,99,1566
-2944892892,99,1556
-3269039233,99,1546
-2624175,99,1546
-3445928818,95,1476
-2321109759,95,1466
-3299144127,95,1456
-930767828,90,726
-3407754893,90,716
-194210021,50,646
-3147511625,50,646
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output_result/risk/count_prod.csv

@@ -1,73 +1,73 @@
 id_product,count
-50,9590
-54,8910
-53,8840
-51,8780
-52,8160
-55,7860
-99,6214
-95,4398
-46,3872
-49,3872
-47,3872
-48,3872
-45,3852
-44,3852
-90,1442
-93,1400
-94,1390
-91,1390
-43,732
-92,700
-39,696
-40,686
-38,662
-41,512
-42,472
-7,150
-11,40
-23,40
-20,40
-69,40
-68,40
-67,40
-15,40
-74,40
+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
-27,40
-60,36
-61,30
-72,30
-79,30
-8,30
-9,30
-12,30
-66,30
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+8,39
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+64,37
+63,36
+15,35
+69,35
+79,34
+24,33
+60,32
+35,31
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-28,30
-30,30
-18,30
-32,30
-33,30
-34,30
-35,30
-73,20
-97,20
-26,20
-29,20
-36,20
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requirements.txt

@@ -16,7 +16,7 @@ Jinja2==3.1.2
 joblib==1.2.0
 kiwisolver==1.4.4
 MarkupSafe==2.1.2
-matplotlib==3.7.5
+matplotlib==3.3.4
 matplotlib-inline==0.1.6
 multiprocess==0.70.14
 mysqlclient==2.1.1

risk_analysis_firm_network.py

@@ -13,7 +13,7 @@ count_dcp = pd.read_csv("output_result/risk/count_dcp.csv",
                             'up_id_firm': str,
                             'down_id_firm': str
                         })
-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()
@@ -42,7 +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 = [(
@@ -53,18 +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="")
+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} {value}" for key, value in node_label.items()}
+node_label = {key: f"{key} " for key, value in node_label.items()}
+node_label = {
+  '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 ** 3, 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")
@@ -79,18 +104,25 @@ nx.draw(G_firm,
         pos,
         node_size=node_size,
         labels=node_label,
-        font_size=2,
-        width=1,
+        font_size=8,
+        width=2,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
         edge_vmax=vmax)
 # 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=4)
 cb.outline.set_visible(False)
-plt.savefig("output_result\\risk\\count_dcp_network_dot")
+plt.savefig("output_result\\risk\\count_dcp_network")
 plt.close()

risk_analysis_prod_network.py

@@ -29,7 +29,7 @@ for code in g_bom.nodes:
     index_list = count_prod[count_prod['id_product'] == code].index.tolist()
     index = index_list[0] if len(index_list) == 1 else -1
     node_attr['count'] = count_prod['count'].get(index, 0)
-    node_attr['node_size'] = (count_prod['count'].get(index, 0) + 10) / 10
+    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_bom, labels_dict)
@@ -45,9 +45,12 @@ 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_bom,
-        pos,
+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=3,
@@ -55,14 +58,35 @@ nx.draw(g_bom,
         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=3)
-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
@@ -91,8 +115,6 @@ list_prod = list(set(list_prod))
 BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
 BomNodes.set_index('Index', inplace=True)
 
-BomNodes.loc[13].to_dict()
-
 g_bom = nx.MultiDiGraph()
 g_bom.add_nodes_from(list_prod)
 
@@ -116,47 +138,86 @@ 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] = 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, 6), dpi=500)
-# plt.subplots_adjust(right=0.7)
-nx.draw(g_bom,
-        pos_new,
-        node_size=25,
+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,
-        width=0.5,
+        width=1.5,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
         edge_vmax=vmax,
-        alpha=1)
-plt.axis('off')
-axis = plt.gca()
-axis.set_xlim([1 * x for x in axis.get_xlim()])
-axis.set_ylim([1 * y for y in axis.get_ylim()])
+        )
+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([0.85, 0.75, 0.01, 0.2])
-cb = plt.colorbar(sm, fraction=0.01, cax=position)
-cb.ax.tick_params(labelsize=3)
-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

@@ -40,12 +40,142 @@ if G_bom_str is None:
 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')
+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
 
-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=3)
-plt.savefig(f"output_result/risk/g_bom_exp_id_{exp_id}.png")
+# 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
@@ -77,14 +207,228 @@ 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="")
 node_label = {key: key for key in nx.get_node_attributes(G_firm, 'Revenue_Log').keys()}
-node_size = [value * 10 for value in nx.get_node_attributes(G_firm, 'Revenue_Log').values()]  # 节点大小扩大10倍
+node_label = {
+    "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 = [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=(12, 12), dpi=500)
-nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=5, width=0.5)
-nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=2)
-plt.axis('equal')  # 锁定坐标轴比例，确保图形内容是正方形
-plt.savefig(f"output_result/risk/g_firm_sample_id_{sample_id}_de.png", bbox_inches='tight', pad_inches=0.1)
+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()
 
 

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

@@ -46,7 +46,7 @@ def visualize_progress():
         ax.text(i, v + 0.5, str(v), ha='center', fontsize=12)
 
     # 刷新绘图
-    plt.pause(3)  # 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()

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")
+