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

@@ -3,7 +3,7 @@
   <component name="CsvFileAttributes">
     <option name="attributeMap">
       <map>
-        <entry key="C:\Users\www\Desktop\半导体数据——暂时的数据\我做的数据\汇总数据\BomCateNet.csv">
+        <entry key="C:\Users\www\Desktop\python项目\数据\抽样第3次数据\firm_amended.csv">
           <value>
             <Attribute>
               <option name="separator" value="," />
@@ -31,6 +31,13 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\input_data\input_firm_data\firm_amended.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\input_data\input_firm_data\firms_devices.csv">
           <value>
             <Attribute>
@@ -129,6 +136,41 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\input_data\产品消耗制造比例.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </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>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\output_result\risk\count_dcp.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>

11.py

@@ -0,0 +1,29 @@
+import pickle
+import os
+
+from 查看进度 import visualize_progress
+
+
+def load_cached_data(file_path):
+    """
+    从指定的缓存文件加载数据。
+    如果文件不存在或加载失败，则返回空字典。
+    """
+    if not os.path.exists(file_path):
+        print(f"Warning: Cache file '{file_path}' does not exist.")
+        return {}
+
+    try:
+        with open(file_path, 'rb') as f:
+            data = pickle.load(f)
+            print(f"Successfully loaded cache from '{file_path}'.")
+            return data
+    except (pickle.UnpicklingError, FileNotFoundError, EOFError) as e:
+        print(f"Error loading cache from '{file_path}': {e}")
+        return {}
+
+
+# 示例用法
+# data_dct = load_cached_data("G_Firm_add_edges.pkl")
+
+visualize_progress()

README.md

@@ -0,0 +1,36 @@
+## 安装内容
+1. 数据库，推荐使用mysql 8.0以上版本
+2. Python 3.8
+3. 通过pip等方法安装*requirements_manual_selected_20230304.txt*文件中的包
+
+## 前期准备工作
+1. 复制整个代码到本地
+2. 用root及密码登录mysql，在本地数据库中创建一个数据库，命名为*iiabmdb*
+3. 在mysql中运行*SQL_db_user_create.sql*里的sql命令，创建数据库用户。如果创建用户报错，需打开该文件，并运行第三行被注释掉的代码。该文件后面的sql命令也需要运行，将数据库用户的权限赋予*iiabmdb*数据库
+4. 之后直接运行controller.py文件，如果没有报错，则说明前期准备工作完成
+
+## 运行程序
+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 --job 6 --reset_db True
+```
+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 --job 6 --reset_db True
+```
+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
+
+### 风险节点分析
+1. 运行*risk_analysis_sum_result.py*文件，该程序自动产生风险节点分析统计数据并放置到output_result/risk文件夹中
+2. 依次运行*risk_analysis_prod_network.py*，*risk_analysis_firm_network.py*文件，将自动产生相关结果放置到output_result/risk文件夹中
+
+### 韧性影响因素分析
+1. 运行*SQL_analysis_experiment.sql*文件，将汇总结果手工复制至output_result/resilience文件夹*experiment_result.csv*文件中
+2. 使用Minitab进行田口设计分析
+3. 新建田口设计（统计——DOE——田口——创建田口设计——混合水平设计），因子数选项设置为8，设计选项设置为L36，水平^列为2^3，3^5，因子选项中将列名依次修改为：is_prf_size，is_prf_conn，ex_cap_type，n_max_trial，ex_cap_para，prob_new_conn，t_max_trial，n_sourcing
+4. 将output_result/resilience文件夹*experiment_result.csv*文件中结果复制入田口设计表格右侧列
+5. 依次对各个韧性指标进行田口设计分析（统计——DOE——田口——分析田口设计），从mean_count_firm_prod，mean_max_ts_firm_prod，mean_n_remove_firm_prod，mean_end_ts中选择一个韧性指标，图形选项勾选均值，分析选项中显示响应表勾选均值，拟合线性模型勾选均值，点击确定
+6. 手工汇总方差分析结果至output_result/resilience文件夹*anova.csv*文件中，汇总响应表结果至*anova_visualization.csv*文件中

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

@@ -0,0 +1,4 @@
+## 下一步计划
+1. 运行with_exp
+2. 方差分析
+3. 优化资源消耗逻辑

computation.py

@@ -1,5 +1,10 @@
+import json
 import os
 import datetime
+import time
+
+import networkx as nx
+import pandas as pd
 from mesa import Model
 
 from typing import TYPE_CHECKING
@@ -36,9 +41,8 @@ class Computation:
         dct_sample_para = {'sample': sample_random,
                            'seed': sample_random.seed,
                            **dct_exp}
+
         model = MyModel(dct_sample_para)
-        for i in range(1):
+
-            model.step()
+        model.step()  # 运行仿真一步
-            print(i, datetime.datetime.now())
+        model.end()  # 汇总结果
-        model.end()
-        return False

controller_db.py

@@ -39,7 +39,7 @@ class ControllerDB:
         self.lst_saved_s_id = []
 
         self.experiment_data = []
-        self.batch_size = 2000
+        self.batch_size = 5000
         # 根据需求设置每批次的大小
 
     def init_tables(self):
@@ -53,7 +53,6 @@ class ControllerDB:
 
         # fill dct_lst_init_disrupt_firm_prod
         # 存储 公司-在供应链结点的位置.. 0 ：‘1.1’
-        list_dct = []  # 存储 公司编码code 和对应的产业链 结点
         if self.is_with_exp:
             # 对于方差分析时候使用
             with open('SQL_export_high_risk_setting.sql', 'r') as f:
@@ -67,41 +66,45 @@ class ControllerDB:
             # 行索引 (index)：这一行在数据帧中的索引值。
             # 行数据 (row)：这一行的数据，是一个 pandas.Series 对象，包含该行的所有列和值。
 
+            # 读取企业与产品关系数据
             firm_industry = pd.read_csv("input_data/firm_industry_relation.csv")
             firm_industry['Firm_Code'] = firm_industry['Firm_Code'].astype('string')
+
+            # 假设已从 BOM 数据构建了 code_to_indices
+            bom_nodes = pd.read_csv("input_data/input_product_data/BomNodes.csv")
+            code_to_indices = bom_nodes.groupby('Code')['Index'].apply(list).to_dict()
+
+            # 初始化存储映射结果的列表
+            list_dct = []
+
+            # 遍历 firm_industry 数据
             for _, row in firm_industry.iterrows():
-                code = row['Firm_Code']
+                firm_code = row['Firm_Code']  # 企业代码
-                row = row['Product_Code']
+                product_code = row['Product_Code']  # 原始产品代码
-                dct = {code: [row]}
+
+                # 使用 code_to_indices 映射 Product_Code 到 Product_Indices
+                mapped_indices = code_to_indices.get(product_code, [])  # 如果找不到则返回空列表
+
+                # 构建企业到产品索引的映射
+                dct = {firm_code: mapped_indices}
                 list_dct.append(dct)
 
         # fill g_bom
         # 结点属性值 相当于 图上点的 原始 产品名称
         bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
         bom_nodes['Code'] = bom_nodes['Code'].astype(str)
-        bom_nodes.set_index('Code', inplace=True)
+        bom_nodes.set_index('Index', inplace=True)
-        # bom_cate_net = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
-        # bom_cate_net.fillna(0, inplace=True)
-        # # 创建 可以多边的有向图 同时 转置操作 使得 上游指向下游结点 也就是 1.1.1 - 1.1 类似这种
-        # # 将第一列转换为字符串类型
-        # print("sssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss")
-        # print(bom_cate_net.columns)
-        # print(bom_cate_net.index)  # 打印行标题（索引）
-        # print(bom_cate_net.iloc[:, 0])  # 打印第一列的内容
-        #
-        # g_bom = nx.from_pandas_adjacency(bom_cate_net.T,
-        #                                  create_using=nx.MultiDiGraph())
 
         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())
         # 填充每一个结点 的具体内容 通过 相同的 code 并且通过BomNodes.loc[code].to_dict()字典化 格式类似 格式 { code（0） :  {level: 0 ,name: 工业互联网   }}
         bom_labels_dict = {}
-        for code in g_bom.nodes:
+        for index in g_bom.nodes:
             try:
-                int_code = int(code)
+                bom_labels_dict[index] = bom_nodes.loc[index].to_dict()
-                bom_labels_dict[code] = bom_nodes.loc[int_code].to_dict()
+                # print(bom_labels_dict[index])
             except KeyError:
-                print(f"节点 {code} 不存在于 bom_nodes 中")
+                print(f"节点 {index} 不存在于 bom_nodes 中")
         # 分配属性 给每一个结点 获得类似 格式：{1: {'label': 'A', 'value': 10},
         nx.set_node_attributes(g_bom, bom_labels_dict)
         # 改为json 格式

firm.py

@@ -2,7 +2,7 @@ from mesa import Agent
 
 
 class FirmAgent(Agent):
-    def __init__(self, unique_id, model, type_region, revenue_log, n_equip_c, a_lst_product,
+    def __init__(self, unique_id, model, type_region, revenue_log, a_lst_product,
                  production_output, demand_quantity, R, P, C):
         # 调用超类的 __init__ 方法
         super().__init__(unique_id, model)
@@ -32,20 +32,20 @@ class FirmAgent(Agent):
         # 包括 产品时间
         self.P1 = {0: P}
         # 企业i的供应商
-        self.upper_i = [agent for u, v in self.firm_network.in_edges(self.unique_id)
+        self.upper_i = [self.model.agent_map[u] for u, v in self.firm_network.in_edges(self.unique_id)
-                        for agent in self.model.company_agents if agent.unique_id == u]
+                        if u in self.model.agent_map]
         # 企业i的客户
-        self.downer_i = [agent for u, v in self.firm_network.out_edges(self.unique_id)
+        self.downer_i = [self.model.agent_map[v] for u, v in self.firm_network.out_edges(self.unique_id)
-                         for agent in self.model.company_agents if agent.unique_id == u]
+                         if v in self.model.agent_map]
         # 设备c的数量 （总量） 使用这个来判断设备数量
-        self.n_equip_c = n_equip_c
+        # self.n_equip_c = n_equip_c
-        # 设备c产量 更具设备量进行估算
+        # 设备c产量 根据设备量进行估算
         self.c_yield = production_output
-        # 消耗材料量 根据设备量进行估算
+        # 消耗材料量 根据设备量进行估算   {           }
         self.c_consumption = demand_quantity
         # 设备c购买价格（初始值）
         # self.c_price = c_price
-        # 资源r补货库存阈值
+        # 资源r补货库存阈值 很重要设置
         self.s_r = 40
         self.S_r = 120
         # 设备补货阙值 可选
@@ -143,41 +143,68 @@ class FirmAgent(Agent):
                 #       f"disrupted supplier of {disrupted_up_prod.code}")
 
     def seek_alt_supply(self, product):
+        # 检查当前产品的尝试次数是否达到最大值
         if self.dct_n_trial_up_prod_disrupted[product] <= self.model.int_n_max_trial:
+            # 初始化候选供应商列表
             if self.dct_n_trial_up_prod_disrupted[product] == 0:
                 self.dct_cand_alt_supp_up_prod_disrupted[product] = [
-                    firm for firm in self.model.company_agents
+                    firm for firm in self.model.company_agents if firm.is_prod_in_current_normal(product)
-                    if firm.is_prod_in_current_normal(product)]
+                ]
-            if self.dct_cand_alt_supp_up_prod_disrupted[product]:
+
+            # 如果没有候选供应商，直接退出
+            if not self.dct_cand_alt_supp_up_prod_disrupted[product]:
+                # print(f"No valid candidates found for product {product.unique_id}")
+                return
+
+            # 查找与当前企业已连接的候选供应商
             lst_firm_connect = []
             if self.is_prf_conn:
-                    for firm in self.dct_cand_alt_supp_up_prod_disrupted[product]:
+                lst_firm_connect = [
-                        if self.firm_network.has_edge(self.unique_id, firm.unique_id) or \
+                    firm for firm in self.dct_cand_alt_supp_up_prod_disrupted[product]
-                                self.firm_network.has_edge(firm.unique_id, self.unique_id):
+                    if self.firm_network.has_edge(self.unique_id, firm.unique_id) or
-                            lst_firm_connect.append(firm)
+                       self.firm_network.has_edge(firm.unique_id, self.unique_id)
-                if len(lst_firm_connect) == 0:
+                ]
-                    if self.is_prf_size:
+
+            # 如果没有连接的供应商
+            if not lst_firm_connect:
+                if self.is_prf_size:  # 根据规模加权选择
                     lst_size = [firm.size_stat[-1][0] for firm in self.dct_cand_alt_supp_up_prod_disrupted[product]]
                     lst_prob = [size / sum(lst_size) for size in lst_size]
-                        select_alt_supply = \
+                    select_alt_supply = self.random.choices(
-                            self.random.choices(self.dct_cand_alt_supp_up_prod_disrupted[product], weights=lst_prob)[0]
+                        self.dct_cand_alt_supp_up_prod_disrupted[product], weights=lst_prob
-                    else:
+                    )[0]
+                else:  # 随机选择
                     select_alt_supply = self.random.choice(self.dct_cand_alt_supp_up_prod_disrupted[product])
-                elif len(lst_firm_connect) > 0:
+            else:  # 如果存在连接的供应商
-                    if self.is_prf_size:
+                if self.is_prf_size:  # 根据规模加权选择
                     lst_firm_size = [firm.size_stat[-1][0] for firm in lst_firm_connect]
                     lst_prob = [size / sum(lst_firm_size) for size in lst_firm_size]
                     select_alt_supply = self.random.choices(lst_firm_connect, weights=lst_prob)[0]
-                    else:
+                else:  # 随机选择
                     select_alt_supply = self.random.choice(lst_firm_connect)
 
-                assert select_alt_supply.is_prod_in_current_normal(product)
+            # 检查选中的供应商是否能够生产产品
+            if not select_alt_supply.is_prod_in_current_normal(product):
+               # print(f"Selected supplier {select_alt_supply.unique_id} cannot produce product {product.unique_id}")
 
+                # 打印供应商的生产状态字典
+                #print(f"Supplier production state: {select_alt_supply.dct_prod_up_prod_stat}")
+
+                # 检查产品是否存在于生产状态字典中
+                if product in select_alt_supply.dct_prod_up_prod_stat:
+                    print(
+                        f"Product {product.unique_id} production state: {select_alt_supply.dct_prod_up_prod_stat[product]['p_stat']}")
+                else:
+                    print(f"Product {product.unique_id} not found in supplier production state.")
+                return
+
+            # 添加到供应商的请求字典
             if product in select_alt_supply.dct_request_prod_from_firm:
                 select_alt_supply.dct_request_prod_from_firm[product].append(self)
             else:
                 select_alt_supply.dct_request_prod_from_firm[product] = [self]
 
+            # 更新尝试次数
             self.dct_n_trial_up_prod_disrupted[product] += 1
 
     def handle_request(self):

input_data/input_firm_data/firm_amended.csv

@@ -0,0 +1,241 @@
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input_data/input_firm_data/firms_devices.csv

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input_data/input_firm_data/firms_materials.csv

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+99,95
+100,77
+100,95

input_data/产品消耗制造比例.csv

@@ -0,0 +1,238 @@
+IndustryID,MaterialID,Quantity
+38,47,1.0
+39,49,1.0
+40,44,1.0
+41,15,0.02
+41,18,0.1
+41,20,0.4
+41,22,0.15
+41,23,0.04
+41,25,0.02
+41,31,0.25
+41,36,1.0
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+42,20,0.4
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+45,7,1.4
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main.py

@@ -3,6 +3,9 @@ import random
 import time
 from multiprocessing import Process
 import argparse
+
+from matplotlib import pyplot as plt
+
 from computation import Computation
 from sqlalchemy.orm import close_all_sessions
 import yaml
@@ -30,11 +33,16 @@ def do_process(target: object, controller_db: ControllerDB, ):
 
     for i in process_list:
         i.join()
+
+    # 所有子进程完成后刷新最终进度
+
+    # 显示最终进度后关闭图表
+
 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
@@ -44,9 +52,9 @@ if __name__ == '__main__':
     # 输入参数
     parser = argparse.ArgumentParser(description='setting')
     parser.add_argument('--exp', type=str, default='without_exp')
-    parser.add_argument('--job', type=int, default='1')
+    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

orm.py

@@ -98,8 +98,8 @@ class Result(Base):
     s_id = Column(Integer, ForeignKey('{}.id'.format(
         f"{db_name_prefix}_sample")), nullable=False)
 
-    id_firm = Column(String(10), nullable=False)
+    id_firm = Column(String(20), nullable=False)
-    id_product = Column(String(10), nullable=False)
+    id_product = Column(String(20), nullable=False)
     ts = Column(Integer, nullable=False)
     status = Column(String(5), nullable=False)
 

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,
+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_dcp_prod.csv

@@ -1,23 +1,710 @@
 up_id_product,down_id_product,count
-2.1.3.7,2.1.3,7
+46,52,8413
-1.3.1.3,1.3.1,5
+48,52,8375
-2.1.3.4,2.1.3,4
+48,55,8345
-2.1.3.2,2.1.3,3
+46,55,8328
-1.3.3.3,1.3.3,3
+45,52,8274
-1.1.1,1.1,2
+44,52,8248
-1.3.4.2,1.3.4,2
+49,52,8246
-2.1.1.5,2.1.1,2
+49,55,8197
-1.3.4.3,1.3.4,2
+44,55,8172
-2.3.1,2.3,2
+45,55,8151
-1.3.1.5,1.3.1,2
+47,52,8077
-1.3.3.4,1.3.3,1
+47,55,8016
-1.1.3,1.1,1
+55,99,7248
-1.4.5.6,1.4.5,1
+52,99,7163
-2.1.1.4,2.1.1,1
+50,99,7085
-1.3.1.7,1.3.1,1
+54,99,7066
-2.1.2.2,2.1.2,1
+51,99,7031
-1.3.1.4,1.3.1,1
+53,99,7010
-1.3.1.1,1.3.1,1
+54,55,5570
-2.1.4.1.1,2.1.4.1,1
+54,52,5539
-2.2,2,1
+53,55,5524
-1.4.3.4,1.4.3,1
+52,55,5462
+51,55,5441
+50,55,5440
+53,52,5438
+55,52,5379
+51,52,5312
+50,52,5296
+55,55,5254
+52,52,5177
+45,99,5036
+46,99,4997
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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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output_result/risk/count_prod.csv

@@ -1,35 +1,73 @@
 id_product,count
-2.1.3,14
+52,5571
-1.3.1,10
+55,5513
-1.3.4,4
+51,4711
-1.3.3,4
+53,4687
-1.1,3
+50,4642
-2.1.3.7,3
+54,4622
-1.3.1.3,3
+95,3416
-2.1.1,3
+49,3272
-2.3,2
+46,3254
-2.1.3.4,2
+48,3233
-2.1.1.5,2
+44,3196
-2.3.1,2
+45,3195
-1.3.3.3,2
+47,3164
-1.3.3.4,1
+99,1384
-2.1.2.2,1
+94,1280
-1.1.3,1
+93,1174
-2.2,1
+91,1122
-2.1.4.1.1,1
+90,911
-2.1.4.1,1
+92,768
-1.3.1.1,1
+41,474
-1.3.1.4,1
+42,458
-2.1.3.2,1
+43,301
-1.3.1.5,1
+39,267
-2.1.2,1
+38,257
-1.3.4.2,1
+40,250
-1.3.1.7,1
+7,138
-2.1.1.4,1
+23,54
-2,1
+11,50
-1.4.5.6,1
+68,47
-1.1.1,1
+20,45
-1.4.3.4,1
+67,45
-1.4.3,1
+34,41
-1.3.4.3,1
+61,41
-1.4.5,1
+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

product.py

@@ -2,13 +2,14 @@ from mesa import Agent
 
 
 class ProductAgent(Agent):
-    def __init__(self, unique_id, model, name, type2, j_comp_data_consumed, j_comp_data_produced):
+    def __init__(self, unique_id, model, name, type2, production_ratio):
         # 调用超类的 __init__ 方法
         super().__init__(unique_id, model)
 
         # 初始化代理属性
         self.name = name
         self.product_network = self.model.product_network
+        self.production_ratio = production_ratio
         if type2 == 0:
             self.is_equip = True
         else:
@@ -16,9 +17,6 @@ class ProductAgent(Agent):
             # depreciation ratio 折旧比值
             #  self.depreciation ratio
 
-        self.j_comp_data_produced = j_comp_data_produced
-        self.j_comp_data_consumed = j_comp_data_consumed
-
     def a_successors(self):
         # 从 product_network 中找到当前代理的后继节点
         successors = list(self.model.product_network.successors(self.unique_id))

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'] > 130]
-count_dcp = count_dcp[count_dcp['count'] > 35]
 
 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="")
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
-node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
+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
+  '343012684': '59',
-    for key in node_label.keys()
+  '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 = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
-BomNodes.set_index('Code', inplace=True)
+# BomNodes.set_index('Code', inplace=True)
-BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
+# BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
-BomCateNet.fillna(0, inplace=True)
+# 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:
+for code in g_bom.nodes:
-    node_attr = BomNodes.loc[code].to_dict()
+    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="")
+pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
-dict_node_name = nx.get_node_attributes(G, 'Name')
+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,
+ax = fig.add_subplot(111)
-        pos,
+
-        node_size=list(nx.get_node_attributes(G, 'node_size').values()),
+# 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 = []
+sm.set_array([])
-position = fig.add_axes([0.01, 0.05, 0.01, 0.3])
+
-cb = plt.colorbar(sm, fraction=0.01, cax=position)
+# 设置颜色条位置和样式
-cb.ax.tick_params(labelsize=8)
+cax = fig.add_axes([0.88, 0.3, 0.015, 0.4])  # 右侧垂直对齐
-cb.outline.set_visible(False)
+cb = plt.colorbar(sm, cax=cax)
-plt.savefig("output_result\\risk\\count_prod_network")
+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 = pd.read_csv('input_data/input_product_data/BomNodes.csv')
-BomNodes.set_index('Code', inplace=True)
+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 = {}
+pos_new = {node: (p[1], p[0]) for node, p in pos.items()}  # 字典推导式优化
-for node, p in pos.items():
-    pos_new[node] = (p[1], p[0])
 
-fig = plt.figure(figsize=(6, 10), dpi=300)
+fig = plt.figure(figsize=(8, 8), dpi=300)
-# plt.subplots_adjust(right=0.7)
+plt.subplots_adjust(right=0.85)  # 关键调整：右侧保留15%空白
-nx.draw(g_bom,
+
-        pos_new,
+# 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)
+        edge_vmax=vmax,
-plt.axis('off')
+        )
-axis = plt.gca()
+main_ax.axis('off')
-axis.set_xlim([1.2*x for x in axis.get_xlim()])
-axis.set_ylim([1.2*y for y in axis.get_ylim()])
 
+# 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 = []
+sm._A = []  # 必需的空数组
-position = fig.add_axes([0.75, 0.1, 0.01, 0.2])
+
-cb = plt.colorbar(sm, fraction=0.01, cax=position)
+# 微调颜色条样式
-cb.ax.tick_params(labelsize=8)
+cbar = fig.colorbar(sm, cax=cbar_ax, orientation='vertical')
-cb.outline.set_visible(False)
+cbar.ax.tick_params(labelsize=4,
-plt.savefig("output_result\\risk\\count_dcp_prod_network")
+                    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,
+result = pd.read_sql(sql=str_sql, con=connection)
-                     con=connection)
+result.to_csv('output_result/risk/count.csv', index=False, encoding='utf-8-sig')
-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(
+G_bom_df = pd.read_sql(
-    sql=text(f'select g_bom from iiabmdb.without_exp_experiment '
+    sql=text(f'select g_bom from iiabmdb.without_exp_experiment where id = {exp_id};'),
-        f'where id = {exp_id};'),
+    con=connection
-    con=connection)['g_bom'].tolist()[0]
+)
+
+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 = {
-# node_labels['1'] = '工业互联网'
+    7: 'Si Raw Mtl.',
-# node_labels['1.1'] = '工业自动化硬件'
+    8: 'Photoresist & Reagents',
-# node_labels['1.4'] = '工业互联网安全管理'
+    9: 'Etch Solution',
-# node_labels['1.2.1'] = '网络互联服务'
+    10: 'SiF4',
-# node_labels['1.2.2'] = '标识解析服务'
+    11: 'Developer',
-# node_labels['1.2.3'] = '数据互通服务'
+    12: 'PCE Superplasticizer',
-# node_labels['1.3.1'] = '设计研发软件'
+    13: 'Metal Protectant',
-# node_labels['1.3.2'] = '采购供应软件'
+    14: 'Deep Hole Cu Plating',
-# node_labels['1.3.3'] = '生产制造软件'
+    15: 'Thinner',
-# node_labels['1.3.4'] = '企业运营软件'
+    16: 'HP Boric Acid (Nuc.)',
-# node_labels['1.3.5'] = '仓储物流软件'
+    17: 'E-Grade Epoxy',
-plt.figure(figsize=(12, 12), dpi=300)
+    18: 'Stripper',
-nx.draw_networkx_nodes(G_bom, pos)
+    19: 'HP-MOC',
-nx.draw_networkx_edges(G_bom, pos)
+    20: 'CMP Slurry & Consumables',
-nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6)
+    21: 'PR Remover',
-# plt.show()
+    22: 'Poly-Si Cutting Fluid',
-plt.savefig(f"output_result\\risk\\g_bom_exp_id_{exp_id}.png")
+    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(
+with open("firm_network.pkl", 'rb') as f:
-    sql=text(f'select g_firm from iiabmdb.without_exp_sample ' 
+    G_firm = pickle.load(f)
-             f'where id = {exp_id};'),
+print(f"Successfully loaded cached data from firm_network.pkl")
-    con=connection)['g_firm'].tolist()[0]
 
-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 = {key: key for key in nx.get_node_attributes(G_firm, 'Revenue_Log').keys()}
-node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
 node_label = {
-    key: key
+    "7": "1",
-    for key in node_label.keys()
+    "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))
+node_size = [value * 5 for value in nx.get_node_attributes(G_firm, 'Revenue_Log').values()]
-edge_label = nx.get_edge_attributes(G_firm, "Product")
+edge_label = {(n1, n2): label for (n1, n2, _), label in nx.get_edge_attributes(G_firm, "Product").items()}
-edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+
-plt.figure(figsize=(12, 12), dpi=300)
+plt.figure(figsize=(15, 15), dpi=500)
-nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=6)
+plt.axis('off')  # 完全关闭坐标轴系统
-nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4)
+
-# plt.show()
+# 分层绘制网络组件
-plt.savefig(f"output_result\\risk\\g_firm_sample_id_{exp_id}_de.png")
+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',
+count_firm_prod.to_csv('output_result/risk/count_firm_prod.csv', index=False, encoding='utf-8-sig')
-                       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',
+count_firm.to_csv('output_result/risk/count_firm.csv', index=False, encoding='utf-8-sig')
-                  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',
+count_prod.to_csv('output_result/risk/count_prod.csv', index=False, encoding='utf-8-sig')
-                  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,
+            down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
-                             ['id_firm', 'id_product']]
             for _, up_row in up.iterrows():
                 for _, down_row in down.iterrows():
-                    row = [sid]
+                    result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
-                    row += up_row.tolist()
-                    row += down_row.tolist()
-                    result_dcp.loc[len(result_dcp.index)] = row
             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'])
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
-count_dcp.name = 'count'
+).reset_index(name='count')
-count_dcp = count_dcp.to_frame().reset_index()
+
-count_dcp.to_csv('output_result\\risk\\count_dcp.csv',
+# 保存文件
-                 index=False, encoding='utf-8-sig')
+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}")

可视化测试.py

@@ -1,60 +0,0 @@
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-import numpy as np
-from matplotlib import rcParams
-
-# 设置中文字体和符号显示
-rcParams['font.sans-serif'] = ['SimHei']  # 或者使用 ['Microsoft YaHei']
-rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
-# 定义节点和边
-nodes = {
-    0: '原材料供应商',
-    1: '零部件制造商',
-    2: '产品制造商',
-    3: '分销商',
-    4: '零售商'
-}
-
-edges = [
-    (0, 1),  # 原材料供应商 -> 零部件制造商
-    (1, 2),  # 零部件制造商 -> 产品制造商
-    (2, 3),  # 产品制造商 -> 分销商
-    (3, 4)   # 分销商 -> 零售商
-]
-
-# 定义节点的三维坐标 (x, y, z)
-positions = {
-    0: (0, 0, 0),    # 原材料供应商
-    1: (1, 0, 1),    # 零部件制造商
-    2: (2, 0, 2),    # 产品制造商
-    3: (3, 0, 3),    # 分销商
-    4: (4, 0, 4)     # 零售商
-}
-
-# 创建3D图形
-fig = plt.figure()
-ax = fig.add_subplot(111, projection='3d')
-
-# 绘制节点
-for node, (x, y, z) in positions.items():
-    ax.scatter(x, y, z, color='b', s=100)  # 绘制每个节点
-    ax.text(x, y, z, nodes[node], size=12, zorder=1, color='k')  # 添加节点标签
-
-# 绘制边（箭头）
-for start, end in edges:
-    start_pos = positions[start]
-    end_pos = positions[end]
-    ax.plot([start_pos[0], end_pos[0]],
-            [start_pos[1], end_pos[1]],
-            [start_pos[2], end_pos[2]], color='r')  # 连接每对节点
-
-# 设置坐标轴标签
-ax.set_xlabel('X轴')
-ax.set_ylabel('Y轴')
-ax.set_zlabel('Z轴')
-
-# 设置视角以便更好地观察3D图形
-ax.view_init(elev=20., azim=-35)  # 角度可根据需要调整
-
-# 显示3D图形
-plt.show()

查看进度.py

@@ -0,0 +1,53 @@
+from matplotlib import rcParams, pyplot as plt
+from sqlalchemy import func
+from orm import db_session, Sample
+
+# 创建全局绘图对象和轴
+fig, ax = plt.subplots(figsize=(8, 5))
+plt.ion()  # 启用交互模式
+
+def visualize_progress():
+    """
+    可视化 `is_done_flag` 的分布，动态更新进度条。
+    """
+
+    # 设置全局字体
+    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
+    result = db_session.query(
+        Sample.is_done_flag, func.count(Sample.id)
+    ).group_by(Sample.is_done_flag).all()
+
+    # 转换为字典
+    data = {flag: count for flag, count in result}
+
+    # 填充缺失的标志为 0
+    for flag in [-1, 0, 1]:
+        data.setdefault(flag, 0)
+
+    # 准备数据
+    labels = ['未完成 (-1)', '计算中(0)', '完成 (1)']
+    values = [data[-1], data[0], data[1]]
+
+    # 清空之前的绘图内容
+    ax.clear()
+
+    # 创建柱状图
+    ax.bar(labels, values, color=['red', 'orange', 'green'])
+    ax.set_title('任务进度分布', fontsize=16)
+    ax.set_xlabel('任务状态', fontsize=14)
+    ax.set_ylabel('数量', fontsize=14)
+    ax.tick_params(axis='both', labelsize=12)
+
+    # 显示具体数量
+    for i, v in enumerate(values):
+        ax.text(i, v + 0.5, str(v), ha='center', fontsize=12)
+
+    # 刷新绘图
+    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")
+