no message

.idea/csv-editor.xml

@@ -10,6 +10,55 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\GA_Agent_0925\vulnerable35_match_results\vulnerable20_match_results_new1121.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\GA_Agent_0925\vulnerable35_match_results\vulnerable20_match_results_new1122.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\GA_Agent_0925\vulnerable35_match_results\vulnerable35_match_results.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\GA_Agent_0925\vulnerable35_match_results\vulnerable35_match_results_new1121.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\count_firm.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\count_firm_prod.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\count_prod.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\input_data\device_salvage_values.csv">
           <value>
             <Attribute>

.idea/sqldialects.xml

@@ -1,6 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="SqlDialectMappings">
+    <file url="file://$PROJECT_DIR$/GA_Agent_0925/SQL_analysis_risk_ga.sql" dialect="MySQL" />
     <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" />

GA_Agent_0925/best_result_with_industry.json

@@ -1,6 +1,6 @@
 {
   "best_individual": [
-    291,
+    23,
     0.24373090607513836,
     0.5512650768804697,
     1,

GA_Agent_0925/conf_experiment.yaml

@@ -8,5 +8,5 @@ test:  # only for test scenarios
   n_iter: 100
 
 not_test:  # normal scenarios
-  n_sample: 1
+  n_sample: 5
-  n_iter: 10
+  n_iter: 60

GA_Agent_0925/config.json

@@ -1,6 +1,6 @@
 {
-  "pop_size": 10,
+  "pop_size": 20,
-  "n_gen": 5,
+  "n_gen": 10,
   "n_var": 12,
   "bound_min": -5,
   "bound_max": 5,
@@ -10,6 +10,6 @@
   "mut_sigma": 0.1,
   "mut_indpb": 0.2,
   "tourn_size": 3,
-  "n_jobs": 1,
+  "n_jobs": 4,
   "seed": 42
 }

GA_Agent_0925/controller_db.py

@@ -11,6 +11,7 @@ import networkx as nx
 import json
 import pickle
 
+
 class ControllerDB:
     is_with_exp: bool
     dct_parameter = None
@@ -382,6 +383,24 @@ class ControllerDB:
         db_session.execute(sql)
         db_session.commit()
 
+    def drop_table(self, table_name, keep_ga_id=None):
+        """
+        删除表中数据或整张表：
+        - 如果 keep_ga_id 为 None，则直接删除整张表。
+        - 如果 keep_ga_id 有值，则只删除除了该 ga_id 外的记录，保留该 ga_id。
+
+        参数:
+            table_name : str
+                数据库表名
+            keep_ga_id : str/int, 可选
+                需要保留的 ga_id
+        """
+        try:
+            sql = text(f"DELETE FROM {table_name} WHERE ga_id != :ga_id")
+            engine.execute(sql, {"ga_id": keep_ga_id})
+            print(f"[DB] Deleted all rows from `{table_name}` except ga_id={keep_ga_id}.")
+        except Exception as e:
+            print(f"[DB] Failed to delete rows from `{table_name}`: {e}")
 if __name__ == '__main__':
     print("Testing the database connection...")
     try:

GA_Agent_0925/creating.py

@@ -11,18 +11,18 @@ def creating():
     toolbox = base.Toolbox()
 
     # 基因注册
-    toolbox.register("n_max_trial", random.randint, 1, 40)
+    toolbox.register("n_max_trial", random.randint, 1, 60)
     toolbox.register("prf_size", random.uniform, 0.0, 1.0)
     toolbox.register("prf_conn", random.uniform, 0.0, 1.0)
     toolbox.register("cap_limit_prob_type", random.randint, 0, 1)
-    toolbox.register("cap_limit_level", random.randint, 5, 50)
+    toolbox.register("cap_limit_level", random.randint, 5, 80)
     toolbox.register("diff_new_conn", random.uniform, 0.0, 1.0)
     toolbox.register("netw_prf_n", random.randint, 1, 20)
-    toolbox.register("s_r", random.uniform, 0.05, 0.5)
+    toolbox.register("s_r", random.uniform, 0.01, 0.5)
     toolbox.register("S_r", random.uniform, 0.5, 1.0)
     toolbox.register("x", random.uniform, 0.0, 1)
-    toolbox.register("k", random.uniform, 0.05, 2.0)
+    toolbox.register("k", random.uniform, 0.1, 5.0)
-    toolbox.register("production_increase_ratio", random.uniform, 0.5, 2.0)
+    toolbox.register("production_increase_ratio", random.uniform, 1, 5.0)
 
     # 个体与种群注册
     toolbox.register(

GA_Agent_0925/evaluate_func.py

@@ -59,8 +59,7 @@ def fitness(individual, controller_db_obj):
     #     print(f"  {key}: {value}")
     # ========== 2️⃣ 调用 ABM 模型 ==========
     # 并行进程数目
-    job=6
+    job=4
-
     do_process(controller_db_obj,ga_id,dct_exp,job)
     # ========== 3️⃣ 获取数据库连接并提取结果 ==========
     simulated_vulnerable_industries = get_vulnerable35_code(connection,ga_id)

GA_Agent_0925/ge_abm_readme.md

@@ -0,0 +1,187 @@
+# 遗传算法优化ABM模型参数项目
+
+## 项目概述
+
+本项目使用遗传算法（Genetic Algorithm）来优化基于代理的模型（Agent-Based Model）的关键参数，目标是使模型生成的脆弱产业集合与目标产业集合尽可能匹配。项目包含完整的遗传算法实现、ABM模型集成、数据库操作和结果分析功能。
+
+## 项目结构
+
+```
+项目根目录/
+├── main.py                    # 遗传算法主程序
+├── evaluate_func.py           # 适应度函数和评估逻辑
+├── creating.py                # 遗传算法工具箱创建
+├── config.json                # 配置文件
+├── controller_db.py           # 数据库控制器（需补充）
+├── my_model.py                # ABM模型实现（需补充）
+├── orm.py                     # 数据库ORM配置（需补充）
+├── GA_Agent_0925/             # 遗传算法相关模块
+│   ├── creating.py           # 工具箱创建
+│   ├── orm.py                # 数据库配置
+│   └── SQL_analysis_risk_ga.sql # 分析SQL脚本
+├── results/                   # 结果输出目录（自动创建）
+└── requirements.txt           # 依赖包列表（需补充）
+```
+
+## 功能特性
+
+### 核心功能
+- **遗传算法优化**：使用DEAP框架实现完整的遗传算法流程
+- **多进程并行计算**：支持多进程运行ABM模型，提高计算效率
+- **实时结果记录**：每代最优个体和适应度实时保存到文件
+- **收敛曲线可视化**：自动生成算法收敛过程图表
+- **产业匹配分析**：详细分析模拟结果与目标产业的匹配情况
+
+### 参数优化范围
+算法优化以下12个关键ABM模型参数：
+1. `n_max_trial` - 最大尝试次数 [1, 60]
+2. `prf_size` - 偏好大小 [0.0, 1.0]
+3. `prf_conn` - 偏好连接 [0.0, 1.0]
+4. `cap_limit_prob_type` - 容量限制概率类型 [0, 1]
+5. `cap_limit_level` - 容量限制水平 [5, 80]
+6. `diff_new_conn` - 新连接差异 [0.0, 1.0]
+7. `netw_prf_n` - 网络偏好N [1, 20]
+8. `s_r` - 小r参数 [0.01, 0.5]
+9. `S_r` - 大R参数 [0.5, 1.0]
+10. `x` - X参数 [0.0, 1.0]
+11. `k` - K参数 [0.1, 5.0]
+12. `production_increase_ratio` - 生产增加比率 [1.0, 5.0]
+
+
+
+## 配置说明
+
+### config.json 示例
+```json
+{
+    "seed": 42,
+    "pop_size": 50,
+    "n_gen": 100,
+    "cx_prob": 0.7,
+    "mut_prob": 0.2
+}
+```
+
+### 参数说明
+- `seed`: 随机数种子，确保结果可重现
+- `pop_size`: 种群大小
+- `n_gen`: 进化代数
+- `cx_prob`: 交叉概率
+- `mut_prob`: 变异概率
+
+## 使用方法
+
+### 1. 准备数据库
+确保数据库服务运行，并配置正确的连接参数在`orm.py`中。
+
+### 2. 运行遗传算法
+```bash
+python main.py
+```
+
+### 3. 监控运行过程
+程序运行时会显示进度条和每代信息：
+- 当前进化代数
+- 最优个体适应度
+- 平均适应度
+- 产业匹配情况
+
+### 4. 查看结果
+运行完成后，在`results/`目录下生成：
+- `best_individual_each_gen.txt` - 每代最优个体记录
+- `best_result_with_industry.json` - 最终最优结果
+- `convergence.png` - 收敛曲线图
+
+## 输出文件说明
+
+### 文本输出文件
+```
+实验开始时间：2024-01-01 14
+以下为每一代的最优个体基因参数：
+第1代最优基因：[参数列表] 最优适应度: 0.8500
+第2代最优基因：[参数列表] 最优适应度: 0.8700
+...
+```
+
+### JSON结果文件
+包含完整的优化结果：
+- 算法配置参数
+- 最优个体基因值
+- 最优适应度值
+- 收敛曲线数据
+- 时间戳信息
+
+### 可视化结果
+生成收敛曲线图，显示最佳适应度和平均适应度随代数的变化趋势。
+
+## 适应度函数逻辑
+
+### 目标函数
+适应度 = -误差，其中误差计算为：
+```
+误差 = |模拟脆弱产业集合 Δ 目标产业集合|
+```
+
+### 产业匹配分析
+- **匹配产业**: 模拟结果与目标重合的产业
+- **多余产业**: 模拟结果中多出的产业
+- **缺失产业**: 目标中未被模拟覆盖的产业
+
+### 目标产业集合
+包含35个关键半导体产业链编号，涵盖：
+- 半导体设备类（光刻机、刻蚀设备等）
+- 半导体材料类（光刻胶、清洗溶剂等）
+- 晶圆制造类
+- 封装测试类
+- 芯片设计类
+
+## 数据库操作
+
+### 样本管理
+- 自动从数据库获取随机样本
+- 样本锁定机制防止重复使用
+- 临时表管理和清理
+
+### 结果存储
+- 每个GA个体分配唯一ID
+- 模拟结果与GA个体关联存储
+- 支持多进程并发访问
+
+## 故障排除
+
+### 常见问题
+1. **数据库连接失败**: 检查`orm.py`中的连接配置
+2. **缺少依赖包**: 运行`pip install -r requirements.txt`
+3. **ABM模型错误**: 检查`my_model.py`的实现
+4. **内存不足**: 减少种群大小或并行进程数
+
+### 日志信息
+程序运行时会输出详细日志，包括：
+- GA个体参数
+- 数据库操作状态
+- 产业匹配统计
+- 错误和异常信息
+
+## 扩展开发
+
+### 添加新参数
+1. 在`creating.py`中注册新基因
+2. 在`evaluate_func.py`的`fitness`函数中映射参数
+3. 更新ABM模型以使用新参数
+
+### 修改目标产业
+编辑`evaluate_func.py`中的`get_target_vulnerable_industries()`函数，更新目标产业列表。
+
+### 调整算法策略
+修改`main.py`中的遗传算子：
+- 选择策略：`tools.selTournament`
+- 交叉算子：`tools.cxTwoPoint`
+- 变异算子：`tools.mutShuffleIndexes`
+
+## 许可证
+
+本项目仅供学术研究使用。
+
+## 技术支持
+
+如有问题请联系项目维护团队。

GA_Agent_0925/ge_abm_研究过程.md

@@ -22,7 +22,7 @@
    1. ~~修改部分 my_model 和 firm 参数~~
    2. ~~修改部分 step 逻辑~~
 ### 第四阶段
-1. 运行GA代码
+~~1. 运行GA代码~~
 ## 目标分析
 1. **将现有制裁与产业链节点关联**，当作仿真模型验证的**benchmark**
    1. 查看制裁表 对比 现在的产业结点

GA_Agent_0925/main.py

@@ -1,5 +1,8 @@
 import json
+import os
 import random
+from datetime import datetime
+
 from deap import tools
 from sqlalchemy.orm import close_all_sessions
 from tqdm import tqdm
@@ -28,7 +31,7 @@ def main():
 
     # 2️⃣ 初始化 ControllerDB（数据库连接）
     controller_db_obj = ControllerDB("without_exp", reset_flag=0)
-    controller_db_obj.reset_db(force_drop=False)
+    controller_db_obj.reset_db(force_drop=True)
     # 准备样本表
     controller_db_obj.prepare_list_sample()
     # 2️⃣ 初始化工具箱
@@ -42,6 +45,22 @@ def main():
     best_list = []
     avg_list = []
 
+    # ============================================================
+    # 🔧 新增内容 1：准备保存每代最优个体的文件
+    # ============================================================
+    results_dir = "results"
+    os.makedirs(results_dir, exist_ok=True)
+
+    # 文件名
+    txt_result_file = os.path.join(results_dir, "best_individual_each_gen.txt")
+    json_result_file = os.path.join(results_dir, "best_result_with_industry.json")
+
+    # 写入第一行：实验时间（年月日+小时）
+    with open(txt_result_file, "w", encoding="utf-8") as f:
+        exp_time = datetime.now().strftime("%Y-%m-%d %H")
+        f.write(f"实验开始时间（年月日-小时）：{exp_time}\n\n")
+        f.write("以下为每一代的最优个体基因参数：\n")
+
     # ==============================
     # 主进化循环
     # ==============================
@@ -81,6 +100,29 @@ def main():
         best_list.append(record["max"])
         avg_list.append(record["avg"])
 
+        # ============================================================
+        # 🔧 新增内容 2：每代实时记录最优基因到文件
+        # ============================================================
+        best_ind = tools.selBest(pop, 1)[0]
+        best_gene = list(map(float, best_ind))
+        best_ga_id = getattr(best_ind, "ga_id", None)  # 获取 ga_id，如果没有就返回 None
+
+        # 写入 TXT 文件
+        with open(txt_result_file, "a", encoding="utf-8") as f:
+            f.write(
+                (f"第 {gen + 1} 代最优基因：{best_gene}   最优适应度: {best_ind.fitness.values[0]:.4f}"
+                 if best_gene else "N/A")
+                + "\n"
+            )
+
+        # ============================================================
+        # 新增：删除上一轮产生的临时表
+        # ============================================================
+
+        # 保留当前代最优 ga_id：
+        controller_db_obj.drop_table("without_exp_result", keep_ga_id=best_ga_id)
+        # 希望彻底删除整张表：
+        # controller_db_obj.drop_table("without_exp_result")
     # ==============================
     # 输出最优结果
     # ==============================
@@ -89,7 +131,7 @@ def main():
     print(f"🌟 最优适应度: {hof[0].fitness.values[0]:.4f}")
 
     # 绘制收敛曲线
-    plt.figure(figsize=(8, 5))
+    plt.figure(figsize=(12, 12))
     plt.plot(best_list, label="Best Fitness", linewidth=2)
     plt.plot(avg_list, label="Average Fitness", linestyle="--")
     plt.title("Genetic Algorithm Convergence")
@@ -98,6 +140,7 @@ def main():
     plt.legend()
     plt.grid(True, alpha=0.3)
     plt.tight_layout()
+    plt.savefig("convergence1.png", dpi=300)
     plt.show()
 
     # ==============================
@@ -105,5 +148,31 @@ def main():
     # ==============================
     print("\n📊 计算最优个体产业匹配情况...")
 
+    # ==============================
+    # 保存结果到文件
+    # ==============================
+
+    results_dir = "results"
+    os.makedirs(results_dir, exist_ok=True)
+
+    # 固定保存文件名
+    result_file = os.path.join(results_dir, "best_result_with_industry.json")
+
+    result_data = {
+        "config": cfg,
+        "best_individual": list(map(float, hof[0])),
+        "best_fitness": float(hof[0].fitness.values[0]),
+        "fitness_curve": {
+            "best_list": best_list,
+            "avg_list": avg_list
+        },
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    }
+
+    with open(result_file, "w", encoding="utf-8") as f:
+        json.dump(result_data, f, indent=4, ensure_ascii=False)
+
+    print(f"\n💾 最优结果已保存至: {result_file}")
+
 if __name__ == "__main__":
     main()

GA_Agent_0925/results/best_individual_each_gen.txt

@@ -0,0 +1,12 @@
+实验开始时间（年月日-小时）：2025-11-22 14
+以下为每一代的最优个体基因参数：
+第 1 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 2 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 3 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 4 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 5 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 6 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 7 代最优基因：[51.0, 0.4550083673391433, 1.2286610916299323, 1.0, 0.06796086337575724, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 24.0, 4.322914089653634, 0.4230074859901629]   最优适应度: -31.0000
+第 8 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 9 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000
+第 10 代最优基因：[51.0, 0.4550083673391433, 0.4230074859901629, 1.0, 24.0, 0.9607789032744504, 3.0, 0.4271578472186195, 0.5342606663518962, 0.06796086337575724, 4.322914089653634, 1.2286610916299323]   最优适应度: -31.0000

GA_Agent_0925/results/best_result_with_industry.json

@@ -0,0 +1,59 @@
+{
+    "config": {
+        "pop_size": 20,
+        "n_gen": 10,
+        "n_var": 12,
+        "bound_min": -5,
+        "bound_max": 5,
+        "cx_prob": 0.5,
+        "mut_prob": 0.2,
+        "cx_alpha": 0.5,
+        "mut_sigma": 0.1,
+        "mut_indpb": 0.2,
+        "tourn_size": 3,
+        "n_jobs": 4,
+        "seed": 42
+    },
+    "best_individual": [
+        51.0,
+        0.4550083673391433,
+        0.4230074859901629,
+        1.0,
+        24.0,
+        0.9607789032744504,
+        3.0,
+        0.4271578472186195,
+        0.5342606663518962,
+        0.06796086337575724,
+        4.322914089653634,
+        1.2286610916299323
+    ],
+    "best_fitness": -31.0,
+    "fitness_curve": {
+        "best_list": [
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0,
+            -31.0
+        ],
+        "avg_list": [
+            -32.9,
+            -32.8,
+            -32.5,
+            -32.5,
+            -32.3,
+            -32.1,
+            -32.0,
+            -32.3,
+            -32.6,
+            -32.5
+        ]
+    },
+    "timestamp": "2025-11-22 19:05:08"
+}

GA_Agent_0925/risk_ay/count_firm.csv

@@ -1,32 +1,32 @@
 id_firm,count
-214851100,350
+214851100,453
-3111603340,340
+3111603340,453
-70634828,340
+70634828,453
-25980377,140
+532328014,227
-395736790,136
+303926772,227
-340093034,133
+395736790,216
-3330358736,120
+340093034,214
-29223617,119
+29223617,208
-532328014,100
+3330358736,177
-303926772,99
+25980377,177
-2326722141,72
+2337727838,80
-331545755,71
+2326722141,80
-2337727838,70
+331545755,80
-3191869223,58
+3191869223,61
-591350440,50
+2327605629,61
-2327605629,47
+2336923756,61
-517675473,46
+591350440,60
-2336923756,46
+728969035,59
-728969035,41
+517675473,59
-2349705416,41
+16116663,54
-16210433,39
+16210433,53
-16116663,37
+471121089,52
-2349179532,34
+2349179532,52
-471121089,29
+2349705416,50
 1452048,5
 515770253,3
-2349345463,2
 6,2
+2349345463,2
 5971532,2
 1,2
 5849940,2

GA_Agent_0925/risk_ay/count_firm_prod.csv

@@ -1,82 +1,82 @@
 id_firm,id_product,count
-395736790,95,136
+303926772,99,227
-340093034,95,133
+532328014,99,227
-29223617,95,119
+395736790,95,216
-532328014,99,100
+340093034,95,214
-303926772,99,99
+29223617,95,208
-2326722141,90,72
+2326722141,90,80
-331545755,90,71
+2337727838,90,80
-2337727838,90,70
+331545755,90,80
-3191869223,91,58
+3191869223,91,61
-591350440,91,50
+2327605629,94,61
-2327605629,94,47
+2336923756,91,61
-2336923756,91,46
+591350440,91,60
-517675473,92,46
+728969035,93,59
-70634828,55,42
+517675473,92,59
-728969035,93,41
+16210433,92,53
-2349705416,94,41
+2349179532,93,52
-214851100,55,40
+471121089,93,52
-70634828,54,40
+2349705416,94,50
-214851100,52,40
+16116663,92,50
-16210433,92,39
+70634828,51,49
-214851100,54,38
+3111603340,51,49
-3111603340,55,38
+214851100,51,49
-214851100,53,38
+214851100,53,48
-3111603340,52,38
+214851100,52,48
-70634828,52,38
+3111603340,52,48
-3111603340,50,37
+3111603340,53,48
-214851100,50,36
+70634828,52,48
-70634828,50,36
+70634828,53,48
-3111603340,51,35
+3111603340,54,46
-70634828,53,35
+70634828,55,46
-70634828,51,35
+214851100,54,46
-3111603340,54,35
+214851100,55,46
-2349179532,93,34
+214851100,50,46
-16116663,92,33
+70634828,54,46
-214851100,51,33
+70634828,50,46
-3111603340,53,33
+3111603340,50,46
-471121089,93,29
+3111603340,55,46
-25980377,43,27
+25980377,38,32
-3111603340,48,26
+3330358736,38,32
-25980377,41,24
+3330358736,39,31
-3330358736,40,23
+3330358736,43,31
-25980377,40,23
+25980377,43,31
-214851100,48,23
+25980377,39,31
-70634828,49,23
+25980377,40,29
-214851100,44,23
+3330358736,40,29
-25980377,39,22
+3111603340,47,29
-25980377,38,22
+70634828,47,29
-25980377,42,22
+214851100,47,29
-70634828,48,22
+214851100,49,28
-3111603340,46,22
+70634828,44,28
-214851100,47,21
+25980377,41,28
-3330358736,43,21
+70634828,49,28
-3111603340,45,21
+3111603340,44,28
-3330358736,39,21
+3111603340,45,28
-70634828,46,21
+3111603340,46,28
-3111603340,47,20
+3111603340,48,28
-214851100,49,20
+3111603340,49,28
-214851100,46,20
+70634828,48,28
-3330358736,38,19
+70634828,45,28
-3330358736,41,19
+70634828,46,28
-70634828,47,18
+214851100,48,28
-214851100,45,17
+3330358736,41,28
-3111603340,49,17
+214851100,46,28
-3330358736,42,17
+214851100,45,28
-3111603340,44,17
+214851100,44,28
-70634828,44,15
+25980377,42,26
-70634828,45,14
+3330358736,42,26
 1452048,9,3
 515770253,9,3
-80158773,69,2
-8,37,2
-79938367,9,2
-420984285,16,2
-495782506,19,2
 33822284,9,2
 3392803162,9,2
-37873062,9,2
 5971532,9,2
+37873062,9,2
+79938367,9,2
+8,37,2
+420984285,16,2
+495782506,19,2
+80158773,69,2
 6,10,2
 1,10,2
 14913649,9,2

GA_Agent_0925/risk_ay/count_prod.csv

@@ -1,73 +1,73 @@
 id_product,count
-95,388
+95,638
-90,213
+99,454
-99,199
+90,240
-91,154
+91,182
-55,120
+93,163
-92,118
+92,162
-52,116
+51,147
-54,113
+53,144
-50,109
+52,144
-53,106
+50,138
-93,104
+55,138
-51,103
+54,138
-94,88
+94,111
+47,87
+44,84
 9,84
-48,71
+49,84
-46,63
+46,84
-49,60
+45,84
-47,59
+48,84
-44,55
+38,64
-45,52
+43,62
-43,48
+39,62
-40,46
+40,58
-41,43
+41,56
-39,43
+42,52
-38,41
-42,39
 10,23
 11,9
-33,3
-7,3
 15,3
 12,3
+33,3
+7,3
 25,3
-19,2
+69,2
 74,2
 68,2
-69,2
 70,2
 71,2
 72,2
 73,2
-8,2
+19,2
 79,2
-18,2
+8,2
-97,2
-13,2
 66,2
-16,2
+18,2
 17,2
+16,2
+13,2
+97,2
 67,2
-27,2
-65,2
-26,2
-29,2
-30,2
-31,2
 32,2
+65,2
+27,2
 34,2
 35,2
 36,2
 37,2
-24,2
+31,2
-64,2
+30,2
-23,2
+29,2
 28,2
+26,2
+64,2
+24,2
+23,2
+20,2
 59,2
 60,2
-61,2
 62,2
 63,2
-20,2
+61,2
 22,1

GA_Agent_0925/risk_ay/risk_sum.py

@@ -0,0 +1,80 @@
+import pickle
+
+from sqlalchemy import text
+from orm import engine, connection
+import pandas as pd
+import networkx as nx
+import json
+import matplotlib.pyplot as plt
+
+# Prepare data
+Firm = pd.read_csv("../../input_data/input_firm_data/firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+BomNodes = pd.read_csv('../../input_data/input_product_data/BomNodes.csv', index_col=0)
+
+# SQL query
+with open('../../SQL_analysis_risk.sql', 'r') as f:
+    str_sql = text(f.read())
+
+result = pd.read_sql(sql=str_sql, con=connection)
+result.to_csv('count.csv', index=False, encoding='utf-8-sig')
+print(result)
+
+# 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('count_firm_prod.csv', index=False, encoding='utf-8-sig')
+print(count_firm_prod)
+
+# Count firm
+count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
+count_firm = count_firm.to_frame().reset_index()
+count_firm.sort_values('count', inplace=True, ascending=False)
+count_firm.to_csv('count_firm.csv', index=False, encoding='utf-8-sig')
+print(count_firm)
+
+# Count product
+count_prod = count_firm_prod.groupby('id_product')['count'].sum()
+count_prod = count_prod.to_frame().reset_index()
+count_prod.sort_values('count', inplace=True, ascending=False)
+count_prod.to_csv('count_prod.csv', index=False, encoding='utf-8-sig')
+print(count_prod)
+
+# DCP disruption causing probability
+result_disrupt_ts_above_0 = result[result['ts'] > 0]
+print(result_disrupt_ts_above_0)
+result_dcp = pd.DataFrame(columns=[
+    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
+])
+
+result_dcp_list = []  # 用列表收集数据，避免DataFrame逐行增长的问题
+for sid, group in result.groupby('s_id'):
+    ts_start = max(group['ts'])
+    while ts_start >= 1:
+        ts_end = ts_start - 1
+        while ts_end >= 0:
+            up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
+            down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
+            for _, up_row in up.iterrows():
+                for _, down_row in down.iterrows():
+                    result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
+            ts_end -= 1
+        ts_start -= 1
+
+# 转换为DataFrame
+result_dcp = pd.DataFrame(result_dcp_list, columns=[
+    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
+])
+
+# 统计
+count_dcp = result_dcp.value_counts(
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
+).reset_index(name='count')
+
+# 保存文件
+count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig')
+
+# 输出结果
+print(count_dcp)

GA_Agent_0925/vulnerable35_match_results/process_vulnerable_products.py

@@ -57,5 +57,5 @@ for ga_id, group in df.groupby("ga_id"):
 # 输出 CSV
 # ===============================
 df_results = pd.DataFrame(all_results)
-df_results.to_csv("vulnerable35_match_results.csv", index=False, encoding='utf-8-sig')
+df_results.to_csv("vulnerable20_match_results_new1122.csv", index=False, encoding='utf-8-sig')
 print("[完成] 已生成 vulnerable35_match_results.csv")

GA_Agent_0925/vulnerable35_match_results/query_top35_products.sql

@@ -30,5 +30,5 @@ ranked_products AS (
 
 SELECT ga_id, id_product, cnt
 FROM ranked_products
-WHERE rn <= 35
+WHERE rn <= 20
 ORDER BY ga_id, rn;

GA_Agent_0925/vulnerable35_match_results/vulnerable20_match_results_new1121.csv

@@ -0,0 +1,52 @@
+ga_id,vulnerable35,matching_count,matching_products,extra_count,extra_products,missing_count,missing_products
+054c7cc7,"95,90,99,91,93,92,52,55,50,51,54,53,94,9,47,46,44,48,45,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+078b3e68,"95,90,99,91,53,93,50,52,92,55,51,54,94,9,48,49,46,44,45,47",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+083205c9,"95,90,99,91,93,92,52,55,53,50,54,51,94,9,46,44,48,47,49,45",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+0d8d3334,"95,99,90,91,54,92,52,53,51,93,50,55,94,9,47,48,46,45,49,44",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+100f9b87,"95,99,90,91,53,51,93,50,52,92,54,55,94,9,48,49,46,44,47,45",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+123cb6af,"95,99,90,91,54,53,50,92,55,51,52,93,94,9,44,46,45,49,48,47",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+255c5ac2,"95,99,90,91,53,52,54,92,55,50,93,51,94,9,49,46,44,48,47,45",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+28d7f65f,"95,90,99,91,53,54,51,92,93,52,50,55,94,9,49,46,47,48,44,45",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+2db71b5c,"95,99,90,91,50,92,93,52,55,51,53,54,94,9,48,47,44,45,49,46",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+2ea87bef,"95,99,90,91,52,51,54,50,53,93,92,55,94,9,47,44,46,48,45,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+2fc29d48,"95,90,99,91,50,93,92,52,55,53,54,51,94,9,48,44,46,45,47,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+3094f188,"95,99,90,91,52,53,93,51,54,55,50,92,94,9,47,48,49,46,45,44",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+37b972af,"95,99,90,91,54,93,52,53,92,50,51,55,94,9,48,44,46,45,47,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+38b30ec1,"95,90,99,91,93,92,54,52,55,50,53,51,94,9,44,46,45,48,47,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+3a30a410,"95,99,90,91,92,55,52,50,93,54,51,53,94,9,46,45,44,47,48,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+3ff697af,"95,99,90,91,54,92,93,53,55,50,51,52,94,9,46,48,47,49,44,45",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
+42bf6268,"95,99,90,91,53,55,50,93,92,52,54,51,94,9,47,46,44,48,45,49",16,"52,48,47,49,9,99,90,46,51,44,53,54,95,55,50,45",4,"94,93,92,91",20,"21,7,96,100,66,61,98,59,56,11,97,27,71,38,8,77,57,58,65,68"
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GA_Agent_0925/vulnerable35_match_results/vulnerable20_match_results_new1122.csv

@@ -0,0 +1,139 @@
+ga_id,vulnerable35,matching_count,matching_products,extra_count,extra_products,missing_count,missing_products
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GA_Agent_0925/vulnerable35_match_results/vulnerable35_match_results_new1121.csv

@@ -0,0 +1,52 @@
+ga_id,vulnerable35,matching_count,matching_products,extra_count,extra_products,missing_count,missing_products
+054c7cc7,"95,90,99,91,93,92,52,55,50,51,54,53,94,9,47,46,44,48,45,49,39,40,38,43,41,42,10,11,12,15,7,25,33,13,16",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,12,25,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+078b3e68,"95,90,99,91,53,93,50,52,92,55,51,54,94,9,48,49,46,44,45,47,39,43,40,38,41,42,10,11,15,7,25,12,33,19,20",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,93,25,12,91,19,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+083205c9,"95,90,99,91,93,92,52,55,53,50,54,51,94,9,46,44,48,47,49,45,43,40,39,38,41,42,10,11,12,15,7,25,33,19,20",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,93,12,25,91,19,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+0d8d3334,"95,99,90,91,54,92,52,53,51,93,50,55,94,9,47,48,46,45,49,44,43,39,38,40,41,42,10,11,25,15,7,12,33,30,31",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,31,10,33,93,25,12,30,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+100f9b87,"95,99,90,91,53,51,93,50,52,92,54,55,94,9,48,49,46,44,47,45,38,39,43,40,42,41,10,11,12,15,7,25,33,26,27",20,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,27,38",15,"39,92,42,26,10,33,93,12,25,91,15,94,43,40,41",16,"77,61,68,96,21,100,57,59,66,97,71,58,98,8,65,56"
+123cb6af,"95,99,90,91,54,53,50,92,55,51,52,93,94,9,44,46,45,49,48,47,43,39,40,38,42,41,10,11,25,15,7,12,33,34,35",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,25,12,34,91,15,35,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+255c5ac2,"95,99,90,91,53,52,54,92,55,50,93,51,94,9,49,46,44,48,47,45,39,43,40,38,41,42,10,11,7,25,15,12,33,30,31",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,31,10,33,93,25,12,30,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+28d7f65f,"95,90,99,91,53,54,51,92,93,52,50,55,94,9,49,46,47,48,44,45,43,39,38,40,41,42,10,11,15,7,25,12,33,23,24",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,23,93,25,12,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+2db71b5c,"95,99,90,91,50,92,93,52,55,51,53,54,94,9,48,47,44,45,49,46,40,43,39,38,41,42,10,11,7,15,25,12,33,17,18",19,"95,9,50,46,99,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,17,10,33,94,93,25,12,91,15,18,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+2ea87bef,"95,99,90,91,52,51,54,50,53,93,92,55,94,9,47,44,46,48,45,49,38,43,39,40,41,42,10,11,15,25,7,12,33,23,26",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,26,10,33,23,93,25,12,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+2fc29d48,"95,90,99,91,50,93,92,52,55,53,54,51,94,9,48,44,46,45,47,49,43,40,39,38,41,42,10,11,12,25,7,15,33,18,19",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,12,25,91,19,15,18,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+3094f188,"95,99,90,91,52,53,93,51,54,55,50,92,94,9,47,48,49,46,45,44,38,39,43,40,41,42,10,11,15,7,25,12,33,28,29",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38",16,"28,39,92,42,10,33,93,25,12,91,29,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+37b972af,"95,99,90,91,54,93,52,53,92,50,51,55,94,9,48,44,46,45,47,49,43,40,38,39,41,42,10,11,12,15,7,25,33,13,16",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,94,93,12,25,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+38b30ec1,"95,90,99,91,93,92,54,52,55,50,53,51,94,9,44,46,45,48,47,49,43,38,39,40,41,42,10,11,7,25,15,12,33,35,27",20,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,27,38",15,"39,92,42,10,33,94,93,25,12,91,15,35,43,40,41",16,"77,61,68,96,21,100,57,59,66,97,71,58,98,8,65,56"
+3a30a410,"95,99,90,91,92,55,52,50,93,54,51,53,94,9,46,45,44,47,48,49,43,40,39,38,41,42,10,11,25,15,7,12,33,27,28",20,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,27,38",15,"28,39,92,42,10,33,93,25,12,91,15,94,43,40,41",16,"77,61,68,96,21,100,57,59,66,97,71,58,98,8,65,56"
+3ff697af,"95,99,90,91,54,92,93,53,55,50,51,52,94,9,46,48,47,49,44,45,43,39,38,40,42,41,10,11,15,7,25,12,33,32,28",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"28,39,32,42,92,10,33,93,25,12,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+42bf6268,"95,99,90,91,53,55,50,93,92,52,54,51,94,9,47,46,44,48,45,49,38,43,39,40,41,42,10,11,15,25,7,12,33,23,24",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,23,93,25,12,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+50da2c7c,"95,99,90,91,93,50,92,54,53,55,52,51,94,9,47,44,46,49,48,45,40,43,38,39,41,42,10,11,25,12,7,15,33,31,97",20,"95,9,50,99,46,54,49,45,47,97,90,52,48,7,11,55,51,44,53,38",15,"39,92,42,31,10,33,93,25,12,91,15,94,43,40,41",16,"77,61,68,96,21,100,27,57,59,66,71,58,98,8,65,56"
+5293f3fb,"95,99,90,91,92,52,53,50,55,54,93,51,94,9,48,45,47,46,49,44,43,40,39,38,41,42,10,11,15,7,25,12,33,23,24",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,23,93,25,12,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+577ae76a,"95,90,99,91,92,55,93,50,52,53,54,51,94,9,48,46,49,45,47,44,43,40,39,38,42,41,10,11,15,7,12,25,33,13,16",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,12,25,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+5e3612fd,"95,99,90,91,53,54,50,51,93,52,92,55,94,9,46,48,45,44,49,47,43,40,39,38,41,42,10,11,25,7,15,12,33,23,24",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,23,93,25,12,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+6a3cf339,"95,90,99,91,55,53,92,54,50,51,52,93,94,9,46,48,47,49,44,45,43,38,39,40,41,42,10,11,12,15,25,7,33,23,24",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,23,93,12,25,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+712eeb2b,"95,99,90,91,54,93,53,52,50,92,51,55,94,9,46,48,47,45,44,49,43,38,40,39,41,42,10,11,7,25,15,12,33,31,16",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,31,10,33,93,25,12,91,16,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+7519503e,"95,90,99,91,93,52,92,54,55,53,50,51,94,9,48,47,46,49,44,45,43,38,39,40,41,42,10,11,15,7,25,12,33,24,26",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,26,10,33,93,25,12,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+7f0a76e2,"95,99,90,91,50,53,54,51,93,52,55,92,94,9,47,49,44,46,45,48,43,39,38,40,42,41,10,11,7,12,15,25,33,23,24",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,23,93,12,25,24,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+820d206e,"95,90,99,91,54,51,93,92,55,50,52,53,94,9,46,47,48,49,45,44,43,39,38,40,41,42,10,11,15,25,7,12,33,20,8",20,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38,8",15,"39,92,42,10,33,93,25,12,91,20,15,94,43,40,41",16,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,65,56"
+83f4f364,"95,90,99,91,52,55,50,92,93,53,54,51,94,9,47,48,46,45,49,44,43,39,38,40,41,42,10,11,15,7,25,12,33,20,23",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,23,93,25,12,91,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+8564ae5b,"95,99,90,91,92,54,52,50,51,93,55,53,94,9,46,47,48,45,44,49,43,38,40,39,41,42,10,11,15,25,7,12,33,30,31",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,31,10,33,93,25,12,30,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+8cbbfbbf,"95,99,90,91,53,54,51,52,93,50,55,92,94,9,46,49,48,44,47,45,43,39,38,40,41,42,10,11,12,15,7,25,33,20,32",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38",16,"39,32,42,92,10,33,93,12,25,91,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+91bd5af3,"95,90,99,91,54,92,93,50,52,53,55,51,94,9,47,46,45,49,48,44,43,39,38,40,41,42,10,11,7,15,25,12,33,97,37",20,"95,9,50,99,46,54,45,49,47,97,90,52,48,7,11,55,51,44,53,38",15,"39,37,42,92,10,33,93,25,12,91,15,94,43,40,41",16,"77,61,68,96,21,100,27,57,59,66,71,58,98,8,65,56"
+932ca023,"95,99,90,91,93,52,55,50,54,51,92,53,94,9,47,48,44,46,49,45,38,43,40,39,41,42,10,11,25,15,7,12,33,20,23",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,23,93,25,12,91,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+9b270ef2,"95,99,90,91,54,93,53,55,50,92,51,52,94,9,46,48,49,47,44,45,43,38,39,40,42,41,10,11,25,7,12,15,33,17,26",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,17,26,10,33,93,25,12,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+9c2f3c0f,"95,99,90,91,93,52,51,54,53,50,55,92,94,9,47,48,46,45,44,49,43,38,39,40,41,42,10,11,12,25,7,15,33,13,16",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,94,93,12,25,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+a166aed4,"95,99,90,91,53,93,52,50,55,54,51,92,94,9,49,48,46,47,44,45,39,40,43,38,41,42,10,11,15,25,7,12,33,18,19",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,25,12,91,19,15,18,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+a9d85b98,"95,90,99,91,53,93,50,92,52,54,55,51,94,9,48,47,46,45,49,44,43,38,40,39,41,42,10,11,12,15,7,25,33,19,20",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,93,12,25,91,19,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+b856e036,"95,99,90,91,53,93,55,52,92,51,54,50,94,9,46,48,45,47,49,44,43,38,39,40,41,42,10,11,7,25,15,12,33,29,30",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,93,25,12,30,29,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+b8b0c514,"95,90,99,91,93,53,52,51,54,55,92,50,94,9,46,47,45,48,49,44,43,39,38,40,41,42,10,11,25,15,7,12,33,13,16",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,94,93,25,12,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+c53eb169,"95,99,90,91,50,93,54,53,52,92,51,55,94,9,48,46,47,45,49,44,38,43,39,40,41,42,10,11,15,25,12,7,33,28,29",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"28,39,92,42,10,33,93,25,12,91,29,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+c6bec45f,"95,99,90,91,53,50,52,92,54,55,93,51,94,9,46,48,49,47,44,45,43,39,40,38,41,42,10,11,7,25,15,12,33,30,31",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,31,10,33,93,25,12,30,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+cd7fd049,"95,99,90,91,50,53,92,54,93,52,55,51,94,9,47,46,48,44,49,45,40,38,43,39,41,42,10,11,12,15,7,25,33,20,23",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,23,93,12,25,91,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+ce50cc2c,"95,99,90,91,93,92,55,53,54,51,52,50,94,9,49,44,47,48,46,45,40,43,39,38,41,42,10,11,25,15,7,12,33,37,17",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,37,42,17,10,92,33,93,25,12,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+cf392eb0,"95,90,99,91,93,53,54,52,92,55,50,51,94,9,48,49,46,47,45,44,43,39,38,40,41,42,10,11,7,15,25,12,33,13,16",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,25,12,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+d4d91d3b,"95,99,90,91,93,53,54,52,50,92,55,51,94,9,46,44,47,49,48,45,43,39,40,38,41,42,10,11,7,15,25,12,33,13,16",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,10,33,94,93,25,12,91,16,15,13,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
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+e22f1c79,"95,90,99,91,92,52,50,54,93,53,51,55,94,9,48,45,44,49,46,47,40,43,39,41,38,42,10,11,15,25,7,12,33,29,30",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,93,25,12,30,29,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
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+e9d9134f,"95,99,90,91,53,55,51,54,50,93,52,92,94,9,46,48,47,49,45,44,43,39,38,40,41,42,10,11,15,7,12,25,33,37,20",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,37,42,92,10,33,93,12,25,91,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+f02ffd08,"95,90,99,91,92,93,52,55,50,53,54,51,94,9,48,49,46,44,45,47,39,43,40,38,41,42,10,11,15,7,25,12,33,17,18",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,17,10,33,94,93,25,12,91,15,18,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+f09b1f3e,"95,99,90,91,53,52,93,51,55,50,92,54,94,9,47,48,46,49,45,44,39,38,43,40,41,42,10,11,25,15,12,7,33,29,30",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,93,25,12,30,29,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+f67dce63,"95,99,90,91,53,54,50,52,51,93,55,92,94,9,44,49,47,48,46,45,39,40,43,38,41,42,10,11,12,15,7,25,33,19,20",19,"95,9,50,99,46,54,49,45,47,90,52,48,7,11,51,55,44,53,38",16,"39,92,42,10,33,93,12,25,91,19,20,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"
+f992e3a6,"95,90,99,91,54,92,53,50,55,93,52,51,94,9,46,44,47,45,49,48,43,38,39,40,42,41,10,11,25,15,7,12,33,17,26",19,"95,9,50,99,46,54,45,49,47,90,52,48,7,11,55,51,44,53,38",16,"39,92,42,17,26,10,33,93,25,12,91,15,94,43,40,41",17,"77,61,68,96,21,100,27,57,59,66,97,71,58,98,8,65,56"

GA_Agent_0925/进度.py

@@ -1,43 +0,0 @@
-from matplotlib import rcParams, pyplot as plt
-from sqlalchemy import func
-from orm import db_session, Sample
-
-# 🔹 全局创建绘图对象
-plt.ion()  # 启用交互模式
-fig, ax = plt.subplots(figsize=(8, 5))
-rcParams['font.family'] = 'Microsoft YaHei'
-rcParams['font.size'] = 12
-
-# 初始化柱状图
-labels = ['未完成 (-1)', '计算中(0)', '完成 (1)']
-initial_values = [0, 0, 0]
-bars = ax.bar(labels, initial_values, color=['red', 'orange', 'green'])
-value_texts = [ax.text(bar.get_x() + bar.get_width()/2, 0, '0',
-                       ha='center', va='bottom', fontsize=12)
-               for bar in bars]
-
-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)
-
-def visualize_progress():
-    """
-    实时更新 Sample 表中 is_done_flag 的分布。
-    """
-    # 查询数据库
-    result = db_session.query(Sample.is_done_flag, func.count(Sample.id))\
-                       .group_by(Sample.is_done_flag).all()
-    data = {flag: count for flag, count in result}
-    for flag in [-1, 0, 1]:
-        data.setdefault(flag, 0)
-    values = [data[-1], data[0], data[1]]
-
-    # 更新柱子高度和文本
-    for bar, new_val, txt in zip(bars, values, value_texts):
-        bar.set_height(new_val)
-        txt.set_y(new_val + 0.5)
-        txt.set_text(str(new_val))
-
-    plt.draw()
-    plt.pause(0.1)  # 刷新图表

conf_db_prefix.yaml

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

main.py

@@ -46,8 +46,8 @@ def do_computation(c_db):
 if __name__ == '__main__':
     # 输入参数
     parser = argparse.ArgumentParser(description='setting')
-    parser.add_argument('--exp', type=str, default='without_exp')
+    parser.add_argument('--exp', type=str, default='with_exp')
-    parser.add_argument('--job', type=int, default='1')
+    parser.add_argument('--job', type=int, default='6')
     parser.add_argument('--reset_sample', type=int, default='0')
     parser.add_argument('--reset_db', type=bool, default=True)
 

my_model.py

@@ -93,6 +93,20 @@ class MyModel(Model):
         self.product_agents = []  # 初始化产品代理列表
         self.company_agents = []  # 初始化公司代理列表
 
+        # 进行参数设定计算
+        # self.n_max_trial = 51.0
+        # self.prf_size = 0.4550083673391433
+        # self.prf_conn = 0.4230074859901629
+        # self.cap_limit_prob_type = "normal"
+        # self.cap_limit_level = 24.0
+        # self.diff_new_conn = 0.9607789032744504
+        # self.netw_prf_n = 3.0
+        # self.s_r = 0.4271578472186195
+        # self.S_r = 0.5342606663518962
+        # self.x = 0.06796086337575724
+        # self.k = 4.322914089653634
+        # self.production_increase_ratio = 1.2286610916299323
+
         # 初始化模型的网络和代理
         # 检查缓存是否存在
         cache_file = "firm_network.pkl"

output_result/risk/count_firm.csv

@@ -1,155 +1,212 @@
 id_firm,count
-2313177432,3832
+3111603340,3409
-4067555184,3742
+214851100,3393
-12098344,3347
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+6,20
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-15482118,12
+1,20
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output_result/risk/count_firm_prod.csv

@@ -1,210 +1,274 @@
 id_firm,id_product,count
-814834276,95,1047
+340093034,95,1306
-3339921892,95,1041
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