no message

2025-12-13 12:44:15 +08:00
parent a6b06735f6
commit 0e52fcb34b
41 changed files with 42682 additions and 34029 deletions
--- a/GA_Agent_0925/pycache/controller_db.cpython-38.pyc
+++ b/GA_Agent_0925/pycache/controller_db.cpython-38.pyc
--- a/GA_Agent_0925/pycache/creating.cpython-38.pyc
+++ b/GA_Agent_0925/pycache/creating.cpython-38.pyc
--- a/GA_Agent_0925/pycache/evaluate_func.cpython-38.pyc
+++ b/GA_Agent_0925/pycache/evaluate_func.cpython-38.pyc
--- a/GA_Agent_0925/best_result_with_industry.json
+++ b/GA_Agent_0925/best_result_with_industry.json
@@ -1,6 +1,6 @@
 {
  "best_individual": [
-    291,
+    23,
    0.24373090607513836,
    0.5512650768804697,
    1,
--- a/GA_Agent_0925/conf_experiment.yaml
+++ b/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_iter: 10
+  n_sample: 5
+  n_iter: 60
--- a/GA_Agent_0925/config.json
+++ b/GA_Agent_0925/config.json
@@ -1,6 +1,6 @@
 {
-  "pop_size": 10,
-  "n_gen": 5,
+  "pop_size": 20,
+  "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
 }
--- a/GA_Agent_0925/controller_db.py
+++ b/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:
--- a/GA_Agent_0925/convergence.png
+++ b/GA_Agent_0925/convergence.png
--- a/GA_Agent_0925/creating.py
+++ b/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("production_increase_ratio", random.uniform, 0.5, 2.0)
+    toolbox.register("k", random.uniform, 0.1, 5.0)
+    toolbox.register("production_increase_ratio", random.uniform, 1, 5.0)

    # 个体与种群注册
    toolbox.register(
--- a/GA_Agent_0925/evaluate_func.py
+++ b/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)
--- a/GA_Agent_0925/ge_abm_readme.md
+++ b/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。
--- a/GA_Agent_0925/ge_abm_研究过程.md
+++ b/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
--- a/GA_Agent_0925/main.py
+++ b/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()
--- a/GA_Agent_0925/results/best_individual_each_gen.txt
+++ b/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
--- a/GA_Agent_0925/results/best_result_with_industry.json
+++ b/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"
+}
--- a/GA_Agent_0925/risk_ay/count.csv
+++ b/GA_Agent_0925/risk_ay/count.csv
--- a/GA_Agent_0925/risk_ay/count_dcp.csv
+++ b/GA_Agent_0925/risk_ay/count_dcp.csv
--- a/GA_Agent_0925/risk_ay/count_firm.csv
+++ b/GA_Agent_0925/risk_ay/count_firm.csv
@@ -1,32 +1,32 @@
 id_firm,count
-214851100,350
-3111603340,340
-70634828,340
-25980377,140
-395736790,136
-340093034,133
-3330358736,120
-29223617,119
-532328014,100
-303926772,99
-2326722141,72
-331545755,71
-2337727838,70
-3191869223,58
-591350440,50
-2327605629,47
-517675473,46
-2336923756,46
-728969035,41
-2349705416,41
-16210433,39
-16116663,37
-2349179532,34
-471121089,29
+214851100,453
+3111603340,453
+70634828,453
+532328014,227
+303926772,227
+395736790,216
+340093034,214
+29223617,208
+3330358736,177
+25980377,177
+2337727838,80
+2326722141,80
+331545755,80
+3191869223,61
+2327605629,61
+2336923756,61
+591350440,60
+728969035,59
+517675473,59
+16116663,54
+16210433,53
+471121089,52
+2349179532,52
+2349705416,50
 1452048,5
 515770253,3
-2349345463,2
 6,2
+2349345463,2
 5971532,2
 1,2
 5849940,2
--- a/GA_Agent_0925/risk_ay/count_firm_prod.csv
+++ b/GA_Agent_0925/risk_ay/count_firm_prod.csv
@@ -1,82 +1,82 @@
 id_firm,id_product,count
-395736790,95,136
-340093034,95,133
-29223617,95,119
-532328014,99,100
-303926772,99,99
-2326722141,90,72
-331545755,90,71
-2337727838,90,70
-3191869223,91,58
-591350440,91,50
-2327605629,94,47
-2336923756,91,46
-517675473,92,46
-70634828,55,42
-728969035,93,41
-2349705416,94,41
-214851100,55,40
-70634828,54,40
-214851100,52,40
-16210433,92,39
-214851100,54,38
-3111603340,55,38
-214851100,53,38
-3111603340,52,38
-70634828,52,38
-3111603340,50,37
-214851100,50,36
-70634828,50,36
-3111603340,51,35
-70634828,53,35
-70634828,51,35
-3111603340,54,35
-2349179532,93,34
-16116663,92,33
-214851100,51,33
-3111603340,53,33
-471121089,93,29
-25980377,43,27
-3111603340,48,26
-25980377,41,24
-3330358736,40,23
-25980377,40,23
-214851100,48,23
-70634828,49,23
-214851100,44,23
-25980377,39,22
-25980377,38,22
-25980377,42,22
-70634828,48,22
-3111603340,46,22
-214851100,47,21
-3330358736,43,21
-3111603340,45,21
-3330358736,39,21
-70634828,46,21
-3111603340,47,20
-214851100,49,20
-214851100,46,20
-3330358736,38,19
-3330358736,41,19
-70634828,47,18
-214851100,45,17
-3111603340,49,17
-3330358736,42,17
-3111603340,44,17
-70634828,44,15
-70634828,45,14
+303926772,99,227
+532328014,99,227
+395736790,95,216
+340093034,95,214
+29223617,95,208
+2326722141,90,80
+2337727838,90,80
+331545755,90,80
+3191869223,91,61
+2327605629,94,61
+2336923756,91,61
+591350440,91,60
+728969035,93,59
+517675473,92,59
+16210433,92,53
+2349179532,93,52
+471121089,93,52
+2349705416,94,50
+16116663,92,50
+70634828,51,49
+3111603340,51,49
+214851100,51,49
+214851100,53,48
+214851100,52,48
+3111603340,52,48
+3111603340,53,48
+70634828,52,48
+70634828,53,48
+3111603340,54,46
+70634828,55,46
+214851100,54,46
+214851100,55,46
+214851100,50,46
+70634828,54,46
+70634828,50,46
+3111603340,50,46
+3111603340,55,46
+25980377,38,32
+3330358736,38,32
+3330358736,39,31
+3330358736,43,31
+25980377,43,31
+25980377,39,31
+25980377,40,29
+3330358736,40,29
+3111603340,47,29
+70634828,47,29
+214851100,47,29
+214851100,49,28
+70634828,44,28
+25980377,41,28
+70634828,49,28
+3111603340,44,28
+3111603340,45,28
+3111603340,46,28
+3111603340,48,28
+3111603340,49,28
+70634828,48,28
+70634828,45,28
+70634828,46,28
+214851100,48,28
+3330358736,41,28
+214851100,46,28
+214851100,45,28
+214851100,44,28
+25980377,42,26
+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
--- a/GA_Agent_0925/risk_ay/count_prod.csv
+++ b/GA_Agent_0925/risk_ay/count_prod.csv
@@ -1,73 +1,73 @@
 id_product,count
-95,388
-90,213
-99,199
-91,154
-55,120
-92,118
-52,116
-54,113
-50,109
-53,106
-93,104
-51,103
-94,88
+95,638
+99,454
+90,240
+91,182
+93,163
+92,162
+51,147
+53,144
+52,144
+50,138
+55,138
+54,138
+94,111
+47,87
+44,84
 9,84
-48,71
-46,63
-49,60
-47,59
-44,55
-45,52
-43,48
-40,46
-41,43
-39,43
-38,41
-42,39
+49,84
+46,84
+45,84
+48,84
+38,64
+43,62
+39,62
+40,58
+41,56
+42,52
 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
-97,2
-13,2
+8,2
 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
-64,2
-23,2
+31,2
+30,2
+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
--- a/GA_Agent_0925/risk_ay/risk_sum.py
+++ b/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)
--- a/GA_Agent_0925/vulnerable35_match_results/process_vulnerable_products.py
+++ b/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")
--- a/GA_Agent_0925/vulnerable35_match_results/query_top35_products.sql
+++ b/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;
--- a/GA_Agent_0925/vulnerable35_match_results/vulnerable20_match_results_new1121.csv
+++ b/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"
+50da2c7c,"95,99,90,91,93,50,92,54,53,55,52,51,94,9,47,44,46,49,48,45",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"
+5293f3fb,"95,99,90,91,92,52,53,50,55,54,93,51,94,9,48,45,47,46,49,44",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"
+577ae76a,"95,90,99,91,92,55,93,50,52,53,54,51,94,9,48,46,49,45,47,44",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"
+5e3612fd,"95,99,90,91,53,54,50,51,93,52,92,55,94,9,46,48,45,44,49,47",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"
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--- a/GA_Agent_0925/vulnerable35_match_results/vulnerable20_match_results_new1122.csv
+++ b/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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+e922e7b5,"95,99,90,91,93,51,92,52,50,54,55,53,94,9,49,48,47,44,45,46",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
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+e97459b6,"95,99,90,91,55,52,93,53,50,92,51,54,9,94,49,46,45,47,44,48",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
+ee6e805a,"95,99,90,91,53,52,92,55,50,93,51,54,94,9,49,47,46,48,39,44",15,"90,52,9,48,95,53,54,50,49,99,46,51,55,47,44",5,"39,92,94,93,91",21,"77,71,7,59,56,21,66,58,11,45,38,98,68,27,100,97,57,96,8,65,61"
+ef1945a4,"95,90,99,91,50,51,52,55,53,92,54,93,94,9,47,46,49,44,48,43",15,"90,52,9,48,95,53,54,50,49,99,46,51,55,47,44",5,"92,94,43,93,91",21,"77,71,7,59,56,21,66,58,11,45,38,98,68,27,100,97,57,96,8,65,61"
+f47039bf,"95,99,90,91,53,52,50,54,55,51,92,93,9,94,49,47,46,48,44,39",15,"90,52,9,48,95,53,54,50,49,99,46,51,55,47,44",5,"39,92,94,93,91",21,"77,71,7,59,56,21,66,58,11,45,38,98,68,27,100,97,57,96,8,65,61"
+f4c0ef46,"95,99,90,91,50,54,55,52,92,53,51,93,94,9,47,46,48,45,44,49",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
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+f8eb9982,"95,99,90,91,55,50,52,92,51,53,54,93,94,9,47,46,49,48,43,44",15,"90,52,9,48,95,53,54,50,49,99,46,51,55,47,44",5,"92,94,43,93,91",21,"77,71,7,59,56,21,66,58,11,45,38,98,68,27,100,97,57,96,8,65,61"
+fae9ffa3,"95,99,90,91,51,50,52,53,55,92,54,93,9,94,47,48,49,45,44,46",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
+fc2834d4,"95,99,90,91,52,50,92,53,55,54,93,51,94,9,47,49,46,48,44,45",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
+fdfc9a10,"95,90,99,91,52,50,93,51,92,55,53,54,94,9,48,49,47,46,44,45",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
+feca356a,"95,90,99,91,52,53,50,92,51,55,54,93,94,9,46,47,49,44,48,43",15,"90,52,9,48,95,53,54,50,49,99,46,51,55,47,44",5,"92,94,43,93,91",21,"77,71,7,59,56,21,66,58,11,45,38,98,68,27,100,97,57,96,8,65,61"
+ff9d6646,"95,99,90,91,52,50,55,53,92,93,51,54,9,94,49,47,46,48,44,45",16,"90,52,9,48,95,53,54,50,49,45,99,46,51,55,47,44",4,"91,92,94,93",20,"77,71,7,59,56,21,66,58,11,38,98,68,27,100,97,57,96,8,65,61"
+ffee76d9,"95,90,99,91,53,52,50,51,92,55,54,93,9,94,49,47,46,48,39,44",15,"90,52,9,48,95,53,54,50,49,99,46,51,55,47,44",5,"39,92,94,93,91",21,"77,71,7,59,56,21,66,58,11,45,38,98,68,27,100,97,57,96,8,65,61"
--- a/GA_Agent_0925/vulnerable35_match_results/vulnerable35_match_results_new1121.csv
+++ b/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"
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--- a/GA_Agent_0925/进度.py
+++ b/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