遗传002

GA_Agent_0925/conf_db.yaml

@@ -0,0 +1,10 @@
+# read by orm
+is_local_db: True
+
+local:
+  user_name: iiabm_user
+  password: iiabm_pwd
+  db_name: iiabmdb_20250925
+  address: 'localhost'
+  port: 3306
+

GA_Agent_0925/conf_experiment.yaml

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

GA_Agent_0925/config.json

@@ -1,5 +1,5 @@
 {
-  "pop_size": 5,
+  "pop_size": 10,
   "n_gen": 5,
   "n_var": 12,
   "bound_min": -5,

GA_Agent_0925/count_firm.csv

@@ -1,204 +1,198 @@
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GA_Agent_0925/count_firm_prod.csv

@@ -1,266 +1,259 @@
 id_firm,id_product,count
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GA_Agent_0925/count_prod.csv

@@ -1,73 +1,73 @@
 id_product,count
-95,1787
+95,388
-90,1004
+90,213
-99,982
+99,199
-91,727
+91,154
-92,581
+55,120
-93,575
+92,118
-53,559
+52,116
-55,551
+54,113
-54,549
+50,109
-52,547
+53,106
-51,539
+93,104
-50,533
+51,103
-94,433
+94,88
-9,338
+9,84
-47,294
+48,71
-45,275
+46,63
-46,272
+49,60
-49,271
+47,59
-44,264
+44,55
-48,255
+45,52
-38,214
+43,48
-43,212
+40,46
-39,208
+41,43
-41,197
+39,43
-40,194
+38,41
-42,185
+42,39
-10,113
+10,23
-11,45
+11,9
-15,15
+33,3
-12,15
+7,3
-33,15
+15,3
-7,15
+12,3
-25,15
+25,3
-69,10
+19,2
-74,10
+74,2
-68,10
+68,2
-70,10
+69,2
-71,10
+70,2
-72,10
+71,2
-73,10
+72,2
-19,10
+73,2
-79,10
+8,2
-8,10
+79,2
-66,10
+18,2
-18,10
+97,2
-17,10
+13,2
-16,10
+66,2
-13,10
+16,2
-97,10
+17,2
-67,10
+67,2
-32,10
+27,2
-65,10
+65,2
-64,10
+26,2
-34,10
+29,2
-35,10
+30,2
-37,10
+31,2
-31,10
+32,2
-30,10
+34,2
-29,10
+35,2
-28,10
+36,2
-27,10
+37,2
-26,10
+24,2
-24,10
+64,2
-23,10
+23,2
-20,10
+28,2
-59,10
+59,2
-60,10
+60,2
-62,10
+61,2
-63,10
+62,2
-61,10
+63,2
-36,8
+20,2
-22,5
+22,1

GA_Agent_0925/evaluate_func.py

@@ -63,7 +63,7 @@ def fitness(individual, controller_db_obj):
 
     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_vulnerable100_code(connection,ga_id)
+    simulated_vulnerable_industries = get_vulnerable35_code(connection,ga_id)
     print(simulated_vulnerable_industries)
     # ========== 4️⃣ 获取目标产业集合 ==========
     target_vulnerable_industries = get_target_vulnerable_industries()
@@ -154,9 +154,11 @@ def get_target_vulnerable_industries():
         {"product": "过程工艺检测", "category": "制程检测", "chain_id": 513740}
     ]
     # 手工转换
-    industry_list_index = vulnerable100_index = \
+    merged_list = [
-        ['100', '58', '61', '9', '7', '98', '57', '8', '65', '68', '66', '38',
+        '61', '68', '65', '66', '59', '71', '77', '7', '38', '95', '58', '90', '56', '57', '97', '98',
-         '90', '21', '96', '71', '27', '74', '99', '95', '11', '77', '59', '56', '97']
+        '27', '8', '11', '9', '21', '96', '99', '100', '44', '45', '46', '47', '48', '49', '50', '51',
+        '52', '53', '54', '55'
+    ]
     # # 提取所有 chain_id，并去重
     # chain_ids = set()
     # for item in industry_list:
@@ -167,9 +169,10 @@ def get_target_vulnerable_industries():
     #     else:
     #         chain_ids.add(str(item["chain_id"]))
 
-    return industry_list_index
+    return merged_list
+
 # 从数据库计算脆弱产业集合
-def get_vulnerable100_code(engine, ga_id):
+def get_vulnerable35_code(engine, ga_id):
     """
     计算最脆弱前100产品的 Code 列表（去重），只针对指定 ga_id。
     """
@@ -221,7 +224,7 @@ def get_vulnerable100_code(engine, ga_id):
     # ===============================
     # 5️⃣ 选出最脆弱的前100个产品（出现次数最多）
     # ===============================
-    vulnerable100_product = count_prod.nlargest(100, "count")["id_product"].tolist()
+    vulnerable100_product = count_prod.nlargest(35, "count")["id_product"].tolist()
     print(f"[信息] ga_id={ga_id} 查询完成，共找到 {len(vulnerable100_product)} 个脆弱产品")
 
     # ===============================

GA_Agent_0925/main.py

@@ -8,7 +8,7 @@ import matplotlib.pyplot as plt
 from GA_Agent_0925.creating import creating
 from GA_Agent_0925.orm import connection
 from controller_db import ControllerDB
-from evaluate_func import fitness, get_vulnerable100_code, get_target_vulnerable_industries
+from evaluate_func import fitness, get_vulnerable35_code, get_target_vulnerable_industries
 
 
 # ==============================
@@ -28,7 +28,7 @@ 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=True)
+    controller_db_obj.reset_db(force_drop=False)
     # 准备样本表
     controller_db_obj.prepare_list_sample()
     # 2️⃣ 初始化工具箱

GA_Agent_0925/vulnerable35_match_results/conf_db.yaml

@@ -0,0 +1,10 @@
+# read by orm
+is_local_db: True
+
+local:
+  user_name: iiabm_user
+  password: iiabm_pwd
+  db_name: iiabmdb_20250925
+  address: 'localhost'
+  port: 3306
+

GA_Agent_0925/vulnerable35_match_results/conf_db_prefix.yaml

@@ -0,0 +1 @@
+db_name_prefix: without_exp

GA_Agent_0925/vulnerable35_match_results/process_vulnerable_products.py

@@ -0,0 +1,61 @@
+import pandas as pd
+from sqlalchemy import text
+
+from orm import engine
+
+# ===============================
+# 数据库连接
+# ===============================
+
+sql_file = "query_top35_products.sql"
+
+# 指定对比列表
+specified_list = [
+    '61','68','65','66','59','71','77','7','38','95','58','90','56','57','97','98',
+    '27','8','11','9','21','96','99','100','44','45','46','47','48','49','50','51',
+    '52','53','54','55'
+]
+specified_set = set(specified_list)
+
+# ===============================
+# 执行 SQL 查询
+# ===============================
+with open(sql_file, "r", encoding="utf-8") as f:
+    str_sql = f.read()
+
+# 使用 connect() 解决 AttributeError
+with engine.connect() as conn:
+    df = pd.read_sql(text(str_sql), conn)
+
+print(f"[信息] 查询完成，获取到 {len(df)} 条记录。")
+
+# ===============================
+# 按 ga_id 分组计算匹配情况
+# ===============================
+all_results = []
+
+for ga_id, group in df.groupby("ga_id"):
+    vulnerable35_product = group['id_product'].astype(str).tolist()
+    vulnerable_set = set(vulnerable35_product)
+
+    matching = vulnerable_set & specified_set
+    extra = vulnerable_set - specified_set
+    missing = specified_set - vulnerable_set
+
+    all_results.append({
+        "ga_id": ga_id,
+        "vulnerable35": ",".join(vulnerable35_product),
+        "matching_count": len(matching),
+        "matching_products": ",".join(matching),
+        "extra_count": len(extra),
+        "extra_products": ",".join(extra),
+        "missing_count": len(missing),
+        "missing_products": ",".join(missing)
+    })
+
+# ===============================
+# 输出 CSV
+# ===============================
+df_results = pd.DataFrame(all_results)
+df_results.to_csv("vulnerable35_match_results.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

@@ -0,0 +1,34 @@
+WITH s_disrupt AS (
+    SELECT s_id, id_firm, id_product, MIN(ts) AS ts, ga_id
+    FROM iiabmdb_20250925.without_exp_result
+    WHERE `status` = 'D'
+    GROUP BY s_id, id_firm, id_product, ga_id
+),
+
+s_multiple AS (
+    -- 筛选出现超过1次的 s_id
+    SELECT s_id, ga_id
+    FROM s_disrupt
+    GROUP BY s_id, ga_id
+    HAVING COUNT(*) > 1
+),
+
+product_count AS (
+    -- 统计每个 ga_id 下每个产品出现次数
+    SELECT s.ga_id, s.id_product, COUNT(*) AS cnt
+    FROM s_disrupt s
+    JOIN s_multiple m ON s.s_id = m.s_id AND s.ga_id = m.ga_id
+    GROUP BY s.ga_id, s.id_product
+),
+
+ranked_products AS (
+    -- 按每个 ga_id 的产品出现次数排序，并取前35
+    SELECT *,
+           ROW_NUMBER() OVER (PARTITION BY ga_id ORDER BY cnt DESC) AS rn
+    FROM product_count
+)
+
+SELECT ga_id, id_product, cnt
+FROM ranked_products
+WHERE rn <= 35
+ORDER BY ga_id, rn;

GA_Agent_0925/vulnerable35_match_results/vulnerable35_match_results.csv

@@ -0,0 +1,49 @@
+ga_id,vulnerable35,matching_count,matching_products,extra_count,extra_products,missing_count,missing_products
+03269d9f,"95,90,91,92,52,93,55,99,51,50,9,94,54,53,47,44,46,48,49,45,41,42,38,43,39,40,10,11,7,12,25,15,33,73,32",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,93,40,91,73,94,41,39,15,10,25,32,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+053c105b,"95,99,90,91,55,50,53,52,92,51,54,93,94,9,48,47,44,46,49,39,38,45,40,43,41,42,10,11,15,33,12,7,25,31,36",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,25,93,31,40,94,41,39,15,10,36,91,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+07257290,"95,99,90,91,52,53,51,50,92,55,93,54,94,9,49,47,48,39,44,46,45,43,38,40,42,41,10,11,25,7,15,12,33,26,18",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,93,18,40,26,94,41,39,15,10,25,91,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+12eb85fc,"95,99,90,91,51,52,93,92,50,53,54,55,9,94,47,49,48,46,44,45,38,40,43,39,41,42,10,11,7,25,12,33,15,37,19",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,93,40,19,94,41,39,37,15,10,25,91,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+1809ec77,"95,99,90,91,93,92,53,52,50,94,55,51,54,9,49,44,47,40,46,45,39,48,38,43,41,42,10,11,20,26,66,16,15,64,61",20,"90,61,44,45,95,49,11,99,52,50,47,9,66,46,54,51,48,55,38,53",15,"42,93,40,26,94,41,39,16,15,20,10,91,43,64,92",16,"98,71,57,56,65,7,97,100,8,68,77,58,21,59,96,27"
+19cae10f,"95,99,90,91,51,50,53,92,52,93,55,54,9,94,47,49,46,48,45,44,38,43,39,40,41,42,10,11,25,12,33,15,7,64,8",20,"90,7,44,45,95,49,11,99,8,52,50,47,9,46,54,51,48,55,38,53",15,"42,93,40,94,41,39,15,64,10,25,91,43,12,33,92",16,"98,71,57,66,56,65,97,100,61,68,77,58,21,59,96,27"
+1a4227c6,"95,99,90,91,52,92,54,53,51,55,50,93,94,9,47,49,46,48,44,45,43,39,38,41,42,40,10,11,7,25,33,15,12,74,68",20,"90,7,68,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",15,"42,93,40,94,74,41,39,15,10,25,91,43,12,33,92",16,"98,71,57,66,65,56,97,100,8,61,77,58,21,59,96,27"
+1f977ec5,"95,99,90,91,52,92,50,55,53,51,93,54,94,9,48,47,46,44,49,45,40,41,38,39,43,42,10,11,12,15,25,33,7,35,64",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,93,35,40,94,41,39,15,64,10,25,91,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+28481dd9,"95,99,90,91,52,51,55,50,92,53,54,93,94,9,47,49,44,46,48,38,43,45,40,39,41,42,10,11,25,7,33,12,15,32,97",20,"90,7,44,45,95,49,11,99,52,50,97,47,9,46,54,51,48,55,38,53",15,"42,93,40,91,94,41,39,15,10,25,32,43,12,33,92",16,"98,71,57,66,56,65,100,8,61,68,77,58,21,59,96,27"
+2ef7bd37,"95,99,90,91,53,52,50,92,54,51,55,93,94,9,49,47,46,44,48,39,45,43,41,40,38,42,10,11,7,25,33,15,12,60,30",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,60,93,40,94,30,41,39,15,10,25,91,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+360d68d3,"95,99,90,91,9,53,92,93,50,52,51,55,54,94,47,44,46,49,48,43,39,45,38,40,41,42,10,11,15,33,7,12,27,69,72",20,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,53,38,27",15,"42,93,40,94,41,69,39,72,15,10,91,43,12,33,92",16,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96"
+400b809d,"95,99,90,91,51,52,50,53,92,55,54,93,9,47,94,49,44,46,39,38,48,43,40,45,41,42,10,11,33,7,25,12,15,24,19",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,93,40,19,94,41,43,39,15,10,25,91,24,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+40408929,"95,99,90,91,53,51,52,93,92,50,55,54,94,9,49,47,46,48,44,45,39,38,43,40,41,42,10,11,25,12,33,7,15,70,29",19,"90,7,44,45,95,49,11,99,52,50,47,9,46,54,51,48,55,38,53",16,"42,93,40,70,94,41,39,29,15,10,25,91,43,12,33,92",17,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,59,96,27"
+4c00c80f,"95,99,90,91,52,51,50,92,54,53,55,93,9,94,46,44,47,49,48,45,43,38,39,40,41,42,10,11,15,7,12,33,25,28,59",20,"90,7,44,45,95,49,11,99,52,50,59,47,9,46,54,51,48,55,38,53",15,"42,93,40,94,28,41,39,15,10,25,91,43,12,33,92",16,"98,71,57,66,56,65,97,100,8,61,68,77,58,21,96,27"
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GA_Agent_0925/多功能.py

@@ -41,7 +41,7 @@ from orm import connection
 
 
 # 读取 SQL
-ga_id="c943f2c6"
+ga_id="c40fd813"
 with open("SQL_analysis_risk_ga.sql", "r", encoding="utf-8") as f:
     str_sql = text(f.read())
     print(str_sql[:300])

firm.py

@@ -1,5 +1,6 @@
 from collections import defaultdict
 
+import numpy as np
 from mesa import Agent
 
 
@@ -81,7 +82,7 @@ class FirmAgent(Agent):
         for product in a_lst_product:
             assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
                 "cap_limit_prob_type must be either 'uniform' or 'normal'"
-            extra_cap_mean = self.size_stat[0][0] / self.flt_cap_limit_level
+            extra_cap_mean = self.size_stat[0][0] / self.flt_cap_limit_level if self.flt_cap_limit_level != 0 else 0
             if self.str_cap_limit_prob_type == 'uniform':
                 extra_cap = self.model.random.uniform(extra_cap_mean - 2, extra_cap_mean + 2)
                 extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
@@ -256,6 +257,9 @@ class FirmAgent(Agent):
             prod_accept = 1.0
         else:
             prod_accept = self.flt_diff_new_conn
+        # 安全处理，防止异常概率
+        prod_accept = np.nan_to_num(prod_accept, nan=0.0)
+        prod_accept = max(0.0, min(1.0, prod_accept))
         if self.model.nprandom.choice([True, False], p=[prod_accept, 1 - prod_accept]):
             self.firm_network.add_edge(self.unique_id, down_firm.unique_id, Product=product.unique_id)
             self.dct_prod_capacity[product] -= 1