no message

.idea/csv-editor.xml

@@ -45,6 +45,20 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\GA_Agent_0925\risk_ay\count_firm.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\GA_Agent_0925\risk_ay\count_prod.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\GA_Agent_0925\vulnerable35_match_results\vulnerable20_match_results_new1121.csv">
           <value>
             <Attribute>

.idea/dataSources.local.xml

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="dataSourceStorageLocal" created-in="PY-242.26775.22">
+  <component name="dataSourceStorageLocal" created-in="PY-261.22158.340">
     <data-source name="@localhost" uuid="3ce7b935-0ff7-47a3-aaa8-91063c963644">
       <database-info product="MySQL" version="8.0.36" jdbc-version="4.2" driver-name="Amazon Web Services (AWS) Advanced JDBC Wrapper" driver-version="Amazon Web Services (AWS) Advanced JDBC Wrapper 2.3.7 ( Revision: 7591851e8da4e1c705ba232a8bd07824a5cfd276 )" dbms="MYSQL" exact-version="8.0.36" exact-driver-version="2.3">
         <extra-name-characters>#@</extra-name-characters>

.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <dataSource name="@localhost">
-  <database-model serializer="dbm" dbms="MYSQL" family-id="MYSQL" format-version="4.53">
+  <database-model serializer="dbm" dbms="MYSQL" family-id="MYSQL" format-version="4.55">
     <root id="1">
       <DefaultCasing>lower/lower</DefaultCasing>
       <DefaultEngine>InnoDB</DefaultEngine>

.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/information_schema.FNRwLQ.meta

@@ -0,0 +1,2 @@
+#n:information_schema
+!<md> [null, 0, null, null, -2147483648, -2147483648]

.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/performance_schema.kIw0nw.meta

@@ -0,0 +1,2 @@
+#n:performance_schema
+!<md> [null, 0, null, null, -2147483648, -2147483648]

.idea/dataSources/data_sources_history.xml

@@ -0,0 +1,26 @@
+<DataSourcesHistory>
+  <DataSourceFromHistory isRemovedFromProject="false">
+    <data-source source="LOCAL" name="@localhost" uuid="3ce7b935-0ff7-47a3-aaa8-91063c963644">
+      <database-info product="MySQL" version="8.0.36" jdbc-version="4.2" driver-name="Amazon Web Services (AWS) Advanced JDBC Wrapper" driver-version="Amazon Web Services (AWS) Advanced JDBC Wrapper 2.3.7 ( Revision: 7591851e8da4e1c705ba232a8bd07824a5cfd276 )" dbms="MYSQL" exact-version="8.0.36" exact-driver-version="2.3">
+        <extra-name-characters>#@</extra-name-characters>
+        <identifier-quote-string>`</identifier-quote-string>
+      </database-info>
+      <case-sensitivity plain-identifiers="lower" quoted-identifiers="lower" />
+      <driver-ref>mysql_aurora.aws_wrapper</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>software.amazon.jdbc.Driver</jdbc-driver>
+      <jdbc-url>jdbc:aws-wrapper:mysql://localhost:3306</jdbc-url>
+      <secret-storage>master_key</secret-storage>
+      <user-name>iiabm_user</user-name>
+      <schema-mapping>
+        <introspection-scope>
+          <node kind="schema">
+            <name qname="@" />
+            <name qname="iiabmdb_20250925" />
+          </node>
+        </introspection-scope>
+      </schema-mapping>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </DataSourceFromHistory>
+</DataSourcesHistory>

GA_Agent_0925/evaluate_func.py

@@ -13,6 +13,7 @@ from my_model import MyModel
 from orm import connection, engine
 
 
+# 🎯 适应度函数（核心目标函数）
 def fitness(individual, controller_db_obj):
     """
     遗传算法适应度函数：用于评估个体（模型参数）的优劣。
@@ -63,10 +64,8 @@ def fitness(individual, controller_db_obj):
     print(simulated_vulnerable_industries)
     # ========== 4️⃣ 获取目标产业集合 ==========
     target_vulnerable_industries = get_target_vulnerable_industries()
-
     """
         Top-K 加权命中误差（越小越好）
-
         simulated_vulnerable_industries : list[str]
             模型输出的产业排序（风险从高到低）
         target_vulnerable_industries : list[str] or set[str]

GA_Agent_0925/废案/GA_random.py

@@ -1,330 +0,0 @@
-# -*- coding: utf-8 -*-  # 文件的编码格式设置为 UTF-8
-from __future__ import division  # 为了兼容 Python 2 和 3，保证除法始终返回浮点数
-
-import multiprocessing
-import random  # 导入 random 库，用于生成随机数
-
-from deap import base  # 从 DEAP 库导入 base 模块，提供一些遗传算法相关的功能
-from deap import creator  # 从 DEAP 库导入 creator 模块，用于定义个体和适应度
-from deap import tools  # 从 DEAP 库导入 tools 模块，提供常用的遗传算法工具（如交叉、变异等）
-
-from my_model import MyModel
-from sqlalchemy import text
-import pandas as pd
-from orm import connection
-
-def main():
-    random.seed(42)  # 可复现结果
-    print("Start of evolution")
-
-    ga = creating()
-    pop = ga.population(n=50)
-    CXPB, MUTPB, NGEN = 0.5, 0.2, 200
-
-    # # 并行计算
-    # pool = multiprocessing.Pool()
-    # ga.register("map", pool.map)
-
-    # 改为：
-    ga.register("map", map)  # 单进程
-
-    # 评估初始种群
-    fitnesses = list(ga.map(ga.evaluate, pop))
-    for ind, fit in zip(pop, fitnesses):
-        ind.fitness.values = fit
-    print(f"Evaluated {len(pop)} individuals")
-
-    best_log = []
-
-    for g in range(NGEN):
-        print(f"-- Generation {g} --")
-
-        # 选择并克隆
-        offspring = list(map(ga.clone, ga.select(pop, len(pop))))
-
-        # 交叉与变异
-        for child1, child2 in zip(offspring[::2], offspring[1::2]):
-            if random.random() < CXPB:
-                ga.mate(child1, child2)
-                del child1.fitness.values
-                del child2.fitness.values
-
-        for mutant in offspring:
-            if random.random() < MUTPB:
-                ga.mutate(mutant)
-                del mutant.fitness.values
-
-        # 重新计算失效适应度
-        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
-        fitnesses = list(ga.map(ga.evaluate, invalid_ind))
-        for ind, fit in zip(invalid_ind, fitnesses):
-            ind.fitness.values = fit
-
-        pop[:] = offspring
-
-        # 最优个体
-        best_ind = tools.selBest(pop, 1)[0]
-        best_log.append((g, best_ind.fitness.values[0]))
-
-        print(f"Best individual {g}: {best_ind}, Fitness: {best_ind.fitness.values[0]:.3f}")
-
-        # 写入数据库
-        result_sql = text(f"""
-            INSERT INTO ga (generation, stu_beta, stu_nmb, gtu_mgf, gtu_discount, fitness, remark)
-            VALUES ({g}, {best_ind[0]}, {best_ind[1]}, {best_ind[2]}, {best_ind[3]}, {best_ind.fitness.values[0]}, 'Random2')
-        """)
-        with connection.connect() as conn:
-            conn.execute(result_sql)
-            conn.commit()
-
-    # pool.close()
-    # pool.join()
-
-    pd.DataFrame(best_log, columns=["generation", "fitness"]).to_csv("ga_log.csv", index=False)
-    print("-- End of (successful) evolution --")
-
-# 目标函数（适应度函数），用于评估个体的适应度
-def fitness(individual):
-    """
-        GA 适应度函数：用于评估个体（模型参数）的效果。
-
-        目标：
-        - individual: 遗传算法中的个体参数列表
-            [n_max_trial, prf_size, prf_conn, cap_limit_prob_type, cap_limit_level,
-             diff_new_conn, netw_prf_n, s_r, S_r, x, k, production_increase_ratio]
-        - target_chain_set: 美国打击的产业链编号集合（整数集合）
-
-        适应度定义：
-        - fitness = -error
-        - error = 脆弱产业集合与 target_chain_set 的差集大小
-        """
-
-    # 1 将 GA 生成的个体参数传入 ABM 模型
-    """
-    n_iter
-    g_bom
-    seed
-    sample
-    dct_lst_init_disrupt_firm_prod
-    remove_t
-    """
-    dct_exp = {
-        'n_max_trial': individual[0],
-        'prf_size': individual[1],
-        'prf_conn': individual[2],
-        'cap_limit_prob_type': individual[3],
-        'cap_limit_level': individual[4],
-        'diff_new_conn': individual[5],
-        'netw_prf_n': individual[6],
-        's_r': individual[7],
-        'S_r': individual[8],
-        'x': individual[9],
-        'k': individual[10],
-        'production_increase_ratio': individual[11]
-    }
-
-    abm_model = MyModel(**dct_exp)
-
-    # 2 运行 ABM，获取模拟结果的“脆弱产业集合”
-    abm_model.step()
-    abm_model.end()
-
-    simulated_vulnerable_industries=get_vulnerable100_code(connection)
-    # 3 获取目标集合（美国打击我们的产业集合）
-    target_vulnerable_industries = get_target_vulnerable_industries()  # list / set
-
-    # 4 计算误差（集合差异度）
-    # 这里可以用 Jaccard 距离、集合交并比、或者简单的匹配数差
-    set_sim = set(simulated_vulnerable_industries)
-    set_target = set(target_vulnerable_industries)
-
-    error = len(set_sim.symmetric_difference(set_target))  # 差异元素个数
-
-    # 5 返回 fitness（GA 目标是最大化）
-    #  因为我们希望误差越小越好，所以 fitness = -error
-    return -error,
-
-def creating():
-    """
-    创建遗传算法工具箱，用于优化 ABM 模型参数，使生成的脆弱产业集合
-    与目标产业集合误差最小化（fitness 最大化）。
-    """
-    if "FitnessMax" not in creator.__dict__:
-        creator.create("FitnessMax", base.Fitness, weights=(1.0,))
-    if "Individual" not in creator.__dict__:
-        creator.create("Individual", list, fitness=creator.FitnessMax)
-    # 定义最大化适应度
-    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
-    # 定义个体类
-    creator.create("Individual", list, fitness=creator.FitnessMax)
-
-    toolbox = base.Toolbox()
-
-    # 定义每个基因的取值范围 / 类型及默认值
-    toolbox.register("n_max_trial", random.randint, 50, 500)  # 最大尝试次数 [50,500]
-    toolbox.register("prf_size", random.uniform, 0.0, 1.0)  # 是否规模偏好参数 [0,1]
-    toolbox.register("prf_conn", random.uniform, 0.0, 1.0)  # 是否已有连接偏好 [0,1]
-    toolbox.register("cap_limit_prob_type", random.randint, 0, 2)  # 额外产能分布类型 {0:正态,1:均匀,2:指数}
-    toolbox.register("cap_limit_level", random.uniform, 0.5, 2.0)  # 额外产能均值放缩因子 [0.5,2.0]
-    toolbox.register("diff_new_conn", random.uniform, 0.0, 1.0)  # 新供应关系构成概率 [0,1]
-    toolbox.register("netw_prf_n", random.randint, 1, 10)  # 在网络中选择供应商目标数量 [1,10]
-    toolbox.register("s_r", random.uniform, 0.1, 0.5)  # 补货下阈值 [0.1,0.5]
-    toolbox.register("S_r", random.uniform, 0.5, 1.0)  # 补货上阈值 [0.5,1.0]
-    toolbox.register("x", random.uniform, 0.0, 0.1)  # 每周期减少残值 [0.0,0.1]
-    toolbox.register("k", random.uniform, 0.1, 1.0)  # 资源消耗比例 [0.1,1.0]
-    toolbox.register("production_increase_ratio", random.uniform, 0.5, 2.0)  # 产品生产比例 [0.5,2.0]
-
-    # 个体由上述基因组成
-    toolbox.register(
-        "individual",
-        tools.initCycle,
-        creator.Individual,
-        (
-            toolbox.n_max_trial,
-            toolbox.prf_size,
-            toolbox.prf_conn,
-            toolbox.cap_limit_prob_type,
-            toolbox.cap_limit_level,
-            toolbox.diff_new_conn,
-            toolbox.netw_prf_n,
-            toolbox.s_r,
-            toolbox.S_r,
-            toolbox.x,
-            toolbox.k,
-            toolbox.production_increase_ratio
-        ),
-        n=1
-    )
-
-    # 种群初始化
-    toolbox.register("population", tools.initRepeat, list, toolbox.individual)
-
-    # 注册 fitness 函数（需要在调用时传入目标产业集合）
-    toolbox.register("evaluate", fitness)  # 可以在 main 中使用 lambda 包装 target_chain_set
-
-    # 交叉、变异和选择操作
-    toolbox.register("mate", tools.cxTwoPoint)
-    toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.1)
-    toolbox.register("select", tools.selTournament, tournsize=3)
-
-    return toolbox
-
-def get_target_vulnerable_industries():
-    """
-       获取行业列表中所有产业链编号的集合（整数形式）。
-       说明：
-       - 输入的 industry_list 是一个字典列表，每个字典包含：
-           {"product": 产品名称, "category": 产品类别, "chain_id": 产业链编号}
-       - 某些 chain_id 可能是复合编号，例如 "11 / 513742"，需要拆分成单独整数。
-       - 输出是一个 set，包含所有 chain_id（去重、整数形式）。
-
-       参数：
-       industry_list : list of dict
-           行业字典列表，每个字典必须包含 "chain_id" 键。
-
-       返回：
-       set
-           所有产业链编号的整数集合。
-       """
-    industry_list = [
-        # ① 半导体设备类
-        {"product": "离子注入机", "category": "离子注入设备", "chain_id": 34538},
-        {"product": "刻蚀设备 / 湿法刻蚀设备", "category": "刻蚀机", "chain_id": 34529},
-        {"product": "沉积设备", "category": "薄膜生长设备（CVD/PVD）", "chain_id": 34539},
-        {"product": "CVD", "category": "薄膜生长设备", "chain_id": 34539},
-        {"product": "PVD", "category": "薄膜生长设备", "chain_id": 34539},
-        {"product": "CMP", "category": "化学机械抛光设备", "chain_id": 34530},
-        {"product": "光刻机", "category": "光刻机", "chain_id": 34533},
-        {"product": "涂胶显影机", "category": "涂胶显影设备", "chain_id": 34535},
-        {"product": "晶圆清洗设备", "category": "晶圆清洗机", "chain_id": 34531},
-        {"product": "测试设备", "category": "测试机", "chain_id": 34554},
-        {"product": "外延生长设备", "category": "薄膜生长设备", "chain_id": 34539},
-
-        # ② 半导体材料与化学品类
-        {"product": "三氯乙烯", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438},
-        {"product": "丙酮", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438},
-        {"product": "异丙醇", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438},
-        {"product": "其他醇类", "category": "清洗溶剂 → 通用湿电子化学品", "chain_id": 32438},
-        {"product": "光刻胶", "category": "光刻胶及配套试剂", "chain_id": 32445},
-        {"product": "显影液", "category": "显影液", "chain_id": 46504},
-        {"product": "蚀刻液", "category": "蚀刻液", "chain_id": 56341},
-        {"product": "光阻去除剂", "category": "光阻去除剂", "chain_id": 32442},
-
-        # ③ 晶圆制造类
-        {"product": "晶圆", "category": "单晶硅片 / 多晶硅片", "chain_id": 32338},
-        {"product": "硅衬底", "category": "硅衬底", "chain_id": 36914},
-        {"product": "外延片", "category": "硅外延片 / GaN外延片 / SiC外延片等", "chain_id": 32338},
-
-        # ④ 封装与测试类
-        {"product": "封装", "category": "IC封装", "chain_id": 10},
-        {"product": "测试", "category": "芯片测试 / 晶圆测试", "chain_id": 513742},
-        {"product": "测试", "category": "芯片测试 / 晶圆测试", "chain_id": 11},
-
-        # ⑤ 芯片与设计EDA类
-        {"product": "芯片（通用）", "category": "集成电路制造", "chain_id": 317589},
-        {"product": "DRAM", "category": "存储芯片 → 集成电路制造", "chain_id": 317589},
-        {"product": "GPU", "category": "图形芯片 → 集成电路制造", "chain_id": 317589},
-        {"product": "处理器（CPU/SoC）", "category": "芯片设计", "chain_id": 9},
-        {"product": "高频芯片", "category": "芯片设计", "chain_id": 9},
-        {"product": "光子芯片（含激光）", "category": "芯片设计 / 功率半导体器件", "chain_id": 9},
-        {"product": "光子芯片（含激光）", "category": "芯片设计 / 功率半导体器件", "chain_id": 2717},
-        {"product": "先进节点制造设备", "category": "集成电路制造", "chain_id": 317589},
-        {"product": "EDA及IP服务", "category": "设计辅助", "chain_id": 2515},
-        {"product": "MPW服务", "category": "多项目晶圆流片", "chain_id": 2514},
-        {"product": "芯片设计验证", "category": "设计验证", "chain_id": 513738},
-        {"product": "过程工艺检测", "category": "制程检测", "chain_id": 513740}
-    ]
-    # 提取所有 chain_id，并去重
-    chain_ids = set()
-    for item in industry_list:
-        # 如果 chain_id 是字符串包含多个编号，用逗号或斜杠拆分
-        if isinstance(item["chain_id"], str):
-            for cid in item["chain_id"].replace("/", ",").split(","):
-                chain_ids.add(cid.strip())
-        else:
-            chain_ids.add(str(item["chain_id"]))
-
-    return chain_ids
-
-def get_vulnerable100_code(connection):
-    """
-    计算最脆弱前100产品的 Code 列表（去重）。
-    参数：
-        connection: 数据库连接对象，用于执行 SQL
-    返回：
-        List[int]: 最脆弱前100产品对应的 Code 列表
-    """
-    #  读取映射表
-    bom_file = r"../../input_data/input_product_data/BomNodes.csv"  # 直接给出路径
-    mapping_df = pd.read_csv(bom_file)
-
-    # 执行 SQL 获取结果
-    with open("../../SQL_analysis_risk.sql", "r", encoding="utf-8") as f:
-        str_sql = text(f.read())
-
-    result = pd.read_sql(sql=str_sql, con=connection)
-
-    # 统计每个 (id_firm, id_product) 出现次数
-    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()
-
-    # 统计每个 id_product 的总 count
-    count_prod = (
-        count_firm_prod
-        .groupby("id_product")["count"]
-        .sum()
-        .reset_index()
-    )
-
-    # 按 count 升序取最脆弱前100 id_product
-    vulnerable100_index = count_prod.nsmallest(100, "count")["id_product"].tolist()
-
-    # 映射 Index -> Code 并去重
-    index_to_code = dict(zip(mapping_df["Index"], mapping_df["Code"]))
-    vulnerable100_code = list({index_to_code[i] for i in vulnerable100_index if i in index_to_code})
-
-    return vulnerable100_code
-
-if __name__ == "__main__":
-    main()

分析.py

@@ -0,0 +1,127 @@
+import pandas as pd
+
+# ====== 填入你的数据 ======
+names = [
+"集成电路制造",
+"晶圆测试",
+"功率半导体器件",
+"二极管",
+"碳化硅外延晶片",
+"氮化镓外延片",
+"晶闸管",
+"氮化铝外延片",
+"磷化铟外延片",
+"LED外延片",
+"晶体管",
+"硅外延片",
+"整流桥",
+"蚀刻液",
+"砷化镓单晶片",
+"多晶硅片",
+"碳化硅单晶和单晶片",
+"磷化铟单晶和单晶片",
+"氮化镓晶体和单晶片",
+"单晶硅片",
+"氮化镓衬底",
+"碳化硅衬底",
+"磷化铟衬底",
+"硅衬底",
+"氮化铝衬底",
+"深紫外LED衬底",
+"氟化硅",
+"显影液",
+"稀释剂",
+"硅原材料",
+"聚羧酸减水剂",
+"表面活性剂",
+"碳化硅",
+"高纯金属有机化合物",
+"半导体电镀设备",
+"晶硅切片机",
+"薄膜生长设备",
+"硅片倒角机",
+"等离子去胶机",
+"晶圆清洗机",
+"熔炼矿热炉",
+"光刻胶及其配套试剂",
+"离子注入设备",
+"剥离液",
+"芯片设计验证",
+"金属保护液",
+"化学机械抛光设备",
+"高纯硼酸（核电）",
+"电子级环氧树脂",
+"光刻机",
+"通用湿电子化学品",
+"单晶生长炉",
+"晶圆测量设备",
+"电子级阻燃材料及化学品",
+"液晶取向剂及配套化学品",
+"功能湿电子化学品",
+"砷化镓",
+"氮化镓",
+"氮化硅",
+"磁性载体",
+"研磨液及配套化学品、研磨垫材料",
+"电子级酚醛树脂",
+"钝化液",
+"电镀化学品及配套材料",
+"涂胶显影设备",
+"硅片研磨机",
+"刻蚀机",
+"氧化/扩散炉",
+"磷化铟",
+"氮化铝",
+"晶圆检测设备",
+"多晶硅切削液"
+]
+
+counts = [
+3726,2171,1915,1423,1141,1132,1127,1113,1111,1104,1092,1082,813,642,558,555,551,535,
+526,520,429,425,419,398,365,351,226,90,30,30,30,30,24,20,20,20,
+20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,
+18,16,16,10
+]
+
+# 检查长度是否一致
+if len(names) != len(counts):
+    raise ValueError(f"名称数量 ({len(names)}) 与 count 数量 ({len(counts)}) 不一致！")
+
+# 创建 DataFrame
+df = pd.DataFrame({"名称": names, "count": counts})
+
+# ====== 定义类别划分规则 ======
+def categorize(name):
+    if any(x in name for x in ["制造","设计验证"]):
+        return "芯片制造与设计"
+    elif any(x in name for x in ["晶圆","外延片","硅片","单晶","多晶"]):
+        return "晶圆及外延片"
+    elif any(x in name for x in ["器件","二极管","晶闸管","晶体管","整流桥"]):
+        return "半导体器件"
+    elif any(x in name for x in ["衬底"]):
+        return "衬底材料"
+    elif any(x in name for x in ["液","试剂","化学品","材料","金属有机化合物","活性剂","减水剂","环氧树脂"]):
+        return "化学品与材料"
+    elif any(x in name for x in ["机","设备","炉","薄膜","测量","光刻"]):
+        return "制造设备"
+    else:
+        return "其他材料与辅助"
+
+# 应用分类
+df["类别"] = df["名称"].apply(categorize)
+
+# ====== 按类别统计 ======
+stats = df.groupby("类别")["count"].agg(['min','max','mean','median','sum']).reset_index()
+stats.rename(columns={
+    "min":"最小值",
+    "max":"最大值",
+    "mean":"均值",
+    "median":"中位数",
+    "sum":"总和"
+}, inplace=True)
+
+# 输出结果
+print(stats)
+
+# 如果需要保存为 Excel
+stats.to_excel("产业类别统计分析.xlsx", index=False)

查看进度.py

@@ -10,7 +10,6 @@ def visualize_progress():
     """
     可视化 `is_done_flag` 的分布，动态更新进度条。
     """
-
     # 设置全局字体
     rcParams['font.family'] = 'Microsoft YaHei'  # 黑体，适用于中文
     rcParams['font.size'] = 12

绘制图.py

@@ -0,0 +1,48 @@
+import matplotlib.pyplot as plt
+import numpy as np
+plt.rcParams['font.sans-serif'] = 'SimHei'
+import matplotlib.pyplot as plt
+import numpy as np
+
+# 数据
+risk_levels = ["高风险", "次高风险", "次低风险", "低风险"]
+material = [41.7, 34.0, 58.3, 36.8]
+equipment = [16.7, 18.0, 8.3, 10.5]
+design = [37.5, 38.0, 33.3, 31.6]
+manufacturing = [4.2, 10.0, 0.0, 21.1]
+
+# 设置柱状图位置
+x = np.arange(len(risk_levels))
+width = 0.6
+
+# 绘制堆叠柱状图
+fig, ax = plt.subplots(figsize=(10,6))
+
+bars_material = ax.bar(x, material, width, label="材料", color="#1f77b4")
+bars_equipment = ax.bar(x, equipment, width, bottom=material, label="设备", color="#ff7f0e")
+bars_design = ax.bar(x, design, width, bottom=np.array(material)+np.array(equipment), label="设计", color="#2ca02c")
+bars_manufacturing = ax.bar(x, manufacturing, width, bottom=np.array(material)+np.array(equipment)+np.array(design), label="制造封测", color="#d62728")
+
+# 添加柱内比例标签
+for i in range(len(x)):
+    # 材料
+    ax.text(x[i], material[i]/2, f"{material[i]:.1f}%", ha='center', va='center', color='white', fontsize=10)
+    # 设备
+    ax.text(x[i], material[i]+equipment[i]/2, f"{equipment[i]:.1f}%", ha='center', va='center', color='white', fontsize=10)
+    # 设计
+    ax.text(x[i], material[i]+equipment[i]+design[i]/2, f"{design[i]:.1f}%", ha='center', va='center', color='white', fontsize=10)
+    # 制造封测
+    if manufacturing[i] > 0:
+        ax.text(x[i], material[i]+equipment[i]+design[i]+manufacturing[i]/2, f"{manufacturing[i]:.1f}%", ha='center', va='center', color='white', fontsize=10)
+
+# 图表美化
+ax.set_xticks(x)
+ax.set_xticklabels(risk_levels)
+ax.set_ylabel("占比 (%)")
+ax.set_title("各风险等级企业结构占比（堆叠柱状图）")
+ax.legend()
+ax.set_ylim(0, 120)
+ax.grid(axis='y', linestyle='--', alpha=0.7)
+
+plt.tight_layout()
+plt.show()