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Cricial
2026-06-17 13:01:05 +08:00
parent a69e272e43
commit b74490c4fa
23 changed files with 222 additions and 335 deletions
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14
.idea/csv-editor.xml generated
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@@ -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>

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.idea/dataSources.local.xml generated
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@@ -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>

2
.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml generated
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@@ -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>

2
.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/information_schema.FNRwLQ.meta generated Normal file
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@@ -0,0 +1,2 @@
#n:information_schema
!<md> [null, 0, null, null, -2147483648, -2147483648]

2
.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/performance_schema.kIw0nw.meta generated Normal file
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#n:performance_schema
!<md> [null, 0, null, null, -2147483648, -2147483648]

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.idea/dataSources/data_sources_history.xml generated Normal file
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<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>

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GA_Agent_0925/evaluate_func.py
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@@ -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]

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330
GA_Agent_0925/废案/GA_random.py
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# -*- 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()

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产业类别统计分析.xlsx Normal file
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分析.py Normal file
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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)

1
查看进度.py
View File

@@ -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

48
绘制图.py Normal file
View File

@@ -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()