330 lines
14 KiB
Python
330 lines
14 KiB
Python
# -*- 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 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
|
||
# 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）。
|
||
"""
|
||
# 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 main():
|
||
# 创建遗传算法的工具箱
|
||
ga = creating()
|
||
|
||
# 初始化种群大小为 50
|
||
pop = ga.population(n=50)
|
||
|
||
# 交叉概率、变异概率和代数
|
||
CXPB, MUTPB, NGEN = 0.5, 0.2, 500
|
||
|
||
print("Start of evolution")
|
||
|
||
# 评估整个种群的适应度
|
||
fitnesses = list(map(ga.evaluate, pop))
|
||
for ind, fit in zip(pop, fitnesses):
|
||
ind.fitness.values = fit
|
||
|
||
print(" Evaluated %i individuals" % len(pop))
|
||
# my_sql = Sql() # 创建 Sql 类的实例，用于与数据库交互
|
||
|
||
# 开始演化
|
||
for g in range(NGEN):
|
||
print("-- Generation %i --" % g)
|
||
|
||
# 选择下一代的个体
|
||
offspring = ga.select(pop, len(pop))
|
||
# 克隆选择的个体
|
||
offspring = list(map(ga.clone, offspring))
|
||
|
||
# 对后代进行交叉和变异
|
||
for child1, child2 in zip(offspring[::2], offspring[1::2]):
|
||
# 以 CXPB 的概率交叉两个个体
|
||
if random.random() < CXPB:
|
||
ga.mate(child1, child2)
|
||
|
||
# 交叉后的适应度值需要重新计算
|
||
del child1.fitness.values
|
||
del child2.fitness.values
|
||
|
||
for mutant in offspring:
|
||
# 以 MUTPB 的概率变异个体
|
||
if random.random() < MUTPB:
|
||
ga.mutate(mutant)
|
||
del mutant.fitness.values
|
||
|
||
# 评估适应度无效的个体
|
||
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
|
||
fitnesses = map(ga.evaluate, invalid_ind)
|
||
for ind, fit in zip(invalid_ind, fitnesses):
|
||
ind.fitness.values = fit
|
||
|
||
print(" Evaluated %i individuals" % len(invalid_ind))
|
||
|
||
# 将种群完全替换为后代
|
||
pop[:] = offspring
|
||
|
||
# 收集所有个体的适应度并打印统计信息
|
||
fits = [ind.fitness.values[0] for ind in pop]
|
||
|
||
# 获取当前最好的个体并打印
|
||
best_ind = tools.selBest(pop, 1)[0]
|
||
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
|
||
|
||
# 将最优个体的信息插入数据库
|
||
result_string = '''INSERT INTO ga (generation, stu_beta, stu_nmb, gtu_mgf, gtu_discount, fitness, remark)
|
||
VALUES ({}, {}, {}, {}, {}, {}, 'Random2')'''.format(g, best_ind[0], best_ind[1], best_ind[2], best_ind[3], best_ind.fitness.values[0])
|
||
# my_sql.insert_one_row_and_return_new_id(result_string)
|
||
|
||
print("-- End of (successful) evolution --")
|
||
|
||
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
|
||
|
||
|
||
import pandas as pd
|
||
from sqlalchemy import text # 用于 SQL 查询
|
||
|
||
|
||
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()
|