diff --git a/.idea/csv-editor.xml b/.idea/csv-editor.xml index 38d096c..d5a8745 100644 --- a/.idea/csv-editor.xml +++ b/.idea/csv-editor.xml @@ -45,6 +45,20 @@ + + + + + + + + + + + + diff --git a/.idea/dataSources.local.xml b/.idea/dataSources.local.xml index 62ebb75..1a9e389 100644 --- a/.idea/dataSources.local.xml +++ b/.idea/dataSources.local.xml @@ -1,6 +1,6 @@ - + #@ diff --git a/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml index 930de16..05d40b0 100644 --- a/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml +++ b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644.xml @@ -1,6 +1,6 @@ - + lower/lower InnoDB diff --git a/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/information_schema.FNRwLQ.meta b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/information_schema.FNRwLQ.meta new file mode 100644 index 0000000..1ff3db2 --- /dev/null +++ b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/information_schema.FNRwLQ.meta @@ -0,0 +1,2 @@ +#n:information_schema +! [null, 0, null, null, -2147483648, -2147483648] diff --git a/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/performance_schema.kIw0nw.meta b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/performance_schema.kIw0nw.meta new file mode 100644 index 0000000..9394db1 --- /dev/null +++ b/.idea/dataSources/3ce7b935-0ff7-47a3-aaa8-91063c963644/storage_v2/_src_/schema/performance_schema.kIw0nw.meta @@ -0,0 +1,2 @@ +#n:performance_schema +! [null, 0, null, null, -2147483648, -2147483648] diff --git a/.idea/dataSources/data_sources_history.xml b/.idea/dataSources/data_sources_history.xml new file mode 100644 index 0000000..5d5a926 --- /dev/null +++ b/.idea/dataSources/data_sources_history.xml @@ -0,0 +1,26 @@ + + + + + #@ + ` + + + mysql_aurora.aws_wrapper + true + software.amazon.jdbc.Driver + jdbc:aws-wrapper:mysql://localhost:3306 + master_key + iiabm_user + + + + + + + + + $ProjectFileDir$ + + + \ No newline at end of file diff --git a/GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc b/GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc index 2f7dded..514989d 100644 Binary files a/GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc and b/GA_Agent_0925/__pycache__/controller_db.cpython-38.pyc differ diff --git a/GA_Agent_0925/__pycache__/creating.cpython-38.pyc b/GA_Agent_0925/__pycache__/creating.cpython-38.pyc index 18bc287..e6be5bb 100644 Binary files a/GA_Agent_0925/__pycache__/creating.cpython-38.pyc and b/GA_Agent_0925/__pycache__/creating.cpython-38.pyc differ diff --git a/GA_Agent_0925/__pycache__/evaluate_func.cpython-38.pyc b/GA_Agent_0925/__pycache__/evaluate_func.cpython-38.pyc index 7d666d4..627ed1b 100644 Binary files a/GA_Agent_0925/__pycache__/evaluate_func.cpython-38.pyc and b/GA_Agent_0925/__pycache__/evaluate_func.cpython-38.pyc differ diff --git a/GA_Agent_0925/convergence.png b/GA_Agent_0925/convergence.png deleted file mode 100644 index 1cb50d7..0000000 Binary files a/GA_Agent_0925/convergence.png and /dev/null differ diff --git a/GA_Agent_0925/convergence0119.png b/GA_Agent_0925/convergence0119.png deleted file mode 100644 index 9301965..0000000 Binary files a/GA_Agent_0925/convergence0119.png and /dev/null differ diff --git a/GA_Agent_0925/convergence1.png b/GA_Agent_0925/convergence1.png deleted file mode 100644 index ec6e472..0000000 Binary files a/GA_Agent_0925/convergence1.png and /dev/null differ diff --git a/GA_Agent_0925/evaluate_func.py b/GA_Agent_0925/evaluate_func.py index 72a037c..9f85c8a 100644 --- a/GA_Agent_0925/evaluate_func.py +++ b/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] diff --git a/GA_Agent_0925/ga_convergence_me_.png b/GA_Agent_0925/ga_convergence_me_.png new file mode 100644 index 0000000..750a728 Binary files /dev/null and b/GA_Agent_0925/ga_convergence_me_.png differ diff --git a/GA_Agent_0925/myplot.png b/GA_Agent_0925/myplot.png deleted file mode 100644 index 7730f5d..0000000 Binary files a/GA_Agent_0925/myplot.png and /dev/null differ diff --git a/GA_Agent_0925/废案/GA_random.py b/GA_Agent_0925/废案/GA_random.py deleted file mode 100644 index 5c75385..0000000 --- a/GA_Agent_0925/废案/GA_random.py +++ /dev/null @@ -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() diff --git a/__pycache__/my_model.cpython-38.pyc b/__pycache__/my_model.cpython-38.pyc index 6cf59b1..6b3585b 100644 Binary files a/__pycache__/my_model.cpython-38.pyc and b/__pycache__/my_model.cpython-38.pyc differ diff --git a/cache/firm_network_1009.pkl b/cache/firm_network_1009.pkl deleted file mode 100644 index 18f2da4..0000000 Binary files a/cache/firm_network_1009.pkl and /dev/null differ diff --git a/cache/firm_network备份.pkl b/cache/firm_network备份.pkl deleted file mode 100644 index 454d453..0000000 Binary files a/cache/firm_network备份.pkl and /dev/null differ diff --git a/产业类别统计分析.xlsx b/产业类别统计分析.xlsx new file mode 100644 index 0000000..9377381 Binary files /dev/null and b/产业类别统计分析.xlsx differ diff --git a/分析.py b/分析.py new file mode 100644 index 0000000..9f68029 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/查看进度.py b/查看进度.py index 37f125c..db8a623 100644 --- a/查看进度.py +++ b/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 diff --git a/绘制图.py b/绘制图.py new file mode 100644 index 0000000..c7f710f --- /dev/null +++ b/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() \ No newline at end of file