遗传算法001

.idea/dataSources.local.xml

@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="dataSourceStorageLocal" created-in="PY-242.23726.102">
-    <data-source name="iiabmdb_20250925@localhost" uuid="753dde79-ca3d-42e1-8fb4-13124f46b86d">
+    <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>
         <identifier-quote-string>`</identifier-quote-string>
@@ -11,7 +11,10 @@
       <user-name>iiabm_user</user-name>
       <schema-mapping>
         <introspection-scope>
-          <node kind="schema" qname="@" />
+          <node kind="schema">
+            <name qname="@" />
+            <name qname="iiabmdb_20250925" />
+          </node>
         </introspection-scope>
       </schema-mapping>
     </data-source>

.idea/dataSources.xml

@@ -1,11 +1,11 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
-    <data-source source="LOCAL" name="iiabmdb_20250925@localhost" uuid="753dde79-ca3d-42e1-8fb4-13124f46b86d">
+    <data-source source="LOCAL" name="@localhost" uuid="3ce7b935-0ff7-47a3-aaa8-91063c963644">
       <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/iiabmdb_20250925</jdbc-url>
+      <jdbc-url>jdbc:aws-wrapper:mysql://localhost:3306</jdbc-url>
       <working-dir>$ProjectFileDir$</working-dir>
     </data-source>
   </component>

.idea/misc.xml

@@ -4,4 +4,7 @@
     <option name="sdkName" value="Python 3.8" />
   </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+  <component name="PythonCompatibilityInspectionAdvertiser">
+    <option name="version" value="3" />
+  </component>
 </project>

GA_Agent_0925/SQL_analysis_risk_ga.sql

@@ -0,0 +1,21 @@
+SELECT *
+FROM (
+    SELECT s_id, id_firm, id_product, MIN(ts) AS ts
+    FROM iiabmdb_20250925.without_exp_result
+    WHERE `status` = 'D'
+      AND ga_id = :ga_id
+    GROUP BY s_id, id_firm, id_product
+) AS s_disrupt
+WHERE s_id IN (
+    SELECT s_id
+    FROM (
+        SELECT s_id, id_firm, id_product, MIN(ts) AS ts
+        FROM iiabmdb_20250925.without_exp_result
+        WHERE `status` = 'D'
+          AND ga_id = :ga_id
+        GROUP BY s_id, id_firm, id_product
+    ) AS t
+    GROUP BY s_id
+    HAVING COUNT(*) > 1
+)
+ORDER BY s_id;

GA_Agent_0925/best_result_with_industry.json

@@ -0,0 +1,136 @@
+{
+  "best_individual": [
+    291,
+    0.24373090607513836,
+    0.5512650768804697,
+    1,
+    0.7859155564218925,
+    0.5993775986748999,
+    3,
+    0.4456107737714353,
+    0.6381237014110205,
+    0.07900061820031135,
+    0.4734481811962107,
+    1.9013725905237802
+  ],
+  "best_fitness": -9999.0,
+  "best_per_gen": [
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0
+  ],
+  "avg_per_gen": [
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0,
+    -9999.0
+  ],
+  "industry_matching": {
+    "matching": [],
+    "extra": [],
+    "missing": [
+      "2515",
+      "34533",
+      "10",
+      "34539",
+      "34529",
+      "513740",
+      "9",
+      "34530",
+      "513742"
+    ]
+  }
+}

GA_Agent_0925/conf_experiment.yaml

@@ -0,0 +1,12 @@
+# read by ControllerDB
+
+# run settings
+meta_seed: 2
+
+test:  # only for test scenarios
+  n_sample: 1
+  n_iter: 100
+
+not_test:  # normal scenarios
+  n_sample: 5
+  n_iter: 10

GA_Agent_0925/config.json

@@ -0,0 +1,15 @@
+{
+  "pop_size": 5,
+  "n_gen": 5,
+  "n_var": 12,
+  "bound_min": -5,
+  "bound_max": 5,
+  "cx_prob": 0.5,
+  "mut_prob": 0.2,
+  "cx_alpha": 0.5,
+  "mut_sigma": 0.1,
+  "mut_indpb": 0.2,
+  "tourn_size": 3,
+  "n_jobs": 1,
+  "seed": 42
+}

GA_Agent_0925/controller_db.py

@@ -0,0 +1,393 @@
+# -*- coding: utf-8 -*-
+from orm import db_session, engine, Base, ins, connection
+from orm import Experiment, Sample, Result
+from sqlalchemy.exc import OperationalError
+from sqlalchemy import text
+import yaml
+import random
+import pandas as pd
+import platform
+import networkx as nx
+import json
+import pickle
+
+class ControllerDB:
+    is_with_exp: bool
+    dct_parameter = None
+    is_test: bool = None
+    db_name_prefix: str = None
+    reset_flag: int
+
+    lst_saved_s_id: list
+
+    def __init__(self, prefix, reset_flag=0):
+        with open('conf_experiment.yaml') as yaml_file:
+            dct_conf_experiment = yaml.full_load(yaml_file)
+        assert prefix in ['test', 'without_exp', 'with_exp'], "db name not in test, without_exp, with_exp"
+
+        self.is_test = prefix == 'test'
+        self.is_with_exp = False if prefix == 'test' or prefix == 'without_exp' else True
+        self.db_name_prefix = prefix
+        dct_para_in_test = dct_conf_experiment['test'] if self.is_test else dct_conf_experiment['not_test']
+        self.dct_parameter = {'meta_seed': dct_conf_experiment['meta_seed'], **dct_para_in_test}
+
+        # print(self.dct_parameter)
+        # 0, not reset; 1, reset self; 2, reset all
+        self.reset_flag = reset_flag
+        self.is_exist = False
+        self.lst_saved_s_id = []
+
+        self.experiment_data = []
+        self.batch_size = 5000
+        # 根据需求设置每批次的大小
+
+    def init_tables(self):
+        self.fill_experiment_table()
+        self.fill_sample_table()
+
+    def fill_experiment_table(self):
+        firm = pd.read_csv("../input_data/input_firm_data/Firm_amended.csv")
+        firm['Code'] = firm['Code'].astype('string')
+        firm.fillna(0, inplace=True)
+
+        # fill dct_lst_init_disrupt_firm_prod
+        # 存储 公司-在供应链结点的位置.. 0 ：‘1.1’
+        if self.is_with_exp:
+            # 对于方差分析时候使用
+            with open('../SQL_export_high_risk_setting.sql', 'r') as f:
+                str_sql = text(f.read())
+            result = pd.read_sql(sql=str_sql, con=connection)
+            result['dct_lst_init_disrupt_firm_prod'] = \
+                result['dct_lst_init_disrupt_firm_prod'].apply(
+                    lambda x: pickle.loads(x))
+            list_dct = result['dct_lst_init_disrupt_firm_prod'].to_list()
+        else:
+            # 行索引 (index)：这一行在数据帧中的索引值。
+            # 行数据 (row)：这一行的数据，是一个 pandas.Series 对象，包含该行的所有列和值。
+
+            # 读取企业与产品关系数据
+            firm_industry = pd.read_csv("../input_data/firm_industry_relation.csv")
+            firm_industry['Firm_Code'] = firm_industry['Firm_Code'].astype('string')
+
+            # 假设已从 BOM 数据构建了 code_to_indices
+            bom_nodes = pd.read_csv("../input_data/input_product_data/BomNodes.csv")
+            code_to_indices = bom_nodes.groupby('Code')['Index'].apply(list).to_dict()
+
+            # 初始化存储映射结果的列表
+            list_dct = []
+
+            # 遍历 firm_industry 数据
+            for _, row in firm_industry.iterrows():
+                firm_code = row['Firm_Code']  # 企业代码
+                product_code = row['Product_Code']  # 原始产品代码
+
+                # 使用 code_to_indices 映射 Product_Code 到 Product_Indices
+                mapped_indices = code_to_indices.get(product_code, [])  # 如果找不到则返回空列表
+
+                # 构建企业到产品索引的映射
+                dct = {firm_code: mapped_indices}
+                list_dct.append(dct)
+
+        # fill g_bom
+        # 结点属性值 相当于 图上点的 原始 产品名称
+        bom_nodes = pd.read_csv('../input_data/input_product_data/BomNodes.csv')
+        bom_nodes['Code'] = bom_nodes['Code'].astype(str)
+        bom_nodes.set_index('Index', inplace=True)
+
+        bom_cate_net = pd.read_csv('../input_data/input_product_data/合成结点.csv')
+        g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())
+        # 填充每一个结点 的具体内容 通过 相同的 code 并且通过BomNodes.loc[code].to_dict()字典化 格式类似 格式 { code（0） :  {level: 0 ,name: 工业互联网   }}
+        bom_labels_dict = {}
+        for index in g_bom.nodes:
+            try:
+                bom_labels_dict[index] = bom_nodes.loc[index].to_dict()
+                # print(bom_labels_dict[index])
+            except KeyError:
+                print(f"节点 {index} 不存在于 bom_nodes 中")
+        # 分配属性 给每一个结点 获得类似 格式：{1: {'label': 'A', 'value': 10},
+        nx.set_node_attributes(g_bom, bom_labels_dict)
+        # 改为json 格式
+        g_product_js = json.dumps(nx.adjacency_data(g_bom))
+
+        # insert exp
+        df_xv = pd.read_csv(
+            "../input_data/"
+            f"xv_{'with_exp' if self.is_with_exp else 'without_exp'}.csv",
+            index_col=None)
+        # read the OA table
+        df_oa = pd.read_csv(
+            "../input_data/"
+            f"oa_{'with_exp' if self.is_with_exp else 'without_exp'}.csv",
+            index_col=None)
+        # .shape[1] 列数  .iloc 访问特定的值 而不是标签
+        df_oa = df_oa.iloc[:, 0:df_xv.shape[1]]
+
+        # idx_scenario 是 0 指行     idx_init_removal 指 索引 0.. dct_init_removal 键 code 公司 g_product_js 图的json数据 dct_exp_para 解码 全局参数xv-
+        for idx_scenario, row in df_oa.iterrows():
+            dct_exp_para = {}
+            for idx_col, para_level in enumerate(row):
+                # 处理 NaN 值，替换为默认值（如 0 或其他合适的值）
+                para_level = para_level if not pd.isna(para_level) else 0
+                # 转换为整数
+                para_level = int(para_level)
+                dct_exp_para[df_xv.columns[idx_col]] = \
+                    df_xv.iloc[para_level, idx_col]
+            # different initial removal 只会得到 键 和 值
+            for idx_init_removal, dct_init_removal in enumerate(list_dct):
+                self.add_experiment_1(idx_scenario,
+                                      idx_init_removal,
+                                      dct_init_removal,
+                                      g_product_js,
+                                      **dct_exp_para)
+                print(f"Inserted experiment for scenario {idx_scenario}, "
+                      f"init_removal {idx_init_removal}!")
+            self.finalize_insertion()
+
+    def add_experiment_1(self, idx_scenario, idx_init_removal,
+                         dct_lst_init_disrupt_firm_prod, g_bom,
+                         n_max_trial, prf_size, prf_conn,
+                         cap_limit_prob_type, cap_limit_level,
+                         diff_new_conn, remove_t, netw_prf_n):
+        e = Experiment(
+            idx_scenario=idx_scenario,
+            idx_init_removal=idx_init_removal,
+            n_sample=int(self.dct_parameter['n_sample']),
+            n_iter=int(self.dct_parameter['n_iter']),
+            dct_lst_init_disrupt_firm_prod=dct_lst_init_disrupt_firm_prod,
+            g_bom=g_bom,
+            n_max_trial=n_max_trial,
+            prf_size=prf_size,
+            prf_conn=prf_conn,
+            cap_limit_prob_type=cap_limit_prob_type,
+            cap_limit_level=cap_limit_level,
+            diff_new_conn=diff_new_conn,
+            remove_t=remove_t,
+            netw_prf_n=netw_prf_n
+        )
+        # 这里我们不立即提交，而是先添加到批量保存的队列中
+        self.experiment_data.append(e)
+
+        # 当批量数据达到一定数量时再提交
+        if len(self.experiment_data) >= self.batch_size:
+            self._commit_batch()
+
+    # 辅助方法：批量提交
+    def _commit_batch(self):
+        db_session.bulk_save_objects(self.experiment_data)
+        db_session.commit()
+        self.experiment_data.clear()  # 清空队列
+
+    def finalize_insertion(self):
+        if self.experiment_data:
+            self._commit_batch()  # 提交剩余的数据
+
+    def fill_sample_table(self):
+        rng = random.Random(self.dct_parameter['meta_seed'])
+        # 根据样本数目 设置 32 位随机整数
+        lst_seed = [
+            rng.getrandbits(32)
+            for _ in range(int(self.dct_parameter['n_sample']))
+        ]
+        lst_exp = db_session.query(Experiment).all()
+
+        lst_sample = []
+        for experiment in lst_exp:
+            # idx_sample: 1-50
+            for idx_sample in range(int(experiment.n_sample)):
+                s = Sample(e_id=experiment.id,
+                           idx_sample=idx_sample + 1,
+                           seed=lst_seed[idx_sample],
+                           is_done_flag=-1)
+                lst_sample.append(s)
+                # 每当达到批量大小时提交一次
+                if len(lst_sample) >= self.batch_size:
+                    db_session.bulk_save_objects(lst_sample)
+                    db_session.commit()
+                    print(f'Inserted {len(lst_sample)} samples!')
+                    lst_sample.clear()  # 清空已提交的样本列表
+
+        # 提交剩余的样本
+        if lst_sample:
+            db_session.bulk_save_objects(lst_sample)
+            db_session.commit()
+        print(f'Inserted {len(lst_sample)} samples!')
+
+    def reset_db(self, force_drop=False):
+        # first, check if tables exist
+        lst_table_obj = [
+            Base.metadata.tables[str_table]
+            for str_table in ins.get_table_names()
+            if str_table.startswith(self.db_name_prefix)
+        ]
+        self.is_exist = len(lst_table_obj) > 0
+        if force_drop:
+            self.force_drop_db(lst_table_obj)
+            # while is_exist:
+            #     a_table = random.choice(lst_table_obj)
+            #     try:
+            #         Base.metadata.drop_all(bind=engine, tables=[a_table])
+            #     except KeyError:
+            #         pass
+            #     except OperationalError:
+            #         pass
+            #     else:
+            #         lst_table_obj.remove(a_table)
+            #         print(
+            #             f"Table {a_table.name} is dropped "
+            #             f"for exp: {self.db_name_prefix}!!!"
+            #         )
+            #     finally:
+            #         is_exist = len(lst_table_obj) > 0
+
+        if self.is_exist:
+            print(
+                f"All tables exist. No need to reset "
+                f"for exp: {self.db_name_prefix}."
+            )
+            # change the is_done_flag from 0 to -1
+            # rerun the in-finished tasks
+            self.is_exist_reset_flag_resset_db()
+            # if self.reset_flag > 0:
+            #     if self.reset_flag == 2:
+            #         sample = db_session.query(Sample).filter(
+            #             Sample.is_done_flag == 0)
+            #     elif self.reset_flag == 1:
+            #         sample = db_session.query(Sample).filter(
+            #             Sample.is_done_flag == 0,
+            #             Sample.computer_name == platform.node())
+            #     else:
+            #         raise ValueError('Wrong reset flag')
+            #     if sample.count() > 0:
+            #         for s in sample:
+            #             qry_result = db_session.query(Result).filter_by(
+            #                 s_id=s.id)
+            #             if qry_result.count() > 0:
+            #                 db_session.query(Result).filter(s_id=s.id).delete()
+            #                 db_session.commit()
+            #             s.is_done_flag = -1
+            #             db_session.commit()
+            #             print(f"Reset the sample id {s.id} flag from 0 to -1")
+
+        else:
+            # 不存在则重新生成所有的表结构
+            Base.metadata.create_all(bind=engine)
+            self.init_tables()
+            print(
+                f"All tables are just created and initialized "
+                f"for exp: {self.db_name_prefix}."
+            )
+
+    def force_drop_db(self, lst_table_obj):
+        self.is_exist = len(lst_table_obj) > 0
+        while self.is_exist:
+            a_table = random.choice(lst_table_obj)
+            try:
+                Base.metadata.drop_all(bind=engine, tables=[a_table])
+            except KeyError:
+                pass
+            except OperationalError:
+                pass
+            else:
+                lst_table_obj.remove(a_table)
+                print(
+                    f"Table {a_table.name} is dropped "
+                    f"for exp: {self.db_name_prefix}!!!"
+                )
+            finally:
+                self.is_exist = len(lst_table_obj) > 0
+
+    def is_exist_reset_flag_resset_db(self):
+        if self.reset_flag > 0:
+            if self.reset_flag == 2:
+                sample = db_session.query(Sample).filter(
+                    Sample.is_done_flag == 0)
+            elif self.reset_flag == 1:
+                sample = db_session.query(Sample).filter(
+                    Sample.is_done_flag == 0,
+                    Sample.computer_name == platform.node())
+            else:
+                raise ValueError('Wrong reset flag')
+            if sample.count() > 0:
+                for s in sample:
+                    qry_result = db_session.query(Result).filter_by(
+                        s_id=s.id)
+                    if qry_result.count() > 0:
+                        db_session.query(Result).filter(s_id=s.id).delete()
+                        db_session.commit()
+                    s.is_done_flag = -1
+                    db_session.commit()
+                    print(f"Reset the sample id {s.id} flag from 0 to -1")
+
+    def prepare_list_sample(self):
+        # 为了符合前面 重置表里面存在 重置本机 或者重置全部 或者不重置的部分 这个部分的 关于样本运行也得重新拿出来
+        # 查找一个风险事件中 50 个样本
+        res = db_session.execute(
+            text(f"SELECT count(*) FROM {self.db_name_prefix}_sample s, "
+                 f"{self.db_name_prefix}_experiment e WHERE s.e_id=e.id"
+                 )).scalar()
+        # 控制 n_sample数量 作为后面的参数
+        n_sample = 0 if res is None else res
+        # print(f'There are a total of {n_sample} samples.')
+        # 查找 is_done_flag = -1 也就是没有运行的 样本 运行后会改为0
+        res = db_session.execute(
+            text(f"SELECT id FROM {self.db_name_prefix}_sample "
+                 f"WHERE is_done_flag = -1"
+                 ))
+        for row in res:
+            s_id = row[0]
+            self.lst_saved_s_id.append(s_id)
+
+    @staticmethod
+    def select_random_sample(lst_s_id):
+        temp_lst = lst_s_id[:]  # 复制列表
+        while temp_lst:
+            s_id = random.choice(temp_lst)
+            temp_lst.remove(s_id)  # 从临时列表删除
+            res = db_session.query(Sample).filter(
+                Sample.id == int(s_id),
+                Sample.is_done_flag == -1
+            )
+            if res.count() == 1:
+                return res[0]
+        # 尝试完所有样本都没找到
+        return None
+
+    def fetch_a_sample(self, s_id=None):
+        # 由Computation 调用 返回 sample对象 同时给出 2中 指定访问模式 抓取特定的 样本 通过s_id
+        # 默认访问 flag为-1的 lst_saved_s_id
+        if s_id is not None:
+            res = db_session.query(Sample).filter(Sample.id == int(s_id))
+            if res.count() == 0:
+                return None
+            else:
+                return res[0]
+
+        sample = self.select_random_sample(self.lst_saved_s_id)
+        if sample is not None:
+            return sample
+
+        return None
+
+    @staticmethod
+    def lock_the_sample(sample: Sample):
+        sample.is_done_flag, sample.computer_name = 0, platform.node()
+        db_session.commit()
+
+    def reset_sample_db(self):
+        """
+            将 iiabmdb_20250925.without_exp_sample 表中
+            所有样本的 is_done_flag 更新为 -1
+            """
+        sql = text("UPDATE iiabmdb_20250925.without_exp_sample SET is_done_flag = -1")
+        db_session.execute(sql)
+        db_session.commit()
+
+if __name__ == '__main__':
+    print("Testing the database connection...")
+    try:
+        controller_db = ControllerDB('test')
+        Base.metadata.create_all(bind=engine)
+    except Exception as e:
+        print("Failed to connect to the database!")
+        print(e)
+        exit(1)

GA_Agent_0925/count_firm.csv

@@ -0,0 +1,204 @@
+id_firm,count
+214851100,1722
+3111603340,1654
+70634828,1548
+25980377,643
+340093034,616
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GA_Agent_0925/count_firm_prod.csv

@@ -0,0 +1,266 @@
+id_firm,id_product,count
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GA_Agent_0925/count_prod.csv

@@ -0,0 +1,73 @@
+id_product,count
+95,1787
+90,1004
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+93,575
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+55,551
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+61,10
+36,8
+22,5

GA_Agent_0925/creating.py

@@ -0,0 +1,56 @@
+import random
+from deap import creator, base, tools
+from evaluate_func import fitness
+
+def creating():
+    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)
+
+    toolbox = base.Toolbox()
+
+    # 基因注册
+    toolbox.register("n_max_trial", random.randint, 1, 40)
+    toolbox.register("prf_size", random.uniform, 0.0, 1.0)
+    toolbox.register("prf_conn", random.uniform, 0.0, 1.0)
+    toolbox.register("cap_limit_prob_type", random.randint, 0, 1)
+    toolbox.register("cap_limit_level", random.randint, 5, 50)
+    toolbox.register("diff_new_conn", random.uniform, 0.0, 1.0)
+    toolbox.register("netw_prf_n", random.randint, 1, 20)
+    toolbox.register("s_r", random.uniform, 0.05, 0.5)
+    toolbox.register("S_r", random.uniform, 0.5, 1.0)
+    toolbox.register("x", random.uniform, 0.0, 1)
+    toolbox.register("k", random.uniform, 0.05, 2.0)
+    toolbox.register("production_increase_ratio", random.uniform, 0.5, 2.0)
+
+    # 个体与种群注册
+    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
+    )
+    toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+
+    # 遗传算子
+    toolbox.register("evaluate", fitness)
+    toolbox.register("mate", tools.cxTwoPoint)
+    toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.1)
+    toolbox.register("select", tools.selTournament, tournsize=3)
+
+    return toolbox

GA_Agent_0925/evaluate_func.py

@@ -0,0 +1,327 @@
+import os
+import time
+import traceback
+import uuid
+from datetime import datetime
+from multiprocessing import Process
+
+import pandas as pd
+from sqlalchemy import text
+from sqlalchemy.orm import sessionmaker
+
+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。
+
+    参数：
+        individual : list
+            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]
+
+    目标：
+        使 ABM 模型生成的“脆弱产业集合”与目标产业集合尽可能相似。
+        - fitness = -error
+        - error = 模拟结果集合与目标集合的差异度（越小越好）
+    """
+    # 生成唯一 GA ID
+    ga_id = str(uuid.uuid4())[:8]  # 简短随机ID
+    individual.ga_id = ga_id  # 将 ga_id 绑定到个体上
+    # ========== 1️⃣ 生成参数字典 ==========
+    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]
+    }
+    # 将 GA 染色体的值映射为实际 ABM 参数
+    if dct_exp['cap_limit_prob_type'] == 0:
+        dct_exp['cap_limit_prob_type'] = "uniform"  # 类型A（例如 uniform）
+    else:
+        dct_exp['cap_limit_prob_type'] = "normal"  # 类型B（例如 normal）
+
+    # 打印 GA ID 和参数
+    print(f"\n 正在执行 GA 个体 {ga_id}，参数如下：")
+    # for key, value in dct_exp.items():
+    #     print(f"  {key}: {value}")
+    # ========== 2️⃣ 调用 ABM 模型 ==========
+    # 并行进程数目
+    job=6
+
+    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)
+    print(simulated_vulnerable_industries)
+    # ========== 4️⃣ 获取目标产业集合 ==========
+    target_vulnerable_industries = get_target_vulnerable_industries()
+
+    # ========== 5️⃣ 计算误差（集合差异度） ==========
+    set_sim = set(simulated_vulnerable_industries)
+    set_target = set(target_vulnerable_industries)
+    error = len(set_sim.symmetric_difference(set_target))
+
+    simulated_set = set(simulated_vulnerable_industries)
+    target_set = set(target_vulnerable_industries)
+
+    matching = simulated_set & target_set  # 交集
+    extra = simulated_set - target_set  # 模拟多出的产业
+    missing = target_set - simulated_set  # 未覆盖产业
+
+    print(f"符合目标的产业数量: {len(matching)}")
+    print(matching)
+    print(f"模拟多出的产业数量: {len(extra)}")
+    print(f"未覆盖目标产业数量: {len(missing)}")
+
+    # ========== 6️⃣ 返回适应度（越大越好） ==========
+    return (float(-error),)
+# 目标产业集合
+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}
+    ]
+    # 手工转换
+    industry_list_index = vulnerable100_index = \
+        ['100', '58', '61', '9', '7', '98', '57', '8', '65', '68', '66', '38',
+         '90', '21', '96', '71', '27', '74', '99', '95', '11', '77', '59', '56', '97']
+    # # 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 industry_list_index
+# 从数据库计算脆弱产业集合
+def get_vulnerable100_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。
+    """
+    # 生成新的 session
+    Session = sessionmaker(bind=engine)
+    session = Session()
+    # 1️⃣ 读取 SQL 文件
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    sql_file = os.path.join(base_dir, "..", "GA_Agent_0925", "SQL_analysis_risk_ga.sql")
+    sql_file = os.path.abspath(sql_file)
+    with open(sql_file, "r", encoding="utf-8") as f:
+        str_sql = f.read()  # 注意这里是 str_sql，不是 tr_sql
+    print(f"[信息] 正在查询 ga_id={ga_id} 的脆弱产品数据...")
+
+    # 2️⃣ 执行 SQL 查询
+    # 2️⃣ 新建 connection
+    # 2️⃣ 新建 session，每次查询都用新的 session/connection
+    Session = sessionmaker(bind=engine)
+    with Session() as session:
+        # 使用 session.connection() 来保证 SQLAlchemy 执行原生 SQL
+        result = pd.read_sql(
+            sql=text(str_sql),
+            con=session.connection(),
+            params={"ga_id": ga_id}  # 绑定参数
+        )
+
+    # ===============================
+    # 2️⃣ 统计每个企业-产品组合出现次数
+    # ===============================
+    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()
+    count_firm_prod.to_csv('count_firm_prod.csv', index=False, encoding='utf-8-sig')
+
+    # ===============================
+    # 3️⃣ 统计每个企业出现的总次数
+    # ===============================
+    count_firm = count_firm_prod.groupby('id_firm')['count'].sum().reset_index()
+    count_firm.sort_values('count', ascending=False, inplace=True)
+    count_firm.to_csv('count_firm.csv', index=False, encoding='utf-8-sig')
+
+    # ===============================
+    # 4️⃣ 统计每个产品出现的总次数
+    # ===============================
+    count_prod = count_firm_prod.groupby('id_product')['count'].sum().reset_index()
+    count_prod.sort_values('count', ascending=False, inplace=True)
+    count_prod.to_csv('count_prod.csv', index=False, encoding='utf-8-sig')
+
+    # ===============================
+    # 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()
+    print(f"[信息] ga_id={ga_id} 查询完成，共找到 {len(vulnerable100_product)} 个脆弱产品")
+
+    # ===============================
+    # # 6️⃣ 过滤 result，只保留前100脆弱产品
+    # # ===============================
+    # result_vulnerable100 = result[result['id_product'].isin(vulnerable100_product)].copy()
+    # print(f"[信息] 筛选后剩余记录数: {len(result_vulnerable100)}")
+    # # ===============================
+    # # 7️⃣ 构造 DCP（Disruption Causing Probability）
+    # # ===============================
+    # result_dcp_list = []
+    # for sid, group in result_vulnerable100.groupby('s_id'):
+    #     ts_start = max(group['ts'])
+    #     while ts_start >= 1:
+    #         ts_end = ts_start - 1
+    #         while ts_end >= 0:
+    #             up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
+    #             down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
+    #             for _, up_row in up.iterrows():
+    #                 for _, down_row in down.iterrows():
+    #                     result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
+    #             ts_end -= 1
+    #         ts_start -= 1
+    #
+    # # 转换为 DataFrame
+    # result_dcp = pd.DataFrame(result_dcp_list, columns=[
+    #     's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
+    # ])
+    #
+    # # ===============================
+    # # 8️⃣ 统计 DCP 出现次数
+    # # ===============================
+    # count_dcp = result_dcp.value_counts(
+    #     subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
+    # ).reset_index(name='count')
+    #
+    # # 保存文件
+    # count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig')
+
+    # 输出结果
+    return vulnerable100_product
+
+def run_ABM_samples(controller_db_obj,ga_id,dct_exp, str_code="GA",):
+    """
+    从数据库获取一个随机样本，锁定它，然后运行模型仿真。
+    参数:
+        controller_db: ControllerDB 对象
+        str_code: 可选标识，用于打印
+    返回:
+        True 如果没有可用样本
+        False 如果成功运行
+    """
+    # 1. 从数据库获取一个随机样本
+    sample_random = controller_db_obj.fetch_a_sample(s_id=None)  # s_id 可根据需要传入
+    if sample_random is None:
+        #print(ga_id+"无样本")
+        return True  # 没有样本，返回 True 表示结束
+    # 2. 锁定该样本
+    controller_db_obj.lock_the_sample(sample_random)
+    # print(f"Pid {pid} ({str_code}) is running sample {sample_random.id} at {datetime.now()}")
+    # print(f"Pid {pid} ({str_code})")
+    # print(f"[信息] 当前正在运行的 GA 个体 ID: {ga_id}, 时间: {datetime.now()}")
+    # 3. 获取 experiment 的所有列及其值
+    dct_exp_new = {column: getattr(sample_random.experiment, column)
+               for column in sample_random.experiment.__table__.c.keys()}
+    # 删除不需要的主键 id
+    dct_exp_new.pop('id', None)
+    dct_exp_new = {'sample': sample_random,
+                       'seed': sample_random.seed,
+                       **dct_exp_new}
+    try:
+        dct_sample_para = {
+            **dct_exp_new,
+            **dct_exp}
+        # 切换工作目录到项目根目录或 ABM 需要的目录
+        project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+        os.chdir(project_root)
+
+        abm_model = MyModel(dct_sample_para)
+        abm_model.step()
+        abm_model.end(ga_id)
+    except Exception as e:
+        print(f"[❌ ABM运行错误] 错误：{e} ")
+        traceback.print_exc()
+
+def do_computation(controller_db_obj,ga_id,dct_exp,):
+    """每个进程执行 ABM 样本运行"""
+    pid = os.getpid()
+    print(f"[启动] 进程 {pid} 已启动 (PID={pid})")
+    while 1:
+        # time.sleep(random.uniform(0, 1))
+        is_all_done = run_ABM_samples(controller_db_obj,ga_id,dct_exp,)
+        if is_all_done:
+            break
+
+def do_process(controller_db,ga_id,dct_exp,job):
+    process_list = []
+    for i in range(job):
+        p = Process(target=do_computation, args=(controller_db,ga_id,dct_exp,))
+        p.start()
+        process_list.append(p)
+    for i in process_list:
+        i.join()

GA_Agent_0925/ga.py

@@ -1,116 +0,0 @@
-import numpy as np  # 引入NumPy库，用于高效的数值计算
-from pySOT.utils import round_vars  # 引入用于四舍五入的函数
-from typing import TYPE_CHECKING  # 引入类型检查工具
-if TYPE_CHECKING:
-    from policy import Policy
-
-class GeneticAlgorithm:  # 定义一个遗传算法类
-    def __init__(self, the_policy: 'Policy'):  # 初始化方法，接收一个 Policy 对象
-        self.n_variables = the_policy.dim  # 从 policy 中获取问题的维度
-        self.lower_boundary = the_policy.lb  # 获取决策变量的下界
-        self.upper_boundary = the_policy.ub  # 获取决策变量的上界
-        self.integer_variables = the_policy.int_var  # 获取整数变量的索引
-
-        self.sigma = 0.2  # 设置变异操作的标准差
-        self.p_mutation = 1.0 / the_policy.dim  # 设置变异概率
-        self.tournament_size = 5  # 设置锦标赛选择的大小
-        self.p_cross = 0.9  # 设置交叉概率
-
-        pop_size = the_policy.arr_init_doe_points.shape[0]  # 获取种群大小
-        self.lst_value = the_policy.lst_y_init_doe_points  # 初始化每个个体的适应度值
-
-        # 如果种群大小是奇数，生成一个随机个体来确保种群大小是偶数
-        if pop_size % 2 == 1:
-            arr_random = np.random.rand(1, self.n_variables)  # 生成一个随机的个体
-            arr_one_random = self.lower_boundary + arr_random * (self.upper_boundary - self.lower_boundary)  # 将随机个体约束在边界内
-            self.lst_value.append(the_policy.eval(arr_one_random[0, :], is_init_points=True))  # 评估该个体的适应度
-            self.population = np.vstack((the_policy.arr_init_doe_points, arr_one_random))  # 将该个体加入到种群中
-        else:
-            self.population = np.copy(the_policy.arr_init_doe_points)  # 直接使用初始种群
-
-        self.n_individuals = self.population.shape[0]  # 获取种群中个体的数量
-        assert self.n_individuals == pop_size or self.n_individuals == pop_size + 1, 'Wrong pop size'  # 确保种群大小正确
-
-        # 如果有整数变量，需要进行位置四舍五入
-        if len(self.integer_variables) > 0:
-            self.population[:, self.integer_variables] = np.round(self.population[:, self.integer_variables])  # 对整数变量四舍五入
-            for i in self.integer_variables:
-                ind = np.where(self.population[:, i] < self.lower_boundary[i])  # 如果超出了下界，修正为下界
-                self.population[ind, i] += 1
-                ind = np.where(self.population[:, i] > self.upper_boundary[i])  # 如果超出了上界，修正为上界
-                self.population[ind, i] -= 1
-
-        self.ind, self.best_individual, self.best_value = None, None, None  # 初始化最优个体和最优值
-        self.pop_next, self.lst_pop_next_is_evaluated = None, None  # 初始化下一代种群和评估标志
-        self.update_info()  # 更新最优解信息
-
-    def update_info(self):
-        # 更新最优个体和适应度值
-        self.ind = np.argmin(self.lst_value)  # 获取适应度最小的个体（假设目标是最小化）
-        self.best_individual = np.copy(self.population[self.ind, :])  # 复制最优个体
-        self.best_value = self.lst_value[self.ind]  # 记录最优值
-
-        self.pop_next, self.lst_pop_next_is_evaluated = self._generate_next_population()  # 生成下一代种群
-        self.lst_value = []  # 清空当前种群的适应度值
-
-    def _generate_next_population(self):
-        # 生成下一代种群
-        competitors = np.random.randint(0, self.n_individuals, (self.n_individuals, self.tournament_size))  # 随机选择竞赛个体
-        ind = np.argmin(np.array(self.lst_value)[competitors], axis=1)  # 选择每轮锦标赛中的最优个体
-        winner_indices = np.zeros(self.n_individuals, dtype=int)  # 用于存储胜利个体的索引
-        for i in range(self.tournament_size):  # 进行锦标赛选择
-            winner_indices[np.where(ind == i)] = competitors[np.where(ind == i), i]
-
-        # 按照锦标赛结果将种群分为父母
-        parent1 = self.population[winner_indices[0: self.n_individuals // 2], :]
-        parent2 = self.population[winner_indices[self.n_individuals // 2: self.n_individuals], :]
-
-        # 交叉操作：对父母个体进行交叉
-        cross = np.where(np.random.rand(self.n_individuals // 2) < self.p_cross)[0]  # 按照概率决定哪些个体进行交叉
-        nn = len(cross)  # 计算交叉个体的数量
-        alpha = np.random.rand(nn, 1)  # 生成交叉系数
-
-        # 创建新的染色体
-        parent1_new = np.multiply(alpha, parent1[cross, :]) + np.multiply(1 - alpha, parent2[cross, :])
-        parent2_new = np.multiply(alpha, parent2[cross, :]) + np.multiply(1 - alpha, parent1[cross, :])
-        parent1[cross, :] = parent1_new
-        parent2[cross, :] = parent2_new
-        arr_new_population = np.concatenate((parent1, parent2))  # 合并两个父代得到新的种群
-
-        # 变异操作
-        scale_factors = self.sigma * (self.upper_boundary - self.lower_boundary)  # 计算变异的尺度
-        perturbation = np.random.randn(self.n_individuals, self.n_variables)  # 生成扰动
-        perturbation = np.multiply(perturbation, scale_factors)  # 根据尺度调整扰动
-        perturbation = np.multiply(
-            perturbation, (np.random.rand(self.n_individuals, self.n_variables) < self.p_mutation)
-        )  # 根据变异概率决定哪些位置进行扰动
-
-        arr_new_population += perturbation  # 将扰动添加到新种群
-        arr_new_population = np.maximum(np.reshape(self.lower_boundary, (1, self.n_variables)), arr_new_population)  # 确保不超过下界
-        arr_new_population = np.minimum(np.reshape(self.upper_boundary, (1, self.n_variables)), arr_new_population)  # 确保不超过上界
-
-        # 如果有整数变量，进行四舍五入
-        if len(self.integer_variables) > 0:
-            arr_new_population = round_vars(arr_new_population, self.integer_variables, self.lower_boundary,
-                                            self.upper_boundary)
-
-        assert arr_new_population.shape[0] == self.n_individuals, 'Wrong arr_new_population shape'  # 确保新种群的大小正确
-        return arr_new_population, [False] * self.n_individuals  # 返回新种群和评估标志（都设为未评估）
-
-    def select_next_point(self):
-        # 选择下一个要评估的个体
-        for idx_ind, is_evaluated in enumerate(self.lst_pop_next_is_evaluated):
-            if not is_evaluated:  # 如果该个体没有被评估
-                return self.pop_next[idx_ind, :]  # 返回该个体
-
-    def receive_sim_value(self, the_value):
-        # 接收评估结果，并更新适应度信息
-        self.lst_value.append(the_value)  # 将评估值添加到适应度列表
-        idx_ind = 0
-        for idx_ind, is_evaluated in enumerate(self.lst_pop_next_is_evaluated):
-            if not is_evaluated:  # 找到未评估的个体
-                self.lst_pop_next_is_evaluated[idx_ind] = True  # 标记该个体为已评估
-                break
-        if idx_ind == len(self.lst_pop_next_is_evaluated) - 1:
-            assert idx_ind == self.n_individuals - 1, 'Wrong index'  # 确保所有个体都已评估
-            self.update_info()  # 更新最优解信息

GA_Agent_0925/main.py

@@ -0,0 +1,109 @@
+import json
+import random
+from deap import tools
+from sqlalchemy.orm import close_all_sessions
+from tqdm import tqdm
+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
+
+
+# ==============================
+# 遗传算法主函数（单进程）
+# ==============================
+def main():
+    # 1️⃣ 加载配置
+    with open("config.json", "r", encoding="utf-8") as f:
+        cfg = json.load(f)
+
+    random.seed(cfg["seed"])
+
+    print("\n📘 参数配置:")
+    for k, v in cfg.items():
+        print(f"  {k}: {v}")
+    print("-" * 40)
+
+    # 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)
+    # 准备样本表
+    controller_db_obj.prepare_list_sample()
+    # 2️⃣ 初始化工具箱
+    toolbox = creating()
+    pop = toolbox.population(n=cfg["pop_size"])
+    hof = tools.HallOfFame(1)
+    stats = tools.Statistics(lambda ind: ind.fitness.values)
+    stats.register("avg", lambda fits: sum(f[0] for f in fits) / len(fits))
+    stats.register("max", lambda fits: max(f[0] for f in fits))
+
+    best_list = []
+    avg_list = []
+
+    # ==============================
+    # 主进化循环
+    # ==============================
+    for gen in tqdm(range(cfg["n_gen"]), desc="进化中", ncols=90):
+        # 计算未评估个体适应度
+        invalid_ind = [ind for ind in pop if not ind.fitness.valid]
+        for ind in invalid_ind:
+            controller_db_obj.reset_sample_db()
+            controller_db_obj.prepare_list_sample()
+            ind.fitness.values = fitness(ind, controller_db_obj=controller_db_obj)
+
+        # 选择、交叉、变异
+        offspring = toolbox.select(pop, len(pop))
+        offspring = list(map(toolbox.clone, offspring))
+
+        for child1, child2 in zip(offspring[::2], offspring[1::2]):
+            if random.random() < cfg["cx_prob"]:
+                toolbox.mate(child1, child2)
+                del child1.fitness.values, child2.fitness.values
+
+        for mutant in offspring:
+            if random.random() < cfg["mut_prob"]:
+                toolbox.mutate(mutant)
+                del mutant.fitness.values
+
+        # 更新适应度
+        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
+        for ind in invalid_ind:
+            controller_db_obj.reset_sample_db()
+            controller_db_obj.prepare_list_sample()
+            ind.fitness.values = fitness(ind, controller_db_obj=controller_db_obj)
+
+        pop[:] = offspring
+        hof.update(pop)
+
+        record = stats.compile(pop)
+        best_list.append(record["max"])
+        avg_list.append(record["avg"])
+
+    # ==============================
+    # 输出最优结果
+    # ==============================
+    print("\n✅ 进化完成！")
+    print(f"🏆 最优个体: {hof[0]}")
+    print(f"🌟 最优适应度: {hof[0].fitness.values[0]:.4f}")
+
+    # 绘制收敛曲线
+    plt.figure(figsize=(8, 5))
+    plt.plot(best_list, label="Best Fitness", linewidth=2)
+    plt.plot(avg_list, label="Average Fitness", linestyle="--")
+    plt.title("Genetic Algorithm Convergence")
+    plt.xlabel("Generation")
+    plt.ylabel("Fitness")
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.show()
+
+    # ==============================
+    # 最优个体产业匹配
+    # ==============================
+    print("\n📊 计算最优个体产业匹配情况...")
+
+if __name__ == "__main__":
+    main()

GA_Agent_0925/orm.py

@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+from datetime import datetime
+from sqlalchemy import create_engine, inspect, Inspector, Float
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy import (Column, Integer, DECIMAL, String, ForeignKey,
+                        BigInteger, DateTime, PickleType, Boolean, Text)
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship, Session
+from sqlalchemy.pool import NullPool
+import yaml
+
+with open('../conf_db.yaml') as file:
+    dct_conf_db_all = yaml.full_load(file)
+    is_local_db = dct_conf_db_all['is_local_db']
+    if is_local_db:
+        dct_conf_db = dct_conf_db_all['local']
+    else:
+        dct_conf_db = dct_conf_db_all['remote']
+
+with open('../conf_db_prefix.yaml') as file:
+    dct_conf_db_prefix = yaml.full_load(file)
+    db_name_prefix = dct_conf_db_prefix['db_name_prefix']
+
+str_login = 'mysql://{}:{}@{}:{}/{}'.format(dct_conf_db['user_name'],
+                                            dct_conf_db['password'],
+                                            dct_conf_db['address'],
+                                            dct_conf_db['port'],
+                                            dct_conf_db['db_name'])
+# print('DB is {}:{}/{}'.format(dct_conf_db['address'], dct_conf_db['port'], dct_conf_db['db_name']))
+
+# must be null pool to avoid connection lost error
+engine = create_engine(str_login, poolclass=NullPool)
+connection = engine.connect()
+ins: Inspector = inspect(engine)
+
+Base = declarative_base()
+
+db_session = Session(bind=engine)
+
+
+class Experiment(Base):
+    __tablename__ = f"{db_name_prefix}_experiment"
+    id = Column(Integer, primary_key=True, autoincrement=True)
+
+    idx_scenario = Column(Integer, nullable=False)
+    idx_init_removal = Column(Integer, nullable=False)
+
+    # fixed parameters
+    n_sample = Column(Integer, nullable=False)
+    n_iter = Column(Integer, nullable=False)
+
+    # variables
+    dct_lst_init_disrupt_firm_prod = Column(PickleType, nullable=False)
+    g_bom = Column(Text(4294000000), nullable=False)
+
+    n_max_trial = Column(Integer, nullable=False)
+    prf_size = Column(Boolean, nullable=False)
+    prf_conn = Column(Boolean, nullable=False)
+    cap_limit_prob_type = Column(String(16), nullable=False)
+    cap_limit_level = Column(DECIMAL(8, 4), nullable=False)
+    diff_new_conn = Column(DECIMAL(8, 4), nullable=False)
+    remove_t = Column(Integer, nullable=False)
+    netw_prf_n = Column(Integer, nullable=False)
+
+    sample = relationship(
+        'Sample', back_populates='experiment', lazy='dynamic')
+
+    def __repr__(self):
+        return f'<Experiment: {self.id}>'
+
+
+class Sample(Base):
+    __tablename__ = f"{db_name_prefix}_sample"
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    e_id = Column(Integer, ForeignKey('{}.id'.format(
+        f"{db_name_prefix}_experiment")), nullable=False)
+
+    idx_sample = Column(Integer, nullable=False)
+    seed = Column(BigInteger, nullable=False)
+    # -1, waiting; 0, running; 1, done
+    is_done_flag = Column(Integer, nullable=False)
+    computer_name = Column(String(64), nullable=True)
+    ts_done = Column(DateTime(timezone=True), onupdate=func.now())
+    stop_t = Column(Integer, nullable=True)
+
+    g_firm = Column(Text(4294000000), nullable=True)
+
+    experiment = relationship(
+        'Experiment', back_populates='sample', uselist=False)
+    result = relationship('Result', back_populates='sample', lazy='dynamic')
+
+    def __repr__(self):
+        return f'<Sample id: {self.id}>'
+
+
+class Result(Base):
+    __tablename__ = f"{db_name_prefix}_result"
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    s_id = Column(Integer, ForeignKey('{}.id'.format(
+        f"{db_name_prefix}_sample")), nullable=False)
+
+    id_firm = Column(String(20), nullable=False)
+    id_product = Column(String(20), nullable=False)
+    ts = Column(Integer, nullable=False)
+    status = Column(String(5), nullable=False)
+
+    sample = relationship('Sample', back_populates='result', uselist=False)
+
+    # 💥 新增 GA 调用 ID，用于标记属于哪一次遗传算法运行
+    ga_id = Column(String(50), nullable=True)
+
+    def __repr__(self):
+        return f'<Product id: {self.id}>'
+
+if __name__ == '__main__':
+    Base.metadata.drop_all()
+    Base.metadata.create_all()

GA_Agent_0925/risk_firm_count.py

@@ -1,23 +0,0 @@
-from sqlalchemy import text
-
-import pandas as pd
-
-from orm import connection
-
-# SQL query
-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)
-# Count firm product
-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()
-
-# Count product
-count_prod = count_firm_prod.groupby('id_product')['count'].sum()
-count_prod = count_prod.to_frame().reset_index()
-count_prod.sort_values('count', inplace=True, ascending=False)
-print(count_prod)
-top100 = count_prod.head(100)['id_product'].tolist()
-

GA_Agent_0925/多功能.py

@@ -0,0 +1,211 @@
+import json
+import os
+import pickle
+import networkx as nx
+from sqlalchemy import text
+
+import pandas as pd
+
+from orm import connection
+
+# """
+# 计算最脆弱前100产品的 Code 列表（去重）。
+# """
+# bom_file = r"../input_data/input_product_data/BomNodes.csv"
+# mapping_df = pd.read_csv(bom_file)
+#
+# 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)
+#
+# 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()
+#
+# count_prod = (
+#     count_firm_prod.groupby("id_product")["count"].sum().reset_index()
+# )
+#
+# vulnerable100_index = count_prod.nsmallest(100, "count")["id_product"].tolist()
+# # 确保 index_to_code 的 key 都是 int
+# index_to_code = {int(k): v for k, v in zip(mapping_df["Index"], mapping_df["Code"])}
+#
+# # vulnerable100_index 也转成 int
+# vulnerable100_index_int = [int(i) for i in vulnerable100_index]
+#
+# # 获取 code
+# vulnerable100_code = [index_to_code[i] for i in vulnerable100_index_int if i in index_to_code]
+#
+# print(vulnerable100_code)
+
+
+# 读取 SQL
+ga_id="c943f2c6"
+with open("SQL_analysis_risk_ga.sql", "r", encoding="utf-8") as f:
+    str_sql = text(f.read())
+    print(str_sql[:300])
+print(f"[信息] 正在查询 ga_id={ga_id} 的脆弱产品数据...")
+
+# 执行 SQL 查询，并绑定参数 ga_id
+result = pd.read_sql(
+    sql=str_sql,
+    con=connection,
+    params={"ga_id": ga_id}  # 绑定参数
+)
+# ===============================
+# 2️⃣ 统计每个企业-产品组合出现次数
+# ===============================
+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()
+count_firm_prod.to_csv('count_firm_prod.csv', index=False, encoding='utf-8-sig')
+
+# ===============================
+# 3️⃣ 统计每个企业出现的总次数
+# ===============================
+count_firm = count_firm_prod.groupby('id_firm')['count'].sum().reset_index()
+count_firm.sort_values('count', ascending=False, inplace=True)
+count_firm.to_csv('count_firm.csv', index=False, encoding='utf-8-sig')
+
+# ===============================
+# 4️⃣ 统计每个产品出现的总次数
+# ===============================
+count_prod = count_firm_prod.groupby('id_product')['count'].sum().reset_index()
+count_prod.sort_values('count', ascending=False, inplace=True)
+count_prod.to_csv('count_prod.csv', index=False, encoding='utf-8-sig')
+
+# ===============================
+# 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()
+print(f"[信息] ga_id={ga_id} 查询完成，共找到 {len(vulnerable100_product)} 个脆弱产品")
+
+# ===============================
+# 6️⃣ 过滤 result，只保留前100脆弱产品
+# ===============================
+result_vulnerable100 = result[result['id_product'].isin(vulnerable100_product)].copy()
+print(f"[信息] 筛选后剩余记录数: {len(result_vulnerable100)}")
+
+# ===============================
+# 7️⃣ 构造 DCP（Disruption Causing Probability）
+# ===============================
+result_dcp_list = []
+for sid, group in result_vulnerable100.groupby('s_id'):
+    ts_start = max(group['ts'])
+    while ts_start >= 1:
+        ts_end = ts_start - 1
+        while ts_end >= 0:
+            up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
+            down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
+            for _, up_row in up.iterrows():
+                for _, down_row in down.iterrows():
+                    result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
+            ts_end -= 1
+        ts_start -= 1
+
+# 转换为 DataFrame
+result_dcp = pd.DataFrame(result_dcp_list, columns=[
+    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
+])
+
+# ===============================
+# 8️⃣ 统计 DCP 出现次数
+# ===============================
+count_dcp = result_dcp.value_counts(
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
+).reset_index(name='count')
+
+# 保存文件
+count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig')
+
+# 输出结果
+print(count_dcp)
+print(type(vulnerable100_product[0]))
+
+# 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"]))
+# print(list(chain_ids))
+# fill g_bom
+# 结点属性值 相当于 图上点的 原始 产品名称
+# bom_nodes = pd.read_csv('../input_data/input_product_data/BomNodes.csv')
+# bom_nodes['Code'] = bom_nodes['Code'].astype(str)
+# bom_nodes.set_index('Index', inplace=True)
+#
+# bom_cate_net = pd.read_csv('../input_data/input_product_data/合成结点.csv')
+# g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())
+# # 填充每一个结点 的具体内容 通过 相同的 code 并且通过BomNodes.loc[code].to_dict()字典化 格式类似 格式 { code（0） :  {level: 0 ,name: 工业互联网   }}
+# bom_labels_dict = {}
+# for index in g_bom.nodes:
+#     try:
+#         bom_labels_dict[index] = bom_nodes.loc[index].to_dict()
+#         # print(bom_labels_dict[index])
+#     except KeyError:
+#         print(f"节点 {index} 不存在于 bom_nodes 中")
+# # 分配属性 给每一个结点 获得类似 格式：{1: {'label': 'A', 'value': 10},
+# nx.set_node_attributes(g_bom, bom_labels_dict)
+# # 改为json 格式
+# g_product_js = json.dumps(nx.adjacency_data(g_bom))
+# # 假设 g_bom 是你的 NetworkX 图
+# g_product_data = nx.adjacency_data(g_bom)
+#
+# # 保存为 pkl 文件
+# with open("g_bom.pkl", "wb") as f:
+#     pickle.dump(g_product_data, f)
+#
+# print("✅ 图数据已保存为 g_bom.pkl")

GA_Agent_0925/废案/GA_random.py

@@ -1,6 +1,7 @@
 # -*- 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
@@ -11,6 +12,77 @@ 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):
     """
@@ -77,6 +149,10 @@ 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
@@ -124,7 +200,7 @@ def creating():
     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
+    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)
@@ -133,76 +209,6 @@ def creating():
 
     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）。
@@ -280,11 +286,6 @@ def get_target_vulnerable_industries():
 
     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）。
@@ -294,11 +295,11 @@ def get_vulnerable100_code(connection):
         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
+    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:
+    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)

GA_Agent_0925/进度.py

@@ -0,0 +1,43 @@
+from matplotlib import rcParams, pyplot as plt
+from sqlalchemy import func
+from orm import db_session, Sample
+
+# 🔹 全局创建绘图对象
+plt.ion()  # 启用交互模式
+fig, ax = plt.subplots(figsize=(8, 5))
+rcParams['font.family'] = 'Microsoft YaHei'
+rcParams['font.size'] = 12
+
+# 初始化柱状图
+labels = ['未完成 (-1)', '计算中(0)', '完成 (1)']
+initial_values = [0, 0, 0]
+bars = ax.bar(labels, initial_values, color=['red', 'orange', 'green'])
+value_texts = [ax.text(bar.get_x() + bar.get_width()/2, 0, '0',
+                       ha='center', va='bottom', fontsize=12)
+               for bar in bars]
+
+ax.set_title('任务进度分布', fontsize=16)
+ax.set_xlabel('任务状态', fontsize=14)
+ax.set_ylabel('数量', fontsize=14)
+ax.tick_params(axis='both', labelsize=12)
+
+def visualize_progress():
+    """
+    实时更新 Sample 表中 is_done_flag 的分布。
+    """
+    # 查询数据库
+    result = db_session.query(Sample.is_done_flag, func.count(Sample.id))\
+                       .group_by(Sample.is_done_flag).all()
+    data = {flag: count for flag, count in result}
+    for flag in [-1, 0, 1]:
+        data.setdefault(flag, 0)
+    values = [data[-1], data[0], data[1]]
+
+    # 更新柱子高度和文本
+    for bar, new_val, txt in zip(bars, values, value_texts):
+        bar.set_height(new_val)
+        txt.set_y(new_val + 0.5)
+        txt.set_text(str(new_val))
+
+    plt.draw()
+    plt.pause(0.1)  # 刷新图表

best_result_with_industry.json

@@ -0,0 +1,192 @@
+{
+  "best_individual": [
+    274,
+    0.12435365817470823,
+    0.6886779912120937,
+    1,
+    0.640080800113344,
+    0.5004721771179256,
+    8,
+    0.27510354542863924,
+    0.7990183310205519,
+    0.08894228892924537,
+    0.7610805309570537,
+    1.405570461418866
+  ],
+  "best_fitness": -9.0,
+  "best_per_gen": [
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0
+  ],
+  "avg_per_gen": [
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0,
+    -9.0
+  ],
+  "industry_matching": {
+    "matching": [],
+    "extra": [
+      56320,
+      56321,
+      56322,
+      56323,
+      8,
+      317586,
+      317589,
+      2714,
+      2715,
+      2716,
+      2717,
+      2718,
+      46505,
+      32432,
+      32433,
+      32434,
+      32435,
+      32436,
+      32437,
+      32438,
+      32439,
+      32440,
+      32441,
+      513720,
+      32443,
+      32445,
+      32446,
+      32447,
+      32448,
+      32449,
+      32450,
+      32451,
+      513738,
+      513742,
+      32338,
+      513751,
+      34524,
+      34525,
+      34526,
+      34527,
+      34528,
+      34529,
+      34530,
+      34531,
+      34532,
+      34533,
+      34534,
+      34535,
+      34537,
+      34538,
+      34539,
+      34543,
+      36914,
+      34557,
+      56319
+    ],
+    "missing": [
+      "34530",
+      "34539",
+      "2515",
+      "10",
+      "513742",
+      "9",
+      "34529",
+      "34533",
+      "513740"
+    ]
+  }
+}

conf_experiment.yaml

@@ -8,5 +8,5 @@ test:  # only for test scenarios
   n_iter: 100
 
 not_test:  # normal scenarios
-  n_sample: 50
+  n_sample: 10
-  n_iter: 100
+  n_iter: 50

controller_db.py

@@ -32,7 +32,7 @@ class ControllerDB:
         dct_para_in_test = dct_conf_experiment['test'] if self.is_test else dct_conf_experiment['not_test']
         self.dct_parameter = {'meta_seed': dct_conf_experiment['meta_seed'], **dct_para_in_test}
 
-        print(self.dct_parameter)
+        # print(self.dct_parameter)
         # 0, not reset; 1, reset self; 2, reset all
         self.reset_flag = reset_flag
         self.is_exist = False
@@ -328,7 +328,7 @@ class ControllerDB:
                  )).scalar()
         # 控制 n_sample数量 作为后面的参数
         n_sample = 0 if res is None else res
-        print(f'There are a total of {n_sample} samples.')
+        # print(f'There are a total of {n_sample} samples.')
         # 查找 is_done_flag = -1 也就是没有运行的 样本 运行后会改为0
         res = db_session.execute(
             text(f"SELECT id FROM {self.db_name_prefix}_sample "
@@ -340,15 +340,18 @@ class ControllerDB:
 
     @staticmethod
     def select_random_sample(lst_s_id):
-        while 1:
+        temp_lst = lst_s_id[:]  # 复制列表
-            if len(lst_s_id) == 0:
+        while temp_lst:
-                return None
+            s_id = random.choice(temp_lst)
-            s_id = random.choice(lst_s_id)
+            temp_lst.remove(s_id)  # 从临时列表删除
-            lst_s_id.remove(s_id)
+            res = db_session.query(Sample).filter(
-            res = db_session.query(Sample).filter(Sample.id == int(s_id),
+                Sample.id == int(s_id),
-                                                  Sample.is_done_flag == -1)
+                Sample.is_done_flag == -1
+            )
             if res.count() == 1:
                 return res[0]
+        # 尝试完所有样本都没找到
+        return None
 
     def fetch_a_sample(self, s_id=None):
         # 由Computation 调用 返回 sample对象 同时给出 2中 指定访问模式 抓取特定的 样本 通过s_id

count_firm.csv

@@ -0,0 +1 @@
+id_firm,count

count_firm_prod.csv

@@ -0,0 +1 @@
+id_firm,id_product,count

count_prod.csv

@@ -0,0 +1 @@
+id_product,count

firm.py

@@ -1,3 +1,5 @@
+from collections import defaultdict
+
 from mesa import Agent
 
 
@@ -20,7 +22,7 @@ class FirmAgent(Agent):
         # 各资源库存信息，库存资源，库存量
         self.R = R
         # 包括库存时间的值 方便后面统计
-        self.R1 = {0: R}
+        self.R1 = R
         # 设备资产信息，持有设备，设备数量， 增加 设备残值 [[1,2,3],[] ]
         self.C = C
         # 包括设备时间步的值
@@ -45,7 +47,7 @@ class FirmAgent(Agent):
         self.c_consumption = demand_quantity
         # 设备c购买价格（初始值）
         # self.c_price = c_price
-        # 资源r补货库存阈值 很重要设置
+        # 资源r补货库存阈值
         self.s_r = s_r
         self.S_r = S_r
         # 设备补货阙值 可选
@@ -344,7 +346,7 @@ class FirmAgent(Agent):
                 list_C[1] -= machi_list[1]
 
     def refresh_R(self):
-        self.R1[self.model.t] = self.R
+        self.R1 = self.R
 
     def refresh_C(self):
         self.C1[self.model.t] = self.C

input_data/firm_industry_relation.csv

@@ -1,364 +1,364 @@
 Firm_Code,Product_Code
 1,7
-5,7
+1,7
-29954548,7
+6,7
-29954548,7
+6,7
-453289520,8
+8,8
-453289520,8
+8,8
-3472022914,2514
+626924399,2514
-79412414,2514
+42106161,2514
-490476776,2514
+8971203,2514
-720737055,2515
+25685135,2515
-850972471,2515
+25685135,2515
-350343208,2714
+2336923756,2714
-37873062,2714
+3191869223,2714
-1266556718,2714
+591350440,2714
-331545755,2715
+16210433,2715
-3193516458,2715
+517675473,2715
-41454763,2715
+16116663,2715
-584019624,2716
+2349179532,2716
-185356903,2716
+728969035,2716
-22751149,2716
+471121089,2716
-27169556,2717
+331545755,2717
-3346538900,2717
+2326722141,2717
-2541265952,2717
+2337727838,2717
-777299215,2718
+2327605629,2718
-18107611,2718
+2349705416,2718
-4067555184,32338
+214851100,32338
-2313177432,32338
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-12098344,32338
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-4208851809,32432
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input_data/input_firm_data/firm_amended.csv

@@ -1,306 +1,315 @@
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-43566171,19425.4177478546,11225.1590676402,27895.4993691358,1763.15281,长白朝鲜族自治县天宝硅藻土功能制品有限公司,吉林省,4.45
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-409663925,63931.7546131924,36943.5614884361,91807.9726072824,5802.7814,三河建华高科有限责任公司,河北省,4.96
-3414534661,227798.54904047,146248.694269867,454874.703635286,46215.3088984286,厦门弘信电子科技集团股份有限公司,福建省,5.66
-3393219477,245.891363896894,142.090621109369,353.107586951086,22.31839,吉林新环诚汽车科技有限公司,吉林省,2.55
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-2321857672,172615.737455619,99747.6160187774,247881.526039662,15667.50978,福建福顺半导体制造有限公司,福建省,5.39
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-2311907103,61082.6578818333,46952.3648285,333112.031847833,76726.8203528333,苏州赛腾精密电子股份有限公司,江苏省,5.52
-3464943902,368.837045845341,213.135931664054,529.661380426629,33.477585,南京波彩汽车服务有限公司,江苏省,2.72
-259923931,1426.16991060198,824.125602434345,2048.0240043163,129.446662,甘肃明辰环保科技有限公司,甘肃省,3.31
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-186745206,322457.554289333,214280.913465333,594869.173440333,31534.1938276667,四川东材科技集团股份有限公司,四川省,5.77
-507827038,690052.603181786,422522.420147987,1589615.70303614,134419.051238286,潮州三环（集团）股份有限公司,广东省,6.2
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-505990558,2704.80500286583,1562.99683220306,3884.18345646195,245.50229,铜陵佳友科技有限公司,安徽省,3.59
-221048382,157338.070008167,103120.752683167,993163.7559685,352447.843621667,云南省贵金属新材料控股集团股份有限公司,云南省,6.0
-2348910693,26926.1598385298,15632.4205943617,40928.6516936667,4641.11761583333,北京康美特科技股份有限公司,北京市,4.61
-2342515031,55858.3214985777,32278.2527620118,80214.2735023883,5069.99426166667,苏州住友电木有限公司,江苏省,4.9
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-2317568755,11034.5563371667,7291.74244,86496.1049655,7858.70136516667,深圳科创新源新材料股份有限公司,广东省,4.94
-2349511062,8646.87845536109,4996.67947221875,12417.1843111667,1190.53784783333,江苏明昊新材料科技股份有限公司,江苏省,4.09
-500189853,22867.8968424112,13214.4277631714,32839.005586451,2075.61027,东莞升洋焊锡材料有限公司,广东省,4.52
-18065940,191013.048499246,96582.5538206939,867380.822769857,143283.010690286,北京君正集成电路股份有限公司,北京市,5.94
-2333993502,206465.644948888,142992.065005962,241630.438272143,17415.7935534286,合肥新汇成微电子股份有限公司,安徽省,5.38
-433384648,13130.5988320941,7587.63916724033,18855.945143188,1191.802026,牡丹江市北亚硬质合金有限责任公司,黑龙江省,4.28
-344181818,162948.972334805,104126.347671517,466055.759069286,79181.2545228571,青岛高测科技股份有限公司,山东省,5.67
-1104420298,86588.4830698333,50706.5640115,1206682.12055083,213731.195733667,中微半导体设备（上海）股份有限公司,上海市,6.08
-2349349655,8916.26246416139,5152.34553759312,12804.0280612,1116.3192482,苏州博洋化学股份有限公司,江苏省,4.11
-420984285,223613.011910333,158257.78831,1015669.78454783,62168.8641543333,深圳新宙邦科技股份有限公司,广东省,6.01
-2329836516,78826.6177775,56812.0715446667,203111.819117167,4786.47693733333,江阴江化微电子材料股份有限公司,江苏省,5.31
-27599908,65243.1752206425,37701.3781343527,93691.2130710213,5921.81281333333,北京航天晨信科技有限责任公司,北京市,4.97
-2350719552,1767.01581175,997.23466825,55246.80543625,11314.5121375,深圳好博窗控技术股份有限公司,广东省,4.74
-3216066502,47519.3914293237,25919.6030558896,183625.261502,20677.0661462857,楚天龙股份有限公司,广东省,5.26
-39698451,107938.314838978,39590.1075543603,412101.092700429,69378.6032017143,东方通信股份有限公司,浙江省,5.62
-2351643794,24220.299343844,13995.9261792729,34781.097314682,2198.361415,亚洲信用卡厂（深圳）有限公司,广东省,4.54
-2351192662,8851.83008107238,5548.49697731345,27867.47160725,3416.11452834962,河北晶禾电子技术股份有限公司,河北省,4.45
-865049663,87774.8053041193,59882.7303796437,298504.191277714,43328.108973,国民技术股份有限公司,广东省,5.47
-891649,85550.3230285109,36367.0451934161,230945.34964,25810.7164891429,恒宝股份有限公司,江苏省,5.36
-3424978618,31885.9688027932,21504.6483379033,177114.294382333,56086.1265501667,深圳市智微智能科技股份有限公司,广东省,5.25
-3145156061,245.891363896894,142.090621109369,353.107586951086,22.31839,北京中星微人工智能芯片技术有限公司,北京市,2.55
-281599332,64820.07552,40154.6568665,558302.417650333,40598.0444641667,金卡智能集团股份有限公司,浙江省,5.75
-79938367,364886.103943167,192426.6793045,3404308.61500033,438207.673933167,海信视像科技股份有限公司,山东省,6.53
-864169770,170444.206583047,110508.776061746,469960.309361,24457.7740034286,深圳市聚飞光电股份有限公司,广东省,5.67
-2310296367,37034.8887722,31909.4172092,800509.2176356,31554.9076322,澜起科技股份有限公司,上海市,5.9
-774611690,87455.3617593286,50536.8975745657,125588.598425603,7937.90737666667,成都大唐线缆有限公司,四川省,5.1
-2350442566,24785.8494808069,14322.7346078244,35593.2447646695,2249.693712,无锡友达电子有限公司,江苏省,4.55
-654461595,14660.1619921667,11316.6484308333,119189.010378333,8316.87563333333,上海灿瑞科技股份有限公司,上海市,5.08
-383463860,498520.151164562,288074.525237136,715890.321784631,45248.303886,广东虹勤通讯技术有限公司,广东省,5.85
-463659395,164973.071000475,103647.445394252,518579.052314833,39793.0929578333,深圳市奋达科技股份有限公司,广东省,5.71
-2347105663,7670.18126121354,4432.28587679994,11014.61699875,4603.6098265,深圳市百泰实业股份有限公司,广东省,4.04
-504638253,84443.6902996838,49855.5442456137,204519.3508715,51230.2114925,珠海市杰理科技股份有限公司,广东省,5.31
-519195163,1924.04639447068,1111.82817851309,2762.99,269.27,深圳市迪浦电子有限公司,广东省,3.44
-3221578464,6393.17546131924,3694.35614884361,9180.79726072824,580.27814,北京电星互动文化传媒有限公司,北京市,3.96
-2316256865,31675.2583182562,18154.1646929991,146872.233407667,30518.2317446667,广州慧智微电子股份有限公司,广东省,5.17
-3269840248,368.837045845341,213.135931664054,529.661380426629,33.477585,安徽创矽电子科技有限公司,安徽省,2.72
-1675147952,16925.06773,12545.4403666,86939.851294,9412.2279396,广州安凯微电子股份有限公司,广东省,4.94
-29223617,49351.6354965,38030.0537195,336478.787652167,25356.7104388333,青岛东软载波科技股份有限公司,山东省,5.53
-2553848709,15982.9386532981,9235.89037210901,22951.9931518205,1450.69535,深圳智微电子科技股份有限公司,广东省,4.36
-168035745,4475.22282292347,2586.04930419052,6426.55808250976,406.194698,成都艾希联科技有限公司,四川省,3.81
-510149116,4130.97491346782,2387.12243463741,5932.20746077824,374.948952,深圳市宝视达科技有限公司,广东省,3.77
-5591349,214284.563403751,124927.075093795,1154545.83667914,44936.3798348571,北京四维图新科技股份有限公司,北京市,6.06
-274839085,5072.9571540186,2931.45566990296,7284.9230285,1251.8898265,上海长合信息技术股份有限公司,上海市,3.86
-4209347174,10573.3286475664,6109.89670770289,15183.6262388967,959.69077,四川锦路通科技有限公司,四川省,4.18
-413142822,33691.2724915977,19012.8064225955,295228.181493667,9677.11797866667,北京弘高创意建筑设计股份有限公司,北京市,5.47
-951988821,25605.8899302788,18756.1741981746,351800.143148667,88668.7767376667,武汉帝尔激光科技股份有限公司,湖北省,5.55
-1587526,37281.2763304483,20821.6870507603,264083.505326,37562.7044782857,北京北信源软件股份有限公司,北京市,5.42
-3402194899,614.728409742234,355.226552773424,882.768967377713,55.795975,吉林省依岚机器人科技有限公司,吉林省,2.95
-730857,170484.678968513,98516.1639691628,244821.260286086,15474.0837333333,北京机械工业自动化研究所有限公司,北京市,5.39
-27085933,39068.3169418316,24985.5123183443,81807.0237951429,3512.25453285714,观典防务技术股份有限公司,北京市,4.91
-24495941,68018.9182948103,41411.5321373808,343277.487442714,80497.7686304286,瑞斯康达科技发展股份有限公司,北京市,5.54
-2424229017,114276.893374667,77926.1367106667,500826.828284333,41132.540103,湖北鼎龙控股股份有限公司,湖北省,5.7
-3118428071,245.891363896894,142.090621109369,353.107586951086,22.31839,浙江出彩智能科技有限公司,浙江省,2.55
-3297178263,6721.03061318177,3883.81031032277,9651.60737666299,610.035993333333,安徽徽昂光电科技有限公司,安徽省,3.98
-441623911,79201.8446421667,54549.9474813333,240891.492164333,46601.7746505,东莞铭普光磁股份有限公司,广东省,5.38
-2316150629,3193.75060666667,2061.597836,93411.1801021667,20442.4057828333,深圳英集芯科技股份有限公司,广东省,4.97
-2327057709,23544.9442314765,13440.4003796565,68644.06669975,18862.75571075,苏州华之杰电讯股份有限公司,江苏省,4.84
-2624175,112528.502696921,68592.2842455949,676577.778208429,45047.728384,北京旋极信息技术股份有限公司,北京市,5.83
-3483100980,491.782727793788,284.181242218739,706.215173902172,44.63678,公主岭市王岩口腔诊所有限公司,吉林省,2.85
-781386116,141735.041227833,75482.3393631667,509573.936014,65785.0702958333,国光电器股份有限公司,广东省,5.71
-3222821993,7991.46932664906,4617.94518605451,11475.9965759103,725.347675,象山金钇光电科技有限公司,浙江省,4.06
-10398718,769033.670522573,491078.248113787,2870403.42429771,537996.506329429,中国长城科技集团股份有限公司,广东省,6.46
-3042364033,245.891363896894,142.090621109369,353.107586951086,22.31839,上海津领信息科技有限公司,上海市,2.55
-2342518227,9173.79330924141,5301.16214161011,13173.8503023333,3045.84492716667,江苏欧密格光电科技股份有限公司,江苏省,4.12
-3068358389,1811.95903660257,1047.05745193863,2602.029095,298.473805182015,无锡汉咏科技股份有限公司,江苏省,3.42
-20751117,15716.6102843333,8183.32674633333,416800.384882667,41335.5048401667,上海贝岭股份有限公司,上海市,5.62
-3449575456,705.462014166667,404.564843666667,98147.2591208333,8331.321303,峰岹科技（深圳）股份有限公司,广东省,4.99
-3440374619,5071.89578685606,2930.84234897554,7283.3988725,2753.40906483333,浙江信测通信股份有限公司,浙江省,3.86
-3168979780,312036.140785158,180312.99818779,448093.527840928,28322.03691,华域视觉科技（上海）有限公司,上海市,5.65
-2962064709,10650.3258155645,6154.39024033911,15294.1965483333,3870.76790783333,上海中基国威电子股份有限公司,上海市,4.18
-3151203276,341884.6623548,201430.568992,477385.6924028,28574.494008,合肥颀中科技股份有限公司,安徽省,5.68
-2334772533,28252.9257821369,16326.2170290132,40572.07333,8232.49431216667,南京泰通科技股份有限公司,江苏省,4.61
-38567125,55215.5674613333,32280.1155033333,195121.699532833,23741.5776591667,杭州星帅尔电器股份有限公司,浙江省,5.29
-2338894532,7130.84955300992,4120.62801217171,10240.1200215815,647.23331,宁波锦澄电子科技股份有限公司,浙江省,4.01
-270141231,72968.2622364032,42165.3918142054,104784.676427735,6622.9822325,贵州雅光电子科技股份有限公司,贵州省,5.02
-220783142,7868.5236447006,4546.89987549982,11299.4427824348,714.18848,麦歌恩电子（上海）有限公司,上海市,4.05
-3407754893,232859.633511805,168326.909166578,411069.08997625,40891.55866,比亚迪半导体股份有限公司,广东省,5.61
-9278530,70406.8938624773,40685.2811776494,101106.472396994,6390.49900333333,北京银联金卡科技有限公司,北京市,5.0
-2323580212,16597.6670630403,9591.11692488244,23834.7621191983,1506.491325,深圳市美莱雅科技有限公司,广东省,4.38
-2331160070,3255.79612835197,1881.39220000014,4675.42371666667,794.0237395,南通光合生物技术股份有限公司,江苏省,3.67
-2349345463,74136.2462149135,42840.3222644749,106461.937465753,6728.994585,成都海威华芯科技有限公司,四川省,5.03
-2350687852,11074.7830890728,6399.66684001862,15903.7302921667,4029.26472366667,江苏钜芯集成电路技术股份有限公司,江苏省,4.2
-2357754148,8015.82834302686,4632.02127116739,11510.9768751667,3729.40010633333,广芯电子技术（上海）股份有限公司,上海市,4.06
-3173999388,16720.6127449888,9662.16223543712,24011.3159126738,1517.65052,东方微电科技（武汉）有限公司,湖北省,4.38
-501323741,66833.3625173174,43673.4697465961,220671.160353143,45867.2232541429,浙江大立科技股份有限公司,浙江省,5.34
-2311337085,19127.0147477191,11036.5029958297,112145.035851286,36647.5253237143,上海安路信息科技股份有限公司,上海市,5.05
-2950325617,19318.22026519,11163.2140011207,27741.5604758333,5855.69548783333,深圳市汇春科技股份有限公司,广东省,4.44
-2350701298,2458.91363896894,1420.90621109369,3531.07586951086,223.1839,深圳市玛琪电子科技有限公司,广东省,3.55
-2322658897,1721.23954727826,994.634347765586,2471.7531086576,156.22873,深圳市好通家实业有限公司,广东省,3.39
-2347015781,41626.2975832227,25542.5692075516,141575.867817429,28732.6177297143,苏州盛科通信股份有限公司,江苏省,5.15
-24610687,6738738.84243147,3834842.07575342,24477540.6526928,2602016.44544083,潍柴动力股份有限公司,山东省,7.39
-181655991,273187.085037167,140652.672426167,1074612.49761317,1582.68336616667,网宿科技股份有限公司,上海市,6.03
-2326956863,124019.549713435,73785.5926887248,540313.3684405,76558.826568734,南京国博电子股份有限公司,江苏省,5.73
-3270918801,15859.9929713496,9164.84506155432,22775.4393583451,1439.536155,浙江康鹏半导体有限公司,浙江省,4.36
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+2311838590,53398.0289114503,26546.600225041,80495.2837445714,310.151853,上海华岭集成电路技术股份有限公司,上海市,4.91
+643954924,48262.2453475,34033.3587131667,287207.000554333,147799.326854167,深圳佰维存储科技股份有限公司,广东省,5.46
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+2348894245,140155.923780404,84124.7869805446,297024.681265143,11981.7573048571,晶瑞电子材料股份有限公司,江苏省,5.47
+29954548,757171.52339162,494430.604177194,1465408.31294514,133292.643730143,多氟多新材料股份有限公司,河南省,6.17
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+29452962,456144.931192807,269318.787489444,1898148.98546986,206077.306495286,广电运通集团股份有限公司,广东省,6.28
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+3392803162,39052.6952507718,22381.1925190257,203314.355079143,53110.2105447143,深圳市好上好信息科技股份有限公司,广东省,5.31
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+2350544061,144065.786951576,99941.0487918116,244530.312333667,28099.7294085,广州广合科技股份有限公司,广东省,5.39
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 314846874,1984.631183,1641.71031766667,17644.9277333333,1747.244057,赛卓电子科技（上海）股份有限公司,上海市,4.25
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+3216845719,3811.31614040186,2202.40462719523,5473.16759774183,345.935045,视旗科技（深圳）有限公司,广东省,3.74
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+3422843097,11065.1113753602,6394.07794992163,15889.8414127989,1004.32755,芜湖华沅微电子有限公司,安徽省,4.2
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+907433543,33933.0082177714,19608.505713093,48728.8469992499,3079.93782,铜陵富仕三佳机器有限公司,安徽省,4.69
+300186799,1913582.75295502,1234953.81045746,2517803.40044929,175073.587292714,天水华天科技股份有限公司,甘肃省,6.4
+1476953321,68733.053832359,41979.990455379,221058.3974475,17772.1128096667,明冠新材料股份有限公司,江西省,5.34
+2326520912,28110.518270206,20015.5750167941,91596.5949761428,13584.5539792857,苏州敏芯微电子技术股份有限公司,江苏省,4.96
+16715045,129249.726051754,70258.0689623939,428791.019708857,17613.1475474286,深圳万润科技股份有限公司,广东省,5.63
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+2349746655,22867.8968424111,13214.4277631714,32839.005586451,2075.61027,美科精微机械（苏州）有限公司,江苏省,4.52
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+3226232,3576962.48064625,2066981.17617931,5136628.51,252962.023232612,中国运载火箭技术研究院,北京市,6.71
+303926772,169492.072533121,116431.299807913,501333.021660429,100913.602984571,烟台睿创微纳技术股份有限公司,山东省,5.7
+532328014,24892.0396571667,19319.3788076667,201784.162114833,48707.7229353333,深圳市杰普特光电股份有限公司,广东省,5.3
+225958786,4186.1910587495,2831.52597113953,62451.907052,18806.0135938333,天津金海通半导体设备股份有限公司,天津市,4.8
+862404568,23851.4622979987,13782.7902476088,34251.4359342554,2164.88383,余姚市龙邦电器有限公司,浙江省,4.53

input_data/input_firm_data/firms_devices.csv

@@ -1,61 +1,61 @@
 Firm_Code,相关细分行业,设备id,种类,设备单价数值,固定资产原值（万元人民币）,设备数量
-2311639124,34524,72,设备,140,737.674091690682,5.26910065493344
+805940123,34524,72,设备,140,64607.9558639089,461.485399027921
-762165453,34524,72,设备,140,614.728409742234,4.39091721244453
+286335813,34524,72,设备,140,14753.4818338136,105.382013098669
-2989649772,34525,64,设备,500,353266.3068118,706.5326136236
+2310534839,34525,64,设备,500,110282.276707757,220.564553415514
-25147774,34525,64,设备,500,122743.43558,245.48687116
+3358892171,34525,64,设备,500,96134.749442,192.269498884
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+280281699,34526,60,设备,210,1475.34818338136,7.02546753991124
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 80158773,34528,69,设备,25,12635.7451105,505.42980442
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+758879940,34530,65,设备,700,50189.7546808333,71.6996495440476
-2311352797,34531,71,设备,140,55205.024134511,394.321600960793
+2989649772,34531,71,设备,140,353266.3068118,2523.33076294143
-22324879,34531,71,设备,140,32642.0785573127,233.157703980805
+2728939,34531,71,设备,140,359757.813985901,2569.69867132786
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+2334430421,34532,73,设备,210,58809.0178653405,280.042942215907
-423388486,34533,66,设备,100000,163646.093171967,1.63646093171967
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-1679596339,34533,66,设备,100000,7602.68963569803,0.0760268963569803
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-3164072929,34534,63,设备,700,798499.18961975,1140.71312802821
+2728939,34534,63,设备,700,359757.813985901,513.939734265573
-22324879,34534,63,设备,700,32642.0785573127,46.631540796161
+205960791,34534,63,设备,700,1610605.73638088,2300.86533768697
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 3312358902,34535,59,设备,700,66438.2226519953,94.9117466457076
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+1208566436,34537,62,设备,140,46908.019639,335.057283135714
-1044103384,34537,62,设备,140,424162.602722142,3029.73287658673
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+2350443114,34538,74,设备,500,3196.58773065962,6.39317546131924
 78979697,34538,74,设备,500,3444820.24820822,6889.64049641644
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+676597455,34539,68,设备,350,40504.6693875284,115.72762682151
-2347561020,34539,68,设备,350,23605.5709341018,67.444488383148
-11169556957,34543,70,设备,35,983.565455587577,28.1018701596451
 2333843479,34543,70,设备,35,25746.1035943333,735.602959838094
-22324879,34544,83,设备,140,32642.0785573127,233.157703980805
+872394725,34543,70,设备,35,25724.5710310483,734.987743744237
-59234665,34544,83,设备,140,251695.047657333,1797.82176898095
+301209792,34544,83,设备,140,51111.2877957612,365.08062711258
-4995239819,34545,81,设备,210,491.782727793788,2.34182251330375
+3386978614,34544,83,设备,140,380298.49535,2716.41782392857
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+3146262808,34545,81,设备,210,491.782727793788,2.34182251330375
-3327312155,34546,84,设备,70,4794.88159598943,68.4983085141347
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+3088175489,34546,84,设备,70,5037.29651092296,71.9613787274709
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+3154580688,34547,87,设备,70,245.891363896894,3.51273376995563
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+3408810051,34547,87,设备,70,245.891363896894,3.51273376995563
+3043532438,34548,89,设备,70,26064.4845730707,372.349779615296
 443872531,34549,85,设备,50,217273.703527667,4345.47407055334
-24673506,34549,85,设备,50,3731195.01695067,74623.9003390134
+2313262775,34549,85,设备,50,573.746515759419,11.4749303151884
-3065971313,34550,75,设备,70,134201.385594667,1917.16265135239
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-830662620,34550,75,设备,70,262637.565598167,3751.96522283096
+533879767,34550,75,设备,70,17933.2750664661,256.189643806659
-2347561020,34551,88,设备,70,23605.5709341018,337.22244191574
+314284469,34551,88,设备,70,93478.8051045442,1335.41150149349
-613464015,34551,88,设备,70,17409.1331735,248.701902478571
+3380230725,34551,88,设备,70,39166.3897750939,559.519853929913
-2453696971,34552,82,设备,70,48907.0477203197,698.672110290281
+1452190980,34552,82,设备,70,1967.13091117515,28.101870159645
-43566171,34552,82,设备,70,19425.4177478546,277.505967826494
+2351960502,34552,82,设备,70,1475.34818338136,21.0764026197337
-2311838590,34553,80,设备,21,53398.0289114503,2542.76328149763
+830662620,34553,80,设备,21,262637.565598167,12506.5507427699
-409663925,34553,80,设备,21,63931.7546131924,3044.36926729488
+3398677646,34553,80,设备,21,36420.8435164999,1734.32588173809
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+2314892072,34554,77,设备,350,14999.3731977105,42.8553519934586
-11175750477,34555,76,设备,21,39342.618223503,1873.458010643
+170176299,34555,76,设备,21,9774.18171490151,465.43722451912
-2311838590,34555,76,设备,21,53398.0289114503,2542.76328149763
+3070844997,34555,76,设备,21,17704.1782005764,843.056104789352
-2321857672,34556,78,设备,70,172615.737455619,2465.93910650884
+3119505684,34556,78,设备,70,8852.08910028818,126.458415718403
-146491012,34557,79,设备,350,172984.813296877,494.242323705363
+441623911,34557,79,设备,350,79201.8446421667,226.290984691905
-2311907103,34557,79,设备,350,61082.6578818333,174.521879662381
+3312358902,34557,79,设备,350,66438.2226519953,189.823493291415
-3464943902,34558,86,设备,70,368.837045845341,5.26910065493344
+11197684487,34558,86,设备,70,245.891363896894,3.51273376995563
-259923931,34558,86,设备,70,1426.16991060198,20.3738558657426
+2345116311,34558,86,设备,70,32603.9042950622,465.770061358031

input_data/input_firm_data/firms_materials.csv

@@ -1,105 +1,105 @@
 Firm_Code,相关细分行业,材料id,种类,材料单价数值,存货（万元人民币）,材料数量
 1,7,10,材料,70.0,3736.5025,40.0339553571429
-5,7,10,材料,70.0,2588.93324,27.7385704285714
+1,7,10,材料,70.0,3736.5025,40.0339553571429
-29954548,7,10,材料,70.0,133292.643730143,1428.13546853725
+6,7,10,材料,70.0,163769.3525,1754.67163392857
-29954548,7,10,材料,70.0,133292.643730143,1428.13546853725
+6,7,10,材料,70.0,163769.3525,1754.67163392857
-453289520,8,37,材料,9.7,193520.088971571,14962.8934771833
+8,8,37,材料,9.7,26230.0375,2028.09568298969
-453289520,8,37,材料,9.7,193520.088971571,14962.8934771833
+8,8,37,材料,9.7,26230.0375,2028.09568298969
-350343208,2714,91,材料,140.0,3903.95977783333,20.9140702383928
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+3191869223,2714,91,材料,140.0,66.95517,0.358688410714286
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+16116663,2715,92,材料,21.0,26734.2883584286,954.796012801021
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-27169556,2717,90,材料,70.0,455436.179933333,4879.67335642857
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 203314437,32433,22,材料,2.0,290.13907,108.80215125
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input_data/input_firm_data/firms_products.csv

@@ -1,350 +1,354 @@
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+31732840,32436,29,50,材料,140,700.797446,1.25142401071429
-366828854,32436,29,50,材料,140,37270.9437623333,66.5552567184523
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 1452048,32448,30,95,材料,35,1884370.44367833,13459.7888834166
-1452048,32448,30,95,材料,35,1884370.44367833,13459.7888834166
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-2961715231,32439,31,41,材料,1000,881.576405,0.22039410125
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-3270918801,56320,32,46,材料,1400,1439.536155,0.257060027678571
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-7299120,56320,32,46,材料,1400,29562.480612,5.279014395
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+551856519,56322,33,47,材料,14000,1830.10798,0.0326804996428571
-2314301730,56322,33,47,材料,14000,22.31839,0.000398542678571429
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 648145286,56323,35,49,材料,1400,509.603238333333,0.0910005782738095
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-3357541349,513691,52,95,材料,35,6191.05493557143,44.2218209683674
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-720737055,2515,56,58,设计,0,315.444274666667,9999999999999.0
+25685135,2515,56,58,设计,0,494.656998333333,999999999999999.0
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-1092796483,34492,58,93,材料,7,21795.4415151429,778.408625540818
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+2962064709,34492,58,95,材料,35,3870.76790783333,27.6483421988095
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+2346894985,34493,58,97,测试,0,12367.5736068333,999999999999999.0
-3344266702,34493,58,90,材料,70,12065.8805295714,43.092430462755
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-2345050363,34494,58,91,材料,140,1262.94723833333,2.25526292559523
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-33171435,34494,58,92,材料,21,61275.9548563333,729.475653051587
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-2961210947,34495,58,93,材料,7,156.22873,5.5795975
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 2350883312,34496,58,90,材料,70,758.82526,2.71009021428571
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-3402194899,49709,58,94,材料,21,55.795975,0.664237797619048
+1408183015,49709,58,94,材料,21,133.91034,1.59417071428571
-730857,49709,58,95,材料,35,15474.0837333333,110.529169523809
+2310406921,49709,58,95,材料,35,178.54712,1.27533657142857
-27085933,49710,58,97,测试,0,3512.25453285714,9999999999999.0
+16116663,49710,58,97,测试,0,26734.2883584286,999999999999999.0
-27085933,49710,58,90,材料,70,3512.25453285714,12.5437661887755
+1651310523,49710,58,90,材料,70,357.09424,1.27533657142857
-3424978618,49711,58,91,材料,140,56086.1265501667,100.153797411012
+2350544061,49711,58,91,材料,140,28099.7294085,50.1780882294643
-24495941,49711,58,92,材料,21,80497.7686304286,958.306769409864
+1605495,49711,58,92,材料,21,1500442.86042433,17862.4150050515
-2424229017,49712,58,93,材料,7,41132.540103,1469.01928939286
+686380489,49712,58,93,材料,7,44.63678,1.59417071428571
-3118428071,49712,58,94,材料,21,22.31839,0.265695119047619
+2424229017,49712,58,94,材料,21,41132.540103,489.673096464286
-3297178263,49713,58,95,材料,35,610.035993333333,4.35739995238095
+872030659,49713,58,95,材料,35,758.3442415,5.41674458214286
-441623911,49713,58,97,测试,0,46601.7746505,9999999999999.0
+3222821993,49713,58,97,测试,0,725.347675,999999999999999.0
-2316150629,49714,58,90,材料,70,20442.4057828333,73.0085920815475
+2343364952,49714,58,90,材料,70,189402.304441,676.436801575
-2327057709,49714,58,91,材料,140,18862.75571075,33.683492340625
+343932526,49714,58,91,材料,140,48343.29187,86.3273069107143
-2624175,49715,58,92,材料,21,45047.728384,536.282480761905
+3196033145,49715,58,92,材料,21,15768.2352323333,187.717086099206
-3483100980,49715,58,93,材料,7,44.63678,1.59417071428571
+265133300,49715,58,93,材料,7,13281.5700693333,474.341788190475
-781386116,49715,58,94,材料,21,65785.0702958333,783.15559875992
+4607820,49715,58,94,材料,21,521023.8754055,6202.66518339881
-3222821993,49716,58,95,材料,35,725.347675,5.18105482142857
+9746245,49716,58,95,材料,35,3484537.2,24889.5514285714
-10398718,49716,58,97,测试,0,537996.506329429,9999999999999.0
+1477049981,49716,58,97,测试,0,18091.6998766667,999999999999999.0
-3042364033,49717,58,90,材料,70,22.31839,0.0797085357142857
+2487067571,49717,58,90,材料,70,22.31839,0.0797085357142857
-2333993502,49717,58,91,材料,140,17415.7935534286,31.0996313454082
+1452048,49717,58,91,材料,140,1884370.44367833,3364.94722085416
-79938367,49718,58,92,材料,21,438207.673933167,5216.75802301389
+24653920,49718,58,92,材料,21,49184.7394994286,585.532613088436
-2342518227,49718,58,93,材料,7,3045.84492716667,108.780175970238
+79938367,49718,58,93,材料,7,438207.673933167,15650.2740690417
-3068358389,49719,58,94,材料,21,298.473805182015,3.55325958550018
+1570236326,49719,58,94,材料,21,14314.7781271667,170.414025323413
-20751117,49719,58,95,材料,35,41335.5048401667,295.253606001191
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-3449575456,49720,58,97,测试,0,8331.321303,9999999999999.0
+24653920,49720,58,97,测试,0,49184.7394994286,999999999999999.0
-3449575456,49720,58,90,材料,70,8331.321303,29.7547189392857
+2326655246,49720,58,90,材料,70,43797.5919677143,156.419971313265
-3440374619,49721,58,91,材料,140,2753.40906483333,4.91680190148809
+2341774429,49721,58,91,材料,140,379.41263,0.677522553571429
-3168979780,49721,58,92,材料,21,28322.03691,337.167106071429
+186924210,49721,58,92,材料,21,22.31839,0.265695119047619
-2962064709,49722,58,93,材料,7,3870.76790783333,138.241710994048
+169978927,49722,58,93,材料,7,65028.9291661667,2322.46175593453
-3151203276,49722,58,94,材料,21,28574.494008,340.172547714286
+696450846,49722,58,94,材料,21,1557.95664833333,18.5471029563492
-3449575456,49723,58,95,材料,35,8331.321303,59.5094378785714
+37873062,49723,58,95,材料,35,184703.551339833,1319.31108099881
-777299215,49723,58,97,测试,0,602.59653,9999999999999.0
+3449575456,49723,58,97,测试,0,8331.321303,999999999999999.0
-24459300,49724,58,90,材料,70,87424.1267993333,312.229024283333
+1008519520,49724,58,90,材料,70,40902.5847475714,146.080659812755
-1675147952,49724,58,91,材料,140,9412.2279396,16.8075498921429
+640700057,49724,58,91,材料,140,81210.7388408333,145.019176501488
-2334772533,49725,58,92,材料,21,8232.49431216667,98.0058846686508
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-38567125,49725,58,93,材料,7,23741.5776591667,847.913487827382
+889661204,49725,58,93,材料,7,468.68619,16.7387925
-2338894532,49726,58,94,材料,21,647.23331,7.70515845238095
+78576577,49726,58,94,材料,21,3278.27611,39.027096547619
-270141231,49726,58,95,材料,35,6622.9822325,47.3070159464286
+2948003547,49726,58,95,材料,35,29467.2679745,210.480485532143
-220783142,49727,58,97,测试,0,714.18848,9999999999999.0
+314846874,49727,58,97,测试,0,1747.244057,999999999999999.0
-3407754893,49727,58,90,材料,70,40891.55866,146.041280928571
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-9278530,49728,58,91,材料,140,6390.49900333333,11.4116053630952
+2948003547,49728,58,91,材料,140,29467.2679745,52.6201213830357
-9278530,49728,58,92,材料,21,6390.49900333333,76.0773690873015
+3222664794,49728,58,92,材料,21,714.18848,8.50224380952381
-2323580212,49729,58,93,材料,7,1506.491325,53.8032616071429
+778745779,49729,58,93,材料,7,22.31839,0.797085357142857
-2331160070,49729,58,94,材料,21,794.0237395,9.45266356547619
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-2349345463,49730,58,95,材料,35,6728.994585,48.0642470357143
+359006365,49730,58,95,材料,35,647.23331,4.62309507142857
-2350687852,49730,58,97,测试,0,4029.26472366667,9999999999999.0
+189427260,49730,58,97,测试,0,100.432755,999999999999999.0
-2357754148,49731,58,90,材料,70,3729.40010633333,13.3192860940476
+466148111,49731,58,90,材料,70,24096.4217366667,86.0586490595239
 2357754148,49731,58,91,材料,140,3729.40010633333,6.6596430470238
-3173999388,49733,58,92,材料,21,1517.65052,18.0672680952381
+3444782013,49733,58,92,材料,21,245.50229,2.92264630952381
-501323741,49733,58,93,材料,7,45867.2232541429,1638.11511621939
+2348987001,49733,58,93,材料,7,3280.80333,117.1715475
-20751117,49734,58,94,材料,21,41335.5048401667,492.089343335318
+658871510,49734,58,94,材料,21,111.59195,1.3284755952381
-20751117,49734,58,95,材料,35,41335.5048401667,295.253606001191
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-2311337085,56247,58,97,测试,0,36647.5253237143,9999999999999.0
+3125087791,56247,58,97,测试,0,62053.129276,999999999999999.0
-2950325617,56247,58,90,材料,70,5855.69548783333,20.9131981708333
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-2350701298,56248,58,91,材料,140,223.1839,0.398542678571429
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-2347015781,56249,58,93,材料,7,28732.6177297143,1026.16491891837
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-15482118,56250,58,97,测试,0,75554.4652471667,9999999999999.0
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-2982872611,317586,58,90,材料,70,764.4048575,2.73001734821429
+3392803162,317586,58,90,材料,70,53110.2105447143,189.67932337398
-3051771738,317586,58,91,材料,140,119431.036400333,213.269707857738
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-1237811030,317586,58,92,材料,21,1350.262595,16.074554702381
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-2358215091,317620,58,93,材料,7,2443.928088,87.283146
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+1261908916,431078,58,97,测试,0,34244.3486247143,999999999999999.0
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-2311676659,431082,58,90,材料,70,8280.12269,29.57186675
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-1048928993,431082,58,91,材料,140,13303.533159,23.7563092125
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 80158773,34528,69,50,材料,140,1474.810014,2.63358931071429
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 2333843479,34543,70,91,材料,140,8385.40003966667,14.9739286422619
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main.py

@@ -33,11 +33,6 @@ def do_process(target: object, controller_db: ControllerDB, ):
 
     for i in process_list:
         i.join()
-
-    # 所有子进程完成后刷新最终进度
-
-    # 显示最终进度后关闭图表
-
 def do_computation(c_db):
     exp = Computation(c_db)
 
@@ -54,7 +49,7 @@ if __name__ == '__main__':
     parser.add_argument('--exp', type=str, default='without_exp')
     parser.add_argument('--job', type=int, default='1')
     parser.add_argument('--reset_sample', type=int, default='0')
-    parser.add_argument('--reset_db', type=bool, default=False)
+    parser.add_argument('--reset_db', type=bool, default=True)
 
     args = parser.parse_args()
     # 几核参与进程

my_model.py

@@ -62,7 +62,7 @@ class MyModel(Model):
         self.cap_limit_level = params['cap_limit_level']  # 产能限制的水平。
         self.diff_new_conn = params['diff_new_conn']  # 是否允许差异化的新连接。
         # 初始化停止时间步，可能是用户通过参数传入
-        self.int_stop_ts = params.get('n_iter', 3)  # 默认停止时间为 100
+        self.int_stop_ts = params.get('n_iter', 100)  # 默认停止时间为 100
 
         # 网络初始化
         self.firm_network = nx.MultiDiGraph()  # 企业之间的有向多重图。
@@ -465,12 +465,9 @@ class MyModel(Model):
             production_output = self.data_produced.loc[self.data_produced['Firm_Code'] == int(ag_node)]
 
             # 获取企业的资源信息，同时处理 R、P、C 的情况
-            try:
+            R = self.firm_resource_R.get(int(ag_node), [])
-                R = self.firm_resource_R.loc[int(ag_node)]
+            P = self.firm_resource_P.get(int(ag_node), {})
-                P = self.firm_resource_P.get(int(ag_node))
+            C = self.firm_resource_C.get(int(ag_node), [])
-                C = self.firm_resource_C.loc[int(ag_node)]
-            except KeyError:
-                R, P, C = [], {}, []  # 如果任何资源不存在，返回空列表
             # 在模型初始化时，构建 unique_id -> agent 的快速映射字典
             self.agent_map = {agent.unique_id: agent for agent in self.company_agents}
 
@@ -717,13 +714,20 @@ class MyModel(Model):
         for firm in self.company_agents:
             if not firm.R:  # 跳过没有资源的企业
                 continue
-
             # 遍历资源列表，检查哪些资源需要补货
-            for resource_id, resource_quantity in firm.R:
+            # purchase_material_firms 用于记录需要补充材料的企业
-                if resource_quantity <= firm.s_r:  # 如果资源低于阈值，记录需求
+            purchase_material_firms = {}
-                    required_quantity = firm.S_r - resource_quantity
+
+            for i, (resource_id, resource_quantity) in enumerate(firm.R):
+                # 对应位置的初始值
+                init_quantity = firm.R1[i][1]  # R1[i] = [resource_id, 初始数量]
+
+                if resource_quantity <= init_quantity * firm.s_r:  # 判断阈值
+                    required_quantity = init_quantity * firm.S_r - resource_quantity
+
                     if firm not in purchase_material_firms:
                         purchase_material_firms[firm] = []
+
                     purchase_material_firms[firm].append((resource_id, required_quantity))
 
         # 寻找供应商并处理补货
@@ -754,7 +758,7 @@ class MyModel(Model):
             # 检查设备残值，记录需要补充的设备
             for equipment in firm.C:
                 equipment_id, equipment_quantity, equipment_salvage = equipment
-                equipment_salvage -= firm.x  # 减少设备残值
+                equipment_salvage -= equipment_salvage*firm.x  # 减少设备残值
                 if equipment_salvage <= 0:  # 如果残值小于等于 0
                     equipment_quantity -= 1
                     required_quantity = 1  # 需要补充的设备数量
@@ -869,22 +873,29 @@ class MyModel(Model):
             else:
                 self.int_stop_ts = self.t
 
-    def end(self):
+    def end(self, ga_id=None):
         """
         结束模型运行并保存结果。
-        - 如果当前样本的结果未保存，则保存所有生产状态为非正常状态的结果。
+        - 按照 (sample.id, ga_id) 双键标识每次运行。
-        - 更新样本状态为完成，并记录相关信息。
+        - 若该组合结果已存在，则不重复保存。
+        - 更新样本状态。
         """
-        # 检查当前样本结果是否已存在
+        # 1️⃣ 检查该 sample + ga_id 是否已有结果
-        if not db_session.query(Result).filter_by(s_id=self.sample.id).first():
+        exists = db_session.query(Result).filter_by(
-            # 生成需要保存的结果列表
+            s_id=self.sample.id,
+            ga_id=ga_id
+        ).first()
+
+        if not exists:
+            # 2️⃣ 构造要保存的结果列表
             lst_result_info = [
                 Result(
                     s_id=self.sample.id,
                     id_firm=firm.unique_id,
                     id_product=prod.unique_id,
                     ts=ts,
-                    status=status
+                    status=status,
+                    ga_id=ga_id,  # 标记当前代次
                 )
                 for firm in self.company_agents
                 for prod, dct_status_supply in firm.dct_prod_up_prod_stat.items()
@@ -892,13 +903,14 @@ class MyModel(Model):
                 for status, ts in dct_status_supply['p_stat']
             ]
 
-            # 批量保存结果到数据库
+            # 3️⃣ 插入结果
             if lst_result_info:
                 db_session.bulk_save_objects(lst_result_info)
                 db_session.commit()
 
-        # 更新样本状态为已完成
+        # 4️⃣ 更新样本状态
         self.sample.is_done_flag = 1
         self.sample.computer_name = platform.node()
         self.sample.stop_t = self.int_stop_ts
         db_session.commit()
+

orm.py

@@ -105,6 +105,9 @@ class Result(Base):
 
     sample = relationship('Sample', back_populates='result', uselist=False)
 
+    # 💥 新增 GA 调用 ID，用于标记属于哪一次遗传算法运行
+    ga_id = Column(String(50), nullable=True)
+
     def __repr__(self):
         return f'<Product id: {self.id}>'