new repo

.vscode/launch.json

@@ -0,0 +1,21 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "main.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--exp", "with_exp",
+                "--reset_db", "True",
+                "--job", "24"
+            ]
+        }
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.formatting.provider": "yapf"
+}

SQL_analysis_experiment.sql

@@ -0,0 +1,85 @@
+select distinct experiment.idx_scenario, 
+n_max_trial, prf_size, prf_conn, cap_limit_prob_type, cap_limit_level, diff_new_conn, remove_t, netw_prf_n,
+mean_count_firm_prod, mean_count_firm, mean_count_prod,
+mean_max_ts_firm_prod, mean_max_ts_firm, mean_max_ts_prod,
+mean_n_remove_firm_prod, mean_n_all_prod_remove_firm, mean_end_ts
+from iiabmdb.with_exp_experiment as experiment
+left join 
+(
+select 
+idx_scenario,
+sum(count_firm_prod) / count(*) as mean_count_firm_prod, # Note to use count(*), to include NULL
+sum(count_firm) / count(*) as mean_count_firm,
+sum(count_prod) / count(*) as mean_count_prod,
+sum(max_ts_firm_prod) / count(*) as mean_max_ts_firm_prod,
+sum(max_ts_firm) / count(*) as mean_max_ts_firm,
+sum(max_ts_prod) / count(*) as mean_max_ts_prod,
+sum(n_remove_firm_prod) / count(*) as mean_n_remove_firm_prod,
+sum(n_all_prod_remove_firm) / count(*) as mean_n_all_prod_remove_firm,
+sum(end_ts) / count(*) as mean_end_ts
+from (
+select sample.id, idx_scenario,
+count_firm_prod, count_firm, count_prod,
+max_ts_firm_prod, max_ts_firm, max_ts_prod,
+n_remove_firm_prod, n_all_prod_remove_firm, end_ts
+from iiabmdb.with_exp_sample as sample
+# 1 2 3 + 9
+left join iiabmdb.with_exp_experiment as experiment
+on sample.e_id = experiment.id
+left join (select s_id, 
+count(distinct id_firm, id_product) as count_firm_prod,
+count(distinct id_firm) as count_firm,
+count(distinct id_product) as count_prod,
+max(ts) as end_ts
+from iiabmdb.with_exp_result group by s_id) as s_count
+on sample.id = s_count.s_id
+# 4
+left join # firm prod
+(select s_id, max(ts) as max_ts_firm_prod from
+(select s_id, id_firm, id_product, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_firm, id_product) as ts
+group by s_id) as s_max_ts_firm_prod
+on sample.id = s_max_ts_firm_prod.s_id
+# 5
+left join # firm
+(select s_id, max(ts) as max_ts_firm from
+(select s_id, id_firm, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_firm) as ts
+group by s_id) as s_max_ts_firm
+on sample.id = s_max_ts_firm.s_id
+# 6
+left join # prod
+(select s_id, max(ts) as max_ts_prod from
+(select s_id, id_product, min(ts) as ts
+from iiabmdb.with_exp_result
+where `status` = "D"
+group by s_id, id_product) as ts
+group by s_id) as s_max_ts_prod
+on sample.id = s_max_ts_prod.s_id
+# 7 
+left join
+(select s_id, count(distinct id_firm, id_product) as n_remove_firm_prod
+from iiabmdb.with_exp_result
+where `status` = "R"
+group by s_id) as s_n_remove_firm_prod
+on sample.id = s_n_remove_firm_prod.s_id
+# 8
+left join
+(select s_id, count(distinct id_firm) as n_all_prod_remove_firm from
+(select s_id, id_firm, count(distinct id_product) as n_remove_prod
+from iiabmdb.with_exp_result
+where `status` = "R"
+group by s_id, id_firm) as s_n_remove_prod
+left join iiabmdb_basic_info.firm_n_prod as firm_n_prod
+on s_n_remove_prod.id_firm = firm_n_prod.code
+where n_remove_prod = n_prod
+group by s_id) as s_n_all_prod_remove_firm
+on sample.id = s_n_all_prod_remove_firm.s_id
+) as secnario_count
+group by idx_scenario
+) as secnario_mean
+on experiment.idx_scenario = secnario_mean.idx_scenario;

SQL_analysis_risk.sql

@@ -0,0 +1,12 @@
+select * from
+(select s_id, id_firm, id_product, min(ts) as ts from iiabmdb.without_exp_result
+where `status` = 'D'
+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.without_exp_result
+where `status` = 'D'
+group by s_id, id_firm, id_product) as t
+group by s_id
+having count(*) > 1)
+order by s_id;

SQL_export_high_risk_setting.sql

@@ -0,0 +1,15 @@
+select e_id, n_disrupt_sample, total_n_disrupt_firm_prod_experiment, dct_lst_init_disrupt_firm_prod from iiabmdb.without_exp_experiment as experiment
+inner join (
+select e_id, count(id) as n_disrupt_sample, sum(n_disrupt_firm_prod_sample) as total_n_disrupt_firm_prod_experiment from iiabmdb.without_exp_sample as sample 
+inner join (
+select * from 
+(select s_id, COUNT(DISTINCT id_firm, id_product) as n_disrupt_firm_prod_sample from iiabmdb.without_exp_result group by s_id
+) as count_disrupt_firm_prod_sample 
+where n_disrupt_firm_prod_sample > 1
+) as  disrupt_sample
+on sample.id = disrupt_sample.s_id
+group by e_id
+) as disrupt_experiment
+on experiment.id = disrupt_experiment.e_id
+order by n_disrupt_sample desc, total_n_disrupt_firm_prod_experiment desc
+limit 0, 95;

SQL_migrate_db.sql

@@ -0,0 +1,13 @@
+CREATE DATABASE iiabmdb20230829;
+RENAME TABLE iiabmdb.not_test_experiment TO iiabmdb20230829.not_test_experiment,
+iiabmdb.not_test_result TO iiabmdb20230829.not_test_result,
+iiabmdb.not_test_sample TO iiabmdb20230829.not_test_sample,
+iiabmdb.test_experiment TO iiabmdb20230829.test_experiment,
+iiabmdb.test_result TO iiabmdb20230829.test_result,
+iiabmdb.test_sample TO iiabmdb20230829.test_sample;
+RENAME TABLE iiabmdb.with_exp_experiment TO iiabmdb20230829.with_exp_experiment,
+iiabmdb.with_exp_result TO iiabmdb20230829.with_exp_result,
+iiabmdb.with_exp_sample TO iiabmdb20230829.with_exp_sample,
+iiabmdb.without_exp_experiment TO iiabmdb20230829.without_exp_experiment,
+iiabmdb.without_exp_result TO iiabmdb20230829.without_exp_result,
+iiabmdb.without_exp_sample TO iiabmdb20230829.without_exp_sample;

computation.py

@@ -0,0 +1,37 @@
+import os
+import datetime
+
+from model import Model
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from controller_db import ControllerDB
+
+
+class Computation:
+
+    def __init__(self, c_db: 'ControllerDB'):
+        self.c_db = c_db
+        self.pid = os.getpid()
+
+    def run(self, str_code='0', s_id=None):
+        sample_random = self.c_db.fetch_a_sample(s_id)
+        if sample_random is None:
+            return True
+
+        # lock this row by update is_done_flag to 0
+        self.c_db.lock_the_sample(sample_random)
+        print(
+            f"Pid {self.pid} ({str_code}) is running "
+            f"sample {sample_random.id} at {datetime.datetime.now()}")
+
+        dct_exp = {column: getattr(sample_random.experiment, column)
+                   for column in sample_random.experiment.__table__.c.keys()}
+        del dct_exp['id']
+
+        dct_sample_para = {'sample': sample_random,
+                           'seed': sample_random.seed,
+                           **dct_exp}
+        model = Model(dct_sample_para)
+        model.run(display=False)
+        return False

conf_db.yaml

@@ -0,0 +1,16 @@
+# read by orm
+is_local_db: True
+
+local:
+  user_name: iiabm_yz
+  password: iiabm_yz
+  db_name: iiabmdb
+  address: 'localhost'
+  port: 3306
+
+remote:
+  user_name: iiabm_yz
+  password: iiabm_yz
+  db_name: iiabmdb
+  address: 'localhost'
+  port: 3307

conf_db_prefix.yaml

@@ -0,0 +1 @@
+db_name_prefix: with_exp

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: 50
+  n_iter: 100

controller_db.py

@@ -0,0 +1,259 @@
+# -*- coding: utf-8 -*-
+from orm import db_session, engine, Base, ins
+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:
+    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.lst_saved_s_id = []
+
+    def init_tables(self):
+        self.fill_experiment_table()
+        self.fill_sample_table()
+
+    def fill_experiment_table(self):
+        Firm = pd.read_csv("input_data/Firm_amended.csv")
+        Firm['Code'] = Firm['Code'].astype('string')
+        Firm.fillna(0, inplace=True)
+
+        # fill dct_lst_init_disrupt_firm_prod
+        list_dct = []
+        if self.is_with_exp:
+            with open('SQL_export_high_risk_setting.sql', 'r') as f:
+                str_sql = f.read()
+            result = pd.read_sql(sql=str_sql, con=engine)
+            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:
+            for _, row in Firm.iterrows():
+                code = row['Code']
+                row = row['1':]
+                for product_code in row.index[row == 1].to_list():
+                    dct = {code: [product_code]}
+                    list_dct.append(dct)
+
+        # fill g_bom
+        BomNodes = pd.read_csv('input_data/BomNodes.csv', index_col=0)
+        BomNodes.set_index('Code', inplace=True)
+        BomCateNet = pd.read_csv('input_data/BomCateNet.csv', index_col=0)
+        BomCateNet.fillna(0, inplace=True)
+        g_bom = nx.from_pandas_adjacency(BomCateNet.T,
+                                         create_using=nx.MultiDiGraph())
+        bom_labels_dict = {}
+        for code in g_bom.nodes:
+            bom_labels_dict[code] = BomNodes.loc[code].to_dict()
+        nx.set_node_attributes(g_bom, bom_labels_dict)
+        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)
+        df_oa = df_oa.iloc[:, 0:df_xv.shape[1]]
+        for idx_scenario, row in df_oa.iterrows():
+            dct_exp_para = {}
+            for idx_col, para_level in enumerate(row):
+                dct_exp_para[df_xv.columns[idx_col]] = \
+                    df_xv.iloc[para_level, idx_col]
+            # different initial removal
+            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}!")
+
+    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
+        )
+        db_session.add(e)
+        db_session.commit()
+
+    def fill_sample_table(self):
+        rng = random.Random(self.dct_parameter['meta_seed'])
+        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:
+            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)
+        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)
+        ]
+        is_exist = len(lst_table_obj) > 0
+        if force_drop:
+            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 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
+            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:
+            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 prepare_list_sample(self):
+        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()
+        n_sample = 0 if res is None else res
+        print(f'There are a total of {n_sample} samples.')
+        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):
+        while 1:
+            if len(lst_s_id) == 0:
+                return None
+            s_id = random.choice(lst_s_id)
+            lst_s_id.remove(s_id)
+            res = db_session.query(Sample).filter(Sample.id == int(s_id),
+                                                  Sample.is_done_flag == -1)
+            if res.count() == 1:
+                return res[0]
+
+    def fetch_a_sample(self, s_id=None):
+        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()

firm.py

@@ -0,0 +1,334 @@
+import agentpy as ap
+
+
+class FirmAgent(ap.Agent):
+    def setup(self, code, name, type_region, revenue_log, a_lst_product):
+        self.firm_network = self.model.firm_network
+        self.product_network = self.model.product_network
+
+        # self parameter
+        self.code = code
+        self.name = name
+        self.type_region = type_region
+        self.size_stat = []
+        self.dct_prod_up_prod_stat = {}
+        self.dct_prod_capacity = {}
+
+        # parameter in trial
+        self.dct_n_trial_up_prod_disrupted = {}
+        self.dct_cand_alt_supp_up_prod_disrupted = {}
+        self.dct_request_prod_from_firm = {}
+
+        # external variable
+        self.is_prf_size = self.model.is_prf_size
+        self.is_prf_conn = bool(self.p.prf_conn)
+        self.str_cap_limit_prob_type = str(self.p.cap_limit_prob_type)
+        self.flt_cap_limit_level = float(self.p.cap_limit_level)
+        self.flt_diff_new_conn = float(self.p.diff_new_conn)
+
+        # initialize size_stat (self parameter)
+        # (size, time step)
+        self.size_stat.append((revenue_log, 0))
+
+        # init dct_prod_up_prod_stat (self parameter)
+        for prod in a_lst_product:
+            self.dct_prod_up_prod_stat[prod] = {
+                # status: (Normal / Disrupted / Removed, time step)
+                'p_stat': [('N', 0)],
+                # supply for each component and respective disrupted supplier
+                # set_disrupt_firm is refreshed to empty at each update
+                's_stat': {up_prod: {'stat': True,
+                                     'set_disrupt_firm': set()}
+                           for up_prod in prod.a_predecessors()}
+                # Note: do not use fromkeys as it's a shallow copy
+            }
+
+        # initialize extra capacity (self parameter)
+        for product in a_lst_product:
+            # initialize extra capacity based on discrete uniform distribution
+            assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
+                "cap_limit_prob_type other than uniform, normal"
+            if self.str_cap_limit_prob_type == 'uniform':
+                extra_cap_mean = \
+                    self.size_stat[0][0] / self.flt_cap_limit_level
+                extra_cap = self.model.nprandom.integers(extra_cap_mean-2,
+                                                         extra_cap_mean+2)
+                extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
+                self.dct_prod_capacity[product] = extra_cap
+            elif self.str_cap_limit_prob_type == 'normal':
+                extra_cap_mean = \
+                    self.size_stat[0][0] / self.flt_cap_limit_level
+                extra_cap = self.model.nprandom.normal(extra_cap_mean, 1)
+                extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
+                self.dct_prod_capacity[product] = extra_cap
+
+    def remove_edge_to_cus(self, disrupted_prod):
+        # parameter disrupted_prod is the product that self got disrupted
+        lst_out_edge = list(
+            self.firm_network.graph.out_edges(
+                self.firm_network.positions[self], keys=True, data='Product'))
+        for n1, n2, key, product_code in lst_out_edge:
+            if product_code == disrupted_prod.code:
+                # update customer up product supplier status
+                customer = ap.AgentIter(self.model, n2).to_list()[0]
+                for prod in customer.dct_prod_up_prod_stat.keys():
+                    if disrupted_prod in \
+                            customer.dct_prod_up_prod_stat[
+                                prod]['s_stat'].keys():
+                        customer.dct_prod_up_prod_stat[
+                            prod]['s_stat'][disrupted_prod][
+                            'set_disrupt_firm'].add(self)
+                        # print(f"{self.name} disrupt {customer.name}'s "
+                        #       f"{prod.code} due to {disrupted_prod.code}")
+                # remove edge to customer
+                self.firm_network.graph.remove_edge(n1, n2, key)
+
+    def disrupt_cus_prod(self, prod, disrupted_up_prod):
+        # parameter prod is the product that has disrupted_up_prod
+        # parameter disrupted_up_prod is the product that
+        # self's component exists disrupted supplier
+        num_lost = \
+            len(self.dct_prod_up_prod_stat[prod]['s_stat']
+                [disrupted_up_prod]['set_disrupt_firm'])
+        num_remain = \
+            len([u for u, _, _, d in
+                 self.firm_network.graph.in_edges(self.get_firm_network_node(),
+                                                  keys=True,
+                                                  data='Product')
+                 if d == disrupted_up_prod.code])
+        lost_percent = num_lost / (num_lost + num_remain)
+        lst_size = \
+            [firm.size_stat[-1][0] for firm in self.model.a_lst_total_firms]
+        std_size = (self.size_stat[-1][0] - min(lst_size) + 1) \
+            / (max(lst_size) - min(lst_size) + 1)
+
+        # calculate probability of disruption
+        prob_disrupt = 1 - std_size * (1 - lost_percent)
+        if self.model.nprandom.choice([True, False],
+                                      p=[prob_disrupt,
+                                         1 - prob_disrupt]):
+            self.dct_n_trial_up_prod_disrupted[disrupted_up_prod] = 0
+            self.dct_prod_up_prod_stat[
+                prod]['s_stat'][disrupted_up_prod]['stat'] = False
+            status, _ = self.dct_prod_up_prod_stat[
+                prod]['p_stat'][-1]
+            if status != 'D':
+                self.dct_prod_up_prod_stat[
+                    prod]['p_stat'].append(('D', self.model.t))
+                # print(f"{self.name}'s {prod.code} turn to D status due to "
+                #       f"disrupted supplier of {disrupted_up_prod.code}")
+
+    def seek_alt_supply(self, product):
+        # parameter product is the product that self is seeking
+        # print(f"{self.name} seek alt supply for {product.code}")
+        if self.dct_n_trial_up_prod_disrupted[
+                product] <= self.model.int_n_max_trial:
+            if self.dct_n_trial_up_prod_disrupted[product] == 0:
+                # select a list of candidate firm that has the product
+                self.dct_cand_alt_supp_up_prod_disrupted[product] = \
+                    self.model.a_lst_total_firms.select([
+                        firm.is_prod_in_current_normal(product)
+                        for firm in self.model.a_lst_total_firms
+                    ])
+            if self.dct_cand_alt_supp_up_prod_disrupted[product]:
+                # select based on connection
+                lst_firm_connect = []
+                if self.is_prf_conn:
+                    for firm in \
+                            self.dct_cand_alt_supp_up_prod_disrupted[product]:
+                        node_self = self.get_firm_network_node()
+                        node_firm = firm.get_firm_network_node()
+                        if self.model.firm_network.graph.\
+                                has_edge(node_self, node_firm) or \
+                                self.model.firm_network.graph.\
+                                has_edge(node_firm, node_self):
+                            lst_firm_connect.append(firm)
+                if len(lst_firm_connect) == 0:
+                    # select based on size or not
+                    if self.is_prf_size:
+                        lst_size = \
+                            [firm.size_stat[-1][0] for firm in
+                                self.dct_cand_alt_supp_up_prod_disrupted[
+                                    product]]
+                        lst_prob = [size / sum(lst_size)
+                                    for size in lst_size]
+                        select_alt_supply = self.model.nprandom.choice(
+                            self.dct_cand_alt_supp_up_prod_disrupted[product],
+                            p=lst_prob)
+                    else:
+                        select_alt_supply = self.model.nprandom.choice(
+                            self.dct_cand_alt_supp_up_prod_disrupted[product])
+                elif len(lst_firm_connect) > 0:
+                    # select based on size of connected firm or not
+                    if self.is_prf_size:
+                        lst_firm_size = \
+                            [firm.size_stat[-1][0]
+                             for firm in lst_firm_connect]
+                        lst_prob = \
+                            [size / sum(lst_firm_size)
+                                for size in lst_firm_size]
+                        select_alt_supply = \
+                            self.model.nprandom.choice(lst_firm_connect,
+                                                       p=lst_prob)
+                    else:
+                        select_alt_supply = \
+                            self.model.nprandom.choice(lst_firm_connect)
+                # print(
+                #     f"{self.name} selct alt supply for {product.code} "
+                #     f"from {select_alt_supply.name}"
+                # )
+                assert select_alt_supply.is_prod_in_current_normal(product), \
+                    f"{select_alt_supply} \
+                        does not produce requested product {product}"
+
+                if product in select_alt_supply.dct_request_prod_from_firm.\
+                        keys():
+                    select_alt_supply.dct_request_prod_from_firm[
+                        product].append(self)
+                else:
+                    select_alt_supply.dct_request_prod_from_firm[product] = [
+                        self
+                    ]
+                # print(
+                #     select_alt_supply.name, 'dct_request_prod_from_firm', {
+                #         key.code: [v.name for v in value]
+                #         for key, value in
+                #         select_alt_supply.dct_request_prod_from_firm.items()
+                #     })
+
+                self.dct_n_trial_up_prod_disrupted[product] += 1
+
+    def handle_request(self):
+        # print(self.name, 'handle_request')
+        for product, lst_firm in self.dct_request_prod_from_firm.items():
+            if self.dct_prod_capacity[product] > 0:
+                if len(lst_firm) == 0:
+                    continue
+                elif len(lst_firm) == 1:
+                    self.accept_request(lst_firm[0], product)
+                elif len(lst_firm) > 1:
+                    # handling based on connection
+                    lst_firm_connect = []
+                    if self.is_prf_conn:
+                        for firm in lst_firm:
+                            node_self = self.get_firm_network_node()
+                            node_firm = firm.get_firm_network_node()
+                            if self.model.firm_network.graph.\
+                                    has_edge(node_self, node_firm) or \
+                                    self.model.firm_network.graph.\
+                                    has_edge(node_firm, node_self):
+                                lst_firm_connect.append(firm)
+                    if len(lst_firm_connect) == 0:
+                        # handling based on size or not
+                        if self.is_prf_size:
+                            lst_firm_size = \
+                                [firm.size_stat[-1][0] for firm in lst_firm]
+                            lst_prob = \
+                                [size / sum(lst_firm_size)
+                                 for size in lst_firm_size]
+                            select_customer = \
+                                self.model.nprandom.choice(lst_firm,
+                                                           p=lst_prob)
+                        else:
+                            select_customer = \
+                                self.model.nprandom.choice(lst_firm)
+                        self.accept_request(select_customer, product)
+                    elif len(lst_firm_connect) > 0:
+                        # handling based on size of connected firm or not
+                        if self.is_prf_size:
+                            lst_firm_size = \
+                                [firm.size_stat[-1][0]
+                                 for firm in lst_firm_connect]
+                            lst_prob = \
+                                [size / sum(lst_firm_size)
+                                 for size in lst_firm_size]
+                            select_customer = \
+                                self.model.nprandom.choice(lst_firm_connect,
+                                                           p=lst_prob)
+                        else:
+                            select_customer = \
+                                self.model.nprandom.choice(lst_firm_connect)
+                        self.accept_request(select_customer, product)
+            else:
+                for down_firm in lst_firm:
+                    down_firm.dct_cand_alt_supp_up_prod_disrupted[
+                        product].remove(self)
+
+                    # print(
+                    #     f"{self.name} denied {product.code} request "
+                    #     f"from {down_firm.name} for lack of capacity"
+                    # )
+
+    def accept_request(self, down_firm, product):
+        # parameter product is the product that self is selling
+        # connected firm has no probability for accepting request
+        node_self = self.get_firm_network_node()
+        node_d_firm = down_firm.get_firm_network_node()
+        if self.model.firm_network.graph.has_edge(node_self, node_d_firm) or \
+                self.model.firm_network.graph.has_edge(node_d_firm, node_self):
+            prod_accept = 1.0
+        else:
+            prod_accept = self.flt_diff_new_conn
+        if self.model.nprandom.choice([True, False],
+                                      p=[prod_accept, 1 - prod_accept]):
+            self.firm_network.graph.add_edges_from([
+                (self.firm_network.positions[self],
+                 self.firm_network.positions[down_firm], {
+                     'Product': product.code
+                })
+            ])
+            self.dct_prod_capacity[product] -= 1
+            self.dct_request_prod_from_firm[product].remove(down_firm)
+
+            for prod in down_firm.dct_prod_up_prod_stat.keys():
+                if product in down_firm.dct_prod_up_prod_stat[
+                        prod]['s_stat'].keys():
+                    down_firm.dct_prod_up_prod_stat[
+                        prod]['s_stat'][product]['stat'] = True
+                    down_firm.dct_prod_up_prod_stat[
+                        prod]['p_stat'].append(('N', self.model.t))
+            del down_firm.dct_n_trial_up_prod_disrupted[product]
+            del down_firm.dct_cand_alt_supp_up_prod_disrupted[product]
+
+            # print(
+            #     f"{self.name} accept {product.code} request "
+            #     f"from {down_firm.name}"
+            # )
+        else:
+            down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
+
+            # print(
+            #     f"{self.name} denied {product.code} request "
+            #     f"from {down_firm.name}"
+            # )
+
+    def clean_before_trial(self):
+        self.dct_request_prod_from_firm = {}
+
+    def clean_before_time_step(self):
+        self.dct_n_trial_up_prod_disrupted = \
+            dict.fromkeys(self.dct_n_trial_up_prod_disrupted.keys(), 0)
+        self.dct_cand_alt_supp_up_prod_disrupted = {}
+
+        # update the status of firm
+        for prod in self.dct_prod_up_prod_stat.keys():
+            status, ts = self.dct_prod_up_prod_stat[prod]['p_stat'][-1]
+            if ts != self.model.t:
+                self.dct_prod_up_prod_stat[prod]['p_stat'].append(
+                    (status, self.model.t))
+            # refresh set_disrupt_firm
+            for up_prod in self.dct_prod_up_prod_stat[prod]['s_stat'].keys():
+                self.dct_prod_up_prod_stat[prod][
+                    's_stat'][up_prod]['set_disrupt_firm'] = set()
+
+    def get_firm_network_node(self):
+        return self.firm_network.positions[self]
+
+    def is_prod_in_current_normal(self, prod):
+        if prod in self.dct_prod_up_prod_stat.keys():
+            if self.dct_prod_up_prod_stat[prod]['p_stat'][-1][0] == 'N':
+                return True
+            else:
+                return False
+        else:
+            return False

input_data/BomCateNet.csv

@@ -0,0 +1,108 @@
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+1,,1,,,,1,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.1,,,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.1.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.2,,,,,,,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.2.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.2.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3,,,,,,,,,,,1,,,,,,,,1,,1,,,,,,,,1,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1,,,,,,,,,,,,1,1,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.1.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.2,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.3.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.4.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.4.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.4.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.3.5.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,1,,,,,,,,1,,,,,,,1,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.1.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.1.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.1.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+1.4.2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.2.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.2.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.2.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.2.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.2.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.2.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1.4.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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input_data/BomNodes.csv

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input_data/Firm_amended.csv

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+3,艾克斯特,,艾克斯特,,北京艾克斯特科技有限公司,,,17.407722170008157,,S,3.1780538303479458,24.0,qichacha,Beijing,,,,,,,,,,,,,,,1.0,1.0,1.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+5,安华金和,,安华金和,,北京安华金和科技有限公司,,,18.503133696279523,,S,4.30406509320417,74.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,安世亚太,,安世亚太,,安世亚太科技股份有限公司,2017.0,902000000.0,18.884414760184505,159000000.0,M,5.117993812416755,167.0,qichacha,Beijing,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,
+7,百望,,百望,,百望股份有限公司,,,20.228513288018362,,L,6.077642243349034,436.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,
+8,梆梆安全,,梆梆安全,,北京梆梆安全科技有限公司,,,20.02307783148127,,M,5.8664680569332965,353.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,北京航天测控,,北京航天测控,,北京航天测控技术有限公司,,,20.12502380787598,,L,5.971261839790462,392.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,北京英贝思,,北京英贝思,,北京京能信息技术有限公司,,,18.997444645787912,,M,4.812184355372417,123.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,北信源,SZ,北信源,300352.SZ,北京北信源软件股份有限公司,2021.0,2831000000.0,20.330223248836806,675000000.0,L,7.4377951216719325,1699.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,大唐软件,,大唐软件,,大唐软件技术股份有限公司,,,19.4654905037987,,M,5.293304824724492,199.0,qichacha,Beijing,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,东方国信,SZ,东方国信,300166.SZ,北京东方国信科技股份有限公司,2021.0,8632000000.0,21.6274839875863,2470000000.0,L,9.069698042173716,8688.0,qichacha,Beijing,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,1.0,1.0,1.0,1.0,,1.0,1.0,,,1.0,1.0,1.0
+14,东华软件,SZ,东华软件,002065.SZ,东华软件股份公司,2021.0,21070000000.0,23.110192078374208,10880000000.0,L,9.3729692954244,11766.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,东软集团,SH,东软集团,600718.SH,东软集团股份有限公司,2021.0,18310000000.0,22.89060378053243,8735000000.0,L,9.793616881400416,17919.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+16,东土科技,SZ,东土科技,300353.SZ,北京东土科技股份有限公司,2021.0,2357000000.0,20.662453697549655,941000000.0,L,7.398174092970465,1633.0,qichacha,Beijing,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0
+17,国保金泰,,国保金泰,,北京国保金泰信息安全技术有限公司,,,17.687586384923698,,S,3.4657359027997265,32.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,国能智深,,国能智深,,国能智深控制技术有限公司,,,19.787215956383957,,M,5.6240175061873385,277.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,国泰网信,,国泰网信,,北京国泰网信科技有限公司,,,18.196615083558456,,S,3.9889840465642745,54.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+20,海基科技,,海基科技,,北京海基科技发展有限责任公司,,,16.338965041542313,,XS,2.0794415416798357,8.0,qichacha,Beijing,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+21,Hexagon,SE,Hexagon,HEXA.B,Hexagon AB,2021.0,4348926705.0,21.583992726504846,2364879075.0,L,9.998797732340453,22000.0,hexagon,Foreign,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,航天云网,,航天云网,,航天云网科技发展有限责任公司,,,18.786278941833256,,S,4.59511985013459,99.0,qichacha,Beijing,,,,,,,1.0,1.0,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,1.0,1.0,1.0,1.0,,1.0,1.0,,,1.0,1.0,1.0
+23,和利时,US,和利时,HOLI,和利时科技集团有限公司,2021.0,10365469659.9,22.303790735242135,4857504838.2,S,4.143134726391533,63.0,hollysys,Beijing,,,,1.0,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,1.0,1.0,1.0
+24,华大电子,HK,华大电子,85,北京中电华大电子设计有限责任公司,2021.0,2486434448.0,21.109740822530977,1471783580.8,M,5.605802066295998,272.0,hkexnews,Beijing,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,华大九天,SZ,华大九天,301269.SZ,北京华大九天科技股份有限公司,2021.0,1802000000.0,20.17681303553727,579000000.0,L,6.492239835020471,660.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+26,寄云科技,,寄云科技,,北京寄云科技有限公司,,,14.99034369816093,,XS,0.6931471805599453,2.0,,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,
+27,江南天安,,江南天安,,北京江南天安科技有限公司,,,19.430654185080233,,M,5.2574953720277815,192.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,金山云,HK,金山云,3896,北京金山云网络技术有限公司,2021.0,19000334000.0,22.12943806762975,4080307000.0,L,7.181591944611865,1315.0,hkexnews,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,
+29,京东工业品,US,京东集团,JD,北京京东数智工业科技有限公司,2021.0,496507000000.0,27.581402208446224,951592000000.0,M,5.8916442118257715,362.0,jd,Beijing,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,可信华泰,,可信华泰,,北京可信华泰信息技术有限公司,,,19.34600737166434,,M,5.170483995038151,176.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,昆仑数据,,昆仑数据,,四川昆仑智汇数据科技有限公司,,,17.013275713233007,,S,2.772588722239781,16.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,1.0,1.0,,,,,
+32,兰光创新,,兰光创新,,北京兰光创新科技有限公司,,,18.756343513389538,,S,4.564348191467836,96.0,qichacha,Beijing,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+33,蓝谷信息,,蓝谷信息,,北汽蓝谷信息技术有限公司,,,18.66057858282117,,S,4.465908118654584,87.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,
+34,力控科技,,力控科技,,北京力控元通科技有限公司,,,19.69132991474639,,M,5.5254529391317835,251.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+35,凌昊智能,,凌昊智能,,北京鲲鹏凌昊智能技术有限公司,,,18.282762012402003,,S,4.07753744390572,59.0,qichacha,Beijing,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,龙芯中科,SH,龙芯中科,688047.SH,龙芯中科技术股份有限公司,2021.0,1989000000.0,20.906420380044256,1201000000.0,L,6.71174039505618,822.0,qichacha,Beijing,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+37,绿盟,SZ,绿盟科技,300369.SZ,绿盟科技集团股份有限公司,2021.0,4765000000.0,21.682232843101,2609000000.0,L,8.394347361417392,4422.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,1.0,,,,1.0,,,,,,,1.0,,,,,,,,,1.0,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,牛刀,,起步科技,,北京起步科技股份有限公司,,,17.12785795509261,,S,2.8903717578961645,18.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,Autodesk,US,Autodesk,ADSK,Autodesk Inc,2021.0,50085024000.0,23.984435187501607,26080016000.0,,,,marketwatch,Foreign,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+40,奇安信,SH,奇安信,688561.SH,奇安信科技集团股份有限公司,2021.0,13480000000.0,22.482674275956807,5809000000.0,L,9.175438319966918,9657.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,1.0,,,,,,1.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,启明星辰,SZ,启明星辰,002439.SZ,启明星辰信息技术集团股份有限公司,2021.0,8936000000.0,22.20168348694211,4386000000.0,L,8.792853288640693,6587.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,1.0,,,,,,,,,,1.0,1.0,,1.0,1.0,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,山大华天,,山大华天,,山东山大华天软件有限公司,,,20.381021533902018,,M,6.234410725718371,510.0,qichacha,Beijing,,,,,,,,,,,,1.0,,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,神舟软件,,神舟软件,,北京神舟航天软件技术股份有限公司,2021.0,3042000000.0,21.130063390288353,1502000000.0,L,6.385194398997726,593.0,qichacha,Beijing,,,,,,,,,,,,,,,,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+44,圣邦微电子,,圣邦微电子,,圣邦微电子（北京）股份有限公司,2021.0,3049000000.0,21.528848446836143,2238000000.0,M,6.754604099487962,858.0,qichacha,Beijing,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+45,石化盈科,,石化盈科,,石化盈科信息技术有限责任公司,,,21.430514178160422,,M,7.313220387090301,1500.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,1.0,1.0,,,,,
+46,适创科技,,适创科技,,北京适创科技有限公司,,,17.9046659348233,,S,3.6888794541139363,40.0,qichacha,Beijing,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+47,首自信,,首自信,,北京首钢自动化信息技术有限公司,,,21.333048113425267,,L,7.213031659834869,1357.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,1.0,1.0,1.0,1.0,,,,,,,1.0,,,,,,,,,,,,,,,
+48,曙光信息,SH,曙光信息,603019.SH,曙光信息产业股份有限公司,2021.0,26190000000.0,23.13917961524746,11200000000.0,L,8.319473692442186,4103.0,qichacha,Non_Beijing,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+49,数码大方,,数码大方,,北京数码大方科技股份有限公司,2017.0,219000000.0,18.61953160269753,122000000.0,M,5.786897381366708,326.0,qichacha,Beijing,,,,,,,,,,,,1.0,,,1.0,,1.0,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,
+50,索为系统,,索为系统,,索为技术股份有限公司,,,20.12502380787598,,M,5.971261839790462,392.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+51,天地和兴,,天地和兴,,北京天地和兴科技有限公司,,,18.796056156413595,,M,4.605170185988092,100.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+52,天空卫士,,天空卫士,,北京天空卫士网络安全技术有限公司,,,18.36189705661439,,S,4.1588830833596715,64.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+53,天融信,SZ,天融信,002212.SZ,天融信科技集团股份有限公司,2021.0,11600000000.0,21.932823019566246,3352000000.0,L,8.743372131273969,6269.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,,,,1.0,1.0,,,,,,,1.0,1.0,1.0,1.0,,,,,1.0,,,,1.0,1.0,,,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+54,网御星云,,网御星云,,北京网御星云信息技术有限公司,,,21.480445986887567,,L,7.364547014255642,1579.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,1.0,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+55,威努特,,威努特,,北京威努特技术有限公司,,,20.09221467868815,,M,5.937536205082426,379.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,,,,1.0,1.0,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+56,芯愿景,,芯愿景,,北京芯愿景软件技术股份有限公司,2020.0,439000000.0,19.0140075892301,181000000.0,L,5.8971538676367405,364.0,qichacha,Beijing,,,1.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+57,亚控科技,,亚控科技,,北京亚控科技发展有限公司,,,19.123384646299442,,M,4.941642422609304,140.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0
+58,用友,SH,用友网络,600588.SH,用友网络科技股份有限公司,2021.0,17330000000.0,22.912906170924323,8932000000.0,L,10.072554993651666,23684.0,qichacha,Beijing,,,,,,,,1.0,,,,,,,,,1.0,,1.0,,,,,,,,,,,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,
+59,优特捷,,优特捷,,北京优特捷信息技术有限公司,,,18.90630509639984,,M,4.718498871295094,112.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+60,宇动源,,宇动源,,北京宇动源科技有限公司,,,18.1217454847229,,S,3.912023005428146,50.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
+61,元年科技,,元年科技,,北京元年科技股份有限公司,2017.0,629000000.0,19.34096349709606,251000000.0,L,5.811140992976701,334.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+62,云道智造,,云道智造,,北京云道智造科技有限公司,,,18.64933192967563,,S,4.454347296253507,86.0,qichacha,Beijing,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,
+63,长扬科技,,长扬科技,,长扬科技（北京）股份有限公司,,,20.05292974490909,,M,5.8971538676367405,364.0,qichacha,Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+64,中电智科,,中电智科,,中电智能科技有限公司,,,19.2286574902081,,M,5.049856007249537,156.0,qichacha,Beijing,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+65,中国电信,SH,中国电信,601728.SH,中国电信股份有限公司,2021.0,762234000000.0,26.80902186334131,439552000000.0,L,12.538687451824082,278922.0,qichacha,Beijing,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+66,中国联通,SH,中国联通,600050.SH,中国联合网络通信股份有限公司,2021.0,593300000000.0,26.515974520797013,327900000000.0,L,12.399420686589707,242661.0,qichacha,Beijing,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+67,中国移动,SH,中国移动,600941.SH,中国移动通信有限公司,2021.0,1840000000000.0,27.466450671748124,848258000000.0,L,8.430545384690566,4585.0,qichacha,Beijing,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+68,中望软件,SH,中望软件,688083.SH,广州中望龙腾软件股份有限公司,2021.0,3117000000.0,20.241999015421964,618000000.0,L,7.430707082545968,1687.0,qichacha,Beijing,,,,,,,,,,,,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+69,紫光集团,SZ,紫光股份,000938.SZ,紫光股份有限公司,2021.0,66430000000.0,24.93746536097679,67640000000.0,L,9.749578510178852,17147.0,qichacha,Beijing,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+70,ABB,US,ABB,ABB,Abb Ltd,2021.0,252378345120.0,25.927786876227803,182093636180.16,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,1.0,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+71,Altair,US,Altair,ALTR,Altair Engineering Inc,2021.0,8003332000.0,22.02110910882454,3661391520.0,,,,marketwatch,Foreign,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+72,ANSYS,US,ANSYS,ANSS,ANSYS Inc,2021.0,43511280320.0,23.297266354944313,13118199200.0,,,,marketwatch,Foreign,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+73,FANUC,JP,FANUC,6954,Fanuc Corp,2021.0,83161023470.0,24.062919525471276,28209355790.0,,,,fanuc,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+74,HoneyWell,US,HoneyWell,HON,Honeywell International Inc,2021.0,443553600000.0,26.189563074869437,236582560000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+75,IBM,US,IBM,IBM,International Business Machines Corp,2021.0,908166880000.0,26.701074769106654,394574880000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+76,MasterCAM,,MasterCAM,,CNC Software LLC,2021.0,,20.061617323445837,516000000.0,,,,zippia,Foreign,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+77,Oracle,US,Oracle,ORCL,Oracle Corp,2021.0,902016160000.0,26.352667810008423,278495520000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+78,OutSystems,,OutSystems,,OutSystems,2021.0,,21.418791810932476,2004763250.0,,,,forbes,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
+79,PTC,US,PTC,PTC,PTC Inc,2021.0,31012012800.0,23.243640487776307,12433253920.0,,,,marketwatch,Foreign,,,,,,,,,,,,1.0,,,1.0,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,1.0,,,,,1.0,,,,,1.0,1.0,1.0
+80,Salesforce,US,Salesforce,CRM,Salesforce Inc,2021.0,456150880000.0,25.70833549748036,146213760000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+81,SAP,US,SAP,SAP,SAP SE,2021.0,489642720000.0,25.97843016085804,191552960000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,1.0,,,,,1.0,,,,,,,
+82,Uptake,,Uptake,,Uptake Technologies Inc,2021.0,,18.02651159558605,67424000.0,,,,zippia,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,
+83,Emerson,US,Emerson,EMR,Emerson Electric Co,2021.0,170039200000.0,25.555117627948732,125443040000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+84,Bosch,,Bosch,,Bosch Ltd,2021.0,780540611500.0,27.070589149510063,570962374000.0,,,,bosch,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,1.0,,,,,,,,,,1.0,,,
+85,Dassault,FR,Dassault,DSY,Dassault Systemes SE,2021.0,1044022266200.0,26.600602947110218,356857702600.0,,,,dassault ,Foreign,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+86,Dell EMC,US,Dell,DELL,Dell Inc,2021.0,849095200000.0,27.115122378990367,596963840000.0,,,,marketwatch,Foreign,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+87,Texas Instruments,US,Texas Instruments,TXN,Texas Instruments Inc,2021.0,169770880000.0,25.56118703444727,126206720000.0,,,,marketwatch,Foreign,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+88,HPE,US,HPE,HPE,Hewlett Packard Enterprise Co,2021.0,396969120000.0,25.979255909913164,191711200000.0,,,,marketwatch,Foreign,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+89,Rockwell,US,Rockwell,ROK,Rockwell Automation Inc,2021.0,75779072000.0,24.59715157408287,48129040000.0,,,,marketwatch,Foreign,,,,1.0,,,1.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+90,Mentor Graphics,,Mentor Graphics,,Siemens EDA,2023.0,,22.399698252124722,5346448000.0,,,,zoominfo,Foreign,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+91,Moxa,,Moxa,,Moxa Inc,2023.0,,21.27355829742095,1733760000.0,,6.913737350659685,1006.0,rocketreach,Foreign,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+92,Omron,JP,Omron,6645,Omron Corp,2021.0,47620285930.0,24.387826332521357,39038974590.0,,,,omron,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+93,Cadence,US,Cadence,CDNS,Cadence Design Systems Inc,2021.0,30177737120.0,23.746570412801468,20559118720.0,,,,marketwatch,Foreign,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+94,Mitsubishi,JP,Mitsubishi,6503,Mitsubishi Electric Corp,2021.0,1121237654040.0,27.507090628204164,883441248760.0,,,,mitsubishi,Foreign,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+95,Schneider,FR,Schneider,SU,Schneider Electric SE,2021.0,400516802200.0,26.080973430818208,212237853000.0,,,,schneider,Foreign,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,
+96,Cisco,US,Cisco,CSCO,Cisco Systems Inc,2021.0,670779360000.0,26.560260853399612,342747840000.0,,,,marketwatch,Foreign,,,,,,,1.0,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+97,General Electric,US,General Electric,GE,General Electric Co,2021.0,1368253120000.0,26.958298173346392,510317120000.0,,,,marketwatch,Foreign,,,,,,1.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+98,Microsoft Azure,US,Microsoft,MSFT,Microsoft Co,2021.0,2296399520000.0,27.77637214067841,1156445440000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+99,Siemens,XE,Siemens,SIE,Siemens AG,2021.0,1023352847200.0,26.84835831045688,457186989000.0,,,,siemens,Foreign,,,,1.0,,,1.0,,,,1.0,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,
+100,Synopsys,US,Synopsys,SNPS,Synopsys Inc,2021.0,60215548800.0,24.08697187131976,28896082560.0,,,,marketwatch,Foreign,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+101,Analog Devices,US,Analog Devices,ADI,Analog Devices Inc,2021.0,359975848480.0,24.642260636441325,50349807680.0,,,,marketwatch,Foreign,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+102,Amazon AWS,US,Amazon,AMZN,Amazon.com Inc,2021.0,2893377120000.0,28.80423838845769,3232375360000.0,,,,marketwatch,Foreign,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,
+103,STMicroelectronics ,US,STMicroelectronics ,STM,STMicroelectronics NV,2021.0,94016180090.4,25.03108635524997,74278423930.24,,,,marketwatch,Foreign,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+104,Infineon,XE,Infineon,IFX,Infineon Technologies AG,2021.0,171332228400.0,25.120293836377957,81209156000.0,,10.825521759037349,50288.0,infineon ,Foreign,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+105,Intel,US,Intel,INTC,Intel Co,2021.0,1158633280000.0,27.02163609268993,543685120000.0,,,,marketwatch,Foreign,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+106,阿里巴巴,US,阿里巴巴,BABA,Alibaba Group Holding Ltd,2021.0,1690218000000.0,27.298744664676118,717289000000.0,,,,alibaba ,Non_Beijing,,1.0,,,,1.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,1.0,,,,
+107,安恒信息,SH,安恒信息,688023.SH,杭州安恒信息技术股份有限公司,2021.0,4852000000.0,21.322102338035116,1820000000.0,L,8.191740021277457,3611.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+108,百度,HK,百度,9888,Baidu Inc,2021.0,380034000000.0,25.547515326370895,124493000000.0,,,,hkexnews,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,1.0,,,,
+109,宝信软件,SH,宝信软件,600845.SH,上海宝信软件股份有限公司,2021.0,17860000000.0,23.187969779416893,11760000000.0,L,8.567506005289827,5258.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+110,晨科软件,,晨科软件,,杭州晨科软件技术有限公司,,,18.66057858282117,,S,4.465908118654584,87.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+111,鼎捷软件,SZ,鼎捷软件,300378.SZ,鼎捷软件股份有限公司,2021.0,2638000000.0,21.304363513697734,1788000000.0,L,8.360305435879093,4274.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,1.0,,,,,1.0,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+112,东华测试,SZ,东华测试,300354.SZ,江苏东华测试技术股份有限公司,2021.0,545000000.0,19.364586642859493,257000000.0,L,6.376726947898627,588.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+113,飞腾信息,,飞腾信息,,飞腾信息技术有限公司,,,19.49912235990601,,M,5.327876168789581,206.0,qichacha,Non_Beijing,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+114,富勒科技,,富勒科技,,上海富勒信息科技有限公司,,,18.796056156413595,,M,4.605170185988092,100.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+115,富士康,,富士康,,富士康工业互联网股份有限公司,2021.0,266600000000.0,26.809131059475877,439600000000.0,L,12.165094388801615,191970.0,qichacha,Non_Beijing,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,
+116,概伦电子,SH,概伦电子,688206.SH,上海概伦电子股份有限公司,2021.0,2342000000.0,19.07820074686916,193000000.0,M,5.683579767338681,294.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+117,格创东智,,格创东智,,格创东智科技有限公司,,,14.99034369816093,,XS,0.6931471805599453,2.0,,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,,1.0,1.0,,,,,
+118,工邦邦,,工邦邦,,上海工邦邦工业技术有限公司,,,18.44907397460875,,S,4.248495242049359,70.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+119,广联达,SZ,广联达,002410.SZ,广联达科技股份有限公司,2021.0,10070000000.0,22.449419549076474,5619000000.0,L,9.157572306437528,9486.0,qichacha,Non_Beijing,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+120,广州数控,,广州数控,,广州数控设备有限公司,,,21.70305470983921,,M,7.59337419312129,1985.0,qichacha,Non_Beijing,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+121,广州智臣,,广州智臣,,广州智臣信息科技有限公司,,,17.975021257984938,,S,3.7612001156935624,43.0,qichacha,Non_Beijing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+0,0,1,1,1,2,0,2,0,1,1,0,1,0,1,0,1,0,2,2,1,0,1
+1,0,1,1,2,0,1,0,0,1,1,0,1,0,1,0,2,1,0,0,2,1,2
+2,0,1,1,0,1,2,1,0,1,1,0,1,0,1,0,0,2,1,1,0,2,0
+0,0,1,1,1,2,1,0,1,0,1,1,0,1,0,0,0,2,1,2,2,1,0
+1,0,1,1,2,0,2,1,1,0,1,1,0,1,0,0,1,0,2,0,0,2,1
+2,0,1,1,0,1,0,2,1,0,1,1,0,1,0,0,2,1,0,1,1,0,2
+0,1,0,1,1,0,2,2,1,0,0,1,1,0,1,0,2,0,1,1,0,1,2
+1,1,0,1,2,1,0,0,1,0,0,1,1,0,1,0,0,1,2,2,1,2,0
+2,1,0,1,0,2,1,1,1,0,0,1,1,0,1,0,1,2,0,0,2,0,1
+0,1,0,1,1,1,2,2,0,1,1,1,0,0,0,1,0,1,0,0,2,2,1
+1,1,0,1,2,2,0,0,0,1,1,1,0,0,0,1,1,2,1,1,0,0,2
+2,1,0,1,0,0,1,1,0,1,1,1,0,0,0,1,2,0,2,2,1,1,0
+0,1,0,0,2,1,0,1,1,1,1,0,1,1,0,0,2,2,0,2,0,1,1
+1,1,0,0,0,2,1,2,1,1,1,0,1,1,0,0,0,0,1,0,1,2,2
+2,1,0,0,1,0,2,0,1,1,1,0,1,1,0,0,1,1,2,1,2,0,0
+0,1,1,1,2,1,1,1,0,0,0,0,1,1,0,1,0,0,2,1,2,0,2
+1,1,1,1,0,2,2,2,0,0,0,0,1,1,0,1,1,1,0,2,0,1,0
+2,1,1,1,1,0,0,0,0,0,0,0,1,1,0,1,2,2,1,0,1,2,1
+0,1,1,0,2,2,2,1,1,0,1,0,0,0,1,1,2,1,1,0,1,0,0
+1,1,1,0,0,0,0,2,1,0,1,0,0,0,1,1,0,2,2,1,2,1,1
+2,1,1,0,1,1,1,0,1,0,1,0,0,0,1,1,1,0,0,2,0,2,2
+0,1,1,0,2,0,1,2,0,1,0,1,0,1,1,0,1,2,0,1,1,2,0
+1,1,1,0,0,1,2,0,0,1,0,1,0,1,1,0,2,0,1,2,2,0,1
+2,1,1,0,1,2,0,1,0,1,0,1,0,1,1,0,0,1,2,0,0,1,2

input_data/oa_without_exp.csv

@@ -0,0 +1,2 @@
+X1,X2,X3,X4,X5,X6,X7,X8
+0,0,0,0,0,0,0,0

input_data/xv_with_exp.csv

@@ -0,0 +1,4 @@
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n
+7,TRUE,TRUE,uniform,5,0.3,3,3
+5,FALSE,FALSE,normal,10,0.5,5,2
+3,,,,15,0.7,7,1

input_data/xv_without_exp.csv

@@ -0,0 +1,2 @@
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n
+5,TRUE,TRUE,uniform,10,0.5,5,2

main.py

@@ -0,0 +1,56 @@
+import os
+import random
+import time
+from multiprocessing import Process
+import argparse
+from computation import Computation
+from sqlalchemy.orm import close_all_sessions
+
+import yaml
+
+
+def do_computation(c_db):
+    exp = Computation(c_db)
+
+    while 1:
+        time.sleep(random.uniform(0, 10))
+        is_all_done = exp.run()
+        if is_all_done:
+            break
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='setting')
+    parser.add_argument('--exp', type=str, default='test')
+    parser.add_argument('--job', type=int, default='3')
+    parser.add_argument('--reset_sample', type=int, default='0')
+    parser.add_argument('--reset_db', type=bool, default=False)
+
+    args = parser.parse_args()
+    assert args.job >= 1, 'Number of jobs should >= 1'
+
+    prefix_file_name = 'conf_db_prefix.yaml'
+    if os.path.exists(prefix_file_name):
+        os.remove(prefix_file_name)
+    with open(prefix_file_name, 'w', encoding='utf-8') as file:
+        yaml.dump({'db_name_prefix': args.exp}, file)
+
+    from controller_db import ControllerDB
+    controller_db = ControllerDB(args.exp, reset_flag=args.reset_sample)
+    # controller_db.reset_db()
+
+    # force drop
+    controller_db.reset_db(force_drop=args.reset_db)
+
+    controller_db.prepare_list_sample()
+
+    close_all_sessions()
+
+    process_list = []
+    for i in range(int(args.job)):
+        p = Process(target=do_computation, args=(controller_db,))
+        p.start()
+        process_list.append(p)
+
+    for i in process_list:
+        i.join()

model.py

@@ -0,0 +1,397 @@
+import agentpy as ap
+import pandas as pd
+import networkx as nx
+from firm import FirmAgent
+from product import ProductAgent
+from orm import db_session, Result
+import platform
+import json
+
+
+class Model(ap.Model):
+    def setup(self):
+        # self parameter
+        self.sample = self.p.sample
+        self.int_stop_ts = 0
+        self.int_n_iter = int(self.p.n_iter)
+        self.product_network = None  # agentpy network
+        self.firm_network = None  # agentpy network
+        self.firm_prod_network = None  # networkx
+        self.dct_lst_init_disrupt_firm_prod = \
+            self.p.dct_lst_init_disrupt_firm_prod
+
+        # external variable
+        self.int_n_max_trial = int(self.p.n_max_trial)
+        self.is_prf_size = bool(self.p.prf_size)
+        # self.proactive_ratio = float(self.p.proactive_ratio) # dropped
+        self.remove_t = int(self.p.remove_t)
+        self.int_netw_prf_n = int(self.p.netw_prf_n)
+
+        # initialize graph bom
+        G_bom = nx.adjacency_graph(json.loads(self.p.g_bom))
+        self.product_network = ap.Network(self, G_bom)
+
+        # initialize graph firm
+        Firm = pd.read_csv("input_data/Firm_amended.csv")
+        Firm['Code'] = Firm['Code'].astype('string')
+        Firm.fillna(0, inplace=True)
+        Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
+        firm_product = []
+        for _, row in Firm.loc[:, '1':].iterrows():
+            firm_product.append(row[row == 1].index.to_list())
+        Firm_attr.loc[:, 'Product_Code'] = firm_product
+        Firm_attr.set_index('Code', inplace=True)
+        G_Firm = nx.MultiDiGraph()
+        G_Firm.add_nodes_from(Firm["Code"])
+
+        firm_labels_dict = {}
+        for code in G_Firm.nodes:
+            firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
+        nx.set_node_attributes(G_Firm, firm_labels_dict)
+
+        # initialize graph firm prod
+        Firm_Prod = pd.read_csv("input_data/Firm_amended.csv")
+        Firm_Prod.fillna(0, inplace=True)
+        firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
+        firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
+        firm_prod.drop('bool', axis=1, inplace=True)
+        firm_prod.rename({'level_0': 'Firm_Code',
+                          'level_1': 'Product_Code'}, axis=1, inplace=True)
+        firm_prod['Firm_Code'] = firm_prod['Firm_Code'].astype('string')
+        G_FirmProd = nx.MultiDiGraph()
+        G_FirmProd.add_nodes_from(firm_prod.index)
+
+        firm_prod_labels_dict = {}
+        for code in firm_prod.index:
+            firm_prod_labels_dict[code] = firm_prod.loc[code].to_dict()
+        nx.set_node_attributes(G_FirmProd, firm_prod_labels_dict)
+
+        # add edge to G_firm according to G_bom
+        for node in nx.nodes(G_Firm):
+            lst_pred_product_code = []
+            for product_code in G_Firm.nodes[node]['Product_Code']:
+                lst_pred_product_code += list(G_bom.predecessors(product_code))
+            lst_pred_product_code = list(set(lst_pred_product_code))
+            # to generate consistant graph
+            lst_pred_product_code = list(sorted(lst_pred_product_code))
+            for pred_product_code in lst_pred_product_code:
+                # for each product predecessor (component) the firm need
+                # get a list of firm producing this component
+                lst_pred_firm = \
+                    Firm['Code'][Firm[pred_product_code] == 1].to_list()
+                # select multiple supplier (multi-sourcing)
+                n_pred_firm = self.int_netw_prf_n
+                if n_pred_firm > len(lst_pred_firm):
+                    n_pred_firm = len(lst_pred_firm)
+                # based on size or not
+                if self.is_prf_size:
+                    lst_pred_firm_size = \
+                        [G_Firm.nodes[pred_firm]['Revenue_Log']
+                            for pred_firm in lst_pred_firm]
+                    lst_prob = \
+                        [size / sum(lst_pred_firm_size)
+                            for size in lst_pred_firm_size]
+                    lst_choose_firm = self.nprandom.choice(lst_pred_firm,
+                                                           n_pred_firm,
+                                                           replace=False,
+                                                           p=lst_prob)
+                else:
+                    lst_choose_firm = self.nprandom.choice(lst_pred_firm,
+                                                           n_pred_firm,
+                                                           replace=False)
+                lst_add_edge = [(pred_firm, node,
+                                {'Product': pred_product_code})
+                                for pred_firm in lst_choose_firm]
+                G_Firm.add_edges_from(lst_add_edge)
+
+                # graph firm prod
+                set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
+                set_pred_succ_code = set(G_bom.successors(pred_product_code))
+                lst_use_pred_prod_code = list(
+                    set_node_prod_code & set_pred_succ_code)
+                for pred_firm in lst_choose_firm:
+                    pred_node = [n for n, v in G_FirmProd.nodes(data=True)
+                                 if v['Firm_Code'] == pred_firm and
+                                 v['Product_Code'] == pred_product_code][0]
+                    for use_pred_prod_code in lst_use_pred_prod_code:
+                        current_node = \
+                            [n for n, v in G_FirmProd.nodes(data=True)
+                             if v['Firm_Code'] == node and
+                             v['Product_Code'] == use_pred_prod_code][0]
+                        G_FirmProd.add_edge(pred_node, current_node)
+        # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
+        # nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
+
+        # connect unconnected nodes
+        for node in nx.nodes(G_Firm):
+            if G_Firm.degree(node) == 0:
+                for product_code in G_Firm.nodes[node]['Product_Code']:
+                    # unconnected node does not have possible suppliers,
+                    # therefore find possible customer instead
+                    # current node in graph firm prod
+                    current_node = \
+                        [n for n, v in G_FirmProd.nodes(data=True)
+                            if v['Firm_Code'] == node and
+                            v['Product_Code'] == product_code][0]
+
+                    lst_succ_product_code = list(
+                        G_bom.successors(product_code))
+                    # different from: for different types of product,
+                    # finding a common supplier (the logic above),
+                    # instead: for different types of product,
+                    # finding a customer for each product
+                    for succ_product_code in lst_succ_product_code:
+                        # for each product successor (finished product)
+                        # the firm sells to,
+                        # get a list of firm producing this finished product
+                        lst_succ_firm = Firm['Code'][
+                            Firm[succ_product_code] == 1].to_list()
+                        # select multiple customer (multi-selling)
+                        n_succ_firm = self.int_netw_prf_n
+                        if n_succ_firm > len(lst_succ_firm):
+                            n_succ_firm = len(lst_succ_firm)
+                        # based on size or not
+                        if self.is_prf_size:
+                            lst_succ_firm_size = \
+                                [G_Firm.nodes[succ_firm]['Revenue_Log']
+                                    for succ_firm in lst_succ_firm]
+                            lst_prob = \
+                                [size / sum(lst_succ_firm_size)
+                                    for size in lst_succ_firm_size]
+                            lst_choose_firm = \
+                                self.nprandom.choice(lst_succ_firm,
+                                                     n_succ_firm,
+                                                     replace=False,
+                                                     p=lst_prob)
+                        else:
+                            lst_choose_firm = \
+                                self.nprandom.choice(lst_succ_firm,
+                                                     n_succ_firm,
+                                                     replace=False)
+                        lst_add_edge = [(node, succ_firm,
+                                        {'Product': product_code})
+                                        for succ_firm in lst_choose_firm]
+                        G_Firm.add_edges_from(lst_add_edge)
+
+                        # graph firm prod
+                        for succ_firm in lst_choose_firm:
+                            succ_node = \
+                                [n for n, v in G_FirmProd.nodes(data=True)
+                                 if v['Firm_Code'] == succ_firm and
+                                 v['Product_Code'] == succ_product_code][0]
+                            G_FirmProd.add_edge(current_node, succ_node)
+
+        self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm))
+        self.firm_network = ap.Network(self, G_Firm)
+        self.firm_prod_network = G_FirmProd
+        # import matplotlib.pyplot as plt
+        # nx.draw(G_FirmProd)
+        # plt.show()
+
+        # initialize product
+        for ag_node, attr in self.product_network.graph.nodes(data=True):
+            product = ProductAgent(self, code=ag_node.label, name=attr['Name'])
+            self.product_network.add_agents([product], [ag_node])
+        self.a_lst_total_products = ap.AgentList(self,
+                                                 self.product_network.agents)
+
+        # initialize firm
+        for ag_node, attr in self.firm_network.graph.nodes(data=True):
+            firm_agent = FirmAgent(
+                self,
+                code=ag_node.label,
+                name=attr['Name'],
+                type_region=attr['Type_Region'],
+                revenue_log=attr['Revenue_Log'],
+                a_lst_product=self.a_lst_total_products.select([
+                    code in attr['Product_Code']
+                    for code in self.a_lst_total_products.code
+                ]))
+
+            self.firm_network.add_agents([firm_agent], [ag_node])
+        self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents)
+
+        # initialize dct_lst_init_disrupt_firm_prod (from string to agent)
+        t_dct = {}
+        for firm_code, lst_product in \
+                self.dct_lst_init_disrupt_firm_prod.items():
+            firm = self.a_lst_total_firms.select(
+                self.a_lst_total_firms.code == firm_code)[0]
+            t_dct[firm] = self.a_lst_total_products.select([
+                code in lst_product for code in self.a_lst_total_products.code
+            ])
+        self.dct_lst_init_disrupt_firm_prod = t_dct
+
+        # set the initial firm product that are disrupted
+        # print('\n', '=' * 20, 'step', self.t, '=' * 20)
+        for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
+            for product in a_lst_product:
+                assert product in firm.dct_prod_up_prod_stat.keys(), \
+                    f"product {product.code} not in firm {firm.code}"
+                firm.dct_prod_up_prod_stat[
+                    product]['p_stat'].append(('D', self.t))
+                # print(f"initial disruption {firm.name} {product.code}")
+
+        # draw network
+        # self.draw_network()
+
+    def update(self):
+        self.a_lst_total_firms.clean_before_time_step()
+
+        # reduce the size of disrupted firm
+        for firm in self.a_lst_total_firms:
+            for prod in firm.dct_prod_up_prod_stat.keys():
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
+                if status == 'D':
+                    size = firm.size_stat[-1][0] - \
+                        firm.size_stat[0][0] \
+                        / len(firm.dct_prod_up_prod_stat.keys()) \
+                        / self.remove_t
+                    firm.size_stat.append((size, self.t))
+                    # print(f'in ts {self.t}, reduce {firm.name} size '
+                    #       f'to {firm.size_stat[-1][0]} due to {prod.code}')
+                    lst_is_disrupt = \
+                        [stat == 'D' for stat, _ in
+                         firm.dct_prod_up_prod_stat[prod]['p_stat']
+                         [-1 * self.remove_t:]]
+                    if all(lst_is_disrupt):
+                        # turn disrupted firm into removed firm
+                        # when last self.remove_t times status is all disrupted
+                        firm.dct_prod_up_prod_stat[
+                            prod]['p_stat'].append(('R', self.t))
+
+        # stop simulation if any firm still in disrupted except initial removal
+        if self.t > 0:
+            for firm in self.a_lst_total_firms:
+                for prod in firm.dct_prod_up_prod_stat.keys():
+                    status, _ = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
+                    is_init = \
+                        firm in self.dct_lst_init_disrupt_firm_prod.keys() \
+                        and prod in self.dct_lst_init_disrupt_firm_prod[firm]
+                    if status == 'D' and not is_init:
+                        # print("not stop because", firm.name, prod.code)
+                        break
+                else:
+                    continue
+                break
+            else:
+                self.int_stop_ts = self.t
+                self.stop()
+
+        if self.t == self.int_n_iter:
+            self.stop()
+
+    def step(self):
+        # print('\n', '=' * 20, 'step', self.t, '=' * 20)
+
+        # remove edge to customer and disrupt customer up product
+        for firm in self.a_lst_total_firms:
+            for prod in firm.dct_prod_up_prod_stat.keys():
+                # repetition of disrupted firm that last for multiple ts is ok,
+                # as their edge has already been removed
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
+                if status == 'D' and ts == self.t-1:
+                    firm.remove_edge_to_cus(prod)
+
+        for firm in self.a_lst_total_firms:
+            for prod in firm.dct_prod_up_prod_stat.keys():
+                for up_prod in firm.dct_prod_up_prod_stat[prod][
+                        's_stat'].keys():
+                    if firm.dct_prod_up_prod_stat[prod][
+                            's_stat'][up_prod]['set_disrupt_firm']:
+                        firm.disrupt_cus_prod(prod, up_prod)
+
+        for n_trial in range(self.int_n_max_trial):
+            # print('=' * 10, 'trial', n_trial, '=' * 10)
+            # seek_alt_supply
+            # shuffle self.a_lst_total_firms
+            self.a_lst_total_firms = self.a_lst_total_firms.shuffle()
+            is_stop_trial = True
+            for firm in self.a_lst_total_firms:
+                lst_seek_prod = []
+                for prod in firm.dct_prod_up_prod_stat.keys():
+                    status = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1][0]
+                    if status == 'D':
+                        for supply in firm.dct_prod_up_prod_stat[
+                                prod]['s_stat'].keys():
+                            if not firm.dct_prod_up_prod_stat[
+                                    prod]['s_stat'][supply]['stat']:
+                                lst_seek_prod.append(supply)
+                # commmon supply only seek once
+                lst_seek_prod = list(set(lst_seek_prod))
+                if len(lst_seek_prod) > 0:
+                    is_stop_trial = False
+                for supply in lst_seek_prod:
+                    firm.seek_alt_supply(supply)
+            if is_stop_trial:
+                break
+
+            # handle_request
+            # shuffle self.a_lst_total_firms
+            self.a_lst_total_firms = self.a_lst_total_firms.shuffle()
+            for firm in self.a_lst_total_firms:
+                if len(firm.dct_request_prod_from_firm) > 0:
+                    firm.handle_request()
+
+            # reset dct_request_prod_from_firm
+            self.a_lst_total_firms.clean_before_trial()
+
+    def end(self):
+        # print('/' * 20, 'output', '/' * 20)
+
+        qry_result = db_session.query(Result).filter_by(s_id=self.sample.id)
+        if qry_result.count() == 0:
+            lst_result_info = []
+            for firm in self.a_lst_total_firms:
+                for prod, dct_status_supply in \
+                        firm.dct_prod_up_prod_stat.items():
+                    lst_is_normal = [stat == 'N' for stat, _
+                                     in dct_status_supply['p_stat']]
+                    if not all(lst_is_normal):
+                        # print(f"{firm.name} {prod.code}:")
+                        # print(dct_status_supply['p_stat'])
+                        for status, ts in dct_status_supply['p_stat']:
+                            db_r = Result(s_id=self.sample.id,
+                                          id_firm=firm.code,
+                                          id_product=prod.code,
+                                          ts=ts,
+                                          status=status)
+                            lst_result_info.append(db_r)
+            db_session.bulk_save_objects(lst_result_info)
+            db_session.commit()
+        self.sample.is_done_flag = 1
+        self.sample.computer_name = platform.node()
+        self.sample.stop_t = self.int_stop_ts
+        db_session.commit()
+
+    def draw_network(self):
+        import matplotlib.pyplot as plt
+        plt.rcParams['font.sans-serif'] = 'SimHei'
+        pos = nx.nx_agraph.graphviz_layout(self.firm_network.graph,
+                                           prog="twopi",
+                                           args="")
+        node_label = nx.get_node_attributes(self.firm_network.graph, 'Name')
+        node_degree = dict(self.firm_network.graph.out_degree())
+        node_label = {
+            key: f"{key} {node_label[key]} {node_degree[key]}"
+            for key in node_label.keys()
+        }
+        node_size = list(
+            nx.get_node_attributes(self.firm_network.graph,
+                                   'Revenue_Log').values())
+        node_size = list(map(lambda x: x**2, node_size))
+        edge_label = nx.get_edge_attributes(self.firm_network.graph, "Product")
+        # multi(di)graphs, the keys are 3-tuples
+        edge_label = {(n1, n2): label
+                      for (n1, n2, _), label in edge_label.items()}
+        plt.figure(figsize=(12, 12), dpi=300)
+        nx.draw(self.firm_network.graph,
+                pos,
+                node_size=node_size,
+                labels=node_label,
+                font_size=6)
+        nx.draw_networkx_edge_labels(self.firm_network.graph,
+                                     pos,
+                                     edge_label,
+                                     font_size=4)
+        plt.savefig("network.png")

orm.py

@@ -0,0 +1,116 @@
+# -*- coding: utf-8 -*-
+from sqlalchemy import create_engine, inspect
+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)
+ins = 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(10), nullable=False)
+    id_product = Column(String(10), nullable=False)
+    ts = Column(Integer, nullable=False)
+    status = Column(String(5), nullable=False)
+
+    sample = relationship('Sample', back_populates='result', uselist=False)
+
+    def __repr__(self):
+        return f'<Product id: {self.id}>'
+
+
+if __name__ == '__main__':
+    Base.metadata.drop_all()
+    Base.metadata.create_all()

output_result/resilience/anova.csv

@@ -0,0 +1,9 @@
+,mean_count_firm_prod,mean_max_ts_firm_prod,mean_n_remove_firm_prod,mean_end_ts
+prf_size,0.391,0.989,0.724,0.549
+prf_conn,0.564,0.315,0.252,0.744
+cap_limit_prob_type,0.001,0,0,0
+n_max_trial,0.001,0.009,0.002,0.007
+cap_limit_level,0,0,0,0
+diff_new_conn,0.046,0.071,0.46,0.119
+remove_t,0.007,0.045,0.74,0
+netw_prf_n,0,0.002,0.254,0.044

output_result/resilience/anova_visualization.csv

@@ -0,0 +1,22 @@
+自变量,level,系统恢复用时R1,产业-企业边累计扰乱次数R2,产业-企业边最大传导深度R3,产业-企业边断裂总数R4
+采购策略P1,三供应商,2.144,2.826,1.156,0.7541
+采购策略P1,双供应商,2.146,2.65,1.133,0.7615
+采购策略P1,单供应商,2.261,2.519,1.121,0.7919
+是否规模偏好P2,倾向,2.196,2.661,1.137,0.7657
+是否规模偏好P2,不倾向,2.171,2.669,1.137,0.7726
+最大尝试次数P3,高,2.141,2.652,1.13,0.739
+最大尝试次数P3,中,2.124,2.652,1.127,0.7431
+最大尝试次数P3,低,2.286,2.691,1.154,0.8254
+是否已有连接偏好P4,倾向,2.191,2.663,1.133,0.7579
+是否已有连接偏好P4,不倾向,2.177,2.668,1.141,0.7804
+额外产能分布P5,均匀分布,2.316,2.681,1.158,0.8403
+额外产能分布P5,正态分布,2.052,2.65,1.115,0.698
+额外产能分布参数P6,高,1.914,2.624,1.098,0.6299
+额外产能分布参数P6,中,2.202,2.666,1.142,0.7655
+额外产能分布参数P6,低,2.436,2.705,1.171,0.9121
+新供应关系构成概率P7,低,2.24,2.672,1.143,0.764
+新供应关系构成概率P7,中,2.132,2.674,1.143,0.7859
+新供应关系构成概率P7,高,2.179,2.649,1.124,0.7575
+最大尝试时间步P8,低,1.726,2.646,1.123,0.7782
+最大尝试时间步P8,中,2.186,2.682,1.144,0.7599
+最大尝试时间步P8,高,2.64,2.667,1.143,0.7694

output_result/resilience/experiment_result.csv

@@ -0,0 +1,37 @@
+idx_scenario,n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n,mean_count_firm_prod,mean_count_firm,mean_count_prod,mean_max_ts_firm_prod,mean_max_ts_firm,mean_max_ts_prod,mean_n_remove_firm_prod,mean_n_all_prod_remove_firm,mean_end_ts
+0,7,1,1,uniform,5.0000,0.3000,3,3,2.7027,2.7027,2.0928,1.0928,1.0928,1.0928,0.6051,0.2131,1.5524
+1,5,1,1,uniform,10.0000,0.5000,5,2,2.7023,2.7004,2.2211,1.2211,1.2211,1.2211,0.9634,0.3309,2.4335
+2,3,1,1,uniform,15.0000,0.7000,7,1,2.5387,2.5360,2.1756,1.1756,1.1756,1.1756,1.0019,0.3293,3.1926
+3,7,1,1,uniform,5.0000,0.3000,3,2,2.5589,2.5589,2.0884,1.0884,1.0884,1.0884,0.6059,0.2139,1.5533
+4,5,1,1,uniform,10.0000,0.5000,5,1,2.5387,2.5356,2.1756,1.1756,1.1756,1.1756,0.9143,0.3143,2.4840
+5,3,1,1,uniform,15.0000,0.7000,7,3,2.9109,2.9074,2.2773,1.2773,1.2773,1.2773,1.1680,0.3872,3.4848
+6,7,1,1,normal,5.0000,0.5000,7,3,2.6356,2.6356,2.1055,1.1055,1.1055,1.1055,0.6008,0.2107,2.1806
+7,5,1,1,normal,10.0000,0.7000,3,2,2.5914,2.5909,2.0857,1.0857,1.0857,1.0857,0.6829,0.2354,1.6272
+8,3,1,1,normal,15.0000,0.3000,5,1,2.5219,2.5189,2.1617,1.1617,1.1617,1.1617,0.8895,0.3088,2.5105
+9,7,1,0,uniform,5.0000,0.7000,5,3,2.7091,2.7091,2.0952,1.0952,1.0952,1.0952,0.6051,0.2131,1.8733
+10,5,1,0,uniform,10.0000,0.3000,7,2,2.7107,2.7086,2.2280,1.2280,1.2280,1.2280,0.9674,0.3320,3.0187
+11,3,1,0,uniform,15.0000,0.5000,3,1,2.5771,2.5731,2.2095,1.2095,1.2095,1.2095,1.1646,0.3939,2.1248
+12,7,1,0,normal,10.0000,0.7000,3,1,2.4051,2.4042,2.0539,1.0539,1.0539,1.0539,0.6888,0.2347,1.6442
+13,5,1,0,normal,15.0000,0.3000,5,3,2.7368,2.7364,2.2015,1.2015,1.2015,1.2015,0.8714,0.3021,2.3029
+14,3,1,0,normal,5.0000,0.5000,7,2,2.5665,2.5665,2.0600,1.0600,1.0600,1.0600,0.6000,0.2105,2.1863
+15,7,1,0,normal,10.0000,0.7000,5,3,2.6705,2.6705,2.1389,1.1389,1.1389,1.1389,0.6943,0.2406,2.0160
+16,5,1,0,normal,15.0000,0.3000,7,2,2.6707,2.6680,2.1577,1.1577,1.1577,1.1577,0.8642,0.2973,2.8053
+17,3,1,0,normal,5.0000,0.5000,3,1,2.4105,2.4103,2.0594,1.0594,1.0594,1.0594,0.6903,0.2394,1.6615
+18,7,0,1,normal,10.0000,0.3000,7,1,2.4520,2.4493,2.1034,1.1034,1.1034,1.1034,0.7008,0.2423,2.6644
+19,5,0,1,normal,15.0000,0.5000,3,3,2.8086,2.8059,2.2133,1.2133,1.2133,1.2133,0.8821,0.3017,1.7964
+20,3,0,1,normal,5.0000,0.7000,5,2,2.5808,2.5808,2.0684,1.0684,1.0684,1.0684,0.6004,0.2105,1.8642
+21,7,0,1,normal,10.0000,0.5000,7,1,2.4149,2.4141,2.0697,1.0697,1.0697,1.0697,0.6594,0.2274,2.4549
+22,5,0,1,normal,15.0000,0.7000,3,3,2.8084,2.8057,2.2131,1.2131,1.2131,1.2131,0.8811,0.3013,1.7956
+23,3,0,1,normal,5.0000,0.3000,5,2,2.5808,2.5808,2.0684,1.0684,1.0684,1.0684,0.6006,0.2107,1.8648
+24,7,0,1,uniform,15.0000,0.5000,3,2,2.6992,2.6935,2.2269,1.2269,1.2269,1.2269,1.1223,0.3851,2.0112
+25,5,0,1,uniform,5.0000,0.7000,5,1,2.3648,2.3646,2.0248,1.0248,1.0248,1.0248,0.6084,0.2118,1.9465
+26,3,0,1,uniform,10.0000,0.3000,7,3,2.8187,2.8160,2.2122,1.2122,1.2122,1.2122,0.9806,0.3446,3.0491
+27,7,0,0,normal,15.0000,0.5000,5,2,2.6756,2.6731,2.1558,1.1558,1.1558,1.1558,0.8455,0.2893,2.2659
+28,5,0,0,normal,5.0000,0.7000,7,1,2.3678,2.3678,2.0261,1.0261,1.0261,1.0261,0.6008,0.2112,2.2375
+29,3,0,0,normal,10.0000,0.3000,3,3,2.7467,2.7463,2.1556,1.1556,1.1556,1.1556,0.7137,0.2482,1.6533
+30,7,0,0,uniform,15.0000,0.7000,7,2,2.6956,2.6899,2.2232,1.2232,1.2232,1.2232,1.1025,0.3741,3.2589
+31,5,0,0,uniform,5.0000,0.3000,3,1,2.4206,2.4183,2.0752,1.0752,1.0752,1.0752,0.8027,0.2905,1.7909
+32,3,0,0,uniform,10.0000,0.5000,5,3,2.8238,2.8211,2.2158,1.2158,1.2158,1.2158,0.9766,0.3423,2.4798
+33,7,0,0,uniform,15.0000,0.3000,5,1,2.5533,2.5451,2.1931,1.1931,1.1931,1.1931,1.0897,0.3749,2.7922
+34,5,0,0,uniform,5.0000,0.5000,7,3,2.6741,2.6741,2.0787,1.0787,1.0787,1.0787,0.6034,0.2114,2.1891
+35,3,0,0,uniform,10.0000,0.7000,3,2,2.6552,2.6518,2.1880,1.1880,1.1880,1.1880,0.9922,0.3379,1.9097

output_result/risk/count_dcp_prod.csv

@@ -0,0 +1,236 @@
+up_id_product,up_name_product,down_id_product,down_name_product,count
+1.4,工业互联网安全,1,供给,946
+1.3,工业软件,1,供给,484
+2.1.3.6,微服务,2.1.3,工业物联网,415
+2.1.3.2,平台基础服务,2.1.3,工业物联网,402
+2.1.3.4,应用管理服务,2.1.3,工业物联网,402
+2.1.3.1,物联网服务,2.1.3,工业物联网,401
+2.1.3.7,制造类API,2.1.3,工业物联网,401
+2.1.3.5,容器服务,2.1.3,工业物联网,400
+2.1.3.3,工业引擎服务,2.1.3,工业物联网,393
+2.3.3,协议转换,2.3,边缘层,359
+2.3.1,工业数据接入,2.3,边缘层,351
+2.3.2,边缘数据处理,2.3,边缘层,343
+2.1.2.4,行业机理模型,2.1.2,工业模型库,320
+1.3.1.2,计算机辅助工程CAE,1.3.1,设计研发,320
+2.1.2.2,业务流程模型,2.1.2,工业模型库,319
+1.3.1.1,计算机辅助设计CAD,1.3.1,设计研发,316
+1.3.1.7,电子设计自动化EDA,1.3.1,设计研发,314
+2.1.2.1,数据算法模型,2.1.2,工业模型库,313
+2.1.2.3,研发仿真模型,2.1.2,工业模型库,312
+1.3.1.6,产品生命周期管理PLM,1.3.1,设计研发,308
+1.3.1.5,产品数据管理PDM,1.3.1,设计研发,306
+1.3.1.3,计算机辅助制造CAM,1.3.1,设计研发,306
+1.3.1.4,计算机辅助工艺过程设计CAPP,1.3.1,设计研发,304
+1.3.2,采购供应,1.3,工业软件,304
+1.4.5,数据安全,1,供给,296
+1.4.5,数据安全,1.4,工业互联网安全,296
+1.4.4,平台安全,1.4,工业互联网安全,294
+1.1.2,工业控制器,1.1,工业自动化,281
+1.3.1,设计研发,1.3,工业软件,274
+1.1.3,工业服务器,1.1,工业自动化,259
+1.1.1,工业计算芯片,1.1,工业自动化,249
+2.1.1.4,组态建模工具,2.1.1,开发工具,239
+2.1.1.1,算法建模工具,2.1.1,开发工具,235
+2.1.1.3,流程开发工具,2.1.1,开发工具,234
+2,工业互联网平台,1,供给,233
+2.1.1.2,低代码开发工具,2.1.1,开发工具,233
+1.2.2,标识解析,1.2,工业互联网网络,221
+1.2.3,数据互通,1.2,工业互联网网络,218
+2.1.1.5,数字孪生建模工具,2.1.1,开发工具,215
+1.3.3,生产制造,1.3,工业软件,213
+1.2.1,网络互联,1.2,工业互联网网络,212
+1.4.4,平台安全,1,供给,210
+1.4.3,网络安全,1,供给,207
+1.4.3,网络安全,1.4,工业互联网安全,207
+1.4.2,控制安全,1.4,工业互联网安全,204
+1.4.2,控制安全,1,供给,204
+1.3.4,企业运营管理,1.3,工业软件,201
+1.3.4.1,企业资源计划ERP,1.3.4,企业运营管理,181
+1.3.4.2,客户关系管理CRM,1.3.4,企业运营管理,181
+1.3.4.3,人力资源管理HRM,1.3.4,企业运营管理,180
+1.3.3.6,运维保障系统MRO,1.3.3,生产制造,176
+2.1.4.1,工业大数据存储,2.1.4,工业大数据,174
+1.3.3.5,企业资产管理系统EAM,1.3.3,生产制造,170
+1.3.3.1,制造执行系统MES,1.3.3,生产制造,167
+1.3.3.7,故障预测与健康管理PHM,1.3.3,生产制造,167
+1.3.3.4,可编程逻揖控制系统PLC,1.3.3,生产制造,165
+2.1.4.2,工业大数据管理,2.1.4,工业大数据,164
+1.3.3.2,分布式控制系统DCS,1.3.3,生产制造,161
+1.3.3.3,数据采集与监视控制系统SCADA,1.3.3,生产制造,160
+1.3.5,仓储物流,1.3,工业软件,156
+1.4.4.1,身份鉴别与访问控制,1.4.4,平台安全,153
+2.1.4.2.1,数据质量管理,2.1.4.2,工业大数据管理,140
+2.1,PaaS,2,工业互联网平台,140
+2.1.4,工业大数据,2.1,PaaS,138
+2.1.4.1.3,实时数据库,2.1.4.1,工业大数据存储,137
+2.1.4.1.4,时序数据库,2.1.4.1,工业大数据存储,135
+1.4.4.4,工业应用行为监控,1.4.4,平台安全,132
+1.4.4.3,接入认证,1.4.4,平台安全,132
+1.4.4.2,密钥管理,1.4.4,平台安全,132
+2.1.4.2.2,数据安全管理,2.1.4.2,工业大数据管理,132
+2.1.4.1.1,关系型数据库,2.1.4.1,工业大数据存储,132
+2.1.3,工业物联网,2.1,PaaS,131
+2.1.4.1.2,分布式数据库,2.1.4.1,工业大数据存储,130
+1.3.2,采购供应,1,供给,121
+2.3,边缘层,2,工业互联网平台,108
+1.2,工业互联网网络,1,供给,108
+1.4.2.4,安全隔离与信息交换系统,1.4.2,控制安全,103
+1.4.4.5,安全态势感知,1.4.4,平台安全,100
+1.3.2.1,供应链管理SCM,1.3,工业软件,100
+1.3.2.1,供应链管理SCM,1.3.2,采购供应,100
+1.1,工业自动化,1,供给,98
+1.4.2.1,工控安全监测与审计,1.4.2,控制安全,93
+1.4.1.3,防毒墙,1.4.1,设备安全,87
+1.4.1.1,工业防火墙,1.4.1,设备安全,86
+1.4.3.3,APT检测,1.4.3,网络安全,85
+1.4.5.4,数据脱敏,1.4.5,数据安全,78
+1.4.5.5,敏感数据发现与监控,1.4.5,数据安全,78
+1.4.5.9,数据防火墙,1.4.5,数据安全,78
+1.4.5.6,数据容灾备份,1.4.5,数据安全,78
+1.3.5,仓储物流,1,供给,78
+1.4.5.3,数据审计系统,1.4.5,数据安全,78
+1.4.5.7,数据恢复,1.4.5,数据安全,78
+1.4.5.2,数据防泄漏系统,1.4.5,数据安全,78
+1.3.5.1,仓储物流管理WMS,1.3.5,仓储物流,77
+2.2,IaaS,2,工业互联网平台,77
+1.4.1,设备安全,1.4,工业互联网安全,77
+2.1.4,工业大数据,2,工业互联网平台,76
+1.4.1,设备安全,1,供给,75
+1.4.1.5,统一威胁管理系统,1.4.1,设备安全,74
+1.4.3.4,攻击溯源,1.4.3,网络安全,74
+1.4.2.5,安全日志与审计,1.4.2,控制安全,74
+1.4.1.4,入侵检测系统,1.4.1,设备安全,74
+1.4.2.2,工控主机卫士,1.4.2,控制安全,74
+1.4.2.6,隐私计算,1.4.2,控制安全,74
+1.4.3.5,负载均衡,1.4.3,网络安全,74
+1.4.3.1,网络漏洞扫描和补丁管理,1.4.3,网络安全,74
+1.4.1.2,下一代防火墙,1.4.1,设备安全,74
+2.1,PaaS,1,供给,70
+2.1.2,工业模型库,2.1,PaaS,61
+2.1.1,开发工具,2.1,PaaS,60
+1.3.5.1,仓储物流管理WMS,1.3,工业软件,56
+1.3.3,生产制造,1,供给,51
+1.4.5.1,恶意代码检测系统,1,供给,50
+1.4.4.5,安全态势感知,1.4,工业互联网安全,50
+1.3.2.1,供应链管理SCM,1,供给,50
+1.4.3.6,沙箱类设备,1.4.3,网络安全,50
+1.4.3.6,沙箱类设备,1.4,工业互联网安全,50
+1.4.3.6,沙箱类设备,1,供给,50
+1.4.5.1,恶意代码检测系统,1.4,工业互联网安全,50
+1.4.4.5,安全态势感知,1,供给,50
+1.4.3.2,流量检测,1.4.3,网络安全,50
+1.4.2.7,工控原生安全,1,供给,50
+1.4.2.7,工控原生安全,1.4.2,控制安全,50
+1.4.2.3,工控漏洞扫描,1,供给,50
+1.4.2.3,工控漏洞扫描,1.4,工业互联网安全,50
+1.4.2.3,工控漏洞扫描,1.4.2,控制安全,50
+1.4.5.8,数据加密,1.4.5,数据安全,50
+1.4.5.8,数据加密,1.4,工业互联网安全,50
+1.4.5.8,数据加密,1,供给,50
+1.4.2.7,工控原生安全,1.4,工业互联网安全,50
+1.4.5.1,恶意代码检测系统,1.4.5,数据安全,50
+1.4.3.2,流量检测,1,供给,50
+1.4.3.2,流量检测,1.4,工业互联网安全,50
+2.1.4,工业大数据,1,供给,38
+1.3.4,企业运营管理,1,供给,36
+1.3.5.1,仓储物流管理WMS,1,供给,28
+1.4.5.2,数据防泄漏系统,1.4,工业互联网安全,26
+1.4.5.2,数据防泄漏系统,1,供给,26
+1.3.3.7,故障预测与健康管理PHM,1.3,工业软件,26
+1.4.5.7,数据恢复,1,供给,26
+1.4.5.7,数据恢复,1.4,工业互联网安全,26
+1.4.5.4,数据脱敏,1.4,工业互联网安全,25
+1.3.1.5,产品数据管理PDM,1.3,工业软件,25
+1.4.5.6,数据容灾备份,1.4,工业互联网安全,25
+1.4.5.6,数据容灾备份,1,供给,25
+1.4.5.5,敏感数据发现与监控,1.4,工业互联网安全,25
+1.4.4.2,密钥管理,1.4,工业互联网安全,25
+1.4.5.4,数据脱敏,1,供给,25
+1.4.5.5,敏感数据发现与监控,1,供给,25
+1.4.4.3,接入认证,1.4,工业互联网安全,24
+1.3.4.2,客户关系管理CRM,1.3,工业软件,23
+1.4.3.1,网络漏洞扫描和补丁管理,1.4,工业互联网安全,23
+1.4.3.1,网络漏洞扫描和补丁管理,1,供给,23
+1.4.2.5,安全日志与审计,1,供给,21
+1.4.2.5,安全日志与审计,1.4,工业互联网安全,21
+1.3.3.5,企业资产管理系统EAM,1.3,工业软件,20
+2.1.4.1,工业大数据存储,2.1,PaaS,20
+1.3.1,设计研发,1,供给,19
+1.4.4.3,接入认证,1,供给,19
+1.3.1.3,计算机辅助制造CAM,1.3,工业软件,19
+1.4.4.4,工业应用行为监控,1.4,工业互联网安全,18
+1.4.5.9,数据防火墙,1.4,工业互联网安全,18
+1.4.3.4,攻击溯源,1.4,工业互联网安全,18
+1.4.3.4,攻击溯源,1,供给,18
+1.4.5.9,数据防火墙,1,供给,18
+2.1.4.2,工业大数据管理,2.1,PaaS,17
+1.3.4.3,人力资源管理HRM,1.3,工业软件,17
+1.4.2.6,隐私计算,1,供给,17
+1.4.2.6,隐私计算,1.4,工业互联网安全,17
+1.4.4.2,密钥管理,1,供给,16
+1.4.2.2,工控主机卫士,1.4,工业互联网安全,14
+1.4.2.2,工控主机卫士,1,供给,14
+1.4.4.4,工业应用行为监控,1,供给,13
+2.1.4.1,工业大数据存储,2,工业互联网平台,12
+1.4.3.5,负载均衡,1,供给,12
+1.4.3.5,负载均衡,1.4,工业互联网安全,12
+2.1.4.2,工业大数据管理,2,工业互联网平台,10
+1.3.3.4,可编程逻揖控制系统PLC,1.3,工业软件,10
+1.3.1.7,电子设计自动化EDA,1.3,工业软件,10
+1.4.1.4,入侵检测系统,1,供给,10
+1.4.4.1,身份鉴别与访问控制,1.4,工业互联网安全,10
+1.4.1.5,统一威胁管理系统,1.4,工业互联网安全,10
+1.4.1.4,入侵检测系统,1.4,工业互联网安全,10
+1.4.1.5,统一威胁管理系统,1,供给,8
+2.1.3.1,物联网服务,2.1,PaaS,8
+1.2.3,数据互通,1,供给,8
+1.3.1.6,产品生命周期管理PLM,1.3,工业软件,7
+1.3.3.3,数据采集与监视控制系统SCADA,1.3,工业软件,6
+2.1.4.1,工业大数据存储,1,供给,6
+1.3.3.5,企业资产管理系统EAM,1,供给,6
+2.1.4.1.2,分布式数据库,2.1.4,工业大数据,6
+1.3.3.7,故障预测与健康管理PHM,1,供给,6
+1.3.4.3,人力资源管理HRM,1,供给,6
+1.3.4.2,客户关系管理CRM,1,供给,5
+1.3.1.2,计算机辅助工程CAE,1.3,工业软件,5
+2.1.4.2,工业大数据管理,1,供给,5
+1.4.3.3,APT检测,1.4,工业互联网安全,4
+1.4.3.3,APT检测,1,供给,4
+1.1.3,工业服务器,1,供给,4
+1.3.1.1,计算机辅助设计CAD,1.3,工业软件,4
+1.4.1.2,下一代防火墙,1,供给,4
+1.3.4.1,企业资源计划ERP,1.3,工业软件,4
+1.4.1.2,下一代防火墙,1.4,工业互联网安全,4
+1.3.3.4,可编程逻揖控制系统PLC,1,供给,4
+2.3,边缘层,1,供给,3
+2.1.3.3,工业引擎服务,2.1,PaaS,3
+2.1.3.2,平台基础服务,2.1,PaaS,3
+2.1.3.4,应用管理服务,2.1,PaaS,3
+1.3.1.3,计算机辅助制造CAM,1,供给,2
+2.2,IaaS,1,供给,2
+2.3.2,边缘数据处理,2,工业互联网平台,2
+1.4.2.4,安全隔离与信息交换系统,1.4,工业互联网安全,2
+1.2.2,标识解析,1,供给,2
+1.4.2.4,安全隔离与信息交换系统,1,供给,2
+2.1.1,开发工具,2,工业互联网平台,2
+2.1.4.1.1,关系型数据库,2.1.4,工业大数据,2
+2.1.3.5,容器服务,2.1,PaaS,2
+2.1.2,工业模型库,2,工业互联网平台,2
+1.3.3.6,运维保障系统MRO,1.3,工业软件,2
+1.3.1.5,产品数据管理PDM,1,供给,2
+1.4.1.1,工业防火墙,1,供给,2
+1.4.1.1,工业防火墙,1.4,工业互联网安全,2
+1.4.4.1,身份鉴别与访问控制,1,供给,2
+2.1.3.7,制造类API,2.1,PaaS,2
+1.4.1.3,防毒墙,1.4,工业互联网安全,1
+1.3.3.3,数据采集与监视控制系统SCADA,1,供给,1
+1.3.1.2,计算机辅助工程CAE,1,供给,1
+1.4.5.3,数据审计系统,1.4,工业互联网安全,1
+2.1.2.3,研发仿真模型,2.1,PaaS,1
+1.4.1.3,防毒墙,1,供给,1
+1.2.1,网络互联,1,供给,1
+2.3.2,边缘数据处理,1,供给,1
+2.1.2,工业模型库,1,供给,1
+2.1.1,开发工具,1,供给,1
+1.4.5.3,数据审计系统,1,供给,1

output_result/risk/count_firm.csv

@@ -0,0 +1,172 @@
+id_firm,Name,count
+126,华为,1965
+170,Pseudo1,1611
+106,阿里巴巴,1515
+99,Siemens,1382
+79,PTC,1301
+142,深信服,1041
+97,General Electric,1023
+81,SAP,921
+41,启明星辰,910
+85,Dassault,718
+148,腾讯,696
+22,航天云网,629
+84,Bosch,575
+58,用友,552
+80,Salesforce,540
+13,东方国信,528
+53,天融信,509
+100,Synopsys,505
+29,京东工业品,478
+39,Autodesk,477
+93,Cadence,473
+73,FANUC,461
+157,新华三,445
+74,HoneyWell,429
+75,IBM,429
+40,奇安信,421
+108,百度,415
+135,浪潮,395
+0,360科技,369
+130,金蝶,318
+102,Amazon AWS,302
+23,和利时,273
+159,徐工集团,254
+47,首自信,247
+26,寄云科技,247
+77,Oracle,245
+140,山石网科,220
+95,Schneider,212
+98,Microsoft Azure,211
+49,数码大方,211
+37,绿盟,201
+124,海尔,193
+86,Dell EMC,191
+67,中国移动,189
+155,小米,189
+45,石化盈科,186
+105,Intel,180
+115,富士康,180
+94,Mitsubishi,173
+168,中控技术,171
+117,格创东智,170
+55,威努特,144
+6,安世亚太,144
+5,安华金和,141
+62,云道智造,134
+3,艾克斯特,131
+38,牛刀,120
+143,沈阳自动化研究所,119
+16,东土科技,117
+82,Uptake,113
+31,昆仑数据,112
+149,天泽智云,111
+78,OutSystems,111
+33,蓝谷信息,109
+144,树根互联,108
+57,亚控科技,107
+68,中望软件,106
+42,山大华天,103
+154,西格数据,103
+165,智能云科,100
+131,九物互联,99
+60,宇动源,98
+137,美林数据,96
+161,研华科技,92
+163,优也科技,91
+111,鼎捷软件,81
+162,壹进制,78
+43,神舟软件,77
+63,长扬科技,75
+54,网御星云,69
+9,北京航天测控,65
+14,东华软件,64
+89,Rockwell,56
+153,武汉开目,54
+56,芯愿景,53
+133,蓝盾股份,47
+70,ABB,46
+27,江南天安,44
+11,北信源,44
+138,启明信息,44
+76,MasterCAM,43
+90,Mentor Graphics,43
+129,华中数控,43
+50,索为系统,43
+152,卫士通,43
+21,Hexagon,42
+72,ANSYS,41
+116,概伦电子,40
+114,富勒科技,40
+158,信大捷安,40
+139,容知日新,39
+96,Cisco,39
+127,华为海思,39
+88,HPE,39
+52,天空卫士,39
+48,曙光信息,38
+119,广联达,38
+30,可信华泰,37
+151,唯智信息,37
+156,芯禾科技,37
+59,优特捷,37
+122,国民技术,37
+146,苏州浩辰,37
+112,东华测试,36
+20,海基科技,36
+71,Altair,36
+107,安恒信息,36
+25,华大九天,36
+145,思普软件,35
+10,北京英贝思,35
+160,亚信科技,35
+164,震坤行,34
+4,爱创科技,34
+110,晨科软件,33
+2,706所,33
+46,适创科技,33
+64,中电智科,33
+134,朗坤智慧,33
+123,海得控制,33
+8,梆梆安全,33
+15,东软集团,33
+19,国泰网信,32
+120,广州数控,32
+92,Omron,32
+51,天地和兴,32
+34,力控科技,31
+118,工邦邦,31
+121,广州智臣,31
+17,国保金泰,31
+61,元年科技,31
+128,华伍股份,30
+1,51WORLD,29
+109,宝信软件,29
+166,中国电子科技网络信息安全,29
+35,凌昊智能,28
+125,华数机器人,28
+32,兰光创新,28
+147,拓邦股份,27
+136,美的,26
+91,Moxa,26
+65,中国电信,25
+83,Emerson,24
+141,上海新华控制,24
+12,大唐软件,24
+18,国能智深,23
+66,中国联通,23
+132,科远智慧,21
+24,华大电子,20
+150,唯捷创芯,19
+103,STMicroelectronics ,18
+7,百望,18
+69,紫光集团,18
+28,金山云,18
+36,龙芯中科,17
+104,Infineon,17
+44,圣邦微电子,17
+167,中环股份,17
+113,飞腾信息,16
+101,Analog Devices,16
+169,中芯国际,15
+87,Texas Instruments,14

output_result/risk/count_firm_prod.csv

@@ -0,0 +1,476 @@
+id_firm,name_firm,id_product,name_product,count
+170,Pseudo1,1,供给,1611
+126,华为,1.4,工业互联网安全,1021
+41,启明星辰,1.4.5,数据安全,696
+142,深信服,1.4.2,控制安全,568
+100,Synopsys,1.3.1,设计研发,505
+29,京东工业品,1.3,工业软件,478
+39,Autodesk,1.3.1,设计研发,477
+93,Cadence,1.3.1,设计研发,473
+106,阿里巴巴,1.3,工业软件,472
+73,FANUC,2.1.3,工业物联网,461
+142,深信服,1.4.3,网络安全,457
+157,新华三,1.4.1,设备安全,445
+75,IBM,1.3.3,生产制造,429
+97,General Electric,1.3.3,生产制造,421
+74,HoneyWell,2.1.3,工业物联网,418
+99,Siemens,1.3.1,设计研发,410
+148,腾讯,2.1.3,工业物联网,408
+126,华为,2.1.3,工业物联网,405
+108,百度,2.1.3,工业物联网,402
+85,Dassault,1.3.1,设计研发,402
+99,Siemens,1.3.3,生产制造,399
+97,General Electric,2.1.3,工业物联网,393
+106,阿里巴巴,2.1.3,工业物联网,387
+0,360科技,1.4.4,平台安全,369
+99,Siemens,2.1,PaaS,345
+40,奇安信,1.4.4,平台安全,340
+79,PTC,2.1.4.1,工业大数据存储,338
+80,Salesforce,2.1.1,开发工具,335
+85,Dassault,2.1.1,开发工具,316
+58,用友,2.1.2,工业模型库,299
+81,SAP,2.1.4.1,工业大数据存储,288
+106,阿里巴巴,2.1.1,开发工具,286
+148,腾讯,2.1.1,开发工具,279
+79,PTC,2.1.2,工业模型库,271
+81,SAP,2.1.2,工业模型库,263
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+124,海尔,2.3,边缘层,185
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+23,和利时,1.4.2.7,工控原生安全,50
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+41,启明星辰,1.4.3.2,流量检测,50
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+55,威努特,1.4.4.4,工业应用行为监控,44
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+37,绿盟,1.4.4.3,接入认证,44
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+56,芯愿景,1.3.1.7,电子设计自动化EDA,42
+22,航天云网,2.1.3.3,工业引擎服务,42
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+21,Hexagon,1.3.1.3,计算机辅助制造CAM,42
+79,PTC,2.1.3.5,容器服务,41
+58,用友,1.3.4.2,客户关系管理CRM,41
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+22,航天云网,2.1.3.5,容器服务,41
+72,ANSYS,1.3.1.2,计算机辅助工程CAE,41
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+79,PTC,2.1.3.2,平台基础服务,41
+116,概伦电子,1.3.1.7,电子设计自动化EDA,40
+135,浪潮,2.1.3.2,平台基础服务,40
+114,富勒科技,1.3.5.1,仓储物流管理WMS,40
+42,山大华天,1.3.1.3,计算机辅助制造CAM,40
+22,航天云网,2.1.3.1,物联网服务,40
+158,信大捷安,1.4.4.1,身份鉴别与访问控制,40
+79,PTC,2.1.3.6,微服务,40
+5,安华金和,1.4.5.9,数据防火墙,39
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+52,天空卫士,1.4.5.5,敏感数据发现与监控,39
+5,安华金和,1.4.5.4,数据脱敏,39
+162,壹进制,1.4.5.7,数据恢复,39
+53,天融信,1.4.5.7,数据恢复,39
+162,壹进制,1.4.5.6,数据容灾备份,39
+53,天融信,1.4.5.2,数据防泄漏系统,39
+53,天融信,1.4.5.6,数据容灾备份,39
+37,绿盟,1.4.5.2,数据防泄漏系统,39
+140,山石网科,1.4.5.4,数据脱敏,39
+88,HPE,1.1.3,工业服务器,39
+140,山石网科,1.4.5.9,数据防火墙,39
+26,寄云科技,2.1.3.7,制造类API,39
+130,金蝶,1.3.4.2,客户关系管理CRM,39
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+135,浪潮,2.1.3.6,微服务,38
+45,石化盈科,1.3.4.2,客户关系管理CRM,38
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+119,广联达,1.3.1.1,计算机辅助设计CAD,38
+26,寄云科技,2.1.3.1,物联网服务,38
+48,曙光信息,1.2.2,标识解析,38
+3,艾克斯特,1.3.1.6,产品生命周期管理PLM,38
+22,航天云网,2.1.3.6,微服务,38
+41,启明星辰,1.4.3.1,网络漏洞扫描和补丁管理,37
+41,启明星辰,1.4.3.5,负载均衡,37
+156,芯禾科技,1.3.1.7,电子设计自动化EDA,37
+40,奇安信,1.4.2.5,安全日志与审计,37
+9,北京航天测控,1.3.3.7,故障预测与健康管理PHM,37
+30,可信华泰,1.4.2.6,隐私计算,37
+122,国民技术,1.4.2.6,隐私计算,37
+37,绿盟,1.4.2.2,工控主机卫士,37
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+55,威努特,1.4.2.2,工控主机卫士,37
+151,唯智信息,1.3.5.1,仓储物流管理WMS,37
+79,PTC,2.3.3,协议转换,37
+23,和利时,2.1.3.6,微服务,37
+26,寄云科技,2.1.3.2,平台基础服务,37
+59,优特捷,1.4.2.5,安全日志与审计,37
+58,用友,1.3.4.3,人力资源管理HRM,37
+53,天融信,1.4.3.4,攻击溯源,37
+146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,37
+3,艾克斯特,1.3.1.5,产品数据管理PDM,37
+53,天融信,1.4.3.5,负载均衡,37
+37,绿盟,1.4.3.1,网络漏洞扫描和补丁管理,37
+26,寄云科技,2.1.3.3,工业引擎服务,36
+25,华大九天,1.3.1.7,电子设计自动化EDA,36
+71,Altair,1.3.1.2,计算机辅助工程CAE,36
+58,用友,1.3.1.6,产品生命周期管理PLM,36
+107,安恒信息,1.4.3.3,APT检测,36
+112,东华测试,1.3.3.7,故障预测与健康管理PHM,36
+79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,36
+20,海基科技,1.3.1.2,计算机辅助工程CAE,36
+49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,35
+145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,35
+10,北京英贝思,1.3.3.5,企业资产管理系统EAM,35
+13,东方国信,2.3.1,工业数据接入,35
+13,东方国信,2.3.3,协议转换,35
+79,PTC,2.3.1,工业数据接入,35
+160,亚信科技,1.4.1.3,防毒墙,35
+26,寄云科技,2.1.3.5,容器服务,35
+16,东土科技,2.3.1,工业数据接入,34
+168,中控技术,2.3.3,协议转换,34
+4,爱创科技,1.2.2,标识解析,34
+164,震坤行,1.3.3.6,运维保障系统MRO,34
+79,PTC,1.3.1.1,计算机辅助设计CAD,34
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+22,航天云网,2.3.3,协议转换,33
+26,寄云科技,2.1.3.4,应用管理服务,33
+64,中电智科,1.1.2,工业控制器,33
+2,706所,1.1.3,工业服务器,33
+46,适创科技,1.3.1.2,计算机辅助工程CAE,33
+111,鼎捷软件,1.3.1.6,产品生命周期管理PLM,33
+110,晨科软件,1.3.3.5,企业资产管理系统EAM,33
+47,首自信,2.1.3.6,微服务,33
+8,梆梆安全,1.4.1.1,工业防火墙,33
+123,海得控制,1.1.2,工业控制器,33
+134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,33
+161,研华科技,2.3.2,边缘数据处理,33
+133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,33
+13,东方国信,2.3.2,边缘数据处理,33
+15,东软集团,1.3.3.5,企业资产管理系统EAM,33
+13,东方国信,1.2.2,标识解析,32
+92,Omron,1.3.3.4,可编程逻揖控制系统PLC,32
+120,广州数控,1.2.3,数据互通,32
+47,首自信,2.1.2.2,业务流程模型,32
+79,PTC,1.3.1.6,产品生命周期管理PLM,32
+68,中望软件,1.3.1.1,计算机辅助设计CAD,32
+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,32
+42,山大华天,1.3.1.4,计算机辅助工艺过程设计CAPP,32
+43,神舟软件,1.3.1.6,产品生命周期管理PLM,32
+144,树根互联,2.1.2.4,行业机理模型,32
+51,天地和兴,1.4.2.1,工控安全监测与审计,32
+19,国泰网信,1.4.2.1,工控安全监测与审计,32
+34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,31
+42,山大华天,1.3.1.1,计算机辅助设计CAD,31
+61,元年科技,1.3.3.3,数据采集与监视控制系统SCADA,31
+58,用友,1.2.2,标识解析,31
+6,安世亚太,2.1.2.1,数据算法模型,31
+68,中望软件,1.3.1.2,计算机辅助工程CAE,31
+82,Uptake,2.1.2.1,数据算法模型,31
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+118,工邦邦,1.3.3.6,运维保障系统MRO,31
+168,中控技术,2.3.1,工业数据接入,31
+17,国保金泰,1.4.2.4,安全隔离与信息交换系统,31
+115,富士康,1.1.3,工业服务器,30
+49,数码大方,1.3.1.6,产品生命周期管理PLM,30
+149,天泽智云,2.1.2.2,业务流程模型,30
+33,蓝谷信息,2.1.2.2,业务流程模型,30
+57,亚控科技,2.3.3,协议转换,30
+168,中控技术,2.3.2,边缘数据处理,30
+16,东土科技,2.3.3,协议转换,30
+128,华伍股份,1.1.2,工业控制器,30
+153,武汉开目,1.3.1.4,计算机辅助工艺过程设计CAPP,30
+53,天融信,1.4.5.3,数据审计系统,30
+6,安世亚太,2.1.2.2,业务流程模型,30
+23,和利时,2.3.2,边缘数据处理,30
+6,安世亚太,1.3.1.2,计算机辅助工程CAE,30
+1,51WORLD,2.1.1.5,数字孪生建模工具,29
+23,和利时,2.3.3,协议转换,29
+62,云道智造,2.1.2.4,行业机理模型,29
+47,首自信,2.1.1.4,组态建模工具,29
+82,Uptake,2.1.2.3,研发仿真模型,29
+140,山石网科,1.4.1.4,入侵检测系统,29
+33,蓝谷信息,2.1.2.4,行业机理模型,29
+166,中国电子科技网络信息安全,1.2.3,数据互通,29
+109,宝信软件,1.3.3.1,制造执行系统MES,29
+149,天泽智云,2.1.2.4,行业机理模型,29
+23,和利时,2.3.1,工业数据接入,29
+26,寄云科技,2.1.3.6,微服务,29
+35,凌昊智能,1.1.3,工业服务器,28
+49,数码大方,2.1.2.4,行业机理模型,28
+144,树根互联,2.1.2.3,研发仿真模型,28
+149,天泽智云,2.1.2.1,数据算法模型,28
+38,牛刀,2.1.1.4,组态建模工具,28
+32,兰光创新,1.2.3,数据互通,28
+161,研华科技,2.3.1,工业数据接入,28
+41,启明星辰,1.4.1.2,下一代防火墙,28
+49,数码大方,1.3.1.1,计算机辅助设计CAD,28
+57,亚控科技,2.3.2,边缘数据处理,28
+47,首自信,2.1.2.4,行业机理模型,28
+22,航天云网,1.2.2,标识解析,28
+9,北京航天测控,1.3.3.6,运维保障系统MRO,28
+6,安世亚太,2.1.2.3,研发仿真模型,28
+165,智能云科,2.1.2.3,研发仿真模型,28
+125,华数机器人,1.2.3,数据互通,28
+62,云道智造,2.1.2.3,研发仿真模型,27
+62,云道智造,2.1.2.1,数据算法模型,27
+70,ABB,1.3.3.4,可编程逻揖控制系统PLC,27
+47,首自信,2.1.2.1,数据算法模型,27
+147,拓邦股份,1.1.2,工业控制器,27
+47,首自信,2.1.1.3,流程开发工具,27
+143,沈阳自动化研究所,2.1.1.3,流程开发工具,27
+111,鼎捷软件,1.3.4.1,企业资源计划ERP,27
+22,航天云网,2.1.1.1,算法建模工具,27
+82,Uptake,2.1.2.4,行业机理模型,27
+79,PTC,2.3.2,边缘数据处理,27
+127,华为海思,1.1.3,工业服务器,27
+49,数码大方,2.1.2.2,业务流程模型,27
+165,智能云科,2.1.2.4,行业机理模型,27
+45,石化盈科,1.3.4.1,企业资源计划ERP,26
+22,航天云网,2.3.1,工业数据接入,26
+91,Moxa,1.2.1,网络互联,26
+22,航天云网,2.3.2,边缘数据处理,26
+53,天融信,1.4.1.5,统一威胁管理系统,26
+23,和利时,1.3.3.4,可编程逻揖控制系统PLC,26
+23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,26
+55,威努特,1.4.1.2,下一代防火墙,26
+49,数码大方,2.1.2.1,数据算法模型,26
+62,云道智造,2.1.2.2,业务流程模型,26
+38,牛刀,2.1.1.1,算法建模工具,26
+130,金蝶,1.3.4.1,企业资源计划ERP,26
+47,首自信,2.1.1.1,算法建模工具,26
+40,奇安信,1.4.3.3,APT检测,26
+22,航天云网,2.1.1.4,组态建模工具,26
+135,浪潮,1.1.3,工业服务器,26
+117,格创东智,2.1.1.2,低代码开发工具,26
+136,美的,1.2.1,网络互联,26
+78,OutSystems,2.1.1.3,流程开发工具,26
+57,亚控科技,2.3.1,工业数据接入,26
+38,牛刀,2.1.1.2,低代码开发工具,26
+82,Uptake,2.1.2.2,业务流程模型,26
+47,首自信,2.1.2.3,研发仿真模型,25
+144,树根互联,2.1.2.2,业务流程模型,25
+33,蓝谷信息,2.1.2.1,数据算法模型,25
+60,宇动源,2.1.1.4,组态建模工具,25
+41,启明星辰,1.4.1.5,统一威胁管理系统,25
+126,华为,2.1.1.5,数字孪生建模工具,25
+165,智能云科,2.1.2.2,业务流程模型,25
+6,安世亚太,2.1.2.4,行业机理模型,25
+33,蓝谷信息,2.1.2.3,研发仿真模型,25
+62,云道智造,1.3.1.2,计算机辅助工程CAE,25
+65,中国电信,1.2.1,网络互联,25
+149,天泽智云,2.1.2.3,研发仿真模型,24
+22,航天云网,2.1.1.2,低代码开发工具,24
+131,九物互联,2.1.1.1,算法建模工具,24
+153,武汉开目,1.3.1.1,计算机辅助设计CAD,24
+12,大唐软件,1.2.1,网络互联,24
+143,沈阳自动化研究所,2.1.1.1,算法建模工具,24
+3,艾克斯特,1.3.4.1,企业资源计划ERP,24
+83,Emerson,1.3.3.2,分布式控制系统DCS,24
+5,安华金和,1.4.5.3,数据审计系统,24
+37,绿盟,1.4.1.4,入侵检测系统,24
+140,山石网科,1.4.5.3,数据审计系统,24
+78,OutSystems,2.1.1.1,算法建模工具,24
+141,上海新华控制,1.3.3.2,分布式控制系统DCS,24
+57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,23
+78,OutSystems,2.1.1.2,低代码开发工具,23
+77,Oracle,1.3.3.6,运维保障系统MRO,23
+14,东华软件,1.3.3.4,可编程逻揖控制系统PLC,23
+60,宇动源,2.1.1.3,流程开发工具,23
+168,中控技术,1.3.3.4,可编程逻揖控制系统PLC,23
+140,山石网科,1.4.1.5,统一威胁管理系统,23
+143,沈阳自动化研究所,2.1.1.4,组态建模工具,23
+95,Schneider,1.2.3,数据互通,23
+58,用友,1.3.4.1,企业资源计划ERP,23
+144,树根互联,2.1.2.1,数据算法模型,23
+66,中国联通,1.2.1,网络互联,23
+143,沈阳自动化研究所,2.1.1.2,低代码开发工具,23
+168,中控技术,1.1.2,工业控制器,23
+18,国能智深,1.3.3.2,分布式控制系统DCS,23
+53,天融信,1.4.3.3,APT检测,23
+154,西格数据,2.1.4.2.2,数据安全管理,22
+131,九物互联,2.1.1.3,流程开发工具,22
+49,数码大方,2.1.2.3,研发仿真模型,22
+89,Rockwell,1.3.3.1,制造执行系统MES,22
+22,航天云网,1.3.3.6,运维保障系统MRO,22
+129,华中数控,1.1.2,工业控制器,22
+31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,22
+60,宇动源,2.1.1.1,算法建模工具,22
+96,Cisco,1.2.3,数据互通,22
+45,石化盈科,2.1.4.2.1,数据质量管理,22
+99,Siemens,1.1.2,工业控制器,22
+143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,22
+129,华中数控,1.2.3,数据互通,21
+132,科远智慧,1.3.3.2,分布式控制系统DCS,21
+135,浪潮,1.3.4.1,企业资源计划ERP,21
+53,天融信,1.4.1.4,入侵检测系统,21
+111,鼎捷软件,1.3.3.1,制造执行系统MES,21
+78,OutSystems,2.1.1.4,组态建模工具,21
+45,石化盈科,1.3.3.1,制造执行系统MES,21
+38,牛刀,2.1.1.5,数字孪生建模工具,20
+13,东方国信,2.1.4.2.2,数据安全管理,20
+117,格创东智,2.1.1.4,组态建模工具,20
+37,绿盟,1.4.1.2,下一代防火墙,20
+154,西格数据,2.1.4.2.1,数据质量管理,20
+38,牛刀,2.1.1.3,流程开发工具,20
+163,优也科技,2.1.4.1.3,实时数据库,20
+24,华大电子,1.1.1,工业计算芯片,20
+47,首自信,2.1.1.2,低代码开发工具,20
+165,智能云科,2.1.2.1,数据算法模型,20
+89,Rockwell,1.1.2,工业控制器,19
+22,航天云网,2.1.1.3,流程开发工具,19
+137,美林数据,2.1.4.1.4,时序数据库,19
+163,优也科技,2.1.4.1.2,分布式数据库,19
+131,九物互联,2.1.1.2,低代码开发工具,19
+70,ABB,1.3.3.2,分布式控制系统DCS,19
+131,九物互联,2.1.1.4,组态建模工具,19
+13,东方国信,2.1.4.2.1,数据质量管理,19
+150,唯捷创芯,1.1.1,工业计算芯片,19
+16,东土科技,1.1.3,工业服务器,19
+45,石化盈科,2.1.4.1.1,关系型数据库,19
+45,石化盈科,2.1.4.1.3,实时数据库,19
+103,STMicroelectronics ,1.1.1,工业计算芯片,18
+40,奇安信,1.4.2.1,工控安全监测与审计,18
+23,和利时,1.3.3.1,制造执行系统MES,18
+7,百望,2.2,IaaS,18
+22,航天云网,2.1.4.2.1,数据质量管理,18
+69,紫光集团,1.1.1,工业计算芯片,18
+117,格创东智,2.1.1.1,算法建模工具,18
+22,航天云网,2.1.1.5,数字孪生建模工具,18
+117,格创东智,2.1.1.3,流程开发工具,18
+154,西格数据,2.1.4.1.3,实时数据库,18
+168,中控技术,1.3.3.1,制造执行系统MES,18
+60,宇动源,2.1.1.2,低代码开发工具,18
+28,金山云,2.2,IaaS,18
+13,东方国信,2.1.4.1.1,关系型数据库,18
+104,Infineon,1.1.1,工业计算芯片,17
+36,龙芯中科,1.1.1,工业计算芯片,17
+96,Cisco,1.2.1,网络互联,17
+163,优也科技,2.1.4.1.4,时序数据库,17
+31,昆仑数据,2.1.4.2.1,数据质量管理,17
+78,OutSystems,2.1.1.5,数字孪生建模工具,17
+13,东方国信,2.1.4.1.4,时序数据库,17
+31,昆仑数据,2.1.4.1.1,关系型数据库,17
+167,中环股份,1.1.1,工业计算芯片,17
+154,西格数据,2.1.4.1.1,关系型数据库,17
+23,和利时,1.1.2,工业控制器,17
+117,格创东智,2.1.4.1.4,时序数据库,17
+44,圣邦微电子,1.1.1,工业计算芯片,17
+45,石化盈科,2.1.4.1.2,分布式数据库,17
+137,美林数据,2.1.4.2.2,数据安全管理,17
+101,Analog Devices,1.1.1,工业计算芯片,16
+113,飞腾信息,1.1.1,工业计算芯片,16
+22,航天云网,2.1.4.1.3,实时数据库,16
+140,山石网科,1.4.1.1,工业防火墙,16
+142,深信服,1.4.1.1,工业防火墙,16
+137,美林数据,2.1.4.1.2,分布式数据库,16
+137,美林数据,2.1.4.1.1,关系型数据库,16
+99,Siemens,1.2.1,网络互联,15
+117,格创东智,2.1.1.5,数字孪生建模工具,15
+55,威努特,1.4.1.3,防毒墙,15
+137,美林数据,2.1.4.2.1,数据质量管理,15
+169,中芯国际,1.1.1,工业计算芯片,15
+131,九物互联,2.1.1.5,数字孪生建模工具,15
+63,长扬科技,1.4.2.4,安全隔离与信息交换系统,15
+89,Rockwell,1.2.1,网络互联,15
+49,数码大方,1.3.3.1,制造执行系统MES,15
+31,昆仑数据,2.1.4.1.4,时序数据库,15
+31,昆仑数据,2.1.4.1.3,实时数据库,15
+22,航天云网,2.1.4.1.2,分布式数据库,14
+22,航天云网,2.1.4.1.4,时序数据库,14
+87,Texas Instruments,1.1.1,工业计算芯片,14
+133,蓝盾股份,1.4.1.3,防毒墙,14
+53,天融信,1.4.2.4,安全隔离与信息交换系统,14
+117,格创东智,2.1.4.1.3,实时数据库,13
+31,昆仑数据,2.1.4.1.2,分布式数据库,13
+154,西格数据,2.1.4.1.2,分布式数据库,13
+31,昆仑数据,2.1.4.2.2,数据安全管理,13
+154,西格数据,2.1.4.1.4,时序数据库,13
+117,格创东智,2.1.4.2.2,数据安全管理,13
+54,网御星云,1.4.1.3,防毒墙,13
+108,百度,2.2,IaaS,13
+137,美林数据,2.1.4.1.3,实时数据库,13
+163,优也科技,2.1.4.1.1,关系型数据库,13
+54,网御星云,1.4.2.4,安全隔离与信息交换系统,12
+127,华为海思,1.1.1,工业计算芯片,12
+163,优也科技,2.1.4.2.2,数据安全管理,12
+168,中控技术,1.3.3.2,分布式控制系统DCS,12
+22,航天云网,2.1.4.1.1,关系型数据库,12
+117,格创东智,2.1.4.1.2,分布式数据库,12
+45,石化盈科,2.1.4.2.2,数据安全管理,12
+13,东方国信,2.1.4.1.3,实时数据库,12
+45,石化盈科,2.1.4.1.4,时序数据库,12
+74,HoneyWell,1.3.3.2,分布式控制系统DCS,11
+23,和利时,1.3.3.2,分布式控制系统DCS,11
+22,航天云网,2.1.4.2.2,数据安全管理,11
+55,威努特,1.4.1.1,工业防火墙,11
+55,威努特,1.4.2.1,工控安全监测与审计,11
+56,芯愿景,1.1.1,工业计算芯片,11
+13,东方国信,2.1.4.1.2,分布式数据库,10
+163,优也科技,2.1.4.2.1,数据质量管理,10
+60,宇动源,2.1.1.5,数字孪生建模工具,10
+63,长扬科技,1.4.1.1,工业防火墙,10
+53,天融信,1.4.1.3,防毒墙,10
+117,格创东智,2.1.4.2.1,数据质量管理,10
+148,腾讯,2.2,IaaS,9
+124,海尔,1.2.1,网络互联,8
+117,格创东智,2.1.4.1.1,关系型数据库,8
+22,航天云网,1.2.1,网络互联,7
+106,阿里巴巴,2.2,IaaS,6
+126,华为,2.2,IaaS,6
+135,浪潮,2.2,IaaS,5

output_result/risk/count_prod.csv

@@ -0,0 +1,108 @@
+id_product,Name,count
+2.1.3,工业物联网,2874
+1.3.1,设计研发,2267
+1,供给,1611
+2.1.2,工业模型库,1324
+1.3.3,生产制造,1249
+2.1.1,开发工具,1216
+2.3,边缘层,1146
+1.4,工业互联网安全,1021
+1.3,工业软件,950
+1.1,工业自动化,879
+1.2,工业互联网网络,741
+1.4.4,平台安全,709
+1.4.5,数据安全,696
+2.1.4.1,工业大数据存储,626
+1.3.4,企业运营管理,625
+1.4.2,控制安全,568
+1.4.3,网络安全,457
+1.4.1,设备安全,445
+2,工业互联网平台,411
+2.1.4,工业大数据,392
+2.1.4.2,工业大数据管理,364
+2.1,PaaS,345
+2.3.3,协议转换,259
+2.1.2.4,行业机理模型,254
+2.1.3.6,微服务,253
+2.1.2.2,业务流程模型,251
+2.3.1,工业数据接入,244
+2.3.2,边缘数据处理,241
+2.1.2.1,数据算法模型,238
+2.1.2.3,研发仿真模型,236
+1.3.1.2,计算机辅助工程CAE,232
+1.1.1,工业计算芯片,227
+1.1.2,工业控制器,226
+1.3.1.1,计算机辅助设计CAD,224
+2.1.3.7,制造类API,210
+2.1.3.4,应用管理服务,209
+2.1.3.1,物联网服务,208
+2.1.3.2,平台基础服务,205
+2.1.3.3,工业引擎服务,205
+2.1.3.5,容器服务,204
+1.1.3,工业服务器,202
+1.3.1.6,产品生命周期管理PLM,201
+1.3.1.4,计算机辅助工艺过程设计CAPP,200
+1.3.1.7,电子设计自动化EDA,198
+2.1.1.1,算法建模工具,191
+2.1.1.4,组态建模工具,191
+1.2.1,网络互联,186
+1.2.3,数据互通,183
+2.1.1.3,流程开发工具,182
+2.1.1.2,低代码开发工具,179
+2.1.1.5,数字孪生建模工具,171
+1.3.2,采购供应,170
+1.3.1.5,产品数据管理PDM,169
+1.3.1.3,计算机辅助制造CAM,168
+1.2.2,标识解析,163
+1.3.4.1,企业资源计划ERP,147
+1.3.3.2,分布式控制系统DCS,145
+1.3.3.1,制造执行系统MES,144
+1.3.3.6,运维保障系统MRO,138
+1.3.3.5,企业资产管理系统EAM,134
+1.3.3.3,数据采集与监视控制系统SCADA,133
+1.3.3.4,可编程逻揖控制系统PLC,131
+2.1.4.2.1,数据质量管理,131
+1.3.5,仓储物流,127
+2.1.4.1.3,实时数据库,126
+2.1.4.1.4,时序数据库,124
+2.1.4.1.1,关系型数据库,120
+2.1.4.2.2,数据安全管理,120
+1.3.4.3,人力资源管理HRM,119
+1.3.4.2,客户关系管理CRM,118
+1.4.4.1,身份鉴别与访问控制,116
+2.1.4.1.2,分布式数据库,114
+1.3.3.7,故障预测与健康管理PHM,112
+1.4.2.4,安全隔离与信息交换系统,103
+1.4.2.1,工控安全监测与审计,93
+1.4.4.4,工业应用行为监控,88
+1.4.4.3,接入认证,88
+1.4.4.2,密钥管理,88
+1.4.1.3,防毒墙,87
+1.4.1.1,工业防火墙,86
+1.4.3.3,APT检测,85
+1.4.5.3,数据审计系统,78
+1.4.5.9,数据防火墙,78
+1.4.5.7,数据恢复,78
+1.4.5.6,数据容灾备份,78
+1.4.5.5,敏感数据发现与监控,78
+1.4.5.4,数据脱敏,78
+1.4.5.2,数据防泄漏系统,78
+1.3.5.1,仓储物流管理WMS,77
+2.2,IaaS,75
+1.4.2.6,隐私计算,74
+1.4.1.2,下一代防火墙,74
+1.4.1.4,入侵检测系统,74
+1.4.1.5,统一威胁管理系统,74
+1.4.2.2,工控主机卫士,74
+1.4.2.5,安全日志与审计,74
+1.4.3.1,网络漏洞扫描和补丁管理,74
+1.4.3.5,负载均衡,74
+1.4.3.4,攻击溯源,74
+1.4.2.7,工控原生安全,50
+1.4.2.3,工控漏洞扫描,50
+1.4.5.8,数据加密,50
+1.4.3.2,流量检测,50
+1.4.3.6,沙箱类设备,50
+1.4.4.5,安全态势感知,50
+1.3.2.1,供应链管理SCM,50
+1.4.5.1,恶意代码检测系统,50

product.py

@@ -0,0 +1,25 @@
+import agentpy as ap
+
+
+class ProductAgent(ap.Agent):
+    def setup(self, code, name):
+        self.product_network = self.model.product_network
+
+        self.code = code
+        self.name = name
+
+    def a_successors(self):
+        # find successors of a product, return in AgentList (ProductAgent)
+        nodes = self.product_network.graph.successors(
+            self.product_network.positions[self])
+        return ap.AgentList(
+            self.model,
+            [ap.AgentIter(self.model, node).to_list()[0] for node in nodes])
+
+    def a_predecessors(self):
+        # find predecessors of a product, return in AgentList (ProductAgent)
+        nodes = self.product_network.graph.predecessors(
+            self.product_network.positions[self])
+        return ap.AgentList(
+            self.model,
+            [ap.AgentIter(self.model, node).to_list()[0] for node in nodes])

requirements.txt

@@ -0,0 +1,54 @@
+agentpy==0.1.5
+alabaster==0.7.13
+Babel==2.12.1
+certifi @ file:///C:/b/abs_85o_6fm0se/croot/certifi_1671487778835/work/certifi
+charset-normalizer==3.0.1
+colorama==0.4.6
+cycler==0.11.0
+decorator==5.1.1
+dill==0.3.6
+docutils==0.19
+greenlet==2.0.2
+idna==3.4
+imagesize==1.4.1
+importlib-metadata==6.0.0
+Jinja2==3.1.2
+joblib==1.2.0
+kiwisolver==1.4.4
+MarkupSafe==2.1.2
+matplotlib==3.3.4
+matplotlib-inline==0.1.6
+multiprocess==0.70.14
+mysqlclient==2.1.1
+networkx==2.5
+numpy==1.20.3
+numpydoc==1.1.0
+packaging==23.0
+pandas==1.4.1
+pandas-stubs==1.2.0.39
+Pillow==9.4.0
+Pygments==2.14.0
+pygraphviz @ file:///C:/Users/ASUS/Downloads/pygraphviz-1.9-cp38-cp38-win_amd64.whl
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytz==2022.7.1
+PyYAML==6.0
+requests==2.28.2
+SALib==1.4.7
+scipy==1.10.1
+six==1.16.0
+snowballstemmer==2.2.0
+Sphinx==6.1.3
+sphinxcontrib-applehelp==1.0.4
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==2.0.1
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.5
+SQLAlchemy==2.0.5.post1
+traitlets==5.9.0
+typing_extensions==4.5.0
+urllib3==1.26.14
+wincertstore==0.2
+yapf @ file:///tmp/build/80754af9/yapf_1615749224965/work
+zipp==3.15.0

requirements_manual_selected_20230304.txt

@@ -0,0 +1,9 @@
+agentpy==0.1.5
+matplotlib==3.3.4
+matplotlib-inline==0.1.6
+networkx==2.5
+numpy==1.20.3
+numpydoc==1.1.0
+pandas==1.4.1
+pandas-stubs==1.2.0.39
+pygraphviz==1.9

risk_analysis_count.py

@@ -0,0 +1,8 @@
+import pandas as pd
+
+count = pd.read_csv("output_result\\risk\\count.csv",
+                    dtype={'s_id': str, 'id_firm': str})
+print(count)
+print(len(count['s_id'].unique()))
+count_max_ts = count.groupby('s_id')['ts'].max()
+print(count_max_ts.value_counts())

risk_analysis_firm_network.py

@@ -0,0 +1,102 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import networkx as nx
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+# count firm category
+count_firm = pd.read_csv("output_result\\risk\\count_firm.csv")
+print(count_firm.describe())
+
+count_dcp = pd.read_csv("output_result\\risk\\count_dcp.csv",
+                        dtype={
+                            'up_id_firm': str,
+                            'down_id_firm': str
+                        })
+# print(count_dcp)
+count_dcp = count_dcp[count_dcp['count'] > 35]
+
+list_firm = count_dcp['up_id_firm'].tolist(
+) + count_dcp['down_id_firm'].tolist()
+list_firm = list(set(list_firm))
+
+# init graph firm
+Firm = pd.read_csv("input_data\\Firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
+firm_product = []
+for _, row in Firm.loc[:, '1':].iterrows():
+    firm_product.append(row[row == 1].index.to_list())
+Firm_attr.loc[:, 'Product_Code'] = firm_product
+Firm_attr.set_index('Code', inplace=True)
+
+G_firm = nx.MultiDiGraph()
+G_firm.add_nodes_from(list_firm)
+
+firm_labels_dict = {}
+for code in G_firm.nodes:
+    firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
+nx.set_node_attributes(G_firm, firm_labels_dict)
+
+count_max = count_dcp['count'].max()
+count_min = count_dcp['count'].min()
+k = 5 / (count_max - count_min)
+for _, row in count_dcp.iterrows():
+    # print(row)
+    lst_add_edge = [(
+        row['up_id_firm'],
+        row['down_id_firm'],
+        {
+            'up_id_product': row['up_id_product'],
+            'up_name_product': row['up_name_product'],
+            'down_id_product': row['down_id_product'],
+            'down_name_product': row['down_name_product'],
+            'edge_label': f"{row['up_id_product']} - {row['down_id_product']}",
+            'edge_width': k * (row['count'] - count_min),
+            'count': row['count']
+        })]
+    G_firm.add_edges_from(lst_add_edge)
+
+# dcp_networkx
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="dot", args="")
+node_label = nx.get_node_attributes(G_firm, 'Name')
+# node_degree = dict(G_firm.out_degree())
+# desensitize
+node_label = {
+    # key: f"{node_label[key]} {node_degree[key]}"
+    # key: f"{node_label[key]}"
+    key: key
+    for key in node_label.keys()
+}
+node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
+node_size = list(map(lambda x: x**2, node_size))
+edge_label = nx.get_edge_attributes(G_firm, "edge_label")
+edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+edge_width = nx.get_edge_attributes(G_firm, "edge_width")
+edge_width = [w for (n1, n2, _), w in edge_width.items()]
+colors = nx.get_edge_attributes(G_firm, "count")
+colors = [w for (n1, n2, _), w in colors.items()]
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+fig = plt.figure(figsize=(10, 8), dpi=300)
+nx.draw(G_firm,
+        pos,
+        node_size=node_size,
+        labels=node_label,
+        font_size=8,
+        width=3,
+        edge_color=colors,
+        edge_cmap=cmap,
+        edge_vmin=vmin,
+        edge_vmax=vmax)
+nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=6)
+sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
+sm._A = []
+position = fig.add_axes([0.95, 0.05, 0.01, 0.3])
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=10)
+cb.outline.set_visible(False)
+plt.savefig("output_result\\risk\\count_dcp_network")
+plt.close()

risk_analysis_prod_network.py

@@ -0,0 +1,157 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import networkx as nx
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+count_prod = pd.read_csv("output_result\\risk\\count_prod.csv")
+print(count_prod)
+
+# category
+print(count_prod.describe())
+
+# prod_networkx
+BomNodes = pd.read_csv('input_data\\BomNodes.csv', index_col=0)
+BomNodes.set_index('Code', inplace=True)
+BomCateNet = pd.read_csv('input_data\\BomCateNet.csv', index_col=0)
+BomCateNet.fillna(0, inplace=True)
+
+G = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph())
+
+labels_dict = {}
+for code in G.nodes:
+    node_attr = BomNodes.loc[code].to_dict()
+    index_list = count_prod[count_prod['id_product'] == code].index.tolist()
+    index = index_list[0] if len(index_list) == 1 else -1
+    node_attr['count'] = count_prod['count'].get(index, 0)
+    node_attr['node_size'] = count_prod['count'].get(index, 0)
+    node_attr['node_color'] = count_prod['count'].get(index, 0)
+    labels_dict[code] = node_attr
+nx.set_node_attributes(G, labels_dict)
+# print(labels_dict)
+
+pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="")
+dict_node_name = nx.get_node_attributes(G, 'Name')
+node_labels = {}
+for node in nx.nodes(G):
+    node_labels[node] = f"{node} {str(dict_node_name[node])}"
+    # node_labels[node] = f"{str(dict_node_name[node])}"
+colors = list(nx.get_node_attributes(G, 'node_color').values())
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+fig = plt.figure(figsize=(10, 10), dpi=300)
+nx.draw(G,
+        pos,
+        node_size=list(nx.get_node_attributes(G, 'node_size').values()),
+        labels=node_labels,
+        font_size=6,
+        node_color=colors,
+        cmap=cmap,
+        vmin=vmin,
+        vmax=vmax,
+        edge_color='grey')
+sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
+sm._A = []
+position = fig.add_axes([0.01, 0.05, 0.01, 0.3])
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=8)
+cb.outline.set_visible(False)
+plt.savefig("output_result\\risk\\count_prod_network")
+plt.close()
+
+# dcp_prod
+count_dcp = pd.read_csv("output_result\\risk\\count_dcp.csv",
+                        dtype={
+                            'up_id_firm': str,
+                            'down_id_firm': str
+                        })
+count_dcp_prod = count_dcp.groupby(
+    ['up_id_product',
+     'up_name_product',
+     'down_id_product',
+     'down_name_product'])['count'].sum()
+count_dcp_prod = count_dcp_prod.reset_index()
+count_dcp_prod.sort_values('count', inplace=True, ascending=False)
+count_dcp_prod.to_csv('output_result\\risk\\count_dcp_prod.csv',
+                      index=False,
+                      encoding='utf-8-sig')
+count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 50]
+# print(count_dcp_prod)
+
+list_prod = count_dcp_prod['up_id_product'].tolist(
+) + count_dcp['down_id_product'].tolist()
+list_prod = list(set(list_prod))
+
+# init graph bom
+
+BomNodes = pd.read_csv('input_data\\BomNodes.csv', index_col=0)
+BomNodes.set_index('Code', inplace=True)
+
+g_bom = nx.MultiDiGraph()
+g_bom.add_nodes_from(list_prod)
+
+bom_labels_dict = {}
+for code in list_prod:
+    dct_attr = BomNodes.loc[code].to_dict()
+    bom_labels_dict[code] = dct_attr
+nx.set_node_attributes(g_bom, bom_labels_dict)
+
+
+count_max = count_dcp_prod['count'].max()
+count_min = count_dcp_prod['count'].min()
+k = 5 / (count_max - count_min)
+for _, row in count_dcp_prod.iterrows():
+    # print(row)
+    lst_add_edge = [(
+        row['up_id_product'],
+        row['down_id_product'],
+        {
+            'count': row['count']
+        })]
+    g_bom.add_edges_from(lst_add_edge)
+
+# dcp_networkx
+pos = nx.nx_agraph.graphviz_layout(g_bom, prog="dot", args="")
+node_labels = nx.get_node_attributes(g_bom, 'Name')
+# rename node 1
+node_labels['1'] = '解决方案'
+temp = {}
+for key, value in node_labels.items():
+    temp[key] = key + " " + value
+node_labels = temp
+colors = nx.get_edge_attributes(g_bom, "count")
+colors = [w for (n1, n2, _), w in colors.items()]
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+
+pos_new = {}
+for node, p in pos.items():
+    pos_new[node] = (p[1], p[0])
+
+fig = plt.figure(figsize=(6, 10), dpi=300)
+# plt.subplots_adjust(right=0.7)
+nx.draw(g_bom,
+        pos_new,
+        node_size=50,
+        labels=node_labels,
+        font_size=5,
+        width=1.5,
+        edge_color=colors,
+        edge_cmap=cmap,
+        edge_vmin=vmin,
+        edge_vmax=vmax)
+plt.axis('off')
+axis = plt.gca()
+axis.set_xlim([1.2*x for x in axis.get_xlim()])
+axis.set_ylim([1.2*y for y in axis.get_ylim()])
+
+sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
+sm._A = []
+position = fig.add_axes([0.75, 0.1, 0.01, 0.2])
+cb = plt.colorbar(sm, fraction=0.01, cax=position)
+cb.ax.tick_params(labelsize=8)
+cb.outline.set_visible(False)
+plt.savefig("output_result\\risk\\count_dcp_prod_network")
+plt.close()

risk_analysis_sum_result.py

@@ -0,0 +1,188 @@
+from orm import engine
+import pandas as pd
+import networkx as nx
+import json
+import matplotlib.pyplot as plt
+
+# prep data
+Firm = pd.read_csv("input_data\\Firm_amended.csv")
+Firm['Code'] = Firm['Code'].astype('string')
+Firm.fillna(0, inplace=True)
+BomNodes = pd.read_csv('input_data\\BomNodes.csv', index_col=0)
+
+with open('SQL_analysis_risk.sql', 'r') as f:
+    str_sql = f.read()
+result = pd.read_sql(sql=str_sql,
+                     con=engine)
+result.to_csv('output_result\\risk\\count.csv',
+              index=False,
+              encoding='utf-8-sig')
+print(result)
+
+# G bom
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+exp_id = 1
+G_bom_str = pd.read_sql(
+    sql=f'select g_bom from iiabmdb.without_exp_experiment '
+        f'where id = {exp_id};',
+    con=engine)['g_bom'].tolist()[0]
+G_bom = nx.adjacency_graph(json.loads(G_bom_str))
+pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="")
+node_labels = nx.get_node_attributes(G_bom, 'Name')
+# rename node 1
+node_labels['1'] = '解决方案'
+plt.figure(figsize=(12, 12), dpi=300)
+nx.draw_networkx_nodes(G_bom, pos)
+nx.draw_networkx_edges(G_bom, pos)
+nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6)
+# plt.show()
+plt.savefig(f"output_result\\risk\\g_bom_exp_id_{exp_id}.png")
+plt.close()
+
+# G firm
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+sample_id = 1
+G_firm_str = pd.read_sql(
+    sql=f'select g_firm from iiabmdb.without_exp_sample where id = {exp_id};',
+    con=engine)['g_firm'].tolist()[0]
+G_firm = nx.adjacency_graph(json.loads(G_firm_str))
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
+node_label = nx.get_node_attributes(G_firm, 'Name')
+# desensitize
+node_label = {
+    key: key
+    for key in node_label.keys()
+}
+node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
+node_size = list(map(lambda x: x**2, node_size))
+edge_label = nx.get_edge_attributes(G_firm, "Product")
+edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+plt.figure(figsize=(12, 12), dpi=300)
+nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=6)
+nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4)
+# plt.show()
+plt.savefig(f"output_result\\risk\\g_firm_sample_id_{exp_id}_de.png")
+plt.close()
+
+# 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_firm_prod = pd.merge(count_firm_prod,
+                           Firm[['Code', 'Name']],
+                           how='left',
+                           left_on='id_firm',
+                           right_on='Code')
+count_firm_prod.drop('Code', axis=1, inplace=True)
+count_firm_prod.rename(columns={'Name': 'name_firm'}, inplace=True)
+count_firm_prod = pd.merge(count_firm_prod,
+                           BomNodes[['Code', 'Name']],
+                           how='left',
+                           left_on='id_product',
+                           right_on='Code')
+count_firm_prod.drop('Code', axis=1, inplace=True)
+count_firm_prod.rename(columns={'Name': 'name_product'}, inplace=True)
+count_firm_prod = count_firm_prod[[
+    'id_firm', 'name_firm', 'id_product', 'name_product', 'count'
+]]
+count_firm_prod.to_csv('output_result\\risk\\count_firm_prod.csv',
+                       index=False,
+                       encoding='utf-8-sig')
+print(count_firm_prod)
+
+# count firm
+count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
+count_firm = count_firm.to_frame().reset_index()
+count_firm = pd.merge(count_firm,
+                      Firm[['Code', 'Name']],
+                      how='left',
+                      left_on='id_firm',
+                      right_on='Code')
+count_firm.drop('Code', axis=1, inplace=True)
+count_firm.sort_values('count', inplace=True, ascending=False)
+count_firm = count_firm[['id_firm', 'Name', 'count']]
+count_firm.to_csv('output_result\\risk\\count_firm.csv',
+                  index=False,
+                  encoding='utf-8-sig')
+print(count_firm)
+
+# count product
+count_prod = count_firm_prod.groupby('id_product')['count'].sum()
+count_prod = count_prod.to_frame().reset_index()
+count_prod = pd.merge(count_prod,
+                      BomNodes[['Code', 'Name']],
+                      how='left',
+                      left_on='id_product',
+                      right_on='Code')
+count_prod.drop('Code', axis=1, inplace=True)
+count_prod.sort_values('count', inplace=True, ascending=False)
+count_prod = count_prod[['id_product', 'Name', 'count']]
+count_prod.to_csv('output_result\\risk\\count_prod.csv',
+                  index=False,
+                  encoding='utf-8-sig')
+print(count_prod)
+
+# DCP disruption causing probability
+result_disrupt_ts_above_0 = result[result['ts'] > 0]
+print(result_disrupt_ts_above_0)
+result_dcp = pd.DataFrame(columns=[
+    's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
+])
+for sid, group in result.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():
+                    row = [sid]
+                    row += up_row.tolist()
+                    row += down_row.tolist()
+                    result_dcp.loc[len(result_dcp.index)] = row
+            ts_end -= 1
+        ts_start -= 1
+count_dcp = result_dcp.value_counts(
+    subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'])
+count_dcp.name = 'count'
+count_dcp = count_dcp.to_frame().reset_index()
+count_dcp = pd.merge(count_dcp,
+                     Firm[['Code', 'Name']],
+                     how='left',
+                     left_on='up_id_firm',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'up_name_firm'}, inplace=True)
+count_dcp = pd.merge(count_dcp,
+                     BomNodes[['Code', 'Name']],
+                     how='left',
+                     left_on='up_id_product',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'up_name_product'}, inplace=True)
+count_dcp = pd.merge(count_dcp,
+                     Firm[['Code', 'Name']],
+                     how='left',
+                     left_on='down_id_firm',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'down_name_firm'}, inplace=True)
+count_dcp = pd.merge(count_dcp,
+                     BomNodes[['Code', 'Name']],
+                     how='left',
+                     left_on='down_id_product',
+                     right_on='Code')
+count_dcp.drop('Code', axis=1, inplace=True)
+count_dcp.rename(columns={'Name': 'down_name_product'}, inplace=True)
+count_dcp = count_dcp[[
+    'up_id_firm', 'up_name_firm', 'up_id_product', 'up_name_product',
+    'down_id_firm', 'down_name_firm', 'down_id_product', 'down_name_product',
+    'count'
+]]
+count_dcp.to_csv('output_result\\risk\\count_dcp.csv',
+                 index=False, encoding='utf-8-sig')
+print(count_dcp)