commit f9ffc6edf40ce9983082dd2b0c6b9adf9bcb9514 Author: Ethan Hao Date: Sun Sep 10 10:35:48 2023 -0400 new repo diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..091bc2f --- /dev/null +++ b/.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" + ] + } + ] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..2b7e46d --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.formatting.provider": "yapf" +} \ No newline at end of file diff --git a/SQL_analysis_experiment.sql b/SQL_analysis_experiment.sql new file mode 100644 index 0000000..7894992 --- /dev/null +++ b/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; \ No newline at end of file diff --git a/SQL_analysis_risk.sql b/SQL_analysis_risk.sql new file mode 100644 index 0000000..621186e --- /dev/null +++ b/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; \ No newline at end of file diff --git a/SQL_export_high_risk_setting.sql b/SQL_export_high_risk_setting.sql new file mode 100644 index 0000000..cfa3a17 --- /dev/null +++ b/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; \ No newline at end of file diff --git a/SQL_migrate_db.sql b/SQL_migrate_db.sql new file mode 100644 index 0000000..e5c0555 --- /dev/null +++ b/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; \ No newline at end of file diff --git a/__pycache__/computation.cpython-38.pyc b/__pycache__/computation.cpython-38.pyc new file mode 100644 index 0000000..ecb5536 Binary files /dev/null and b/__pycache__/computation.cpython-38.pyc differ diff --git a/__pycache__/computation.cpython-39.pyc b/__pycache__/computation.cpython-39.pyc new file mode 100644 index 0000000..82f0851 Binary files /dev/null and b/__pycache__/computation.cpython-39.pyc differ diff --git a/__pycache__/controller_db.cpython-38.pyc b/__pycache__/controller_db.cpython-38.pyc new file mode 100644 index 0000000..0b75ad0 Binary files /dev/null and b/__pycache__/controller_db.cpython-38.pyc differ diff --git a/__pycache__/firm.cpython-38.pyc b/__pycache__/firm.cpython-38.pyc new file mode 100644 index 0000000..34335df Binary files /dev/null and b/__pycache__/firm.cpython-38.pyc differ diff --git a/__pycache__/model.cpython-38.pyc b/__pycache__/model.cpython-38.pyc new file mode 100644 index 0000000..0fa3479 Binary files /dev/null and b/__pycache__/model.cpython-38.pyc differ diff --git a/__pycache__/model.cpython-39.pyc b/__pycache__/model.cpython-39.pyc new file mode 100644 index 0000000..81ed1e8 Binary files /dev/null and b/__pycache__/model.cpython-39.pyc differ diff --git a/__pycache__/orm.cpython-38.pyc b/__pycache__/orm.cpython-38.pyc new file mode 100644 index 0000000..39d29fa Binary files /dev/null and b/__pycache__/orm.cpython-38.pyc differ diff --git a/__pycache__/product.cpython-38.pyc b/__pycache__/product.cpython-38.pyc new file mode 100644 index 0000000..d25c6a9 Binary files /dev/null and b/__pycache__/product.cpython-38.pyc differ diff --git a/computation.py b/computation.py new file mode 100644 index 0000000..ab54369 --- /dev/null +++ b/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 diff --git a/conf_db.yaml b/conf_db.yaml new file mode 100644 index 0000000..f1618a7 --- /dev/null +++ b/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 diff --git a/conf_db_prefix.yaml b/conf_db_prefix.yaml new file mode 100644 index 0000000..1391188 --- /dev/null +++ b/conf_db_prefix.yaml @@ -0,0 +1 @@ +db_name_prefix: with_exp diff --git a/conf_experiment.yaml b/conf_experiment.yaml new file mode 100644 index 0000000..77309ea --- /dev/null +++ b/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 diff --git a/controller_db.py b/controller_db.py new file mode 100644 index 0000000..9965334 --- /dev/null +++ b/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() diff --git a/firm.py b/firm.py new file mode 100644 index 0000000..e322b84 --- /dev/null +++ b/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 diff --git a/input_data/BomCateNet.csv b/input_data/BomCateNet.csv new file mode 100644 index 0000000..565c59d --- /dev/null +++ b/input_data/BomCateNet.csv @@ -0,0 +1,108 @@ 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+1.4.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.3.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.3.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.3.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.3.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.3.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.3.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.4.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.4.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.4.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.4.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.4.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.8,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1.4.5.9,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,,, +2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,1,,,,,1,,,,,,,,1,,,,,,,,,,,,, +2.1.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.1.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.1.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.1.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,,,,,,,,,,,,,,,,,,,,,, +2.1.2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.2.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.2.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.2.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,1,1,1,,,,,,,,,,,,,, +2.1.3.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3.6,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.3.7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,1,,,,,,, +2.1.4.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1,1,,,,,,,, +2.1.4.1.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.4.1.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.4.1.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.4.1.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.4.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,,,,, +2.1.4.2.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.1.4.2.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 +2.3.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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+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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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+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,,,,,,,,,,,,,,, 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+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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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+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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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+3,,,,15,0.7,7,1 diff --git a/input_data/xv_without_exp.csv b/input_data/xv_without_exp.csv new file mode 100644 index 0000000..caa6fef --- /dev/null +++ b/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 diff --git a/main.py b/main.py new file mode 100644 index 0000000..485fd3c --- /dev/null +++ b/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() diff --git a/model.py b/model.py new file mode 100644 index 0000000..8b623c8 --- /dev/null +++ b/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") diff --git a/orm.py b/orm.py new file mode 100644 index 0000000..f497b0e --- /dev/null +++ b/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'' + + +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'' + + +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'' + + +if __name__ == '__main__': + Base.metadata.drop_all() + Base.metadata.create_all() diff --git a/output_result/resilience/anova.csv b/output_result/resilience/anova.csv new file mode 100644 index 0000000..1ab2b35 --- /dev/null +++ b/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 diff --git a/output_result/resilience/anova_visualization.csv b/output_result/resilience/anova_visualization.csv new file mode 100644 index 0000000..ebae0aa --- /dev/null +++ b/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 diff --git a/output_result/resilience/experiment_result.csv b/output_result/resilience/experiment_result.csv new file mode 100644 index 0000000..c4df5bf --- /dev/null +++ b/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 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and b/output_result/resilience/resilience3.png differ diff --git a/output_result/resilience/resilience4.png b/output_result/resilience/resilience4.png new file mode 100644 index 0000000..2a5c9c4 Binary files /dev/null and b/output_result/resilience/resilience4.png differ diff --git a/output_result/resilience/resilience5.png b/output_result/resilience/resilience5.png new file mode 100644 index 0000000..7ee1afd Binary files /dev/null and b/output_result/resilience/resilience5.png differ diff --git a/output_result/resilience/resilience6.png b/output_result/resilience/resilience6.png new file mode 100644 index 0000000..98a27ff Binary files /dev/null and b/output_result/resilience/resilience6.png differ diff --git a/output_result/risk/count.csv b/output_result/risk/count.csv new file mode 100644 index 0000000..f000386 --- /dev/null +++ b/output_result/risk/count.csv @@ -0,0 +1,32578 @@ +s_id,id_firm,id_product,ts +1,170,1,2 +1,126,1.4,1 +1,0,1.4.4,0 +3,126,1.4,1 +3,0,1.4.4,0 +3,170,1,2 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+29,京东工业品,1.3,工业软件,170,Pseudo1,1,供给,241 +142,深信服,1.4.3,网络安全,170,Pseudo1,1,供给,207 +142,深信服,1.4.3,网络安全,126,华为,1.4,工业互联网安全,207 +142,深信服,1.4.2,控制安全,170,Pseudo1,1,供给,204 +142,深信服,1.4.2,控制安全,126,华为,1.4,工业互联网安全,204 +0,360科技,1.4.4,平台安全,126,华为,1.4,工业互联网安全,149 +40,奇安信,1.4.4,平台安全,126,华为,1.4,工业互联网安全,145 +98,Microsoft Azure,2,工业互联网平台,170,Pseudo1,1,供给,118 +102,Amazon AWS,2,工业互联网平台,170,Pseudo1,1,供给,115 +0,360科技,1.4.4,平台安全,170,Pseudo1,1,供给,108 +40,奇安信,1.4.4,平台安全,170,Pseudo1,1,供给,102 +130,金蝶,1.3.5,仓储物流,106,阿里巴巴,1.3,工业软件,78 +130,金蝶,1.3.5,仓储物流,170,Pseudo1,1,供给,78 +130,金蝶,1.3.5,仓储物流,29,京东工业品,1.3,工业软件,78 +130,金蝶,1.3.2,采购供应,29,京东工业品,1.3,工业软件,77 +157,新华三,1.4.1,设备安全,126,华为,1.4,工业互联网安全,77 +58,用友,1.3.2,采购供应,29,京东工业品,1.3,工业软件,77 +157,新华三,1.4.1,设备安全,170,Pseudo1,1,供给,75 +58,用友,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,75 +130,金蝶,1.3.2,采购供应,106,阿里巴巴,1.3,工业软件,75 +99,Siemens,2.1,PaaS,98,Microsoft Azure,2,工业互联网平台,70 +99,Siemens,2.1,PaaS,170,Pseudo1,1,供给,70 +99,Siemens,2.1,PaaS,102,Amazon AWS,2,工业互联网平台,70 +58,用友,1.3.2,采购供应,170,Pseudo1,1,供给,62 +130,金蝶,1.3.2,采购供应,170,Pseudo1,1,供给,59 +115,富士康,2.1.4,工业大数据,99,Siemens,2.1,PaaS,54 +53,天融信,1.4.2.3,工控漏洞扫描,142,深信服,1.4.2,控制安全,50 +140,山石网科,1.4.5.1,恶意代码检测系统,41,启明星辰,1.4.5,数据安全,50 +140,山石网科,1.4.5.1,恶意代码检测系统,170,Pseudo1,1,供给,50 +140,山石网科,1.4.5.1,恶意代码检测系统,126,华为,1.4,工业互联网安全,50 +63,长扬科技,1.4.4.5,安全态势感知,170,Pseudo1,1,供给,50 +53,天融信,1.4.2.3,工控漏洞扫描,126,华为,1.4,工业互联网安全,50 +23,和利时,1.4.2.7,工控原生安全,170,Pseudo1,1,供给,50 +53,天融信,1.4.2.3,工控漏洞扫描,170,Pseudo1,1,供给,50 +63,长扬科技,1.4.4.5,安全态势感知,126,华为,1.4,工业互联网安全,50 +135,浪潮,1.3.2.1,供应链管理SCM,106,阿里巴巴,1.3,工业软件,50 +135,浪潮,1.3.2.1,供应链管理SCM,130,金蝶,1.3.2,采购供应,50 +53,天融信,1.4.3.6,沙箱类设备,126,华为,1.4,工业互联网安全,50 +53,天融信,1.4.3.6,沙箱类设备,142,深信服,1.4.3,网络安全,50 +135,浪潮,1.3.2.1,供应链管理SCM,170,Pseudo1,1,供给,50 +135,浪潮,1.3.2.1,供应链管理SCM,29,京东工业品,1.3,工业软件,50 +135,浪潮,1.3.2.1,供应链管理SCM,58,用友,1.3.2,采购供应,50 +63,长扬科技,1.4.4.5,安全态势感知,40,奇安信,1.4.4,平台安全,50 +53,天融信,1.4.3.6,沙箱类设备,170,Pseudo1,1,供给,50 +41,启明星辰,1.4.3.2,流量检测,170,Pseudo1,1,供给,50 +53,天融信,1.4.5.8,数据加密,41,启明星辰,1.4.5,数据安全,50 +53,天融信,1.4.5.8,数据加密,126,华为,1.4,工业互联网安全,50 +53,天融信,1.4.5.8,数据加密,170,Pseudo1,1,供给,50 +41,启明星辰,1.4.3.2,流量检测,126,华为,1.4,工业互联网安全,50 +63,长扬科技,1.4.4.5,安全态势感知,0,360科技,1.4.4,平台安全,50 +41,启明星辰,1.4.3.2,流量检测,142,深信服,1.4.3,网络安全,50 +23,和利时,1.4.2.7,工控原生安全,126,华为,1.4,工业互联网安全,50 +23,和利时,1.4.2.7,工控原生安全,142,深信服,1.4.2,控制安全,50 +84,Bosch,2.1.4,工业大数据,99,Siemens,2.1,PaaS,44 +97,General Electric,1.3.3,生产制造,29,京东工业品,1.3,工业软件,41 +97,General Electric,1.3.3,生产制造,106,阿里巴巴,1.3,工业软件,40 +102,Amazon AWS,2.1.4,工业大数据,99,Siemens,2.1,PaaS,40 +114,富勒科技,1.3.5.1,仓储物流管理WMS,130,金蝶,1.3.5,仓储物流,40 +53,天融信,1.4.5.6,数据容灾备份,41,启明星辰,1.4.5,数据安全,39 +77,Oracle,1.3.4,企业运营管理,106,阿里巴巴,1.3,工业软件,39 +37,绿盟,1.4.5.2,数据防泄漏系统,41,启明星辰,1.4.5,数据安全,39 +162,壹进制,1.4.5.6,数据容灾备份,41,启明星辰,1.4.5,数据安全,39 +162,壹进制,1.4.5.7,数据恢复,41,启明星辰,1.4.5,数据安全,39 +52,天空卫士,1.4.5.5,敏感数据发现与监控,41,启明星辰,1.4.5,数据安全,39 +53,天融信,1.4.5.7,数据恢复,41,启明星辰,1.4.5,数据安全,39 +5,安华金和,1.4.5.9,数据防火墙,41,启明星辰,1.4.5,数据安全,39 +5,安华金和,1.4.5.5,敏感数据发现与监控,41,启明星辰,1.4.5,数据安全,39 +5,安华金和,1.4.5.4,数据脱敏,41,启明星辰,1.4.5,数据安全,39 +140,山石网科,1.4.5.4,数据脱敏,41,启明星辰,1.4.5,数据安全,39 +140,山石网科,1.4.5.9,数据防火墙,41,启明星辰,1.4.5,数据安全,39 +53,天融信,1.4.5.2,数据防泄漏系统,41,启明星辰,1.4.5,数据安全,39 +55,威努特,1.4.2.2,工控主机卫士,142,深信服,1.4.2,控制安全,37 +59,优特捷,1.4.2.5,安全日志与审计,142,深信服,1.4.2,控制安全,37 +41,启明星辰,1.4.3.5,负载均衡,142,深信服,1.4.3,网络安全,37 +100,Synopsys,1.3.1,设计研发,29,京东工业品,1.3,工业软件,37 +37,绿盟,1.4.2.2,工控主机卫士,142,深信服,1.4.2,控制安全,37 +53,天融信,1.4.3.4,攻击溯源,142,深信服,1.4.3,网络安全,37 +41,启明星辰,1.4.3.4,攻击溯源,142,深信服,1.4.3,网络安全,37 +41,启明星辰,1.4.3.1,网络漏洞扫描和补丁管理,142,深信服,1.4.3,网络安全,37 +122,国民技术,1.4.2.6,隐私计算,142,深信服,1.4.2,控制安全,37 +40,奇安信,1.4.2.5,安全日志与审计,142,深信服,1.4.2,控制安全,37 +53,天融信,1.4.3.5,负载均衡,142,深信服,1.4.3,网络安全,37 +151,唯智信息,1.3.5.1,仓储物流管理WMS,130,金蝶,1.3.5,仓储物流,37 +30,可信华泰,1.4.2.6,隐私计算,142,深信服,1.4.2,控制安全,37 +37,绿盟,1.4.3.1,网络漏洞扫描和补丁管理,142,深信服,1.4.3,网络安全,37 +99,Siemens,1.3.3,生产制造,29,京东工业品,1.3,工业软件,36 +107,安恒信息,1.4.3.3,APT检测,142,深信服,1.4.3,网络安全,36 +27,江南天安,1.4.4.2,密钥管理,0,360科技,1.4.4,平台安全,35 +53,天融信,1.4.4.4,工业应用行为监控,0,360科技,1.4.4,平台安全,35 +54,网御星云,1.4.4.3,接入认证,0,360科技,1.4.4,平台安全,35 +37,绿盟,1.4.4.3,接入认证,0,360科技,1.4.4,平台安全,35 +160,亚信科技,1.4.1.3,防毒墙,157,新华三,1.4.1,设备安全,35 +55,威努特,1.4.4.4,工业应用行为监控,0,360科技,1.4.4,平台安全,35 +106,阿里巴巴,1.2,工业互联网网络,170,Pseudo1,1,供给,35 +11,北信源,1.4.4.2,密钥管理,0,360科技,1.4.4,平台安全,35 +93,Cadence,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,35 +81,SAP,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,35 +77,Oracle,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,34 +75,IBM,1.3.3,生产制造,29,京东工业品,1.3,工业软件,34 +81,SAP,2.1.4.1,工业大数据存储,115,富士康,2.1.4,工业大数据,34 +79,PTC,2.1.4.1,工业大数据存储,115,富士康,2.1.4,工业大数据,33 +80,Salesforce,1.3.4,企业运营管理,29,京东工业品,1.3,工业软件,33 +8,梆梆安全,1.4.1.1,工业防火墙,157,新华三,1.4.1,设备安全,33 +81,SAP,2.1.4.1,工业大数据存储,84,Bosch,2.1.4,工业大数据,33 +19,国泰网信,1.4.2.1,工控安全监测与审计,142,深信服,1.4.2,控制安全,32 +99,Siemens,1.3.3,生产制造,106,阿里巴巴,1.3,工业软件,32 +79,PTC,2.1.4.1,工业大数据存储,84,Bosch,2.1.4,工业大数据,32 +51,天地和兴,1.4.2.1,工控安全监测与审计,142,深信服,1.4.2,控制安全,32 +80,Salesforce,1.3.4,企业运营管理,106,阿里巴巴,1.3,工业软件,32 +85,Dassault,1.3.1,设计研发,29,京东工业品,1.3,工业软件,32 +79,PTC,2.1.4.2,工业大数据管理,115,富士康,2.1.4,工业大数据,31 +55,威努特,1.4.4.4,工业应用行为监控,40,奇安信,1.4.4,平台安全,31 +53,天融信,1.4.4.4,工业应用行为监控,40,奇安信,1.4.4,平台安全,31 +121,广州智臣,1.4.2.4,安全隔离与信息交换系统,142,深信服,1.4.2,控制安全,31 +37,绿盟,1.4.4.3,接入认证,40,奇安信,1.4.4,平台安全,31 +81,SAP,2.1.4.2,工业大数据管理,115,富士康,2.1.4,工业大数据,31 +27,江南天安,1.4.4.2,密钥管理,40,奇安信,1.4.4,平台安全,31 +97,General Electric,1.2,工业互联网网络,170,Pseudo1,1,供给,31 +54,网御星云,1.4.4.3,接入认证,40,奇安信,1.4.4,平台安全,31 +17,国保金泰,1.4.2.4,安全隔离与信息交换系统,142,深信服,1.4.2,控制安全,31 +11,北信源,1.4.4.2,密钥管理,40,奇安信,1.4.4,平台安全,31 +81,SAP,2.1.4.2,工业大数据管理,84,Bosch,2.1.4,工业大数据,30 +79,PTC,2.1.4.2,工业大数据管理,84,Bosch,2.1.4,工业大数据,30 +53,天融信,1.4.5.3,数据审计系统,41,启明星辰,1.4.5,数据安全,30 +75,IBM,1.3.3,生产制造,106,阿里巴巴,1.3,工业软件,30 +158,信大捷安,1.4.4.1,身份鉴别与访问控制,0,360科技,1.4.4,平台安全,29 +100,Synopsys,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,29 +152,卫士通,1.4.4.1,身份鉴别与访问控制,40,奇安信,1.4.4,平台安全,29 +140,山石网科,1.4.1.4,入侵检测系统,157,新华三,1.4.1,设备安全,29 +85,Dassault,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,29 +81,SAP,1.3.4,企业运营管理,106,阿里巴巴,1.3,工业软件,28 +41,启明星辰,1.4.1.2,下一代防火墙,157,新华三,1.4.1,设备安全,28 +86,Dell EMC,1.1,工业自动化,170,Pseudo1,1,供给,27 +93,Cadence,1.3.1,设计研发,29,京东工业品,1.3,工业软件,27 +152,卫士通,1.4.4.1,身份鉴别与访问控制,0,360科技,1.4.4,平台安全,26 +55,威努特,1.4.1.2,下一代防火墙,157,新华三,1.4.1,设备安全,26 +40,奇安信,1.4.3.3,APT检测,142,深信服,1.4.3,网络安全,26 +53,天融信,1.4.1.5,统一威胁管理系统,157,新华三,1.4.1,设备安全,26 +99,Siemens,1.3.1,设计研发,106,阿里巴巴,1.3,工业软件,25 +158,信大捷安,1.4.4.1,身份鉴别与访问控制,40,奇安信,1.4.4,平台安全,25 +41,启明星辰,1.4.1.5,统一威胁管理系统,157,新华三,1.4.1,设备安全,25 +108,百度,2.1.3,工业物联网,99,Siemens,2.1,PaaS,25 +105,Intel,1.1,工业自动化,170,Pseudo1,1,供给,25 +14,东华软件,1.3.4.3,人力资源管理HRM,77,Oracle,1.3.4,企业运营管理,24 +133,蓝盾股份,1.4.4.1,身份鉴别与访问控制,0,360科技,1.4.4,平台安全,24 +73,FANUC,2.1.3,工业物联网,99,Siemens,2.1,PaaS,24 +14,东华软件,1.3.4.3,人力资源管理HRM,81,SAP,1.3.4,企业运营管理,24 +5,安华金和,1.4.5.3,数据审计系统,41,启明星辰,1.4.5,数据安全,24 +140,山石网科,1.4.5.3,数据审计系统,41,启明星辰,1.4.5,数据安全,24 +37,绿盟,1.4.1.4,入侵检测系统,157,新华三,1.4.1,设备安全,24 +126,华为,1.2,工业互联网网络,170,Pseudo1,1,供给,23 +140,山石网科,1.4.1.5,统一威胁管理系统,157,新华三,1.4.1,设备安全,23 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Electric,1.3.3,生产制造,11 +31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,11 +79,PTC,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,11 +26,寄云科技,2.1.3.5,容器服务,106,阿里巴巴,2.1.3,工业物联网,11 +120,广州数控,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,11 +135,浪潮,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,11 +31,昆仑数据,2.1.4.1.1,关系型数据库,81,SAP,2.1.4.1,工业大数据存储,11 +49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,10 +53,天融信,1.4.1.3,防毒墙,157,新华三,1.4.1,设备安全,10 +47,首自信,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,10 +3,艾克斯特,1.3.1.5,产品数据管理PDM,39,Autodesk,1.3.1,设计研发,10 +9,北京航天测控,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,10 +2,706所,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,10 +31,昆仑数据,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,10 +26,寄云科技,2.1.3.4,应用管理服务,126,华为,2.1.3,工业物联网,10 +34,力控科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,10 +53,天融信,1.4.3.4,攻击溯源,126,华为,1.4,工业互联网安全,10 +2,706所,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,10 +47,首自信,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,10 +13,东方国信,2.1.4.1.1,关系型数据库,79,PTC,2.1.4.1,工业大数据存储,10 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+22,航天云网,2.1.3.3,工业引擎服务,97,General Electric,2.1.3,工业物联网,10 +91,Moxa,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,10 +119,广联达,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,10 +42,山大华天,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,10 +42,山大华天,1.3.1.3,计算机辅助制造CAM,39,Autodesk,1.3.1,设计研发,10 +25,华大九天,1.3.1.7,电子设计自动化EDA,85,Dassault,1.3.1,设计研发,10 +13,东方国信,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,10 +13,东方国信,2.3.2,边缘数据处理,84,Bosch,2.3,边缘层,10 +92,Omron,1.3.3.4,可编程逻揖控制系统PLC,99,Siemens,1.3.3,生产制造,10 +53,天融信,1.4.3.4,攻击溯源,170,Pseudo1,1,供给,10 +22,航天云网,2.1.3.5,容器服务,108,百度,2.1.3,工业物联网,10 +96,Cisco,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,10 +22,航天云网,2.1.3.5,容器服务,148,腾讯,2.1.3,工业物联网,10 +45,石化盈科,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,10 +22,航天云网,2.1.3.7,制造类API,126,华为,2.1.3,工业物联网,10 +22,航天云网,2.1.3.7,制造类API,106,阿里巴巴,2.1.3,工业物联网,10 +22,航天云网,2.1.3.2,平台基础服务,126,华为,2.1.3,工业物联网,10 +22,航天云网,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,10 +22,航天云网,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,10 +130,金蝶,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,10 +124,海尔,2.3,边缘层,102,Amazon AWS,2,工业互联网平台,10 +23,和利时,2.3.3,协议转换,95,Schneider,2.3,边缘层,10 +47,首自信,2.1.1.3,流程开发工具,148,腾讯,2.1.1,开发工具,10 +47,首自信,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,10 +38,牛刀,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,10 +22,航天云网,1.2.2,标识解析,97,General Electric,1.2,工业互联网网络,10 +23,和利时,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,10 +22,航天云网,1.3.3.6,运维保障系统MRO,99,Siemens,1.3.3,生产制造,10 +38,牛刀,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,10 +90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,10 +99,Siemens,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,10 +22,航天云网,2.1.1.2,低代码开发工具,80,Salesforce,2.1.1,开发工具,10 +45,石化盈科,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,10 +45,石化盈科,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,10 +45,石化盈科,2.1.4.1.3,实时数据库,81,SAP,2.1.4.1,工业大数据存储,10 +22,航天云网,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,10 +45,石化盈科,2.1.4.1.2,分布式数据库,81,SAP,2.1.4.1,工业大数据存储,10 +45,石化盈科,2.1.4.1.2,分布式数据库,79,PTC,2.1.4.1,工业大数据存储,10 +22,航天云网,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,10 +22,航天云网,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,10 +10,北京英贝思,1.3.3.5,企业资产管理系统EAM,75,IBM,1.3.3,生产制造,10 +23,和利时,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,10 +52,天空卫士,1.4.5.5,敏感数据发现与监控,170,Pseudo1,1,供给,10 +166,中国电子科技网络信息安全,1.2.3,数据互通,97,General Electric,1.2,工业互联网网络,10 +78,OutSystems,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,10 +71,Altair,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,10 +71,Altair,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,10 +79,PTC,1.3.1.1,计算机辅助设计CAD,39,Autodesk,1.3.1,设计研发,10 +57,亚控科技,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,10 +135,浪潮,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,10 +79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,93,Cadence,1.3.1,设计研发,10 +156,芯禾科技,1.3.1.7,电子设计自动化EDA,100,Synopsys,1.3.1,设计研发,10 +143,沈阳自动化研究所,2.1.1.3,流程开发工具,106,阿里巴巴,2.1.1,开发工具,10 +149,天泽智云,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,10 +60,宇动源,2.1.1.4,组态建模工具,106,阿里巴巴,2.1.1,开发工具,10 +79,PTC,2.3.3,协议转换,155,小米,2.3,边缘层,10 +7,百望,2.2,IaaS,102,Amazon AWS,2,工业互联网平台,10 +154,西格数据,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,10 +79,PTC,2.1.4.1,工业大数据存储,99,Siemens,2.1,PaaS,10 +162,壹进制,1.4.5.7,数据恢复,126,华为,1.4,工业互联网安全,10 +68,中望软件,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,10 +162,壹进制,1.4.5.7,数据恢复,170,Pseudo1,1,供给,10 +149,天泽智云,2.1.2.1,数据算法模型,79,PTC,2.1.2,工业模型库,10 +78,OutSystems,2.1.1.3,流程开发工具,80,Salesforce,2.1.1,开发工具,10 +57,亚控科技,1.3.3.3,数据采集与监视控制系统SCADA,97,General Electric,1.3.3,生产制造,10 +81,SAP,2.1.4.1,工业大数据存储,99,Siemens,2.1,PaaS,10 +145,思普软件,1.3.1.4,计算机辅助工艺过程设计CAPP,100,Synopsys,1.3.1,设计研发,10 +16,东土科技,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,10 +16,东土科技,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,10 +144,树根互联,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,10 +134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,97,General Electric,1.3.3,生产制造,10 +77,Oracle,1.3.3.6,运维保障系统MRO,75,IBM,1.3.3,生产制造,10 +6,安世亚太,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,10 +146,苏州浩辰,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,10 +81,SAP,1.3.4,企业运营管理,170,Pseudo1,1,供给,10 +135,浪潮,2.1.3.3,工业引擎服务,73,FANUC,2.1.3,工业物联网,10 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+55,威努特,1.4.4.4,工业应用行为监控,126,华为,1.4,工业互联网安全,10 +135,浪潮,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,10 +135,浪潮,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,10 +117,格创东智,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,10 +137,美林数据,2.1.4.2.2,数据安全管理,81,SAP,2.1.4.2,工业大数据管理,10 +165,智能云科,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,10 +64,中电智科,1.1.2,工业控制器,126,华为,1.1,工业自动化,10 +137,美林数据,2.1.4.1.1,关系型数据库,79,PTC,2.1.4.1,工业大数据存储,10 +68,中望软件,1.3.1.1,计算机辅助设计CAD,99,Siemens,1.3.1,设计研发,10 +16,东土科技,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,10 +68,中望软件,1.3.1.2,计算机辅助工程CAE,100,Synopsys,1.3.1,设计研发,10 +163,优也科技,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,10 +140,山石网科,1.4.5.9,数据防火墙,170,Pseudo1,1,供给,10 +140,山石网科,1.4.5.9,数据防火墙,126,华为,1.4,工业互联网安全,10 +62,云道智造,2.1.2.1,数据算法模型,58,用友,2.1.2,工业模型库,10 +68,中望软件,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,10 +62,云道智造,2.1.2.1,数据算法模型,81,SAP,2.1.2,工业模型库,10 +163,优也科技,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,10 +149,天泽智云,2.1.2.4,行业机理模型,58,用友,2.1.2,工业模型库,10 +79,PTC,2.1.3.2,平台基础服务,106,阿里巴巴,2.1.3,工业物联网,10 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+22,航天云网,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,9 +22,航天云网,2.1.4.2.1,数据质量管理,79,PTC,2.1.4.2,工业大数据管理,9 +42,山大华天,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,9 +144,树根互联,2.1.2.2,业务流程模型,159,徐工集团,2.1.2,工业模型库,9 +126,华为,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,9 +129,华中数控,1.2.3,数据互通,67,中国移动,1.2,工业互联网网络,9 +79,PTC,2.1.3.1,物联网服务,74,HoneyWell,2.1.3,工业物联网,9 +135,浪潮,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,9 +79,PTC,2.1.3.7,制造类API,74,HoneyWell,2.1.3,工业物联网,9 +31,昆仑数据,2.1.4.1.3,实时数据库,81,SAP,2.1.4.1,工业大数据存储,9 +26,寄云科技,2.1.3.4,应用管理服务,97,General Electric,2.1.3,工业物联网,9 +13,东方国信,1.2.2,标识解析,126,华为,1.2,工业互联网网络,9 +26,寄云科技,2.1.3.5,容器服务,73,FANUC,2.1.3,工业物联网,9 +26,寄云科技,2.1.3.5,容器服务,74,HoneyWell,2.1.3,工业物联网,9 +135,浪潮,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,9 +13,东方国信,2.1.3.1,物联网服务,106,阿里巴巴,2.1.3,工业物联网,9 +135,浪潮,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,9 +3,艾克斯特,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,9 +125,华数机器人,1.2.3,数据互通,126,华为,1.2,工业互联网网络,9 +11,北信源,1.4.4.2,密钥管理,170,Pseudo1,1,供给,9 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+23,和利时,2.3.3,协议转换,155,小米,2.3,边缘层,9 +78,OutSystems,2.1.1.4,组态建模工具,106,阿里巴巴,2.1.1,开发工具,9 +37,绿盟,1.4.2.2,工控主机卫士,170,Pseudo1,1,供给,9 +37,绿盟,1.4.2.2,工控主机卫士,126,华为,1.4,工业互联网安全,9 +79,PTC,1.3.1.1,计算机辅助设计CAD,100,Synopsys,1.3.1,设计研发,9 +143,沈阳自动化研究所,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,9 +79,PTC,1.3.1.1,计算机辅助设计CAD,85,Dassault,1.3.1,设计研发,9 +79,PTC,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,9 +25,华大九天,1.3.1.7,电子设计自动化EDA,93,Cadence,1.3.1,设计研发,9 +143,沈阳自动化研究所,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,9 +26,寄云科技,2.1.3.1,物联网服务,126,华为,2.1.3,工业物联网,9 +79,PTC,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,9 +79,PTC,2.1.3.7,制造类API,97,General Electric,2.1.3,工业物联网,9 +143,沈阳自动化研究所,2.1.1.1,算法建模工具,80,Salesforce,2.1.1,开发工具,9 +33,蓝谷信息,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,9 +95,Schneider,2.3,边缘层,98,Microsoft Azure,2,工业互联网平台,9 +66,中国联通,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,9 +13,东方国信,2.3.1,工业数据接入,155,小米,2.3,边缘层,9 +6,安世亚太,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,9 +59,优特捷,1.4.2.5,安全日志与审计,170,Pseudo1,1,供给,9 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+3,艾克斯特,1.3.1.4,计算机辅助工艺过程设计CAPP,85,Dassault,1.3.1,设计研发,6 +60,宇动源,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,6 +135,浪潮,2.1.3.1,物联网服务,97,General Electric,2.1.3,工业物联网,6 +79,PTC,2.3.2,边缘数据处理,155,小米,2.3,边缘层,6 +79,PTC,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,6 +13,东方国信,2.1.3.3,工业引擎服务,97,General Electric,2.1.3,工业物联网,6 +13,东方国信,2.1.3.3,工业引擎服务,74,HoneyWell,2.1.3,工业物联网,6 +79,PTC,2.3.1,工业数据接入,84,Bosch,2.3,边缘层,6 +135,浪潮,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,6 +108,百度,2.2,IaaS,98,Microsoft Azure,2,工业互联网平台,6 +79,PTC,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,6 +60,宇动源,2.1.1.4,组态建模工具,80,Salesforce,2.1.1,开发工具,6 +136,美的,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,6 +136,美的,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,6 +137,美林数据,2.1.4.1.2,分布式数据库,81,SAP,2.1.4.1,工业大数据存储,6 +138,启明信息,1.3.1.5,产品数据管理PDM,29,京东工业品,1.3,工业软件,6 +129,华中数控,1.2.3,数据互通,126,华为,1.2,工业互联网网络,6 +129,华中数控,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,6 +129,华中数控,1.1.2,工业控制器,86,Dell EMC,1.1,工业自动化,6 +79,PTC,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,6 +104,Infineon,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,6 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+22,航天云网,2.1.3.4,应用管理服务,108,百度,2.1.3,工业物联网,6 +26,寄云科技,2.1.3.6,微服务,97,General Electric,2.1.3,工业物联网,6 +32,兰光创新,1.2.3,数据互通,126,华为,1.2,工业互联网网络,6 +168,中控技术,2.3.1,工业数据接入,126,华为,2.3,边缘层,6 +3,艾克斯特,1.3.1.6,产品生命周期管理PLM,39,Autodesk,1.3.1,设计研发,6 +49,数码大方,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,6 +26,寄云科技,2.1.3.3,工业引擎服务,108,百度,2.1.3,工业物联网,6 +43,神舟软件,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,6 +22,航天云网,2.1.3.6,微服务,74,HoneyWell,2.1.3,工业物联网,6 +49,数码大方,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,6 +49,数码大方,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,6 +22,航天云网,2.3.2,边缘数据处理,124,海尔,2.3,边缘层,6 +33,蓝谷信息,2.1.2.1,数据算法模型,84,Bosch,2.1.2,工业模型库,6 +168,中控技术,1.3.3.1,制造执行系统MES,97,General Electric,1.3.3,生产制造,6 +117,格创东智,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,6 +168,中控技术,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,6 +12,大唐软件,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,6 +22,航天云网,2.3.3,协议转换,155,小米,2.3,边缘层,6 +12,大唐软件,1.2.1,网络互联,126,华为,1.2,工业互联网网络,6 +26,寄云科技,2.1.3.4,应用管理服务,73,FANUC,2.1.3,工业物联网,6 +26,寄云科技,2.1.3.5,容器服务,126,华为,2.1.3,工业物联网,6 +3,艾克斯特,1.3.4.1,企业资源计划ERP,77,Oracle,1.3.4,企业运营管理,6 +126,华为,2.3,边缘层,102,Amazon AWS,2,工业互联网平台,6 +47,首自信,2.1.3.6,微服务,73,FANUC,2.1.3,工业物联网,6 +26,寄云科技,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,6 +47,首自信,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,6 +2,706所,1.1.3,工业服务器,105,Intel,1.1,工业自动化,6 +47,首自信,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,6 +3,艾克斯特,1.3.4.1,企业资源计划ERP,81,SAP,1.3.4,企业运营管理,6 +169,中芯国际,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,6 +49,数码大方,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,6 +30,可信华泰,1.4.2.6,隐私计算,170,Pseudo1,1,供给,6 +47,首自信,2.1.1.3,流程开发工具,85,Dassault,2.1.1,开发工具,6 +49,数码大方,1.3.1.6,产品生命周期管理PLM,99,Siemens,1.3.1,设计研发,6 +22,航天云网,1.2.2,标识解析,126,华为,1.2,工业互联网网络,6 +168,中控技术,2.3.3,协议转换,84,Bosch,2.3,边缘层,6 +168,中控技术,2.3.3,协议转换,124,海尔,2.3,边缘层,6 +49,数码大方,1.3.1.4,计算机辅助工艺过程设计CAPP,99,Siemens,1.3.1,设计研发,6 +22,航天云网,2.1.1.1,算法建模工具,106,阿里巴巴,2.1.1,开发工具,6 +46,适创科技,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,6 +31,昆仑数据,1.3.3.3,数据采集与监视控制系统SCADA,99,Siemens,1.3.3,生产制造,6 +22,航天云网,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,6 +22,航天云网,2.1.1.3,流程开发工具,106,阿里巴巴,2.1.1,开发工具,6 +23,和利时,1.1.2,工业控制器,106,阿里巴巴,1.1,工业自动化,6 +30,可信华泰,1.4.2.6,隐私计算,126,华为,1.4,工业互联网安全,6 +123,海得控制,1.1.2,工业控制器,126,华为,1.1,工业自动化,6 +26,寄云科技,2.1.3.2,平台基础服务,108,百度,2.1.3,工业物联网,6 +23,和利时,2.3.2,边缘数据处理,126,华为,2.3,边缘层,6 +33,蓝谷信息,2.1.2.3,研发仿真模型,81,SAP,2.1.2,工业模型库,6 +23,和利时,1.3.3.3,数据采集与监视控制系统SCADA,75,IBM,1.3.3,生产制造,6 +38,牛刀,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,6 +23,和利时,2.3.3,协议转换,126,华为,2.3,边缘层,6 +168,中控技术,1.1.2,工业控制器,106,阿里巴巴,1.1,工业自动化,6 +33,蓝谷信息,2.1.2.2,业务流程模型,81,SAP,2.1.2,工业模型库,6 +23,和利时,2.1.3.6,微服务,106,阿里巴巴,2.1.3,工业物联网,6 +33,蓝谷信息,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,6 +23,和利时,2.1.3.6,微服务,108,百度,2.1.3,工业物联网,6 +26,寄云科技,2.1.3.2,平台基础服务,148,腾讯,2.1.3,工业物联网,6 +26,寄云科技,2.1.3.2,平台基础服务,74,HoneyWell,2.1.3,工业物联网,6 +167,中环股份,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,6 +36,龙芯中科,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,6 +36,龙芯中科,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,6 +38,牛刀,2.1.1.2,低代码开发工具,85,Dassault,2.1.1,开发工具,6 +23,和利时,2.3.1,工业数据接入,155,小米,2.3,边缘层,6 +23,和利时,2.3.2,边缘数据处理,155,小米,2.3,边缘层,5 +79,PTC,2.3.2,边缘数据处理,99,Siemens,2.3,边缘层,5 +22,航天云网,2.1.1.2,低代码开发工具,106,阿里巴巴,2.1.1,开发工具,5 +89,Rockwell,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,5 +23,和利时,2.3.1,工业数据接入,95,Schneider,2.3,边缘层,5 +68,中望软件,1.3.1.2,计算机辅助工程CAE,85,Dassault,1.3.1,设计研发,5 +23,和利时,2.3.1,工业数据接入,99,Siemens,2.3,边缘层,5 +45,石化盈科,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,5 +68,中望软件,1.3.1.3,计算机辅助制造CAM,106,阿里巴巴,1.3,工业软件,5 +68,中望软件,1.3.1.3,计算机辅助制造CAM,29,京东工业品,1.3,工业软件,5 +112,东华测试,1.3.3.7,故障预测与健康管理PHM,29,京东工业品,1.3,工业软件,5 +135,浪潮,1.1.3,工业服务器,86,Dell EMC,1.1,工业自动化,5 +150,唯捷创芯,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,5 +66,中国联通,1.2.1,网络互联,126,华为,1.2,工业互联网网络,5 +23,和利时,2.3.3,协议转换,84,Bosch,2.3,边缘层,5 +82,Uptake,2.1.2.4,行业机理模型,81,SAP,2.1.2,工业模型库,5 +35,凌昊智能,1.1.3,工业服务器,94,Mitsubishi,1.1,工业自动化,5 +62,云道智造,1.3.1.2,计算机辅助工程CAE,39,Autodesk,1.3.1,设计研发,5 +62,云道智造,1.3.1.2,计算机辅助工程CAE,99,Siemens,1.3.1,设计研发,5 +82,Uptake,2.1.2.2,业务流程模型,58,用友,2.1.2,工业模型库,5 +2,706所,1.1.3,工业服务器,126,华为,1.1,工业自动化,5 +47,首自信,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,5 +24,华大电子,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,5 +47,首自信,2.1.2.3,研发仿真模型,159,徐工集团,2.1.2,工业模型库,5 +131,九物互联,2.1.1.5,数字孪生建模工具,80,Salesforce,2.1.1,开发工具,5 +47,首自信,2.1.2.2,业务流程模型,84,Bosch,2.1.2,工业模型库,5 +20,海基科技,1.3.1.2,计算机辅助工程CAE,93,Cadence,1.3.1,设计研发,5 +33,蓝谷信息,2.1.2.3,研发仿真模型,84,Bosch,2.1.2,工业模型库,5 +62,云道智造,2.1.2.4,行业机理模型,159,徐工集团,2.1.2,工业模型库,5 +132,科远智慧,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,5 +85,Dassault,1.3.1,设计研发,170,Pseudo1,1,供给,5 +150,唯捷创芯,1.1.1,工业计算芯片,94,Mitsubishi,1.1,工业自动化,5 +64,中电智科,1.1.2,工业控制器,94,Mitsubishi,1.1,工业自动化,5 +26,寄云科技,2.1.3.6,微服务,126,华为,2.1.3,工业物联网,5 +69,紫光集团,1.1.1,工业计算芯片,106,阿里巴巴,1.1,工业自动化,5 +134,朗坤智慧,1.3.3.5,企业资产管理系统EAM,106,阿里巴巴,1.3,工业软件,5 +149,天泽智云,2.1.2.3,研发仿真模型,58,用友,2.1.2,工业模型库,5 +31,昆仑数据,2.1.4.2.2,数据安全管理,79,PTC,2.1.4.2,工业大数据管理,5 +69,紫光集团,1.1.1,工业计算芯片,86,Dell EMC,1.1,工业自动化,5 +22,航天云网,2.3.2,边缘数据处理,95,Schneider,2.3,边缘层,5 +78,OutSystems,2.1.1.4,组态建模工具,85,Dassault,2.1.1,开发工具,5 +78,OutSystems,2.1.1.5,数字孪生建模工具,106,阿里巴巴,2.1.1,开发工具,5 +78,OutSystems,2.1.1.5,数字孪生建模工具,85,Dassault,2.1.1,开发工具,5 +79,PTC,2.1.3.6,微服务,148,腾讯,2.1.3,工业物联网,5 +42,山大华天,1.3.1.1,计算机辅助设计CAD,93,Cadence,1.3.1,设计研发,5 +143,沈阳自动化研究所,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,5 +23,和利时,1.3.3.2,分布式控制系统DCS,75,IBM,1.3.3,生产制造,5 +22,航天云网,2.3.2,边缘数据处理,155,小米,2.3,边缘层,5 +143,沈阳自动化研究所,2.1.1.2,低代码开发工具,148,腾讯,2.1.1,开发工具,5 +38,牛刀,2.1.1.3,流程开发工具,106,阿里巴巴,2.1.1,开发工具,5 +89,Rockwell,1.2.1,网络互联,97,General Electric,1.2,工业互联网网络,5 +89,Rockwell,1.2.1,网络互联,67,中国移动,1.2,工业互联网网络,5 +137,美林数据,2.1.4.2.1,数据质量管理,81,SAP,2.1.4.2,工业大数据管理,5 +138,启明信息,1.3.1.5,产品数据管理PDM,106,阿里巴巴,1.3,工业软件,5 +89,Rockwell,1.1.2,工业控制器,126,华为,1.1,工业自动化,5 +22,航天云网,2.3.3,协议转换,84,Bosch,2.3,边缘层,5 +141,上海新华控制,1.3.3.2,分布式控制系统DCS,99,Siemens,1.3.3,生产制造,5 +14,东华软件,1.3.4.3,人力资源管理HRM,106,阿里巴巴,1.3,工业软件,5 +78,OutSystems,2.1.1.4,组态建模工具,148,腾讯,2.1.1,开发工具,5 +22,航天云网,2.1.4.1.4,时序数据库,81,SAP,2.1.4.1,工业大数据存储,5 +154,西格数据,2.1.4.1.4,时序数据库,79,PTC,2.1.4.1,工业大数据存储,5 +53,天融信,1.4.3.5,负载均衡,126,华为,1.4,工业互联网安全,5 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+21,Hexagon,1.3.1.3,计算机辅助制造CAM,29,京东工业品,1.3,工业软件,1 +82,Uptake,2.1.2.3,研发仿真模型,99,Siemens,2.1,PaaS,1 +152,卫士通,1.4.4.1,身份鉴别与访问控制,170,Pseudo1,1,供给,1 +22,航天云网,1.2.1,网络互联,106,阿里巴巴,1.2,工业互联网网络,1 +65,中国电信,1.2.1,网络互联,126,华为,1.2,工业互联网网络,1 +65,中国电信,1.2.1,网络互联,170,Pseudo1,1,供给,1 +15,东软集团,1.3.3.5,企业资产管理系统EAM,170,Pseudo1,1,供给,1 +15,东软集团,1.3.3.5,企业资产管理系统EAM,106,阿里巴巴,1.3,工业软件,1 +46,适创科技,1.3.1.2,计算机辅助工程CAE,29,京东工业品,1.3,工业软件,1 +68,中望软件,1.3.1.3,计算机辅助制造CAM,170,Pseudo1,1,供给,1 +45,石化盈科,2.1.4.1.1,关系型数据库,84,Bosch,2.1.4,工业大数据,1 +70,ABB,1.3.3.4,可编程逻揖控制系统PLC,29,京东工业品,1.3,工业软件,1 +45,石化盈科,2.1.4.1.1,关系型数据库,115,富士康,2.1.4,工业大数据,1 +158,信大捷安,1.4.4.1,身份鉴别与访问控制,170,Pseudo1,1,供给,1 +127,华为海思,1.1.3,工业服务器,170,Pseudo1,1,供给,1 +16,东土科技,1.1.3,工业服务器,170,Pseudo1,1,供给,1 +49,数码大方,1.3.1.6,产品生命周期管理PLM,29,京东工业品,1.3,工业软件,1 +167,中环股份,1.1.1,工业计算芯片,105,Intel,1.1,工业自动化,1 +54,网御星云,1.4.2.4,安全隔离与信息交换系统,126,华为,1.4,工业互联网安全,1 +54,网御星云,1.4.2.4,安全隔离与信息交换系统,170,Pseudo1,1,供给,1 +55,威努特,1.4.1.2,下一代防火墙,126,华为,1.4,工业互联网安全,1 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b/output_result/risk/count_dcp_prod_network.png differ diff --git a/output_result/risk/count_firm.csv b/output_result/risk/count_firm.csv new file mode 100644 index 0000000..a316880 --- /dev/null +++ b/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 diff --git a/output_result/risk/count_firm_prod.csv b/output_result/risk/count_firm_prod.csv new file mode 100644 index 0000000..e62dafc --- /dev/null +++ b/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 +159,徐工集团,2.1.2,工业模型库,254 +84,Bosch,2.1.2,工业模型库,237 +77,Oracle,1.3.4,企业运营管理,222 +98,Microsoft Azure,2,工业互联网平台,211 +97,General Electric,1.2,工业互联网网络,209 +80,Salesforce,1.3.4,企业运营管理,205 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+55,威努特,1.4.4.4,工业应用行为监控,44 +53,天融信,1.4.4.4,工业应用行为监控,44 +135,浪潮,2.1.3.7,制造类API,44 +27,江南天安,1.4.4.2,密钥管理,44 +37,绿盟,1.4.4.3,接入认证,44 +54,网御星云,1.4.4.3,接入认证,44 +11,北信源,1.4.4.2,密钥管理,44 +90,Mentor Graphics,1.3.1.7,电子设计自动化EDA,43 +76,MasterCAM,1.3.1.3,计算机辅助制造CAM,43 +135,浪潮,2.1.3.5,容器服务,43 +135,浪潮,2.1.3.3,工业引擎服务,43 +68,中望软件,1.3.1.3,计算机辅助制造CAM,43 +135,浪潮,2.1.3.1,物联网服务,43 +152,卫士通,1.4.4.1,身份鉴别与访问控制,43 +13,东方国信,2.1.3.7,制造类API,43 +79,PTC,2.1.3.3,工业引擎服务,43 +22,航天云网,2.1.3.4,应用管理服务,43 +50,索为系统,1.3.1.5,产品数据管理PDM,43 +79,PTC,2.1.3.7,制造类API,42 +135,浪潮,2.1.3.4,应用管理服务,42 +13,东方国信,2.1.3.1,物联网服务,42 +22,航天云网,2.1.3.7,制造类API,42 +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 +22,航天云网,2.1.3.2,平台基础服务,42 +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 +13,东方国信,2.1.3.3,工业引擎服务,41 +130,金蝶,1.3.4.3,人力资源管理HRM,41 +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 +14,东华软件,1.3.4.3,人力资源管理HRM,41 +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 +5,安华金和,1.4.5.5,敏感数据发现与监控,39 +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 +139,容知日新,1.3.3.7,故障预测与健康管理PHM,39 +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 +13,东方国信,2.1.3.6,微服务,38 +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 +41,启明星辰,1.4.3.4,攻击溯源,37 +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 +16,东土科技,2.3.2,边缘数据处理,34 +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 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a/output_result/risk/g_firm_sample_id_1_de.png b/output_result/risk/g_firm_sample_id_1_de.png new file mode 100644 index 0000000..de74bae Binary files /dev/null and b/output_result/risk/g_firm_sample_id_1_de.png differ diff --git a/product.py b/product.py new file mode 100644 index 0000000..78d33fa --- /dev/null +++ b/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]) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d3a9f65 --- /dev/null +++ b/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 diff --git a/requirements_manual_selected_20230304.txt b/requirements_manual_selected_20230304.txt new file mode 100644 index 0000000..91f2ba8 --- /dev/null +++ b/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 diff --git a/risk_analysis_count.py b/risk_analysis_count.py new file mode 100644 index 0000000..60f6caf --- /dev/null +++ b/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()) diff --git a/risk_analysis_firm_network.py b/risk_analysis_firm_network.py new file mode 100644 index 0000000..80d1cae --- /dev/null +++ b/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() diff --git a/risk_analysis_prod_network.py b/risk_analysis_prod_network.py new file mode 100644 index 0000000..1f37ee1 --- /dev/null +++ b/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() diff --git a/risk_analysis_sum_result.py b/risk_analysis_sum_result.py new file mode 100644 index 0000000..4e0fa8d --- /dev/null +++ b/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)