From 8b231696f6e0f5cd9ed31a1616cff0fefad7a327 Mon Sep 17 00:00:00 2001 From: Cricial <2911646453@qq.com> Date: Fri, 20 Sep 2024 09:26:39 +0800 Subject: [PATCH] =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E9=80=BB=E8=BE=91=E4=BF=AE?= =?UTF-8?q?=E6=94=B9=E5=AE=8C=E6=88=90=EF=BC=8C=E6=B2=A1=E6=9C=89=E5=86=85?= =?UTF-8?q?=E5=AE=B9=E7=9A=84=E8=BE=93=E5=87=BA=20=E5=92=8C=E5=AF=B9?= =?UTF-8?q?=E4=BA=8E=20=E6=95=B0=E6=8D=AE=E5=BA=93=E7=9A=84=E8=B0=83?= =?UTF-8?q?=E6=95=B4=20=E4=B9=9F=E6=B2=A1=E6=9C=89=E8=B7=91=E4=BB=A3?= =?UTF-8?q?=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .idea/csv-editor.xml | 70 ++++ __pycache__/controller_db.cpython-38.pyc | Bin 8257 -> 8317 bytes __pycache__/firm.cpython-38.pyc | Bin 7799 -> 8462 bytes __pycache__/my_model.cpython-38.pyc | Bin 10329 -> 10789 bytes __pycache__/product.cpython-38.pyc | Bin 1446 -> 1331 bytes controller_db.py | 13 +- firm.py | 124 +++++- input_data/Firm_amended.csv | 2 +- input_data/firm_industry_relation.csv | 476 ++++++++++++++++++++++ input_data/xv_with_exp.csv | 2 +- input_data/测试 BomNodes.csv | 108 +++++ input_data/测试 Firm_amended 170.csv | 171 ++++++++ my_model.py | 191 ++++++++- product.py | 12 +- 测试数据 产业 数据.py | 43 ++ 测试数据 产业-原材料消耗-产品生产量.py | 42 ++ 测试数据 BomNodes.csv.py | 15 + 测试数据 Firm_amended.csv.py | 36 ++ 测试数据 companies_devices.csv | 11 + 测试数据 companies_materials.csv | 11 + 测试数据 companies_products.csv | 11 + 测试数据 consumed_materials.csv | 24 ++ 测试数据 device_salvage_values.csv | 11 + 测试数据 material_device_product_ids.csv | 101 +++++ 测试数据 produced_products.csv | 22 + 测试数据 products_materials_equipment.csv | 101 +++++ 测试数据 产品通编码.py | 25 ++ 测试数据 材料设备产品.py | 42 ++ 测试数据 设备id和设备残值.py | 25 ++ 测试数据_firm_industry_relation.csv.py | 17 + 30 files changed, 1671 insertions(+), 35 deletions(-) create mode 100644 input_data/firm_industry_relation.csv create mode 100644 input_data/测试 BomNodes.csv create mode 100644 input_data/测试 Firm_amended 170.csv create mode 100644 测试数据 产业 数据.py create mode 100644 测试数据 产业-原材料消耗-产品生产量.py create mode 100644 测试数据 BomNodes.csv.py create mode 100644 测试数据 Firm_amended.csv.py create mode 100644 测试数据 companies_devices.csv create mode 100644 测试数据 companies_materials.csv create mode 100644 测试数据 companies_products.csv create mode 100644 测试数据 consumed_materials.csv create mode 100644 测试数据 device_salvage_values.csv create mode 100644 测试数据 material_device_product_ids.csv create mode 100644 测试数据 produced_products.csv create mode 100644 测试数据 products_materials_equipment.csv create mode 100644 测试数据 产品通编码.py create mode 100644 测试数据 材料设备产品.py create mode 100644 测试数据 设备id和设备残值.py create mode 100644 测试数据_firm_industry_relation.csv.py diff --git a/.idea/csv-editor.xml b/.idea/csv-editor.xml index c775f95..f5b85e2 100644 --- a/.idea/csv-editor.xml +++ b/.idea/csv-editor.xml @@ -24,6 +24,13 @@ + + + + + + @@ -52,6 +59,69 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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lst_firm_size = [firm.size_stat[-1][0] for firm in lst_firm_material_connect] + lst_prob = [size / sum(lst_firm_size) for size in lst_firm_size] + select_alt_supply = self.random.choices(lst_firm_material_connect, weights=lst_prob)[0] + else: + select_alt_supply = self.random.choice(lst_firm_material_connect) + return select_alt_supply + + def seek_machinery_supply(self, machinery_type): + lst_firm_machinery_connect = [] # 符合条件 可选择的上游 + upper_i_machinery = [] # 特定 资源的上游 企业集合 + for firm in self.upper_i: + for sub_list in firm.R: + if sub_list[0] == machinery_type: + upper_i_machinery.append(firm) + # 没有 上游 没有 材料的情况，也就是紊乱的情况 + # if len(upper_i_machinery)==0: + + if self.is_prf_conn: + for firm in upper_i_machinery: + if self.firm_network.has_edge(self.unique_id, firm.unique_id) or self.firm_network.has_edge( + firm.unique_id, self.unique_id): + lst_firm_machinery_connect.append(firm) + if len(lst_firm_machinery_connect) == 0: + if self.is_prf_size: + lst_size = [firm.size_stat[-1][0] for firm in upper_i_machinery] + lst_prob = [size / sum(lst_size) for size in lst_size] + select_alt_supply = \ + self.random.choices(upper_i_machinery, weights=lst_prob)[0] + else: + select_alt_supply = self.random.choice(upper_i_machinery) + elif len(lst_firm_machinery_connect) > 0: + if self.is_prf_size: + lst_firm_size = [firm.size_stat[-1][0] for firm in lst_firm_machinery_connect] + lst_prob = [size / sum(lst_firm_size) for size in lst_firm_size] + select_alt_supply = self.random.choices(lst_firm_machinery_connect, weights=lst_prob)[0] + else: + select_alt_supply = self.random.choice(lst_firm_machinery_connect) + return select_alt_supply + + def handle_material_request(self, material_list): + for list in self.P: + if list[0] == material_list[0]: + list[1] -= material_list[1] + + def handle_machinery_request(self, machinery_list): + for list in self.C: + if list[0] == machinery_list[0]: + list[1] -= machinery_list[1] + + def refresh_R(self): + self.R1[self.model.t] = self.R + + def refresh_C(self): + self.C1[self.model.t] = self.C + + def refresh_P(self): + self.P1[self.model.t] = self.P + def clean_before_trial(self): self.dct_request_prod_from_firm = {} diff --git a/input_data/Firm_amended.csv b/input_data/Firm_amended.csv index 9561f52..55bac16 100644 --- a/input_data/Firm_amended.csv +++ b/input_data/Firm_amended.csv @@ -169,4 +169,4 @@ 167,SZ,2021,78220000000,24.43927396,41100000000,L,9.500843462,13371,qichacha,Non_Beijing,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 168,SH,2021,10350000000,22.23155657,4519000000,L,8.528528701,5057,qichacha,Non_Beijing,,,,1,,,,,,,,,,,,,,,,,,1,1,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,1 169,SH,2021,2.30233E+11,24.27012079,34704000000,L,8.55120807,5173,qichacha,Non_Beijing,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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+14,1.3.1.4,3,计算机辅助工艺过程设计CAPP,0 +15,1.3.1.5,3,产品数据管理PDM,1 +16,1.3.1.6,3,产品生命周期管理PLM,1 +17,1.3.1.7,3,电子设计自动化EDA,1 +18,1.3.2,2,采购供应软件,1 +19,1.3.2.1,3,供应链管理SCM,1 +20,1.3.3,2,生产制造软件,1 +21,1.3.3.1,3,制造执行系统MES,1 +22,1.3.3.2,3,分布式控制系统DCS,0 +23,1.3.3.3,3,数据采集与监视控制系统SCADA,1 +24,1.3.3.4,3,可编程逻揖控制系统PLC,1 +25,1.3.3.5,3,企业资产管理系统EAM,1 +26,1.3.3.6,3,运维保障系统MRO,1 +27,1.3.3.7,3,故障预测与健康管理PHM,1 +28,1.3.4,2,企业运营管理软件,1 +29,1.3.4.1,3,企业资源计划ERP,1 +30,1.3.4.2,3,客户关系管理CRM,1 +31,1.3.4.3,3,人力资源管理HRM,1 +32,1.3.5,2,仓储物流软件,0 +33,1.3.5.1,3,仓储物流管理WMS,1 +34,1.4,1,工业互联网安全管理,1 +35,1.4.1,2,设备安全,1 +36,1.4.1.1,3,工业防火墙,1 +37,1.4.1.2,3,下一代防火墙,0 +38,1.4.1.3,3,防毒墙,1 +39,1.4.1.4,3,入侵检测系统,1 +40,1.4.1.5,3,统一威胁管理系统,1 +41,1.4.2,2,控制安全,1 +42,1.4.2.1,3,工控安全监测与审计,1 +43,1.4.2.2,3,工控主机卫士,1 +44,1.4.2.3,3,工控漏洞扫描,0 +45,1.4.2.4,3,安全隔离与信息交换系统,1 +46,1.4.2.5,3,安全日志与审计,1 +47,1.4.2.6,3,隐私计算,1 +48,1.4.2.7,3,工控原生安全,1 +49,1.4.3,2,网络安全,1 +50,1.4.3.1,3,网络漏洞扫描和补丁管理,0 +51,1.4.3.2,3,流量检测,1 +52,1.4.3.3,3,APT检测,0 +53,1.4.3.4,3,攻击溯源,0 +54,1.4.3.5,3,负载均衡,1 +55,1.4.3.6,3,沙箱类设备,0 +56,1.4.4,2,平台安全,1 +57,1.4.4.1,3,身份鉴别与访问控制,0 +58,1.4.4.2,3,密钥管理,0 +59,1.4.4.3,3,接入认证,0 +60,1.4.4.4,3,工业应用行为监控,1 +61,1.4.4.5,3,安全态势感知,0 +62,1.4.5,2,数据安全,0 +63,1.4.5.1,3,恶意代码检测系统,1 +64,1.4.5.2,3,数据防泄漏系统,1 +65,1.4.5.3,3,数据审计系统,0 +66,1.4.5.4,3,数据脱敏,1 +67,1.4.5.5,3,敏感数据发现与监控,0 +68,1.4.5.6,3,数据容灾备份,0 +69,1.4.5.7,3,数据恢复,1 +70,1.4.5.8,3,数据加密,1 +71,1.4.5.9,3,数据防火墙,0 +72,2,0,工业互联网平台,1 +73,2.1,1,PaaS,0 +74,2.1.1,2,开发工具,1 +75,2.1.1.1,3,算法建模工具,1 +76,2.1.1.2,3,低代码开发工具,0 +77,2.1.1.3,3,流程开发工具,1 +78,2.1.1.4,3,组态建模工具,1 +79,2.1.1.5,3,数字孪生建模工具,1 +80,2.1.2,2,工业模型库,1 +81,2.1.2.1,3,数据算法模型,0 +82,2.1.2.2,3,业务流程模型,0 +83,2.1.2.3,3,研发仿真模型,0 +84,2.1.2.4,3,行业机理模型,1 +85,2.1.3,2,工业物联网,1 +86,2.1.3.1,3,物联网服务,0 +87,2.1.3.2,3,平台基础服务,1 +88,2.1.3.3,3,工业引擎服务,1 +89,2.1.3.4,3,应用管理服务,1 +90,2.1.3.5,3,容器服务,1 +91,2.1.3.6,3,微服务,1 +92,2.1.3.7,3,制造类API,0 +93,2.1.4,2,工业大数据,1 +94,2.1.4.1,3,工业大数据存储,1 +95,2.1.4.1.1,4,关系型数据库,0 +96,2.1.4.1.2,4,分布式数据库,1 +97,2.1.4.1.3,4,实时数据库,0 +98,2.1.4.1.4,4,时序数据库,0 +99,2.1.4.2,3,工业大数据管理,0 +100,2.1.4.2.1,4,数据质量管理,1 +101,2.1.4.2.2,4,数据安全管理,1 +102,2.2,1,IaaS,0 +103,2.3,1,边缘层,0 +104,2.3.1,2,工业数据接入,0 +105,2.3.2,2,边缘数据处理,0 +106,2.3.3,2,协议转换,1 diff --git a/input_data/测试 Firm_amended 170.csv b/input_data/测试 Firm_amended 170.csv new file mode 100644 index 0000000..a8bd955 --- /dev/null +++ b/input_data/测试 Firm_amended 170.csv @@ -0,0 +1,171 @@ +Code,Company Name,原材料,库存商品,设备数量,Revenue,Total Employees (People),Type_Region,Self-supply Business (Yes/No),Revenue_Log,production_output,demand_quantity +1,Company_1,284.02,982.67,452.15,29692.44,963,Suburban,Yes,10.298647746934053,204.215,481.402 +2,Company_2,591.75,232.7,597.47,37552.56,222,Urban,No,10.533496830634064,553.747,253.175 +3,Company_3,514.2,466.73,388.52,23557.62,355,Urban,No,10.067204613987071,227.852,377.42 +4,Company_4,893.84,633.71,580.73,89135.78,496,Urban,No,11.397916104118977,221.073,483.384 +5,Company_5,306.54,844.63,474.67,60818.82,117,Suburban,Yes,11.015654559530484,391.467,209.654 +6,Company_6,830.89,831.11,177.37,73695.09,279,Rural,No,11.207691454519859,372.737,473.089 +7,Company_7,483.95,603.67,603.02,73826.05,832,Rural,Yes,11.209466929335226,186.302,485.395 +8,Company_8,483.1,525.24,116.64,83568.26,242,Rural,Yes,11.333419061909991,437.664,500.31 +9,Company_9,958.73,267.31,682.18,36015.98,351,Suburban,No,10.491718007837608,433.217,460.873 +10,Company_10,946.82,215.02,393.99,26255.05,324,Suburban,No,10.175613630469309,472.399,381.682 +11,Company_11,454.76,689.55,232.49,84782.37,81,Suburban,Yes,11.3478428992222,243.249,260.476 +12,Company_12,323.83,177.09,624.04,26639.31,170,Suburban,No,10.19014322341799,321.404,148.382 +13,Company_13,425.17,396.05,274.2,31290.59,265,Rural,No,10.351072692349652,287.42,490.517 +14,Company_14,109.55,739.42,406.11,87814.36,788,Suburban,No,11.38298031978054,148.611,401.955 +15,Company_15,202.82,923.13,100.91,43238.15,487,Suburban,No,10.674478486379028,210.091,336.282 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+165,Company_165,785.54,427.37,589.4,84393.08,683,Suburban,No,11.343240686701314,288.94,373.554 +166,Company_166,992.29,576.28,173.55,43056.45,352,Rural,No,10.670267324417088,470.355,363.229 +167,Company_167,346.54,528.08,728.16,68761.38,309,Rural,Yes,11.138397529103418,195.816,262.654 +168,Company_168,523.22,482.95,699.79,58914.32,735,Rural,No,10.983839464028723,344.979,293.322 +169,Company_169,891.58,418.18,511.08,28763.31,576,Urban,Yes,10.26685589561593,178.108,286.158 +170,Company_170,712.0,906.86,583.05,16881.59,957,Urban,Yes,9.73397895802674,309.305,519.2 diff --git a/my_model.py b/my_model.py index 1792522..891cb67 100644 --- a/my_model.py +++ b/my_model.py @@ -14,7 +14,6 @@ from product import ProductAgent class MyModel(Model): def __init__(self, params): - # 属性 self.is_prf_size = params['prf_size'] self.prf_conn = params['prf_conn'] @@ -60,12 +59,15 @@ class MyModel(Model): self.is_prf_size = bool(params['prf_size']) self.remove_t = int(params['remove_t']) self.int_netw_prf_n = int(params['netw_prf_n']) + # 方法执行 self.initialize_product_network(params) self.initialize_firm_network() self.initialize_firm_product_network() self.add_edges_to_firm_network() self.connect_unconnected_nodes() + self.resource_integration() + self.j_comp_consumed_produced() self.initialize_agents() self.initialize_disruptions() @@ -74,18 +76,33 @@ class MyModel(Model): self.product_network = nx.adjacency_graph(json.loads(params['g_bom'])) except Exception as e: print(f"Failed to initialize product network: {e}") + # 赋予 产业的量 + # 产业种类 + data = pd.read_csv('测试数据 products_materials_equipment.csv') + self.type = data + # 设备c折旧比值 + device_salvage_values = pd.read_csv('测试数据 device_salvage_values.csv') + self.device_salvage_values = device_salvage_values def initialize_firm_network(self): # Read the firm data - firm = pd.read_csv("input_data/Firm_amended.csv") + + firm = pd.read_csv("input_data/测试 Firm_amended 170.csv") + firm['Code'] = firm['Code'].astype('string') firm.fillna(0, inplace=True) - firm_attr = firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]] + + firm_attr = firm.loc[:, ["Code", "Type_Region", "Revenue_Log", "原材料", "设备数量", "库存商品"]] + + firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv") + firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string') 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 + + grouped = firm_industry_relation.groupby('Firm_Code')['Product_Code'].apply(list) + firm_product.append(grouped) + + firm_attr['Product_Code'] = firm_attr['Code'].map(grouped) firm_attr.set_index('Code', inplace=True) self.G_Firm.add_nodes_from(firm["Code"]) @@ -97,21 +114,15 @@ class MyModel(Model): self.Firm = firm def initialize_firm_product_network(self): - """ Initialize the firm-product network """ - # Read the firm-product data - Firm_Prod = pd.read_csv("input_data/Firm_amended.csv") - Firm_Prod.fillna(0, inplace=True) - # Stack the firm-product relationships into a DataFrame - 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') + firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv") + firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string') + # 将 'firm_prod' 表中的每一行作为图中的节点 + self.G_FirmProd.add_nodes_from(firm_industry_relation.index) - self.G_FirmProd.add_nodes_from(firm_prod.index) - # Assign attributes to the firm-product nodes - firm_prod_labels_dict = {code: firm_prod.loc[code].to_dict() for code in firm_prod.index} + # 为每个节点分配属性 + firm_prod_labels_dict = {code: firm_industry_relation.loc[code].to_dict() for code in + firm_industry_relation.index} nx.set_node_attributes(self.G_FirmProd, firm_prod_labels_dict) def add_edges_to_firm_network(self): @@ -204,19 +215,52 @@ class MyModel(Model): def initialize_agents(self): """ Initialize agents and add them to the model. """ for ag_node, attr in self.product_network.nodes(data=True): - product = ProductAgent(ag_node, self, name=attr['Name']) + # 产业种类 + type2 = self.type.loc[ag_node, '种类'] + + device_salvage_values = self.type.loc[ag_node, '设备残值'] + + j_comp_data_consumed = self.data_consumed.loc[ag_node] + + j_comp_data_produced = self.data_consumed.loc[ag_node] + + product = ProductAgent(ag_node, self, name=attr['Name'], type2=type2, + device_salvage_values=device_salvage_values, + j_comp_data_consumed=j_comp_data_consumed, + j_comp_data_produced=j_comp_data_produced, ) + self.add_agent(product) # self.grid.place_agent(product, ag_node) - ##print(f"Product agent created: {product.name}, ID: {product.unique_id}") for ag_node, attr in self.firm_network.nodes(data=True): a_lst_product = [agent for agent in self.product_agents if agent.unique_id in attr['Product_Code']] + + n_equip_c = self.Firm.loc[ag_node, '设备数量'] + + demand_quantity = self.Firm.loc[ag_node, 'production_output'] + + production_output = self.Firm.loc[ag_node, 'demand_quantity'] + + c_price = self.Firm.loc[ag_node, 'c_price'] + + # 资源 资源库存信息 利用 firm_resource + R = self.firm_resource_R.loc[ag_node] + P = self.firm_resource_R.loc[ag_node] + C = self.firm_resource_R.loc[ag_node] + firm_agent = FirmAgent( ag_node, self, type_region=attr['Type_Region'], revenue_log=attr['Revenue_Log'], + n_equip_c=n_equip_c, a_lst_product=a_lst_product, + demand_quantity=demand_quantity, + production_output=production_output, + c_price=c_price, + R=R, + P=P, + C=C ) self.add_agent(firm_agent) @@ -258,6 +302,33 @@ class MyModel(Model): elif isinstance(agent, ProductAgent): self.product_agents.append(agent) + def resource_integration(self): + data_R = pd.read_csv("测试数据 companies_materials.csv") + data_C = pd.read_csv("测试数据 companies_devices.csv") + data_P = pd.read_csv("测试数据 companies_products.csv") + firm_resource_R = (data_R.groupby('Firm_Code')[['材料id', '材料数量']] + .apply(lambda x: x.values.tolist())) + firm_resource_C = (data_C.groupby('Firm_Code')[['材料id', '材料数量']] + .apply(lambda x: x.values.tolist())) + + firm_resource_P = (data_P.groupby('Firm_Code')[['材料id', '材料数量']] + .apply(lambda x: x.values.tolist())) + + self.firm_resource_R = firm_resource_R + self.firm_resource_C = firm_resource_C + self.firm_resource_P = firm_resource_P + + def j_comp_consumed_produced(self): + data_consumed = pd.read_csv('测试数据 consumed_materials.csv') + data_produced = pd.read_csv('测试数据 produced_products.csv') + data_consumed = (data_consumed.groupby('产业id')[['消耗材料id', '消耗材料数量']] + .apply(lambda x: x.values.tolist())) + data_produced = (data_produced.groupby('产业id')[['制造产品id', '制造产品数量']] + .apply(lambda x: x.values.tolist())) + + self.data_consumed = data_consumed + self.data_produced = data_produced + def step(self): # 1. Remove edge to customer and disrupt customer up product for firm in self.company_agents: @@ -303,5 +374,83 @@ class MyModel(Model): for firm in self.company_agents: firm.clean_before_trial() + # 3. 判断是否需要购买资源 判断是否需要购买机器 + purchase_material_firms = {} + purchase_machinery_firms = {} + material_list = [] + machinery_list = [] + list_seek_material_firm = [] # 每一个收到请求的企业 + list_seek_machinery_firm = [] # 每一个收到请求的企业 + for firm in self.company_agents: + # 资源 + for sub_list in firm.R: + if sub_list[1] <= firm.s_r: + required_material_quantity = firm.S_r - sub_list[1] + (material_list + .append([sub_list[0], required_material_quantity])) + purchase_material_firms[firm] = material_list + # 设备 + for sub_list in firm.C: + # 对于设备的required_machinery_quantity 要有所改变 根据残值而言！ 每一个周期固定减少残值值 x firm 里面定义 + sub_list[2] -= firm.x + if sub_list[2] <= 0: # 残值小于等于 0 时 + sub_list[1] -= 1 + required_machinery_quantity = firm.C1[0][1] - sub_list[1] # 补回原来的量 也就是 1 + (machinery_list + .append([sub_list[0], required_machinery_quantity])) + purchase_machinery_firms[firm] = machinery_list + + # 寻源并发送请求 决定是否接受供应 并更新 + + for material_firm, sub_list in purchase_material_firms: + for material_list in sub_list: + (list_seek_material_firm + .append(material_firm.seek_material_supply(material_list[0]))) + if len(list_seek_material_firm) != 0: + for seek_material_firm in list_seek_material_firm: + seek_material_firm.handle_material_request(material_list) # 更新产品 + for R_list in firm.R: + R_list[1] = firm.S_r + + for machinery_firm, sub_list in purchase_machinery_firms: + for machinery_list in sub_list: + (list_seek_machinery_firm + .append(machinery_firm.seek_machinery_supply(machinery_list[0]))) + if len(list_seek_machinery_firm) != 0: + for seek_machinery_firm in list_seek_machinery_firm: + seek_machinery_firm.handle_machinery_request(machinery_list) + for C_list, C0_list in zip(firm.C, firm.C0): + C_list[1] = C0_list[1] # 赋值回去 + C_list[2] = C0_list[2] + + # 消耗资源过程 + consumed_material = [] + for product in firm.a_lst_product: + for sub_list_data_consumed in product.j_comp_data_consumed: + consumed_material_id = sub_list_data_consumed[0] + consumed_material_num = sub_list_data_consumed[1] + consumed_material.append([consumed_material_id, consumed_material_num]) + for sub_list_consumed_material in consumed_material: + for sub_list_material in firm.R: + if sub_list_material[0] == sub_list_consumed_material[0]: + sub_list_material[1] = sub_list_material[1] - sub_list_consumed_material[1] + # 生产产品过程 + produced_products = [] + for product in firm.a_lst_product: + for sub_list_produced_products in product.j_comp_data_consumed: + produced_products_id = sub_list_produced_products[0] + produced_products_num = sub_list_produced_products[1] + produced_products.append([produced_products_id, produced_products_num]) + for sub_list_data_produced_products in produced_products: + for sub_list_products in firm.P: + if sub_list_products[0] == sub_list_data_produced_products[0]: + sub_list_products[1] = sub_list_products[1] - sub_list_data_produced_products[1] + # 刷新 R状态 + firm.refresh_R() + # 刷新 C状态 + firm.refresh_C() + # 刷新 P状态 + firm.refresh_P() + # Increment the time step self.t += 1 diff --git a/product.py b/product.py index 51181ed..8d32c77 100644 --- a/product.py +++ b/product.py @@ -1,13 +1,22 @@ from mesa import Agent + class ProductAgent(Agent): - def __init__(self, unique_id, model, name): + def __init__(self, unique_id, model, name, type2, device_salvage_values, j_comp_data_consumed, j_comp_data_produced): # 调用超类的 __init__ 方法 super().__init__(unique_id, model) # 初始化代理属性 self.name = name self.product_network = self.model.product_network + if type2 == 0: + self.is_equip = True + else: + self.is_mater = True + self.device_salvage_values = device_salvage_values + + self.j_comp_data_produced = j_comp_data_produced + self.j_comp_data_consumed = j_comp_data_consumed def a_successors(self): # 从 product_network 中找到当前代理的后继节点 @@ -22,4 +31,3 @@ class ProductAgent(Agent): # 通过 unique_id 查找前驱节点对应的代理对象，直接从 self.product_agents 列表中获取 return [agent for agent in self.model.product_agents if agent.unique_id in predecessors] - diff --git a/测试数据 产业 数据.py b/测试数据 产业 数据.py new file mode 100644 index 0000000..70db33a --- /dev/null +++ b/测试数据 产业 数据.py @@ -0,0 +1,43 @@ +import pandas as pd +import numpy as np + +# 设置随机种子 +np.random.seed(42) + +# 生成企业和设备数据 +num_rows = 10 # 每个表的行数 + +# 构造数据 +company_ids = np.random.randint(1000, 1100, size=num_rows) +device_ids = np.random.randint(100, 200, size=num_rows) +material_ids = np.random.randint(0, 100, size=num_rows) +product_ids = np.random.randint(0, 200, size=num_rows) + +device_quantities = np.random.randint(50, 200, size=num_rows) +material_quantities = np.random.randint(100,200, size=num_rows) +product_quantities = np.random.randint(20, 100, size=num_rows) + +# 创建三个表格的数据框 +df_devices = pd.DataFrame({ + '企业id': company_ids, + '设备id': device_ids, + '设备数量': device_quantities +}) + +df_materials = pd.DataFrame({ + '企业id': company_ids, + '材料id': material_ids, + '材料数量': material_quantities +}) + +df_products = pd.DataFrame({ + '企业id': company_ids, + '产品id': product_ids, + '产品数量': product_quantities +}) + +# 保存为CSV文件 +df_devices.to_csv('测试数据 companies_devices.csv', index=False) +df_materials.to_csv('测试数据 companies_materials.csv', index=False) +df_products.to_csv('测试数据 companies_products.csv', index=False) + diff --git a/测试数据 产业-原材料消耗-产品生产量.py b/测试数据 产业-原材料消耗-产品生产量.py new file mode 100644 index 0000000..930c2cb --- /dev/null +++ b/测试数据 产业-原材料消耗-产品生产量.py @@ -0,0 +1,42 @@ +import pandas as pd +import numpy as np + +# 设置随机种子，确保结果可重复 +np.random.seed(42) + +# 定义产业数量 +num_industries = 10 + +# 创建产业ID列表 +industry_ids = [i for i in range(0, num_industries + 1)] + +# 为每个产业生成随机的材料id、消耗量、产品id和制造量 +consumed_materials_data = [] +produced_products_data = [] + +for industry in industry_ids: + # 每个产业消耗的材料（生成1到3个随机材料ID和消耗量） + num_materials = np.random.randint(1, 4) + for _ in range(num_materials): + material_id = np.random.randint(0, 100) + consumption_quantity = np.random.randint(50, 500) + consumed_materials_data.append([industry, material_id, consumption_quantity]) + + # 每个产业制造的产品（生成1到3个随机产品ID和制造量） + num_products = np.random.randint(1, 4) + for _ in range(num_products): + product_id = np.random.randint(100, 201) + production_quantity = np.random.randint(100, 1000) + produced_products_data.append([industry, product_id, production_quantity]) + +# 创建两个数据框 +df_consumed_materials = pd.DataFrame(consumed_materials_data, columns=['产业ID', '消耗材料ID', '消耗量']) +df_produced_products = pd.DataFrame(produced_products_data, columns=['产业ID', '制造产品ID', '制造量']) + +# 保存两个数据框为CSV文件 +file_path_consumed = '测试数据 consumed_materials.csv' +file_path_produced = '测试数据 produced_products.csv' + +df_consumed_materials.to_csv(file_path_consumed, index=False) +df_produced_products.to_csv(file_path_produced, index=False) + diff --git a/测试数据 BomNodes.csv.py b/测试数据 BomNodes.csv.py new file mode 100644 index 0000000..95ddd87 --- /dev/null +++ b/测试数据 BomNodes.csv.py @@ -0,0 +1,15 @@ +import pandas as pd +import random +import numpy as np + +# 生成170条测试数据的函数 +data=pd.read_csv('input_data/BomNodes.csv') +# 生成数据 +df = pd.DataFrame(data) +df['产业种类'] = [random.choice([0, 1]) for _ in range(107)] + +# 显示前几行 +print(df.head()) + +# 保存数据到CSV文件 +df.to_csv('input_data/测试 BomNodes.csv', index=False) diff --git a/测试数据 Firm_amended.csv.py b/测试数据 Firm_amended.csv.py new file mode 100644 index 0000000..016c029 --- /dev/null +++ b/测试数据 Firm_amended.csv.py @@ -0,0 +1,36 @@ +import pandas as pd +import random +import numpy as np + + +# 生成170条测试数据的函数 +def generate_test_data(num_rows=170): + data = { + 'Company ID': [i for i in range(1, num_rows + 1)], # 生成1到170的公司ID + 'Company Name': [f'Company_{i}' for i in range(1, num_rows + 1)], # 生成公司名称 + '原材料': [round(random.uniform(100, 1000), 2) for _ in range(num_rows)], # 原材料 + '库存商品': [round(random.uniform(100, 1000), 2) for _ in range(num_rows)], # 库存商品 + '固定资产原值': [round(random.uniform(100, 1000), 2) for _ in range(num_rows)], # 固定资产原值 + 'Revenue': [round(random.uniform(10000, 100000), 2) for _ in range(num_rows)], # Revenue + 'Total Employees (People)': [random.randint(50, 1000) for _ in range(num_rows)], # 员工总数 + 'Type_Region': [random.choice(['Urban', 'Rural', 'Suburban']) for _ in range(num_rows)], # 区域类型 + 'Self-supply Business (Yes/No)': [random.choice(['Yes', 'No']) for _ in range(num_rows)] # 自营业务 + } + + df = pd.DataFrame(data) + + # 添加Revenue_Log列 + df['Revenue_Log'] = np.log(df['Revenue']) + df['production_output'] = df['固定资产原值'] / 10+np.random.randint(100, 500, size=len(df)) + df['demand_quantity'] = df['原材料'] / 10 +np.random.randint(100, 500, size=len(df)) + return df + + +# 生成数据 +df_test_data = generate_test_data() + +# 显示前几行 +print(df_test_data.head()) + +# 保存数据到CSV文件 +df_test_data.to_csv('input_data/测试 Firm_amended 170.csv', index=False) diff --git a/测试数据 companies_devices.csv b/测试数据 companies_devices.csv new file mode 100644 index 0000000..ae382cb --- /dev/null +++ b/测试数据 companies_devices.csv @@ -0,0 +1,11 @@ +企业id,设备id,设备数量 +1051,187,104 +1092,199,113 +1014,123,180 +1071,102,100 +1060,121,184 +1020,152,70 +1082,101,122 +1086,187,67 +1074,129,181 +1074,137,138 diff --git a/测试数据 companies_materials.csv b/测试数据 companies_materials.csv new file mode 100644 index 0000000..7b42404 --- /dev/null +++ b/测试数据 companies_materials.csv @@ -0,0 +1,11 @@ +企业id,材料id,材料数量 +1051,1,159 +1092,63,113 +1014,59,108 +1071,20,189 +1060,32,152 +1020,75,101 +1082,57,183 +1086,21,191 +1074,88,159 +1074,48,170 \ No newline at end of file diff --git a/测试数据 companies_products.csv b/测试数据 companies_products.csv new file mode 100644 index 0000000..725ee0b --- /dev/null +++ b/测试数据 companies_products.csv @@ -0,0 +1,11 @@ +企业id,产品id,产品数量 +1051,58,63 +1092,169,27 +1014,187,66 +1071,14,54 +1060,189,97 +1020,189,55 +1082,174,69 +1086,189,23 +1074,50,21 +1074,107,25 diff --git a/测试数据 consumed_materials.csv b/测试数据 consumed_materials.csv new file mode 100644 index 0000000..8348aef --- /dev/null +++ b/测试数据 consumed_materials.csv @@ -0,0 +1,24 @@ +产业ID,消耗材料ID,消耗量 +0,51,398 +0,14,156 +0,71,238 +1,74,137 +1,99,409 +1,23,180 +2,37,435 +2,63,493 +3,21,302 +3,88,98 +4,61,224 +4,61,100 +5,72,216 +6,8,395 +6,52,435 +7,80,469 +7,49,409 +8,62,451 +9,47,320 +9,71,264 +9,61,345 +10,52,329 +10,25,266 diff --git a/测试数据 device_salvage_values.csv b/测试数据 device_salvage_values.csv new file mode 100644 index 0000000..47d1118 --- /dev/null +++ b/测试数据 device_salvage_values.csv @@ -0,0 +1,11 @@ +设备id,设备残值 +151,97 +192,382 +114,109 +171,881 +160,673 +120,140 +182,671 +186,318 +174,779 +174,353 diff --git a/测试数据 material_device_product_ids.csv b/测试数据 material_device_product_ids.csv new file mode 100644 index 0000000..53a9fc6 --- /dev/null +++ b/测试数据 material_device_product_ids.csv @@ -0,0 +1,101 @@ +材料id,设备id,产品id +51,192,14 +71,160,20 +82,186,74 +74,187,116 +99,123,130 +21,152,1 +87,129,37 +1,163,187 +20,132,57 +21,188,48 +90,158,169 +91,159,14 +61,161,174 +61,150,107 +54,163,130 +50,106,20 +72,138,17 +3,188,59 +13,108,89 +52,101,83 +91,159,198 +43,107,174 +34,177,80 +35,149,103 +3,101,133 +53,103,190 +17,189,43 +33,173,189 +99,113,94 +47,114,199 +77,186,189 +39,184,81 +52,123,153 +88,159,123 +40,128,14 +44,164,88 +70,108,87 +0,107,62 +10,180,135 +34,134,32 +4,140,27 +6,172,71 +11,133,32 +47,122,61 +87,136,98 +43,185,34 +64,198,100 +46,177,130 +0,104,141 +26,108,14 +89,141,123 +76,150,62 +95,151,131 +93,200,150 +14,142,28 +35,112,159 +70,158,85 +27,165,169 +44,161,184 +5,127,27 +43,183,29 +61,174,127 +91,188,189 +96,100,120 +26,161,120 +76,102,197 +71,126,136 +61,136,50 +43,123,58 +31,195,179 +61,157,51 +11,138,129 +2,200,112 +55,180,186 +1,101,53 +86,200,128 +18,101,52 +43,189,159 +69,131,67 +54,174,183 +16,137,23 +68,197,138 +15,196,200 +58,169,92 +2,119,186 +35,118,89 +66,118,147 +95,170,51 +32,139,127 +38,181,103 +0,110,184 +88,149,150 +30,193,41 +98,106,143 +89,159,112 +1,100,47 +11,168,36 +31,108,98 +18,147,130 +19,123,53 diff --git a/测试数据 produced_products.csv b/测试数据 produced_products.csv new file mode 100644 index 0000000..8631620 --- /dev/null +++ b/测试数据 produced_products.csv @@ -0,0 +1,22 @@ +产业ID,制造产品ID,制造量 +0,182,314 +1,152,869 +1,187,591 +2,132,559 +3,158,610 +3,141,575 +3,159,882 +4,163,604 +4,102,584 +4,150,746 +5,103,700 +5,159,113 +6,159,554 +6,143,608 +6,107,134 +7,105,665 +7,103,921 +8,143,261 +8,173,369 +9,179,848 +10,140,256 diff --git a/测试数据 products_materials_equipment.csv b/测试数据 products_materials_equipment.csv new file mode 100644 index 0000000..0e5ae40 --- /dev/null +++ b/测试数据 products_materials_equipment.csv @@ -0,0 +1,101 @@ +产品id,种类 +1,材料 +2,材料 +3,材料 +4,材料 +5,材料 +6,材料 +7,材料 +8,材料 +9,材料 +10,材料 +11,材料 +12,材料 +13,材料 +14,材料 +15,材料 +16,材料 +17,材料 +18,材料 +19,材料 +20,材料 +21,材料 +22,材料 +23,材料 +24,材料 +25,材料 +26,材料 +27,材料 +28,材料 +29,材料 +30,材料 +31,材料 +32,材料 +33,材料 +34,材料 +35,材料 +36,材料 +37,材料 +38,材料 +39,材料 +40,材料 +41,材料 +42,材料 +43,材料 +44,材料 +45,材料 +46,材料 +47,材料 +48,材料 +49,材料 +50,材料 +51,材料 +52,材料 +53,材料 +54,材料 +55,材料 +56,材料 +57,材料 +58,材料 +59,材料 +60,材料 +61,材料 +62,设备 +63,设备 +64,设备 +65,设备 +66,设备 +67,设备 +68,设备 +69,设备 +70,设备 +71,设备 +72,设备 +73,设备 +74,设备 +75,设备 +76,设备 +77,设备 +78,设备 +79,设备 +80,设备 +81,设备 +82,设备 +83,设备 +84,设备 +85,设备 +86,设备 +87,设备 +88,设备 +89,设备 +90,设备 +91,设备 +92,设备 +93,设备 +94,设备 +95,设备 +96,设备 +97,设备 +98,设备 +99,设备 +100,设备 diff --git a/测试数据 产品通编码.py b/测试数据 产品通编码.py new file mode 100644 index 0000000..d7a4b31 --- /dev/null +++ b/测试数据 产品通编码.py @@ -0,0 +1,25 @@ +import pandas as pd +import numpy as np + +# 设置数据行数 +total_rows = 100 # 总共100行 +material_count = 61 # 前61行为材料 + +# 生成产品id +product_ids = np.arange(1, total_rows + 1) + +# 生成种类，前61行是材料，后面是设备 +categories = ['材料'] * material_count + ['设备'] * (total_rows - material_count) + +# 创建数据框 +df_products = pd.DataFrame({ + '产品id': product_ids, + '种类': categories +}) + +# 保存为CSV文件 +file_path_products = '测试数据 products_materials_equipment.csv' +df_products.to_csv(file_path_products, index=False) # index=False 不保存行索引 + +# 打印文件路径 +print(f"CSV 文件已生成，路径为: {file_path_products}") diff --git a/测试数据 材料设备产品.py b/测试数据 材料设备产品.py new file mode 100644 index 0000000..1172cbf --- /dev/null +++ b/测试数据 材料设备产品.py @@ -0,0 +1,42 @@ +import pandas as pd +import numpy as np + +# 设置随机种子，以便结果可重复 +np.random.seed(42) + +# 定义生成数据的行数 +num_rows = 100 # 生成 100 行数据 + +# 创建空列表来存储生成的ID +material_ids = [] +device_ids = [] +product_ids = [] + +# 生成材料、设备、产品的ID，确保同一行内的ID不重复 +for _ in range(num_rows): + mat_id = np.random.randint(0, 100) # 材料ID范围 0-99 + dev_id = np.random.randint(100, 201) # 设备ID范围 100-199 + + # 确保产品ID在当前行与材料ID和设备ID不重复 + while True: + prod_id = np.random.randint(0, 201) + if prod_id != mat_id and prod_id != dev_id: + break + + material_ids.append(mat_id) + device_ids.append(dev_id) + product_ids.append(prod_id) + +# 创建数据框，将三个ID列结合起来 +df_ids = pd.DataFrame({ + '材料id': material_ids, + '设备id': device_ids, + '产品id': product_ids +}) + +# 指定文件路径并保存为CSV文件 +file_path_ids = '测试数据 material_device_product_ids.csv' +df_ids.to_csv(file_path_ids, index=False) # index=False 表示不保存行索引 + +# 打印文件路径 +print(f"CSV 文件已生成，路径为: {file_path_ids}") diff --git a/测试数据 设备id和设备残值.py b/测试数据 设备id和设备残值.py new file mode 100644 index 0000000..cb313e3 --- /dev/null +++ b/测试数据 设备id和设备残值.py @@ -0,0 +1,25 @@ +import pandas as pd +import numpy as np + +# 设置随机种子以确保结果可重复 +np.random.seed(42) + +# 定义行数，即生成多少个设备 +num_rows = 10 + +# 生成设备id（例如100到200之间的设备ID） +device_ids = np.random.randint(100, 200, size=num_rows) + +# 生成设备残值，假设范围在1000到10000之间 +device_salvage_values = np.random.randint(10, 1000, size=num_rows) + +# 创建数据框，将设备id和设备残值结合起来 +df_devices = pd.DataFrame({ + '设备id': device_ids, + '设备残值': device_salvage_values +}) + +# 保存为CSV文件 +file_path_devices = '测试数据 device_salvage_values.csv' +df_devices.to_csv(file_path_devices, index=False) + diff --git a/测试数据_firm_industry_relation.csv.py b/测试数据_firm_industry_relation.csv.py new file mode 100644 index 0000000..fd989f7 --- /dev/null +++ b/测试数据_firm_industry_relation.csv.py @@ -0,0 +1,17 @@ +import pandas as pd + +Firm_Prod = pd.read_csv("input_data/Firm_amended.csv") +Firm_Prod.fillna(0, inplace=True) + +# Stack the firm-product relationships into a DataFrame +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') + +# 保存为新的 CSV 文件 +output_file_path = 'input_data/firm_industry_relation.csv' +firm_prod.to_csv(output_file_path, index=False) + +print(f"新的 CSV 文件已保存到: {output_file_path}")