模型逻辑修改完成，没有内容的输出 和对于 数据库的调整 也没有跑代码

.idea/csv-editor.xml

@@ -24,6 +24,13 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\input_data\firm_industry_relation.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\input_data\oa_with_exp.csv">
           <value>
             <Attribute>
@@ -52,6 +59,69 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\input_data\测试 BomNodes.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\input_data\测试 Firm_amended 170.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 companies_devices.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 companies_materials.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 companies_products.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 consumed_materials.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 industry_raw_materials_products.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 produced_products.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\测试数据 products_materials_equipment.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
       </map>
     </option>
   </component>

controller_db.py

@@ -64,11 +64,12 @@ class ControllerDB:
             # 行索引 (index)：这一行在数据帧中的索引值。
             # 行数据 (row)：这一行的数据，是一个 pandas.Series 对象，包含该行的所有列和值。
 
-            for _, row in firm.iterrows():
+            firm_industry=pd.read_csv("input_data/firm_industry_relation.csv")
-                code = row['Code']
+            firm_industry['Firm_Code'] = firm_industry['Firm_Code'].astype('string')
-                row = row['1':]
+            for _, row in firm_industry.iterrows():
-                for product_code in row.index[row == 1].to_list():
+                code = row['Firm_Code']
-                    dct = {code: [product_code]}
+                row = row['Product_Code']
+                dct = {code: [row]}
                 list_dct.append(dct)
 
         # fill g_bom

firm.py

@@ -2,21 +2,54 @@ from mesa import Agent
 
 
 class FirmAgent(Agent):
-    def __init__(self, unique_id, model, type_region, revenue_log, a_lst_product):
+    def __init__(self, unique_id, model, type_region, revenue_log, n_equip_c, a_lst_product,
+                 production_output, demand_quantity, c_price, R, P, C):
         # 调用超类的 __init__ 方法
         super().__init__(unique_id, model)
 
         # 初始化模型中的网络引用
         self.firm_network = self.model.firm_network
         self.product_network = self.model.product_network
-
         # 初始化代理自身的属性
         self.type_region = type_region
-
         self.size_stat = []
         self.dct_prod_up_prod_stat = {}
         self.dct_prod_capacity = {}
-
+        # 企业涉及的产业
+        self.indus_i = a_lst_product
+        # 各资源库存信息，库存资源，库存量
+        self.R = R
+        # 包括库存时间的值 方便后面统计
+        self.R1 = {0: R}
+        # 设备资产信息，持有设备，设备数量， 增加 设备残值 [[1,2,3],[] ]
+        self.C = C
+        # 包括设备时间步的值
+        self.C1 = {0: C}
+        # 复制一份
+        self.C0 = C
+        # 产品库存信息 库存产品，库存量 ID 数量
+        self.P = P
+        # 包括 产品时间
+        self.P1={0:P}
+        # 企业i的供应商
+        self.upper_i = [u for u, v in self.firm_network.in_edges(self.unique_id)]
+        # 企业i的客户
+        self.downer_i = [u for u, v in self.firm_network.out_edges(self.unique_id)]
+        # 设备c的数量 （总量） 使用这个来判断设备数量
+        self.n_equip_c = n_equip_c
+        # 设备c产量 更具设备量进行估算
+        self.c_yield = production_output
+        # 消耗材料量 根据设备量进行估算
+        self.c_consumption = demand_quantity
+        # 设备c购买价格（初始值）
+        self.c_price = c_price
+        # 资源r补货库存阈值
+        self.s_r = 40
+        self.S_r = 120
+        # 设备补货阙值 可选
+        # self.ss_r = 70
+        # 每一个周期步减少残值：x
+        self.x = 20
         # 试验中的参数
         self.dct_n_trial_up_prod_disrupted = {}
         self.dct_cand_alt_supp_up_prod_disrupted = {}
@@ -200,6 +233,89 @@ class FirmAgent(Agent):
         else:
             down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
 
+    def seek_material_supply(self, material_type):
+        lst_firm_material_connect = []  # 符合条件 可选择的上游
+        upper_i_material = []  # 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] == material_type:
+                    upper_i_material.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_material)==0:
+
+        if self.is_prf_conn:
+            for firm in upper_i_material:
+                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_material_connect.append(firm)
+        if len(lst_firm_material_connect) == 0:
+            if self.is_prf_size:
+                lst_size = [firm.size_stat[-1][0] for firm in upper_i_material]
+                lst_prob = [size / sum(lst_size) for size in lst_size]
+                select_alt_supply = \
+                    self.random.choices(upper_i_material, weights=lst_prob)[0]
+            else:
+                select_alt_supply = self.random.choice(upper_i_material)
+        elif len(lst_firm_material_connect) > 0:
+            if self.is_prf_size:
+                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 = {}
 

input_data/firm_industry_relation.csv

@@ -0,0 +1,476 @@
+Firm_Code,Product_Code
+0,1.4.4
+1,2.1.1.5
+2,1.1.3
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+6,1.3.1.2
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input_data/xv_with_exp.csv

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input_data/测试 BomNodes.csv

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input_data/测试 Firm_amended 170.csv

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+83,Company_83,819.04,221.6,785.02,16422.34,471,Urban,No,9.706397881988158,424.502,273.904
+84,Company_84,495.01,235.24,793.64,18346.41,797,Suburban,No,9.817189194015675,465.36400000000003,229.501
+85,Company_85,189.98,626.37,530.14,49793.11,669,Suburban,No,10.815631900027373,492.014,420.998
+86,Company_86,786.61,983.8,224.61,27511.88,420,Urban,No,10.222373190369527,296.461,200.661
+87,Company_87,775.36,813.93,242.21,94867.05,336,Rural,Yes,11.460231716720575,458.221,433.536
+88,Company_88,316.03,569.38,505.27,31548.83,265,Suburban,Yes,10.35929178328807,307.527,299.603
+89,Company_89,411.42,968.1,873.75,17001.83,274,Suburban,No,9.741076264303647,220.375,534.142
+90,Company_90,360.91,720.1,917.41,33781.07,582,Suburban,Yes,10.427655865407793,473.741,252.091
+91,Company_91,358.16,611.27,878.08,94174.16,361,Urban,Yes,11.452901112955821,500.808,146.816
+92,Company_92,353.58,598.11,973.92,46314.41,797,Suburban,No,10.743208422753485,540.392,251.358
+93,Company_93,796.81,261.4,254.01,36160.77,589,Suburban,No,10.495730108527182,202.401,310.681
+94,Company_94,479.78,168.81,256.88,40033.55,561,Rural,No,10.597473131541856,467.688,210.978
+95,Company_95,500.33,585.43,362.28,58187.29,708,Urban,No,10.971422224990354,253.228,302.033
+96,Company_96,267.69,499.92,214.82,79427.15,318,Suburban,No,11.282595528333824,179.482,373.769
+97,Company_97,755.05,985.95,179.24,82469.43,673,Suburban,Yes,11.320182958199297,140.924,446.505
+98,Company_98,302.62,320.94,594.75,51363.17,367,Suburban,No,10.84667665764297,510.475,362.262
+99,Company_99,714.96,972.27,684.7,26131.27,620,Rural,No,10.170887960471719,552.47,208.496
+100,Company_100,945.42,505.3,509.87,57539.11,222,Urban,Yes,10.960220169485046,174.987,338.54200000000003
+101,Company_101,160.02,165.31,985.52,97438.02,772,Urban,Yes,11.486971762540222,329.552,243.002
+102,Company_102,445.92,108.59,838.78,65823.19,990,Rural,No,11.094727486834918,310.878,233.59199999999998
+103,Company_103,591.79,603.5,225.38,17043.42,511,Suburban,Yes,9.74351948447026,211.538,287.179
+104,Company_104,252.06,450.61,405.59,57402.73,571,Rural,No,10.95784714215228,316.55899999999997,462.206
+105,Company_105,165.62,951.39,306.5,45833.17,398,Rural,Yes,10.73276334377794,173.65,386.562
+106,Company_106,255.06,128.16,259.17,13244.48,616,Urban,No,9.491336140837323,395.91700000000003,362.506
+107,Company_107,447.35,180.41,643.05,70700.8,449,Rural,No,11.166212167237902,476.305,380.735
+108,Company_108,781.83,434.34,550.93,81277.04,720,Suburban,Yes,11.305618844826347,450.093,377.183
+109,Company_109,169.85,134.46,977.71,94591.41,473,Suburban,No,11.45732194753443,239.77100000000002,273.985
+110,Company_110,125.7,708.42,777.62,11615.85,473,Rural,Yes,9.360125823758061,452.762,334.57
+111,Company_111,866.33,981.73,892.43,14848.63,251,Suburban,Yes,9.605662884082752,436.243,392.63300000000004
+112,Company_112,725.77,590.82,558.44,15328.79,949,Urban,Yes,9.63748803854849,397.844,518.577
+113,Company_113,958.57,191.31,188.92,97104.67,310,Urban,Yes,11.483544747870583,305.892,306.85699999999997
+114,Company_114,729.39,511.32,120.71,69592.56,191,Urban,No,11.150412944056944,457.071,390.93899999999996
+115,Company_115,365.17,638.89,288.41,18567.32,982,Rural,Yes,9.829158325138039,372.841,401.517
+116,Company_116,265.79,429.39,476.28,86839.57,716,Urban,No,11.371817672344777,372.628,356.579
+117,Company_117,559.33,264.97,113.88,15724.66,744,Rural,Yes,9.66298545971327,444.388,394.933
+118,Company_118,781.96,443.32,862.47,74245.47,273,Rural,No,11.215132044702743,427.247,508.196
+119,Company_119,890.02,331.77,697.16,83693.72,772,Rural,Yes,11.334919223794108,531.716,428.002
+120,Company_120,358.86,588.14,768.94,84336.55,101,Urban,No,11.342570620606638,188.894,371.886
+121,Company_121,589.37,192.81,994.71,30198.07,651,Urban,Yes,10.315533294036035,451.471,426.937
+122,Company_122,808.28,297.7,455.05,30809.46,875,Urban,Yes,10.335577064660097,157.505,494.828
+123,Company_123,988.78,307.96,869.8,44511.43,310,Suburban,Yes,10.703501289105592,455.98,326.878
+124,Company_124,461.81,849.69,521.11,84053.28,549,Suburban,Yes,11.339206162465574,425.111,217.18099999999998
+125,Company_125,438.73,683.75,458.4,34747.89,771,Rural,Yes,10.455874129724979,386.84,305.873
+126,Company_126,791.33,299.04,650.17,84231.54,634,Urban,Yes,11.341324714414265,208.017,494.13300000000004
+127,Company_127,369.59,143.99,204.6,27639.11,536,Rural,Yes,10.22698707765262,338.46,479.959
+128,Company_128,524.05,885.87,431.91,22997.06,766,Suburban,Yes,10.04312166065388,507.19100000000003,410.405
+129,Company_129,677.07,490.74,311.48,21524.13,781,Rural,No,9.97692991036063,444.148,564.707
+130,Company_130,178.2,260.51,858.09,83651.2,562,Rural,No,11.334411051799224,185.809,333.82
+131,Company_131,691.31,398.92,644.53,87442.53,582,Rural,No,11.378737056558094,443.453,357.131
+132,Company_132,189.46,575.78,346.71,13257.65,284,Rural,Yes,9.492330023297646,296.671,473.946
+133,Company_133,488.33,731.67,953.18,26643.92,489,Rural,Yes,10.190316260976394,559.318,406.83299999999997
+134,Company_134,369.02,465.67,667.29,78924.61,793,Suburban,No,11.27624837201392,238.72899999999998,205.902
+135,Company_135,738.44,879.57,465.81,92477.88,609,Suburban,No,11.434724759768017,365.581,546.844
+136,Company_136,758.99,786.45,658.38,52134.69,989,Urban,Yes,10.861585841104548,218.838,231.899
+137,Company_137,464.53,270.67,133.65,25976.63,552,Suburban,Yes,10.164952566645338,168.365,274.453
+138,Company_138,580.02,793.08,931.46,40643.76,471,Rural,Yes,10.612600597657721,362.146,444.002
+139,Company_139,274.51,795.06,500.42,35139.32,233,Rural,Yes,10.46707601038709,281.04200000000003,458.451
+140,Company_140,959.71,868.21,107.06,49994.95,428,Rural,Yes,10.81967727930944,361.706,578.971
+141,Company_141,354.71,563.01,350.48,42908.83,610,Urban,No,10.666832911242361,272.048,357.471
+142,Company_142,396.03,603.89,128.42,39303.37,668,Urban,No,10.579065544817109,332.842,170.603
+143,Company_143,892.61,311.16,670.46,43027.27,864,Suburban,Yes,10.669589379711221,549.046,279.26099999999997
+144,Company_144,486.76,626.8,943.16,39879.69,50,Rural,No,10.593622450725602,318.31600000000003,189.676
+145,Company_145,917.34,288.69,643.88,55254.01,873,Suburban,Yes,10.919696195931778,281.38800000000003,371.73400000000004
+146,Company_146,435.58,284.0,802.41,14442.55,517,Suburban,Yes,9.577933989653928,362.241,241.558
+147,Company_147,202.67,381.12,247.09,64747.68,871,Rural,Yes,11.07825314880404,335.709,272.267
+148,Company_148,394.8,297.97,982.49,64826.8,825,Suburban,No,11.079474377086543,261.249,240.48000000000002
+149,Company_149,703.7,551.4,223.33,52703.5,186,Rural,No,10.872437145986403,325.333,248.37
+150,Company_150,271.26,639.62,669.86,54145.21,286,Urban,No,10.899424790529425,392.986,361.126
+151,Company_151,741.21,534.21,734.27,99208.27,649,Urban,Yes,11.504976656733618,459.427,365.121
+152,Company_152,946.16,900.51,456.7,12725.19,836,Rural,Yes,9.451338772544975,533.67,282.616
+153,Company_153,230.74,396.22,221.36,44064.93,469,Rural,No,10.693419506970148,279.136,139.074
+154,Company_154,390.33,162.83,592.72,41020.68,592,Urban,Yes,10.621831608768346,283.272,484.033
+155,Company_155,729.59,982.05,479.26,33549.17,191,Suburban,No,10.420767402898578,377.926,280.959
+156,Company_156,873.45,147.99,568.87,10530.31,621,Urban,No,9.262013044390788,262.887,409.345
+157,Company_157,799.45,990.69,784.19,76686.49,803,Rural,No,11.247480831032389,299.419,216.945
+158,Company_158,491.74,143.25,906.25,59283.77,370,Suburban,No,10.990090854439025,558.625,390.174
+159,Company_159,367.87,208.07,473.62,83903.97,372,Suburban,Yes,11.33742820957182,282.362,264.787
+160,Company_160,564.46,355.27,733.19,61663.32,609,Urban,No,11.029444543649397,275.319,363.446
+161,Company_161,607.02,300.46,718.76,99731.85,231,Suburban,No,11.510240363309093,538.876,257.702
+162,Company_162,363.2,676.84,915.1,81358.78,101,Urban,No,11.306624035526434,493.51,285.32
+163,Company_163,976.79,481.49,329.12,87221.77,246,Urban,No,11.37620923470151,221.912,377.679
+164,Company_164,738.36,396.44,379.5,95322.53,249,Rural,No,11.465021473034172,364.95,406.836
+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

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())
+        grouped = firm_industry_relation.groupby('Firm_Code')['Product_Code'].apply(list)
-        firm_attr.loc[:, 'Product_Code'] = firm_product
+        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_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv")
-        firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
+        firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string')
-        firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
+        # 将 'firm_prod' 表中的每一行作为图中的节点
-        firm_prod.drop('bool', axis=1, inplace=True)
+        self.G_FirmProd.add_nodes_from(firm_industry_relation.index)
-        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')
 
-        self.G_FirmProd.add_nodes_from(firm_prod.index)
+        # 为每个节点分配属性
-        # Assign attributes to the firm-product nodes
+        firm_prod_labels_dict = {code: firm_industry_relation.loc[code].to_dict() for code in
-        firm_prod_labels_dict = {code: firm_prod.loc[code].to_dict() for code in firm_prod.index}
+                                 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

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]
-

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)
+

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)
+

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)

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)

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

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

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

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

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

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

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

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

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}")

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}")

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)
+

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}")