diff --git a/.idea/csv-editor.xml b/.idea/csv-editor.xml
index ec19d70..c775f95 100644
--- a/.idea/csv-editor.xml
+++ b/.idea/csv-editor.xml
@@ -3,6 +3,13 @@
   <component name="CsvFileAttributes">
     <option name="attributeMap">
       <map>
+        <entry key="\input_data\BomCateNet.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\input_data\BomNodes.csv">
           <value>
             <Attribute>
diff --git a/__pycache__/controller_db.cpython-38.pyc b/__pycache__/controller_db.cpython-38.pyc
index a3bf1d7..38608ab 100644
Binary files a/__pycache__/controller_db.cpython-38.pyc and b/__pycache__/controller_db.cpython-38.pyc differ
diff --git a/__pycache__/firm.cpython-38.pyc b/__pycache__/firm.cpython-38.pyc
index c831dbf..17f3ddf 100644
Binary files a/__pycache__/firm.cpython-38.pyc and b/__pycache__/firm.cpython-38.pyc differ
diff --git a/__pycache__/my_model.cpython-38.pyc b/__pycache__/my_model.cpython-38.pyc
index 6df0db6..033717c 100644
Binary files a/__pycache__/my_model.cpython-38.pyc and b/__pycache__/my_model.cpython-38.pyc differ
diff --git a/firm.py b/firm.py
index 823e19d..561ea80 100644
--- a/firm.py
+++ b/firm.py
@@ -1,5 +1,4 @@
 from mesa import Agent
-import numpy as np
 
 
 class FirmAgent(Agent):
@@ -55,47 +54,71 @@ class FirmAgent(Agent):
             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.neighbors(self.unique_id))
-        for n2 in lst_out_edge:
-            edge_data = self.firm_network.edges[self.unique_id, n2]
-            if edge_data.get('Product') == disrupted_prod.code:
-                customer = self.model.schedule.get_agent(n2)
+            self.firm_network.out_edges(
+                self.unique_id, keys=True, data='Product'))
+        for n1, n2, key, product_code in lst_out_edge:
+            if product_code == disrupted_prod.unique_id:
+                # update customer up product supplier status
+                customer = next(agent for agent in self.model.company_agents if agent.unique_id == n2)
+
                 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)
-                self.firm_network.remove_edge(self.unique_id, n2)
+                        # print(f"{self.name} disrupt {customer.name}'s "
+                        #       f"{prod.code} due to {disrupted_prod.code}")
+                # remove edge to customer
+                self.firm_network.remove_edge(n1, n2, key)
 
     def disrupt_cus_prod(self, prod, disrupted_up_prod):
-        num_lost = len(self.dct_prod_up_prod_stat[prod]['s_stat'][disrupted_up_prod]['set_disrupt_firm'])
-        num_remain = len([
-            u for u in self.firm_network.neighbors(self.unique_id)
-            if self.firm_network.G.edges[u, self.unique_id].get('Product') == disrupted_up_prod.code])
+        # 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.in_edges(self.get_firm_network_unique_id(),
+                                            keys=True,
+                                            data='Product')
+                 if d == disrupted_up_prod.unique_id])
         lost_percent = num_lost / (num_lost + num_remain)
-        lst_size = [firm.size_stat[-1][0] for firm in self.model.schedule.agents]
-        std_size = (self.size_stat[-1][0] - min(lst_size) + 1) / (max(lst_size) - min(lst_size) + 1)
+        lst_size = \
+            [firm.size_stat[-1][0] for firm in self.model.company_agents]
+        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.random.choice([True, False], p=[prob_disrupt, 1 - prob_disrupt]):
+        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]
+            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.schedule.time))
+                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):
         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:
                 self.dct_cand_alt_supp_up_prod_disrupted[product] = [
-                    firm for firm in self.model.schedule.agents
+                    firm for firm in self.model.company_agents
                     if firm.is_prod_in_current_normal(product)]
             if self.dct_cand_alt_supp_up_prod_disrupted[product]:
                 lst_firm_connect = []
                 if self.is_prf_conn:
                     for firm in self.dct_cand_alt_supp_up_prod_disrupted[product]:
-                        if self.firm_network.G.has_edge(self.unique_id, firm.unique_id) or \
-                                self.firm_network.G.has_edge(firm.unique_id, self.unique_id):
+                        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_connect.append(firm)
                 if len(lst_firm_connect) == 0:
                     if self.is_prf_size:
@@ -133,8 +156,8 @@ class FirmAgent(Agent):
                     lst_firm_connect = []
                     if self.is_prf_conn:
                         for firm in lst_firm:
-                            if self.firm_network.G.has_edge(self.unique_id, firm.unique_id) or \
-                                    self.firm_network.G.has_edge(firm.unique_id, self.unique_id):
+                            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_connect.append(firm)
                     if len(lst_firm_connect) == 0:
                         if self.is_prf_size:
@@ -156,26 +179,26 @@ class FirmAgent(Agent):
                 for down_firm in lst_firm:
                     down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
 
-                    def accept_request(self, down_firm, product):
-                        if self.firm_network.G.has_edge(self.unique_id, down_firm.unique_id) or \
-                                self.firm_network.G.has_edge(down_firm.unique_id, self.unique_id):
-                            prod_accept = 1.0
-                        else:
-                            prod_accept = self.flt_diff_new_conn
-                        if self.random.choice([True, False], p=[prod_accept, 1 - prod_accept]):
-                            self.firm_network.G.add_edge(self.unique_id, down_firm.unique_id, Product=product.code)
-                            self.dct_prod_capacity[product] -= 1
-                            self.dct_request_prod_from_firm[product].remove(down_firm)
+    def accept_request(self, down_firm, product):
+        if self.firm_network.has_edge(self.unique_id, down_firm.unique_id) or \
+                self.firm_network.has_edge(down_firm.unique_id, self.unique_id):
+            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.add_edge(self.unique_id, down_firm.unique_id, Product=product.unique_id)
+            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']:
-                                    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.schedule.time))
-                            del down_firm.dct_n_trial_up_prod_disrupted[product]
-                            del down_firm.dct_cand_alt_supp_up_prod_disrupted[product]
-                        else:
-                            down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
+            for prod in down_firm.dct_prod_up_prod_stat.keys():
+                if product in down_firm.dct_prod_up_prod_stat[prod]['s_stat']:
+                    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]
+        else:
+            down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
 
     def clean_before_trial(self):
         self.dct_request_prod_from_firm = {}
@@ -188,13 +211,25 @@ class FirmAgent(Agent):
         # Update the status of products and refresh disruption sets
         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.schedule.time:
-                self.dct_prod_up_prod_stat[prod]['p_stat'].append((status, self.model.schedule.time))
+            if ts != self.model.t:
+                self.dct_prod_up_prod_stat[prod]['p_stat'].append((status, self.model.t))
 
             # Refresh the set of disrupted firms
             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_unique_id(self):
+        return self.unique_id
+
+    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
+
     def step(self):
         # 在每个时间步进行的操作
         pass
diff --git a/my_model.py b/my_model.py
index d87d93f..cf868b2 100644
--- a/my_model.py
+++ b/my_model.py
@@ -6,8 +6,8 @@ import pandas as pd
 from mesa import Model
 from mesa.space import MultiGrid, NetworkGrid
 from mesa.datacollection import DataCollector
-from mesa.time import RandomActivation
 
+import numpy as np
 from firm import FirmAgent
 from product import ProductAgent
 
@@ -35,10 +35,20 @@ class MyModel(Model):
         self.data_collector = DataCollector(
             agent_reporters={"Product": "name"}
         )
+        # initialize graph bom
+        self.G_bom = nx.adjacency_graph(json.loads(params['g_bom']))
+
+
+        # Create the firm-product network graph
+        self.G_FirmProd = nx.MultiDiGraph()
+        # Create the firm network graph
+        self.G_Firm = nx.MultiDiGraph()
 
         self.company_agents = []
         self.product_agents = []
 
+        self.nprandom = np.random.default_rng(params['seed'])
+
         # Initialize parameters from `params`
         self.sample = params['sample']
         self.int_stop_ts = 0
@@ -62,121 +72,136 @@ class MyModel(Model):
     def initialize_product_network(self, params):
         try:
             self.product_network = nx.adjacency_graph(json.loads(params['g_bom']))
-            print(
-                f"Product network initialized with {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
         except Exception as e:
             print(f"Failed to initialize product network: {e}")
 
     def initialize_firm_network(self):
-        """ Initialize the firm network and add it to the model. """
+        """ Initialize the firm network by reading firm data from a CSV file """
+        # Read the firm data
         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", "Type_Region", "Revenue_Log"]]
-        firm_product = [row[row == 1].index.to_list() for _, row in Firm.loc[:, '1':].iterrows()]
+
+        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)
 
-        self.firm_network = nx.MultiDiGraph()
-        self.firm_network.add_nodes_from(Firm["Code"])
 
-        firm_labels_dict = {code: Firm_attr.loc[code].to_dict() for code in self.firm_network.nodes}
-        nx.set_node_attributes(self.firm_network, firm_labels_dict)
+        self.G_Firm.add_nodes_from(Firm["Code"])
+
+        # Assign attributes to the firm nodes
+        firm_labels_dict = {code: Firm_attr.loc[code].to_dict() for code in self.G_Firm.nodes}
+        nx.set_node_attributes(self.G_Firm, firm_labels_dict)
+
+        self.Firm = Firm
 
     def initialize_firm_product_network(self):
-        """ Initialize the firm-product network and add it to the model. """
+        """ 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')
 
-        self.firm_prod_network = nx.MultiDiGraph()
-        self.firm_prod_network.add_nodes_from(firm_prod.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}
-        nx.set_node_attributes(self.firm_prod_network, firm_prod_labels_dict)
-
-        self.add_edges_to_firm_network()
-        self.connect_unconnected_nodes()
-
-        print(
-            f"Firm network has {self.firm_network.number_of_nodes()} nodes and {self.firm_network.number_of_edges()} edges.")
-        print(
-            f"Product network has {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
+        nx.set_node_attributes(self.G_FirmProd, firm_prod_labels_dict)
 
     def add_edges_to_firm_network(self):
-        """ Add edges to the firm network based on product BOM. """
-        Firm = pd.read_csv("input_data/Firm_amended.csv")
-        Firm['Code'] = Firm['Code'].astype('string')
-        Firm.fillna(0, inplace=True)
-        for node in nx.nodes(self.firm_network):
+        """ Add edges between firms based on the product BOM relationships """
+        # Add edges to G_Firm according to G_bom
+        for node in nx.nodes(self.G_Firm):
             lst_pred_product_code = []
-            for product_code in self.firm_network.nodes[node]['Product_Code']:
-                lst_pred_product_code += list(self.product_network.predecessors(product_code))
+            for product_code in self.G_Firm.nodes[node]['Product_Code']:
+                lst_pred_product_code += list(self.G_bom.predecessors(product_code))
             lst_pred_product_code = list(set(lst_pred_product_code))
-            lst_pred_product_code = list(sorted(lst_pred_product_code))
+            lst_pred_product_code = list(sorted(lst_pred_product_code))  # Ensure consistency
 
             for pred_product_code in lst_pred_product_code:
-                lst_pred_firm = Firm['Code'][Firm[pred_product_code] == 1].to_list()
+                # Get a list of firms producing the component (pred_product_code)
+                lst_pred_firm = self.Firm['Code'][self.Firm[pred_product_code] == 1].to_list()
+
+                # Select multiple suppliers (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)
+
                 if self.is_prf_size:
-                    lst_pred_firm_size = [self.firm_network.nodes[pred_firm]['Revenue_Log'] for pred_firm in
+                    lst_pred_firm_size = [self.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.random.choices(lst_pred_firm, k=n_pred_firm, weights=lst_prob)
+                    lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False, p=lst_prob)
                 else:
-                    lst_choose_firm = self.random.choices(lst_pred_firm, k=n_pred_firm)
+                    lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False)
+
+                # Add edges from predecessor firms to current node (firm)
                 lst_add_edge = [(pred_firm, node, {'Product': pred_product_code}) for pred_firm in lst_choose_firm]
-                self.firm_network.add_edges_from(lst_add_edge)
+                self.G_Firm.add_edges_from(lst_add_edge)
 
-                # Add edges to firm-prod network
-                set_node_prod_code = set(self.firm_network.nodes[node]['Product_Code'])
-                set_pred_succ_code = set(self.product_network.successors(pred_product_code))
-                lst_use_pred_prod_code = list(set_node_prod_code & set_pred_succ_code)
+                # Add edges to firm-product network
+                self.add_edges_to_firm_product_network(node, pred_product_code, lst_choose_firm)
 
-                for pred_firm in lst_choose_firm:
-                    pred_node = [n for n, v in self.firm_prod_network.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 self.firm_prod_network.nodes(data=True) if
-                                        v['Firm_Code'] == node and v['Product_Code'] == use_pred_prod_code][0]
-                        self.firm_prod_network.add_edge(pred_node, current_node)
+    def add_edges_to_firm_product_network(self, node, pred_product_code, lst_choose_firm):
+        """ Helper function to add edges to the firm-product network """
+        set_node_prod_code = set(self.G_Firm.nodes[node]['Product_Code'])
+        set_pred_succ_code = set(self.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 self.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 self.G_FirmProd.nodes(data=True) if
+                                v['Firm_Code'] == node and v['Product_Code'] == use_pred_prod_code][0]
+                self.G_FirmProd.add_edge(pred_node, current_node)
 
     def connect_unconnected_nodes(self):
-        """ Connect unconnected nodes in the firm network. """
-        Firm = pd.read_csv("input_data/Firm_amended.csv")
-        Firm['Code'] = Firm['Code'].astype('string')
-        Firm.fillna(0, inplace=True)
-        for node in nx.nodes(self.firm_network):
-            if self.firm_network.degree(node) == 0:
-                for product_code in self.firm_network.nodes[node]['Product_Code']:
-                    current_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
+        """ Connect unconnected nodes in the firm network """
+        for node in nx.nodes(self.G_Firm):
+            if self.G_Firm.degree(node) == 0:
+                for product_code in self.G_Firm.nodes[node]['Product_Code']:
+                    current_node = [n for n, v in self.G_FirmProd.nodes(data=True) if
                                     v['Firm_Code'] == node and v['Product_Code'] == product_code][0]
-                    lst_succ_product_code = list(self.product_network.successors(product_code))
+                    lst_succ_product_code = list(self.G_bom.successors(product_code))
+
                     for succ_product_code in lst_succ_product_code:
-                        lst_succ_firm = Firm['Code'][Firm[succ_product_code] == 1].to_list()
+                        lst_succ_firm = self.Firm['Code'][self.Firm[succ_product_code] == 1].to_list()
+
                         n_succ_firm = self.int_netw_prf_n
                         if n_succ_firm > len(lst_succ_firm):
                             n_succ_firm = len(lst_succ_firm)
+
                         if self.is_prf_size:
-                            lst_succ_firm_size = [self.firm_network.nodes[succ_firm]['Revenue_Log'] for succ_firm in
+                            lst_succ_firm_size = [self.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.random.choices(lst_succ_firm, k=n_succ_firm, weights=lst_prob)
+                            lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False,
+                                                                   p=lst_prob)
                         else:
-                            lst_choose_firm = self.random.choices(lst_succ_firm, k=n_succ_firm)
-                        lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in lst_choose_firm]
-                        self.firm_network.add_edges_from(lst_add_edge)
+                            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]
+                        self.G_Firm.add_edges_from(lst_add_edge)
+
+                        # Add edges to firm-product network
                         for succ_firm in lst_choose_firm:
-                            succ_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
+                            succ_node = [n for n, v in self.G_FirmProd.nodes(data=True) if
                                          v['Firm_Code'] == succ_firm and v['Product_Code'] == succ_product_code][0]
-                            self.firm_prod_network.add_edge(current_node, succ_node)
+                            self.G_FirmProd.add_edge(current_node, succ_node)
+
+        self.sample.g_firm = json.dumps(nx.adjacency_data(self.G_Firm))
+        self.firm_network = self.G_Firm  # 直接使用 networkx 图对象
+        self.firm_prod_network = self.G_FirmProd  # 直接使用 networkx 图对象
 
     def initialize_agents(self):
         """ Initialize agents and add them to the model. """
@@ -189,7 +214,6 @@ class MyModel(Model):
 
         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']]
-            print(a_lst_product)
             firm_agent = FirmAgent(
                 ag_node, self,
                 type_region=attr['Type_Region'],
@@ -208,7 +232,8 @@ class MyModel(Model):
         # 遍历初始公司-产品干扰数据，将其转化为基于公司和产品的映射
         for firm_code, lst_product in self.dct_lst_init_disrupt_firm_prod.items():
             # 从 company_agents 列表中选择指定公司
-            firm = next(firm for firm in self.company_agents if firm.unique_id == firm_code)
+            firms = [firm for firm in self.company_agents if firm.unique_id == firm_code]
+            firm = firms[0] if firms else None
 
             # 从总产品列表中选择该公司受干扰的产品
             disrupted_products = [product for product in self.product_agents if product.unique_id in lst_product]
@@ -219,6 +244,7 @@ class MyModel(Model):
         # 更新 self.dct_lst_init_disrupt_firm_prod 字典，存储公司及其受干扰的产品
         self.dct_lst_init_disrupt_firm_prod = t_dct
 
+
         # 设置初始受干扰的公司产品状态
         for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
             for product in a_lst_product:
@@ -236,7 +262,6 @@ class MyModel(Model):
             self.product_agents.append(agent)
 
     def step(self):
-        print(f"Running step {self.t}")
         # 1. Remove edge to customer and disrupt customer up product
         for firm in self.company_agents:
             for prod in firm.dct_prod_up_prod_stat.keys():