A system for mining and analyzing investment leads based on large-scale intelligent agents

Abstract

The present invention relates to the field of investment attraction technology, and discloses an investment attraction clue mining and analysis system based on a large model intelligent agent, comprising the following modules: a key link information collection module for collecting key link information; a No. 1 graph structure data encoding module for encoding the key link information into No. 1 graph structure data; a key link acquisition module for inputting the No. 1 graph structure data into a key link model and outputting key links representing the industrial chain required by the economic development zone; an investment attraction clue information collection module for collecting investment attraction clue information; a No. 2 graph structure data encoding module for encoding the investment attraction clue information into No. 2 graph structure data; and an investment attraction plan acquisition module for inputting the No. 2 graph structure data into an investment attraction model and outputting an investment attraction plan. The present invention can more accurately carry out investment attraction work for key links of different industrial chains in different regions based on the results.

Description

Technical Field

[0001] The present invention relates to the technical field of investment promotion, and more specifically, to an investment promotion clue mining and analysis system based on a large model intelligent body. Background Art

[0002] The new energy vehicle industry is a key area of ​​global economic development and holds significant significance for the transformation, upgrading, and sustainable development of local economies. Many regions are actively developing new energy vehicle industries, hoping to seize these opportunities and create new engines for local economic development. Against this backdrop, economic development zones, serving as crucial platforms for navigating industrial transfers and fostering emerging industries, have proposed plans to develop new energy vehicle industry clusters.

[0003] To achieve this goal, the economic development zone needs to introduce a number of key enterprises in the key links of the new energy vehicle industry chain to form a more complete industrial ecosystem. What the economic development zone needs is a more complete industrial chain. Introducing only specific key links may not constitute a complete industrial chain. Moreover, each economic development zone has different conditions, and the key links required at different times may also vary. Therefore, in order to achieve more precise investment attraction for key enterprises in these key links, it is necessary to conduct in-depth research on the key links and key enterprises required by the economic development zone, and a more complete investment attraction plan is needed to carry out targeted investment attraction work. Summary of the Invention

[0004] The present invention provides an investment promotion clue mining and analysis system based on a large model intelligent body, which solves the technical problems in related technologies that the key links of the industrial chain required by different economic development zones in different periods are different and it is difficult to obtain a more complete investment promotion plan.

[0005] The present invention provides a method for mining and analyzing investment leads based on a large model agent, comprising the following steps:

[0006] Step 100: Collect key link information, which includes all links in the industrial chain and information on economic development zones;

[0007] The industry chain includes: battery material supply, motor material supply, power electronic device supply, lightweight material supply, power battery production, drive motor production, power electronic control system production, vehicle design, body manufacturing, vehicle assembly, charging station and charging pile construction, operator service system, battery recycling, material reuse, core software system development, testing and inspection services, logistics and marketing services;

[0008] Information on economic development zones includes: location advantages, resources, industry foundation, human resources, supporting facilities, policy support, and development goals;

[0009] Step 200: Encode the key link information into No. 1 graph structure data; the graph structure data includes nodes and edges connecting the nodes, and each node represents an industrial chain link, location advantage, resource information, industrial foundation, human resources, supporting facilities, policy support, or development goals;

[0010] It indicates that there are edges between the nodes of all industrial chain links; when it indicates that the location advantage brings convenience to the industrial chain link, there is an edge between the industrial chain link and the location advantage; when it indicates that the resource information is conducive to the production and processing of the industrial chain link, there is an edge between the industrial chain link and the resource information; when the existing industrial base and the industrial chain link have an upstream and downstream relationship or a collaborative relationship, there is an edge between the industrial chain link and the industrial base; when it indicates that the quality or quantity of human resources meets the production needs of the industrial chain, there is an edge between the industrial chain link and human resources; when it indicates that the supporting facilities can support the smooth operation of the industrial chain link, there is an edge between the industrial chain link and the supporting facilities; when it indicates that policy support can attract and promote the development of the industrial chain link, there is an edge between the industrial chain link and policy support; when it indicates that the development goal attaches importance to the industrial chain link, there is an edge between the industrial chain link and the development goal;

[0011] Indicates that the characteristics of the industrial chain links, location advantages, resource information, industrial base, human resources, supporting facilities, policy support, and development goals are equal to the corresponding node characteristics;

[0012] The characteristics of industrial chain links, location advantages, resource information, industrial base, human resources, supporting facilities, policy support, and development goals are obtained through feature engineering;

[0013] In step 300, the No. 1 graph structure data is input into the key link model. The key link model includes a first hidden layer and a first output layer. The first hidden layer inputs the No. 1 graph structure data and outputs the first hidden state to the first output layer. The output of the first output layer represents the key link of the industrial chain required by the economic development zone.

[0014] In a preferred embodiment, the calculation formula of the first hidden layer is as follows:

[0015] ;

[0016] in Indicates the first diagram structure data The first hidden state of the node, It is the node of the No. 1 graph structure data The set of nodes with edges between them, Indicates the first diagram structure data The node characteristics of the nodes, is a normalizing constant (such as node degree), represents the weight parameter of the No. 1 graph, Indicates the bias parameter of Figure 1, is a sigmoid function.

[0017] In a preferred embodiment, the calculation formula of the first output layer is as follows:

[0018] ;

[0019] in Represents a vector, the cth component of the vector represents the probability of the cth key link, Indicates the first diagram structure data The first hidden state of the node, Indicates concatenating the first hidden states of all nodes. A collection of nodes representing the links in the industrial chain, is the first weight parameter, is the first bias parameter, Represents the sigmoid function.

[0020] Step 400: Collect investment lead information, which includes key links in the industrial chain, enterprise information, and economic development zone information;

[0021] Enterprise information includes: enterprise name, type, scale, establishment time, legal representative, business scope, registered address, registered capital, number of employees, core technology, number of patents, and market share.

[0022] Step 500: Encode the investment lead information into second graph structure data. The graph structure data includes nodes and edges connecting the nodes. A node represents a key link in the industrial chain or an enterprise or economic development zone.

[0023] There are edges between the nodes representing all key links. There are edges between enterprises when there is a supply chain relationship or a competitive relationship between enterprises. There are edges between enterprises and key links when enterprises are engaged in a certain key link. There are edges between economic development zones and all nodes representing key links.

[0024] The node characteristics of the node representing the enterprise are equal to the enterprise characteristics, the node characteristics of the node representing the key link are equal to the key link characteristics, and the node characteristics of the node representing the economic development zone are equal to the economic development zone characteristics;

[0025] Enterprise characteristics, key link characteristics and economic development zone characteristics are obtained through feature engineering;

[0026] Encode the investment lead information of all nodes into sequence data, the sequence data includes n sequence units, and the t-th sequence unit represents the investment lead information of all nodes at the t-th moment;

[0027] Step 600: Input the graph structure data No. 2 into the investment attraction model. The model includes a second hidden layer, a third hidden layer, and a second output layer. The second hidden layer inputs the graph structure data No. 2 and outputs the second hidden state to the third hidden layer. The third hidden layer outputs the third hidden state to the second output layer. The output of the second output layer represents the investment attraction plan.

[0028] In a preferred embodiment, the calculation formula of the second hidden layer is as follows:

[0029] ;

[0030] in The tth sequence unit of the second sequence data is represented by The second hidden state of the node, The tth sequence unit of the second sequence data is represented by The aggregation coefficient of nodes, represents the set of nodes that have edges with the vth node of the tth sequence unit of the second sequence data, represents the activation function, represents the graph weight coefficient;

[0031] ;

[0032] ;

[0033] ;

[0034] in and The tth sequence unit of the second sequence data is represented by and The node characteristics of the nodes, and The tth sequence unit of the second sequence data is represented by and The linear transformation characteristics of nodes, represents the aggregation weight coefficient, represents the splicing weight coefficient, T represents transposition, represents an exponential function with a natural constant as the base, LeakyRelu represents a corrected linear unit function, Indicates the The set of nodes that have edges.

[0035] In a preferred embodiment, the calculation formula of the third hidden layer is as follows:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] in, 、 、 represents the first, second and third sequence weight parameters, 、 、 represents the first, second and third sequence bias parameters, represents the dot product, 、 and Represent the first, second, and third intermediate states respectively, where , The tth sequence unit of the second sequence data is represented by The second hidden state of the node, Indicates the first t-th sequence unit of the second sequence data The set of nodes with which there are edges. and Represent the t-th and t-1-th third hidden states respectively, n≥t≥1, n represents the total number of sequence units of the second sequence data, when t=1 , tanh is the hyperbolic tangent function, Represents the sigmoid function.

[0041] In a preferred embodiment, the calculation formula of the second output layer is as follows:

[0042] ;

[0043] in Represents a vector, the i-th component of the vector represents the probability value of the i-th action; the set of all actions is called the action space, which contains all possible actions that may be selected. represents the t-th third hidden state, Indicates the concatenation of the 1st to nth third hidden states, n represents the total number of sequence units of the second sequence data, is the second weight parameter, is the second bias parameter, Represents the sigmoid function.

[0044] In a preferred embodiment, both the key link model and the investment attraction model are trained using reinforcement learning, wherein the model to be trained is the key link model or the investment attraction model;

[0045] The steps of training the model to be trained include:

[0046] Step 201: Build a target network, and copy the model to be trained to the target network;

[0047] Step 202: randomly obtain an experience from the experience pool. The experience includes the state at time t. , the action performed at time t , the state at time e+1 , perform the action Rewards received ;

[0048] The states at time e and e+1 represent the key link information or investment lead information at time e and e+1 of the input to the model to be trained respectively;

[0049] Then calculate the state The target network output corresponds to The probability value of ;

[0050] ;

[0051] express The maximum probability value output by the target network;

[0052] Step 203, according to and Calculate the loss value loss and update the model to be trained;

[0053] ;

[0054] express The output of the model to be trained corresponds to The probability value of

[0055] is the discount factor, a value between 0 and 1;

[0056] Step 204: Update the target network every fixed number of network updates so that its parameters are the same as those of the current model to be trained;

[0057] Step 205: The step is terminated until the number of collected experiences reaches a set threshold or the model to be trained converges.

[0058] In a preferred embodiment, a large-scale intelligent agent-based investment lead mining and analysis system is characterized by including the following modules:

[0059] A No. 1 graph structure data encoding module is used to encode key link information into No. 1 graph structure data; the graph structure data includes nodes and edges connecting nodes, and each node represents an industrial chain link, location advantage, resource information, industrial foundation, human resources, supporting facilities, policy support, or development goals;

[0060] a key link acquisition module, configured to input the No. 1 graph structure data into a key link model, wherein the key link model includes a first hidden layer and a first output layer, wherein the first hidden layer inputs the No. 1 graph structure data and outputs a first hidden state to the first output layer, and the first output layer outputs a key link of the industrial chain required by the economic development zone;

[0061] The investment lead information collection module is used to collect investment lead information, including key links in the industrial chain, enterprise information and economic development zone information;

[0062] A second graph structure data encoding module is used to encode investment lead information into second graph structure data. The graph structure data includes nodes and edges connecting nodes. A node represents a key link in the industrial chain, an enterprise, or an economic development zone.

[0063] The investment promotion plan acquisition module is used to input the No. 2 graph structure data into the investment promotion model. The model includes a second hidden layer, a third hidden layer and a second output layer. The second hidden layer inputs the No. 2 graph structure data and outputs the second hidden state to the third hidden layer. The third hidden layer outputs the third hidden state to the second output layer. The output of the second output layer represents the investment promotion plan.

[0064] The beneficial effects of the present invention are:

[0065] The present invention first determines the key links of the industrial chain required locally, and then determines the investment promotion plan required for each key link through the key links. According to the results, investment promotion work can be carried out more accurately for key links of different industrial chains in different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a method for mining and analyzing investment leads based on a large model agent of the present invention;

[0067] Figure 2 This is a module diagram of a large-model intelligent agent-based investment lead mining and analysis system of the present invention. DETAILED DESCRIPTION

[0068] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. Furthermore, features described in some examples may be combined in other examples.

[0069] At least one embodiment of the present invention discloses a method for mining and analyzing investment leads based on a large model agent, such as Figure 1 As shown, the following steps are included:

[0070] Step 100: Collect key link information, which includes all links in the industrial chain and information on economic development zones;

[0071] The industry chain includes: battery material supply, motor material supply, power electronic device supply, lightweight material supply, power battery production, drive motor production, power electronic control system production, vehicle design, body manufacturing, vehicle assembly, charging station and charging pile construction, operator service system, battery recycling, material reuse, core software system development, testing and inspection services, logistics and marketing services;

[0072] Information on economic development zones includes: location advantages, resources, industry foundation, human resources, supporting facilities, policy support, and development goals;

[0073] Step 200: Encode key link information into No. 1 graph structure data; No. 1 graph structure data includes nodes and edges connecting nodes, where a node represents an industrial chain link, location advantage, resource information, industrial foundation, human resources, supporting facilities, policy support, or development goals;

[0074] It indicates that there are edges between the nodes of all industrial chain links; when it indicates that the location advantage brings convenience to the industrial chain link, there is an edge between the industrial chain link and the location advantage; when it indicates that the resource information is conducive to the production and processing of the industrial chain link, there is an edge between the industrial chain link and the resource information; when the existing industrial base and the industrial chain link have an upstream and downstream relationship or a collaborative relationship, there is an edge between the industrial chain link and the industrial base; when it indicates that the quality or quantity of human resources meets the production needs of the industrial chain, there is an edge between the industrial chain link and human resources; when it indicates that the supporting facilities can support the smooth operation of the industrial chain link, there is an edge between the industrial chain link and the supporting facilities; when it indicates that policy support can attract and promote the development of the industrial chain link, there is an edge between the industrial chain link and policy support; when it indicates that the development goal attaches importance to the industrial chain link, there is an edge between the industrial chain link and the development goal;

[0075] Indicates that the characteristics of the industrial chain links, location advantages, resource information, industrial base, human resources, supporting facilities, policy support, and development goals are equal to the corresponding node characteristics;

[0076] The characteristics of industrial chain links, location advantages, resource information, industrial base, human resources, supporting facilities, policy support and development goals are obtained through feature engineering.

[0077] Step 300: Input the graph structure data No. 1 into a key link model. The key link model includes a first hidden layer and a first output layer. The first hidden layer inputs the graph structure data No. 1 and outputs a first hidden state to the first output layer. The first output layer outputs a key link of the industrial chain required by the economic development zone.

[0078] In one embodiment of the present invention, the calculation formula of the first hidden layer is as follows:

[0079] ;

[0080] in Indicates the first diagram structure data The first hidden state of the node, It is the node of the No. 1 graph structure data The set of nodes with edges between them, Indicates the first diagram structure data The node characteristics of the nodes, is a normalizing constant (such as node degree), represents the weight parameter of the No. 1 graph, Indicates the bias parameter of Figure 1, is a sigmoid function.

[0081] In one embodiment of the present invention, the calculation formula of the first output layer is as follows:

[0082] ;

[0083] in Represents a vector, the cth component of the vector represents the probability of the cth key link, Indicates the first diagram structure data The first hidden state of the node, Indicates concatenating the first hidden states of all nodes. A collection of nodes representing the links in the industrial chain, is the first weight parameter, is the first bias parameter, Represents the sigmoid function.

[0084] In one embodiment of the present invention, the key links of the industrial chain required by the economic development zone output by the key link model are decoded to obtain specific key links before proceeding to subsequent steps.

[0085] Step 400: Collect investment lead information, which includes key links in the industrial chain, enterprise information, and economic development zone information;

[0086] Enterprise information includes: enterprise name, type, scale, establishment time, legal representative, business scope, registered address, registered capital, number of employees, core technology, number of patents, and market share.

[0087] Step 500: Encode the investment lead information into graph structure data No. 2, which includes nodes and edges connecting the nodes. A node represents a key link in the industrial chain, an enterprise, or an economic development zone.

[0088] There are edges between the nodes representing all key links. There are edges between enterprises when there is a supply chain relationship or a competitive relationship between enterprises. There are edges between enterprises and key links when enterprises are engaged in a certain key link. There are edges between economic development zones and all nodes representing key links.

[0089] The node characteristics of the node representing the enterprise are equal to the enterprise characteristics, the node characteristics of the node representing the key link are equal to the key link characteristics, and the node characteristics of the node representing the economic development zone are equal to the economic development zone characteristics;

[0090] Enterprise characteristics, key link characteristics and economic development zone characteristics are obtained through feature engineering;

[0091] Encode the investment lead information of all nodes into second sequence data, the second sequence data includes n sequence units, and the t-th sequence unit represents the investment lead information of all nodes at the t-th moment;

[0092] Step 600: Input the graph structure data No. 2 into the investment attraction model. The model includes a second hidden layer, a third hidden layer, and a second output layer. The second hidden layer inputs the graph structure data No. 2 and outputs the second hidden state to the third hidden layer. The third hidden layer outputs the third hidden state to the second output layer. The output of the second output layer represents the investment attraction plan.

[0093] In one embodiment of the present invention, the key link information and investment lead information are in text mode, and the WordEmbedding algorithm is used as a feature engineering method.

[0094] In one embodiment of the present invention, the calculation formula of the second hidden layer is as follows:

[0095] ;

[0096] in The tth sequence unit of the second sequence data is represented by The second hidden state of the node, The tth sequence unit of the second sequence data is represented by The aggregation coefficient of nodes, represents the set of nodes that have edges with the vth node of the tth sequence unit of the second sequence data, represents the activation function, represents the graph weight coefficient;

[0097] ;

[0098] ;

[0099] ;

[0100] in and The tth sequence unit of the second sequence data is represented by and The node characteristics of the nodes, and The tth sequence unit of the second sequence data is represented by and The linear transformation characteristics of nodes, represents the aggregation weight coefficient, represents the splicing weight coefficient, T represents transposition, represents an exponential function with a natural constant as the base, LeakyRelu represents a corrected linear unit function, Indicates the The set of nodes with which there are edges.

[0101] In one embodiment of the present invention, the calculation formula of the third hidden layer is as follows:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] in, 、 、 represents the first, second and third sequence weight parameters, 、 、 represents the first, second and third sequence bias parameters, represents the dot product, 、 and Represent the first, second, and third intermediate states respectively, where , The tth sequence unit of the second sequence data is represented by The second hidden state of the node, Indicates the first t-th sequence unit of the second sequence data The set of nodes with which there are edges. and Represent the t-th and t-1-th third hidden states respectively, n≥t≥1, n represents the total number of sequence units of the second sequence data, when t=1 , tanh is the hyperbolic tangent function, Represents the sigmoid function.

[0107] In one embodiment of the present invention, the calculation formula of the second output layer is as follows:

[0108] ;

[0109] in Represents a vector, the i-th component of the vector represents the probability value of the i-th action; the set of all actions is called the action space, which contains all possible actions that may be selected. represents the t-th third hidden state, Indicates the concatenation of the 1st to nth third hidden states, n represents the total number of sequence units of the second sequence data, is the second weight parameter, is the second bias parameter, Represents the sigmoid function.

[0110] In one embodiment of the present invention, an action is an investment promotion plan, and an action is represented as a matrix. The first row and j column of the matrix represents the type of enterprise required by the jth key link, the second row and j column represents the number of enterprises required by the jth key link, the third row and j column represents the scale of the enterprise required by the jth key link, and the fourth row and j column represents the priority of the enterprise required by the jth key link.

[0111] In one embodiment of the present invention, the actions in the action space are to randomly combine the enterprise type, number of enterprises, enterprise scale, and investment promotion priority. The enterprise type and industrial chain link division are the same, the number of enterprises is an integer value between 1-10, the enterprise scale is divided into three levels: small, medium, and large, and the investment promotion priority is divided into three levels: one, two, and three.

[0112] In one embodiment of the present invention, the investment promotion plan output by the investment promotion model is decoded to obtain a specific investment promotion plan, which is then implemented by relevant departments of the local government.

[0113] In one embodiment of the present invention, Figure 2 As shown, a large-scale intelligent agent-based investment lead mining and analysis system includes the following modules:

[0114] The key link information collection module 101 is used to collect key link information, which includes information on all links in the industrial chain and economic development zones;

[0115] The first graph structure data encoding module 102 is used to encode key link information into the first graph structure data; the graph structure data includes nodes and edges connecting nodes, and each node represents an industrial chain link, location advantage, resource information, industrial foundation, human resources, supporting facilities, policy support, or development goals;

[0116] The key link acquisition module 103 is used to input the No. 1 graph structure data into a key link model. The key link model includes a first hidden layer and a first output layer. The first hidden layer inputs the No. 1 graph structure data and outputs a first hidden state to the first output layer. The first output layer outputs the key links of the industrial chain required by the economic development zone.

[0117] The investment lead information collection module 104 is used to collect investment lead information, which includes key links in the industrial chain, enterprise information and economic development zone information;

[0118] The second graph structure data encoding module 105 is used to encode the investment lead information into the second graph structure data. The graph structure data includes nodes and edges connecting nodes. A node represents a key link in the industrial chain or an enterprise or economic development zone.

[0119] The investment promotion plan acquisition module 106 is used to input the No. 2 graph structure data into the investment promotion model. The model includes a second hidden layer, a third hidden layer and a second output layer. The second hidden layer inputs the No. 2 graph structure data and outputs the second hidden state to the third hidden layer. The third hidden layer outputs the third hidden state to the second output layer. The output of the second output layer represents the investment promotion plan.

[0120] In one embodiment of the present invention, both the key link model and the investment attraction model are trained using reinforcement learning, wherein the model to be trained is the key link model or the investment attraction model;

[0121] The steps of training the model to be trained include:

[0122] Step 201: Build a target network, and copy the model to be trained to the target network;

[0123] Step 202: randomly obtain an experience from the experience pool. The experience includes the state at time t. , the action performed at time t , the state at time e+1 , perform the action Rewards received ;

[0124] The states at time e and e+1 represent the key link information or investment lead information at time e and e+1 of the input to the model to be trained respectively;

[0125] Then calculate the state The target network output corresponds to The probability value of ;

[0126] ;

[0127] express The maximum probability value output by the target network;

[0128] Step 203, according to and Calculate the loss value loss and update the model to be trained;

[0129] ;

[0130] express The output of the model to be trained corresponds to The probability value of

[0131] is the discount factor, a value between 0 and 1;

[0132] Step 204: Update the target network every fixed number of network updates so that its parameters are the same as those of the current model to be trained;

[0133] Step 205: The step is terminated until the number of collected experiences reaches a set threshold or the model to be trained converges.

[0134] In one embodiment of the present invention, the experience is derived from the record of historical action execution.

[0135] In one embodiment of the present invention, the reward of the key clue model The calculation formula is as follows:

[0136]

[0137] ;

[0138] in Indicates the number of key links from time e to e+1, represents the total number of links in the industrial chain, represents the location adaptation score given by experts from time e to e+1, represents the natural resource adaptation score given by experts from time e to e+1, represents the human resource adaptation score given by experts from time e to e+1, represents the policy support adaptation score given by experts from time e to e+1, 、 、 、 、 Represent the first, second, third, fourth, and fifth weight coefficients respectively.

[0139] In one embodiment of the present invention, the incentives of the investment model The calculation formula is as follows:

[0140]

[0141] ;

[0142] in It represents the new tax revenue of the economic development zone from time e to e+1. Indicates the total tax revenue of the economic development zone in the previous year, It represents the newly added employment population in the economic development zone from time e to e+1. represents the total number of employees in the economic development zone in the previous year, It represents the average salary of the investment project from time e to e+1. represents the average wage in the economic development zone, It represents the capital investment in the economic development zone from time e to time e+1. Indicates the total annual capital investment in the economic development zone, It represents the land area investment of the economic development zone from time e to e+1. It represents the total annual land area invested in the economic development zone. 、 、 、 、 They represent the sixth, seventh, eighth, ninth and tenth weight coefficients respectively.

[0143] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A method for mining and analyzing investment leads based on a large model agent, characterized in that: The following steps are involved: Step 100: Collect information on key links, including all links and economic development zones in the industrial chain. The industrial chain includes battery material supply, motor material supply, power electronic device supply, lightweight material supply, power battery production, drive motor production, power electronic control system production, vehicle design, body manufacturing, vehicle assembly, charging station and charging pile construction, operator service system, battery recycling, material reuse, core software system development, testing and inspection services, and logistics and marketing services. Information on economic development zones includes: location advantages, resources, industry foundation, human resources, supporting facilities, policy support, and development goals; Step 200: Encode the key link information into No. 1 graph structure data; the graph structure data includes nodes and edges connecting the nodes, and each node represents an industrial chain link, location advantage, resource information, industrial foundation, human resources, supporting facilities, policy support, or development goals; It indicates that there are edges between the nodes of all industrial chain links; when it indicates that the location advantage brings convenience to the industrial chain link, there is an edge between the industrial chain link and the location advantage; when it indicates that the resource information is conducive to the production and processing of the industrial chain link, there is an edge between the industrial chain link and the resource information; when the existing industrial base and the industrial chain link have an upstream and downstream relationship or a collaborative relationship, there is an edge between the industrial chain link and the industrial base; when it indicates that the quality or quantity of human resources meets the production needs of the industrial chain, there is an edge between the industrial chain link and human resources; when it indicates that the supporting facilities can support the smooth operation of the industrial chain link, there is an edge between the industrial chain link and the supporting facilities; when it indicates that policy support can attract and promote the development of the industrial chain link, there is an edge between the industrial chain link and policy support; when it indicates that the development goal attaches importance to the industrial chain link, there is an edge between the industrial chain link and the development goal; Indicates that the characteristics of the industrial chain links, location advantages, resource information, industrial base, human resources, supporting facilities, policy support, and development goals are equal to the corresponding node characteristics; The characteristics of industrial chain links, location advantages, resource information, industrial base, human resources, supporting facilities, policy support, and development goals are obtained through feature engineering; Step 300: Input the No. 1 graph structure data into the key link model. The key link model includes a first hidden layer and a first output layer. The first hidden layer inputs the No. 1 graph structure data and outputs a first hidden state to the first output layer. The first output layer outputs the key links of the industrial chain required by the economic development zone. The calculation formula of the first hidden layer is as follows: ； in Indicates the first diagram structure data The first hidden state of the node, It is the node of the No. 1 graph structure data The set of nodes with edges between them, Indicates the first diagram structure data The node characteristics of the nodes, is a normalizing constant, represents the weight parameter of the No. 1 graph, Indicates the bias parameter of Figure 1, is a sigmoid function; The calculation formula of the first output layer is as follows: ； in Represents a vector, the cth component of the vector represents the probability of the cth key link, Indicates the first diagram structure data The first hidden state of the node, Indicates concatenating the first hidden states of all nodes. A collection of nodes representing the links in the industrial chain, is the first weight parameter, is the first bias parameter, represents the sigmoid function; Step 400: Collect information on investment leads, including key links in the industrial chain, enterprise information, and economic development zone information. Enterprise information includes: enterprise name, type, scale, establishment time, legal representative, business scope, registered address, registered capital, number of employees, core technology, number of patents, and market share. Step 500: Encode the investment lead information into second graph structure data. The graph structure data includes nodes and edges connecting the nodes. A node represents a key link in the industrial chain or an enterprise or economic development zone. There are edges between the nodes representing all key links. There are edges between enterprises when there is a supply chain relationship or a competitive relationship between enterprises. There are edges between enterprises and key links when enterprises are engaged in a certain key link. There are edges between economic development zones and all nodes representing key links. The node characteristics of the node representing the enterprise are equal to the enterprise characteristics, the node characteristics of the node representing the key link are equal to the key link characteristics, and the node characteristics of the node representing the economic development zone are equal to the economic development zone characteristics; Enterprise characteristics, key link characteristics and economic development zone characteristics are obtained through feature engineering; Encode the investment lead information of all nodes into sequence data, the sequence data includes n sequence units, and the t-th sequence unit represents the investment lead information of all nodes at the t-th moment; Step 600: Input the graph structure data No. 2 into the investment attraction model. The model includes a second hidden layer, a third hidden layer, and a second output layer. The second hidden layer inputs the graph structure data No. 2 and outputs the second hidden state to the third hidden layer. The third hidden layer outputs the third hidden state to the second output layer. The output of the second output layer represents the investment attraction plan. The calculation formula for the second hidden layer is as follows: ； in The tth sequence unit of the second sequence data is represented by The second hidden state of the node, The tth sequence unit of the second sequence data is represented by The aggregation coefficient of nodes, represents the set of nodes that have edges with the vth node of the tth sequence unit of the second sequence data, represents the activation function, represents the graph weight coefficient; ； ； ； in and The tth sequence unit of the second sequence data is represented by and The node characteristics of the nodes, and The tth sequence unit of the second sequence data is represented by and The linear transformation characteristics of nodes, represents the aggregation weight coefficient, represents the splicing weight coefficient, T represents transposition, represents an exponential function with a natural constant as the base, LeakyRelu represents a corrected linear unit function, Indicates the The set of nodes with which there are edges; The calculation formula for the third hidden layer is as follows: ； ； ； ； in, 、 、 represents the first, second and third sequence weight parameters, 、 、 represents the first, second and third sequence bias parameters, represents the dot product, 、 and Represent the first, second, and third intermediate states respectively, where , The tth sequence unit of the second sequence data is represented by The second hidden state of the node, Indicates the first t-th sequence unit of the second sequence data The set of nodes with which there are edges. and Represent the t-th and t-1-th third hidden states respectively, n≥t≥1, n represents the total number of sequence units of the second sequence data, when t=1 , tanh is the hyperbolic tangent function, represents the sigmoid function; The calculation formula of the second output layer is as follows: ； in Represents a vector, the i-th component of the vector represents the probability value of the i-th action; the set of all actions is called the action space, which contains all possible actions that may be selected. represents the t-th third hidden state, Indicates the concatenation of the 1st to nth third hidden states, n represents the total number of sequence units of the second sequence data, is the second weight parameter, is the second bias parameter, represents the sigmoid function; Among them, an action is a business investment plan, and an action is represented by a matrix. The first row and the jth column of the matrix represent the type of enterprise required by the jth key link, the second row and the jth column represent the number of enterprises required by the jth key link, the third row and the jth column represent the scale of the enterprises required by the jth key link, and the fourth row and the jth column represent the priority of the enterprises required by the jth key link. The actions in the action space are random combinations of enterprise type, number of enterprises, enterprise scale, and investment promotion priority. The enterprise type and industrial chain link divisions are the same. The number of enterprises is an integer value between 1 and 10. The enterprise scale is divided into three levels: small, medium, and large. The investment promotion priority is divided into three levels: one, two, and three.

2. The method for mining and analyzing investment leads based on a large model agent according to claim 1, characterized in that: Both the key link model and the investment attraction model are trained using reinforcement learning, where the model to be trained is the key link model or the investment attraction model; The steps of training the model to be trained include: Step 201: Build a target network, and copy the model to be trained to the target network; Step 202: randomly obtain an experience from the experience pool. The experience includes the state at time t. , the action performed at time t , the state at time e+1 , perform the action Rewards received ; The states at time e and e+1 represent the key link information or investment lead information at time e and e+1 of the input to the model to be trained respectively; Then calculate the state The target network output corresponds to The probability value of ; ； express The maximum probability value output by the target network; Step 203, according to and Calculate the loss value loss and update the model to be trained; ； express The output of the model to be trained corresponds to The probability value of is the discount factor, a value between 0 and 1; Step 204: Update the target network every fixed number of network updates so that its parameters are the same as those of the current model to be trained; Step 205: The step is terminated until the number of collected experiences reaches a set threshold or the model to be trained converges.

3. A large-scale intelligent agent-based investment lead mining and analysis system, characterized in that: It is used to execute the investment lead mining and analysis method based on a large model agent as claimed in claim 1, and includes the following modules: A No. 1 graph structure data encoding module is used to encode key link information into No. 1 graph structure data; the graph structure data includes nodes and edges connecting nodes, and each node represents an industrial chain link, location advantage, resource information, industrial foundation, human resources, supporting facilities, policy support, or development goals; a key link acquisition module, configured to input the No. 1 graph structure data into a key link model, wherein the key link model includes a first hidden layer and a first output layer, wherein the first hidden layer inputs the No. 1 graph structure data and outputs a first hidden state to the first output layer, and the first output layer outputs a key link of the industrial chain required by the economic development zone; The investment lead information collection module is used to collect investment lead information, including key links in the industrial chain, enterprise information and economic development zone information; A second graph structure data encoding module is used to encode investment lead information into second graph structure data. The graph structure data includes nodes and edges connecting nodes. A node represents a key link in the industrial chain, an enterprise, or an economic development zone. The investment promotion plan acquisition module is used to input the No. 2 graph structure data into the investment promotion model. The model includes a second hidden layer, a third hidden layer and a second output layer. The second hidden layer inputs the No. 2 graph structure data and outputs the second hidden state to the third hidden layer. The third hidden layer outputs the third hidden state to the second output layer. The output of the second output layer represents the investment promotion plan.

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