Industrial project auxiliary decision support system based on administration of government, industry, university and research fund

Abstract

The invention relates to the technical field of industrial decision support, and discloses an industrial project auxiliary decision support system based on administration of government, industry and university research fund. The system comprises a relation network construction module, a time sequence track reduction module, a region heat calculation module and an intelligent deduction core module. The system constructs an industrial association map by receiving multi-source data, and restores an industrial project development trajectory chain with continuous time attributes from the industrial association map. Meanwhile, a dynamic regional industry thermal distribution diagram is generated according to the geographic positions of the track chain nodes. And the intelligent deduction core module deduces the landing probability and the resource demand vector of an industrial project in a specified area by synchronously utilizing the trajectory chain and the thermal distribution map and combining machine learning and an area heat value under the structural constraint of the correlation map. According to the scheme, the reduction of the full life cycle time sequence context of the industrial project is realized, and intelligent deduction can be carried out under the dual dynamic constraints of the industrial network and the space popularity.

Description

Technical Field

[0001] This invention relates to the field of industrial decision support technology, specifically to an industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application. Background Technology

[0002] Current industrial project management and regional economic analysis generally rely on fragmented databases and independent analytical tools. Tracking industrial projects typically involves using project management software or spreadsheets to record key milestones. These records are discrete and isolated, reflecting only the project's status at a single point in time. They cannot automatically identify and connect the complete, continuous lifecycle evolution path of a project from concept to implementation from heterogeneous data from multiple sources such as government, industry, academia, research, finance, and application. Data from different project stages is scattered across reports in different systems, lacking semantic connections between them. This makes it difficult to extract chronologically logical development patterns from historical projects and to intuitively understand the dynamic evolution of projects within complex innovation networks.

[0003] In predicting industrial layout and project implementation, existing methods often employ statistical models based on historical macroeconomic data or evaluation models based on the project's own attributes. These methods tend to treat projects as independent entities, neglecting the constraints of the actual industrial ecosystem network in which the project operates. Furthermore, the regional industrial environment is typically simplified to static economic indicators, failing to reflect real-time spatial heat changes resulting from the dynamic aggregation of specific projects. This makes the predictions potentially detached from the actual industrial network structure and unable to accurately match the region's real-time carrying capacity and resource endowment, thus limiting their value for decision-making. Summary of the Invention

[0004] The purpose of this invention is to provide an auxiliary decision support system for industrial projects based on government, industry, academia, research, finance, and application, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides an industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application, the system comprising: The relational network construction module is used to receive raw multi-source data from government, industry, academia, research, finance, and application. By parsing the entity and event relationships between data, it generates a knowledge node with a unique identifier for each data point and constructs an initial industry association graph containing multi-dimensional connection edges based on the semantic and logical dependencies between nodes. The time-series trajectory restoration module is used to receive the initial industry association map, extract the status markers and time points related to the life cycle of the industry project from the knowledge nodes, connect different status nodes of the same industry project in chronological order, and generate an industry project development trajectory chain with continuous time attributes. The regional heat map calculation module is used to receive the development trajectory chain of the industrial projects, aggregate the nodes into the corresponding geographic grids according to the geographical location tags contained in the nodes in the trajectory chain, calculate the number and weight of new nodes added in each grid in different time windows, and dynamically generate a regional industrial heat map. The intelligent simulation core module is used to simultaneously receive the development trajectory chain of the industrial project and the regional industrial heat map. Based on machine learning methods, under the structural constraints of the industrial association map, it simulates the next stage state of the industrial project along the development trajectory chain, and combines the heat value of the regional industrial heat map to infer the probability of the industrial project's implementation and resource demand vector within a specified geographical grid, and outputs a structured decision simulation report.

[0006] Preferably, the relationship network construction module performs the following steps: Entity identification and relationship extraction are performed on the original multi-source data. Each identified government, industry, academia, research, finance and service institution, policy document, technology patent, financial product and service project is defined as an entity. A knowledge node is created for each entity, and each knowledge node is given metadata containing type, source and key attributes. Between the knowledge nodes, based on event co-occurrence, semantic similarity, and preset rule templates, connection edges representing cooperation, membership, reference, support, and conflict relationships are established, and each connection edge has a relationship type and confidence weight. Integrate all knowledge nodes and their connecting edges to form a network structure with multiple relational dimensions, which is the initial industry association graph. The initial industry association graph is stored in the form of a node list and an adjacency matrix.

[0007] Preferably, the time-series trajectory restoration module performs the following steps: From the initial industry association map, knowledge nodes of type industry project are selected as the starting point of the trajectory; Starting from the knowledge node of the industry project, a breadth-first traversal is performed along the connecting edges in the initial industry association graph to find other knowledge nodes that have event associations with the industry project node. The event associations include project application, project approval, R&D stage, financing events, and results release. For each associated knowledge node found, the time point recorded in its attributes is extracted. The starting node of the industry project is linearly connected with each associated node in the order of time from early to late, forming a node sequence sorted by time. In the node sequence, the time interval and stage name are labeled for each adjacent node, and the development trajectory chain of the industry project is finally generated.

[0008] Preferably, the regional heat calculation module performs the following steps: Read the development trajectory chain of the industry project, analyze the attributes of each knowledge node in the chain, and extract the geographical location description information that is explicitly recorded or implicit. The geographic location description information is converted into a standard geographic grid code using a geocoding service. Set a sliding time window, count the number of knowledge nodes that fall into the same geographic grid code in each time window, and pre-assign different weight coefficients according to the type of knowledge node. Calculate the total weighted number of nodes of the geographic grid in the time window as the original heat value. The original heat values ​​of the same geographic grid at different time windows are standardized to eliminate dimensional differences and generate a standard heat value sequence. Based on this, a dynamically changing regional industrial heat distribution map is drawn.

[0009] Preferably, the step of assigning metadata to each knowledge node in the relationship network construction module includes: Assign a globally unique string identifier to each identified entity; Based on the data source and inherent attributes of the entity, determine the type of knowledge node corresponding to the entity. The types include government departments, research institutions, production enterprises, financial institutions, service platforms, policy provisions, technical topics, and financial instruments. Key attributes of the entity are extracted from the raw data in a structured manner or predicted through machine learning models. These key attributes include establishment time, industry, scale level, effective period, and technological maturity. The globally unique string identifier, the type, and the key attributes are encapsulated to form the complete metadata of the knowledge node.

[0010] Preferably, the step of performing a breadth-first traversal along the connecting edges in the time-series trajectory reconstruction module includes: Initialize a queue to be accessed, add the industry project knowledge nodes to the queue, and mark them as accessed; Take a node from the queue and search in the adjacency matrix of the initial industry association graph for all neighboring nodes that are directly connected to the industry project knowledge node; Check the relationship type of the connection edge between each neighbor node and the current node. If the relationship type belongs to the preset industry project development related event type, add the neighbor node to the queue to be visited and record its event relationship with the current node. Repeat the process of retrieving nodes from the queue to be visited, finding neighboring nodes, checking relationship types, and updating the queue until the queue to be visited is empty or the preset traversal depth is reached, and finally obtain the set of all event nodes related to the starting industry project node.

[0011] Preferably, the step of calculating the total number of weighted nodes in the regional heat calculation module includes: A positive real number is assigned as a weighting coefficient for each type of knowledge node, with node types that represent the core progress of industrial projects having a higher weighting coefficient. For each knowledge node that falls within a specific geographic grid and a specific time window, query the corresponding weight coefficient based on its type; The weight coefficients of all knowledge nodes within the specific geographic grid and the specific window are summed, and the resulting sum is the original heat value.

[0012] Preferably, the steps in the intelligent simulation core module for simulating the next stage of an industrial project include: The current state node of the development trajectory chain of the industry project is used as the input; In the initial industry association map, find the knowledge nodes of all other industry projects that have historically been connected to the terminal state node, and use the subsequent development trajectories of these industry projects as reference samples; By utilizing sequence pattern mining methods in machine learning, the potential patterns of state transitions in industrial projects are learned from the reference samples, and a state transition probability model is constructed. The features of the terminal state node are input into the state transition probability model to calculate the probability distribution of possible transitions to the next state node.

[0013] Preferably, the steps in the intelligent simulation core module for simulating the probability of implementation and the resource demand vector include: From the probability distribution output by the state transition probability model, select the candidate next states with the highest probabilities; For each candidate next state, based on its attribute characteristics, the most suitable geographical grid is matched in the regional industrial heat distribution map, and the current standard heat value of the matching grid is used as the environmental adaptation factor. The environmental adaptation factor and the transfer probability are fused together to calculate the comprehensive probability of the industrial project achieving the candidate next state in the geographic grid. Meanwhile, based on the average value of various resources consumed by industrial projects in similar states in similar hot areas in historical data, a multi-dimensional resource demand vector is generated. The dimensions of the resource demand vector include funding, talent, policy, and technology.

[0014] Preferably, the step of the intelligent inference core module outputting the decision inference report includes: This will summarize all candidate next states for an industrial project and their corresponding comprehensive implementation probability, target geographic grid, and resource demand vector; The simulation results are sorted from highest to lowest based on the overall probability of implementation, forming a list of projection results; The list of projection results, along with the original industrial project development trajectory chain and related regional industrial heat map segments, are packaged and integrated into a complete structured document containing current status retrospective, future projection, spatial suggestions, and resource prediction, namely the decision projection report.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By extracting state markers and time points related to the lifecycle of industry projects from the knowledge nodes of the constructed initial industry association graph, and connecting different state nodes of the same industry project in chronological order, a development trajectory chain of industry projects with continuous time attributes is generated. This technology automatically identifies and reorganizes fragmented information representing different stages of a project, which was originally scattered across multi-source data, based on unique identifiers and temporal logic. This allows the complete development path of an industry project to be clearly presented, connecting key stages such as project initiation, R&D, and industrialization into a visualized trajectory chain, thereby revealing the continuous process and stage transition patterns of project development, and providing a temporal data foundation for analyzing project growth patterns.

[0016] When simulating the next stage of an industrial project using machine learning methods, structural constraints from the industrial association graph were simultaneously applied, and the heat values ​​from the regional industrial heat map were incorporated. The simulation process not only considered the project's own historical trajectory but also enforced adherence to the existing network of relationships between entities, while introducing dynamic heat characteristics reflecting spatial agglomeration effects. This joint simulation mechanism under multi-dimensional constraints ensures that the simulated next stage of the project and its implementation probability simultaneously satisfy the internal logic of the industrial network and the external conditions of the regional environment. The output resource demand vector also incorporates the dual influences of network structure and spatial heat, making the simulation conclusions more closely aligned with the complex realities of the industrial system. Attached Figure Description

[0017] Figure 1 This is a sequence diagram of the industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application, as described in this invention. Figure 2 A flowchart illustrating the operation of the timing trajectory reconstruction module; Figure 3 A flowchart illustrating the operation of the regional heat calculation module; Figure 4 A comparison chart of the average confidence level and sample size distribution for different association types; Figure 5 This is a comparison chart of standard heat values ​​for geographic grids. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides an auxiliary decision support system for industrial projects based on government, industry, academia, research, finance, and application. The system includes: firstly, relying on the aggregation and processing of multi-source heterogeneous data from government, industry, academia, research, finance, and application, constructing a relational graph, reconstructing development trajectories, calculating regional heat maps, and finally performing intelligent inference. Specifically, the system operates sequentially according to a modular process: the relational network construction module receives the original multi-source data, identifies institutions, policies, technologies, funds, and service entities through knowledge extraction technology and defines them as knowledge nodes, then extracts various semantic and event relationships between entities to form connection edges, thereby constructing a multi-level, multi-relational dimension initial industrial relational graph. The time-series trajectory reconstruction module receives this graph, identifies core nodes representing industrial projects, and uses these nodes as starting points to traverse the graph to find various related event nodes. Based on the time information recorded in the event node attributes, these nodes are connected in chronological order, thus forming industrial project development trajectory chains that record key nodes from project inception to maturity. The regional heat map module receives these trajectory chains, analyzes the geographical location information carried by the nodes in the chains, and maps them to a unified spatial grid through geocoding. Then, within a sliding time window, it performs weighted statistics and standardization on the number and importance of newly added nodes in each grid, dynamically generating a regional industry heat map reflecting the level of industrial activity in different regions at different times. The intelligent inference core module simultaneously receives the industrial project development trajectory chains and the regional industry heat map. Starting from the current state of the trajectory chain, under the constraints of the structural relationships contained in the industry association map, it uses machine learning methods to learn the historical project state transition patterns, predicts the next stage state of the project, and combines the environmental adaptability reflected in the heat map of the target region to comprehensively infer the probability of the project's implementation and development in a specific region and the composition of various resources required, ultimately outputting a structured decision inference report.

[0020] Example 1: The relational network construction module performs entity recognition and relation extraction on the acquired raw multi-source data. From unstructured text, semi-structured tables, and structured database records, it identifies entities such as government departments, universities, research institutes, enterprises, banks, investment funds, and service organizations, as well as specific policy provisions, patent documents, financial products, and service case entities. Each identified entity is defined as a knowledge node. Metadata containing type, source, and key attributes is assigned to each knowledge node. In the step of assigning metadata to each knowledge node, a globally unique string identifier is assigned to each identified entity. Based on the entity's data source and inherent attributes, the type of knowledge node corresponding to the entity is determined. These types include government departments, research institutions, manufacturing enterprises, financial institutions, service platforms, policy provisions, technology themes, and funding instruments. Key attributes of the entity are extracted from the raw data in a structured manner or predicted through machine learning models. These key attributes include establishment time, industry, scale level, effective period, and technological maturity. The globally unique string identifier, type, and key attributes are encapsulated to constitute the complete metadata of the knowledge node. Between knowledge nodes, connections are established based on event co-occurrence, semantic similarity, and pre-defined rule templates. These connections represent cooperation, affiliation, referencing, support, and conflict, and each connection contains two attributes: relationship type and confidence weight. By integrating all knowledge nodes with metadata and their attributed connections, the system constructs and stores an initial industry association graph in the form of a node list and an adjacency matrix.

[0021] In its implementation, the relationship network construction module processes multi-source data from the field of "new energy vehicle battery technology." The module performs entity identification and relationship extraction on the original multi-source data. Data sources include government industry planning documents, academic paper databases, enterprise R&D project announcements, venture capital news, and technical service contract filing information. From a PDF document titled "Guiding Opinions on the Development of High-tech Industries in City A in 2023," the entity identification process identifies "City A Science and Technology Bureau," "new energy vehicles," and "financial subsidies," defining "City A Science and Technology Bureau" as a government department entity and "Article 5 of the financial subsidy clause" as a policy clause entity. In the metadata of an academic paper titled "Research on the Cycle Stability of High-Nickel Ternary Cathode Materials," "University B Lithium Battery Laboratory" and "Researcher Wang Ming" are identified. "University B Lithium Battery Laboratory" is defined as a research institution entity, and "a coating modification method" mentioned in the paper is defined as a technical topic entity. From Company C's annual report, "C New Energy Technology Co., Ltd." and "2GWh Power Battery Project" were identified. "C New Energy Technology Co., Ltd." was defined as a production enterprise entity, and "2GWh Power Battery Project" was defined as an industrial project entity.

[0022] A knowledge node is created for each entity, and metadata containing type, source, and key attributes is assigned to each knowledge node. In the step of assigning metadata to each knowledge node, a globally unique string identifier "Gov_A_001" is assigned to the identified entity "Science and Technology Bureau of City A", and a globally unique string identifier "Res_B_005" is assigned to the entity "Lithium Battery Laboratory of University B". Based on the entity's data source and inherent attributes, the type of the knowledge node corresponding to entity "Article 5 of the Fiscal Subsidy Clause" is determined to be "Policy Clause", and the type of the knowledge node corresponding to entity "C New Energy Technology Co., Ltd." is determined to be "Manufacturing Enterprise". Key attributes of the entities are extracted from the raw data in a structured manner or predicted using a machine learning model. The "Establishment Time" attribute value of "C New Energy Technology Co., Ltd." is extracted from the structured database fields and is set to "2018-05-10". The "Technology Maturity" attribute value of "A Coating Modification Method" is predicted to be "Laboratory Stage" using a text classification model. The globally unique string identifier, type, and key attributes are encapsulated to form the complete metadata of the knowledge node. For example, the metadata of the knowledge node "Gov_A_001" is encapsulated as {ID: "Gov_A_001", Type: "Government Department", Source: "Guiding Opinions on the Development of High-tech Industries in City A in 2023.pdf", Properties: {"Field of Origin": "New Energy Vehicles", "Administrative Level": "City Level"}}.

[0023] Between knowledge nodes, connections are established based on event co-occurrence, semantic similarity, and preset rule templates. Based on event co-occurrence, the "Science and Technology Bureau of City A" node and the "C New Energy Technology Co., Ltd." node appear in the same project approval document. The module establishes a connection between them with a relationship type of "support" and an initial confidence weight of 0.9, according to the preset "Government Agency-Support-Enterprise" rule template. Based on semantic similarity calculation, the descriptive text vector similarity between the technical topic node "A Coating Modification Method" and another technical topic node "Preparation Process of High-Temperature Resistant Electrode Material" in the patent database reaches 0.85. The module establishes a connection between them with a relationship type of "Technology Related" and a confidence weight of 0.85. Each connection has a relationship type and a confidence weight. The calculation of the confidence weight can employ various quantification methods in specific implementations. In some embodiments, for edges generated based on rule templates, their weight is determined by the preset baseline weight of the template and the completeness of the data; for edges generated based on statistical or machine learning models, their weight directly corresponds to the probability value or similarity score output by the model. An optional method for calculating confidence weights is shown in the formula:

[0024] in: The final confidence weight of the connecting edge. The normalized value representing the co-occurrence frequency of an entity in the original data. This represents the semantic similarity of entities calculated using a neural network model. and It is a weighting coefficient used to adjust the contribution ratio of co-occurrence features and semantic features, and The module integrates all knowledge nodes and their connecting edges to form a network structure with multiple relational dimensions, which is the initial industry association graph. In specific implementation, the module integrates the nodes "City A Science and Technology Bureau", "C New Energy Technology Co., Ltd.", "Annual Production of 2GWh Power Battery Project", and "Article 5 of Fiscal Subsidy Clause", as well as the "Support", "Belong to", and "Based on" connecting edges established between them. The initial industry association graph is stored in the form of a node list and an adjacency matrix. The node list records the globally unique string identifiers and metadata of all knowledge nodes, while the adjacency matrix records whether there is a connecting edge between any two nodes and the type and weight of the edge.

[0025] Example 2: See Figure 2 The time-series trajectory reconstruction module selects knowledge nodes of type "industry project" from the initial industry association graph based on their type attributes, using these nodes as the starting points of the trajectory. Starting with an industry project knowledge node, it performs a breadth-first traversal along the connecting edges in the initial industry association graph to find other knowledge nodes with event associations related to that industry project node. These event associations include project application, project approval, R&D stage, financing events, and results release. The breadth-first traversal along the connecting edges includes: initializing a queue to be visited, adding the selected industry project knowledge nodes to the queue, and marking them as visited; retrieving a node from the queue and searching for all directly connected neighbor nodes in the adjacency matrix of the initial industry association graph; checking the relationship type of the connecting edges between each neighbor node and the current node; if the relationship type belongs to a preset set of industry project development-related event types, such as "obtaining financing" or "passing acceptance," then adding the neighbor node to the queue to be visited and recording its event relationship with the current node. The process of retrieving nodes from the queue, finding neighboring nodes, checking relationship types, and updating the queue is repeated until the queue is empty or the preset traversal depth is reached, ultimately obtaining a set of all event nodes related to the starting industry project node. For each found associated knowledge node, the explicitly recorded time point in its metadata attributes is extracted. Following the order of time points from earliest to latest, the starting industry project node is linearly connected to each associated event node, forming a time-sorted node sequence. Within this node sequence, the time interval and stage name are labeled for each adjacent node, ultimately generating an industry project development trajectory chain.

[0026] In its implementation, the time-series trajectory reconstruction module filters knowledge nodes of type "industrial project" from the stored initial industry association graph based on their type attributes. Several knowledge nodes in the initial industry association graph are marked with the type attribute "industrial project," such as the node "2GWh annual production capacity power battery project" with the identifier "Proj_C_001," and the node "solid electrolyte pilot production line" with the identifier "Proj_D_001." The module uses these industrial project nodes as the starting point for subsequent trajectory reconstruction. Starting with a specific industrial project knowledge node "2GWh annual production capacity power battery project," the time-series trajectory reconstruction module performs a breadth-first search along the connecting edges in the initial industry association graph, looking for other knowledge nodes that are event-related to the "2GWh annual production capacity power battery project" node. The preset industrial project development-related event types include "project application," "project approval," "R&D stage," "financing event," and "results release," and these event types correspond to the relationship type definitions of the connecting edges.

[0027] The steps for breadth-first traversal along the connecting edges include: initializing a queue to be visited, adding the "2GWh annual production capacity power battery project" node to the queue, and marking the "2GWh annual production capacity power battery project" node as visited. Then, the "2GWh annual production capacity power battery project" node is retrieved from the queue, and all neighboring nodes directly connected to it are searched in the adjacency matrix of the initial industry association graph. The query result of the adjacency matrix returns multiple neighboring nodes, such as the "A City Science and Technology Bureau" node, the "C New Energy Technology Co., Ltd." node, and the "High-performance battery separator R&D" node. Check the relationship type of the connection edges between each neighbor node and the "2GWh Power Battery Project" node. If the relationship type belongs to the preset industry project development-related event types, for example, the relationship type between the "C New Energy Technology Co., Ltd." node and the "2GWh Power Battery Project" node is "undertaking," and "undertaking" belongs to the project-related event type, then add the "C New Energy Technology Co., Ltd." node to the queue to be visited, and record the event relationship between the "C New Energy Technology Co., Ltd." node and the "2GWh Power Battery Project" node as "undertaking." If the relationship type does not belong to the preset industry project development-related event types, for example, the relationship type between the "A City Science and Technology Bureau" node and the "2GWh Power Battery Project" node is "administrative affiliation," and "administrative affiliation" does not belong to the preset event type set, then the "A City Science and Technology Bureau" node is not added to the queue to be visited. Repeatedly execute the process of retrieving nodes from the queue to be visited, finding neighboring nodes, checking relationship types, and updating the queue to be visited. For example, the next step is to retrieve the "C New Energy Technology Co., Ltd." node from the queue to be visited and find its neighboring nodes until the queue to be visited is empty or the preset traversal depth is reached. Finally, obtain the set of all event nodes related to the starting industry project node "Annual Production of 2GWh Power Battery Project". This set includes the "Project Approval Letter (Approval Number: 2023-001)" node, the "Angel Round Financing Agreement (Signing Party: XX Capital)" node, and the "First Generation Prototype Battery Test Report" node.

[0028] For each associated knowledge node found, the time-series trajectory reconstruction module extracts the time points recorded in its attributes. The "Approval Date" attribute value "2023-03-15" is extracted from the metadata attributes of the "Project Approval Document (Approval Number: 2023-001)" node; the "Signing Date" attribute value "2023-08-22" is extracted from the metadata attributes of the "Angel Round Financing Agreement (Signing Party: XX Capital)" node; and the "Completion Date" attribute value "2024-01-10" is extracted from the metadata attributes of the "First Generation Prototype Battery Test Report" node. Following the timeline from earliest to latest, the time-series trajectory reconstruction module linearly connects the starting node of the industrial project, "Annual Production of 2GWh Power Battery Project," with all related nodes, forming a time-ordered node sequence: ["Annual Production of 2GWh Power Battery Project" (starting point, no time point) -> "Project Approval Document (Approval Number: 2023-001)" (2023-03-15) -> "Angel Round Financing Agreement (Signed by: XX Capital)" (2023-08-22) -> "First Generation Prototype Battery Test Report" (2024-01-10)]. Within the node sequence, each adjacent node is labeled with a time interval and stage name. For example, the time interval between the "Project Approval Document" node and the "Angel Round Financing Agreement" node is marked as "160 days," and the stage name is "Financing Preparation Period"; the time interval between the "Angel Round Financing Agreement" node and the "First Generation Prototype Battery Test Report" node is marked as "141 days," and the stage name is "R&D Testing Period." Finally, the time-series trajectory reconstruction module generates a structured industrial project development trajectory chain. In some embodiments, this chain is stored in the system database as a graph structure or a JSON linked list with timestamps and relationship tags. It is understood that in other embodiments, for certain event nodes without explicit time attributes, the module performs time-series interpolation or sequence inference based on their logical connections with other time-series nodes in the initial industrial association graph. An optional formula for evaluating the influence of associated nodes during traversal to determine whether to include them in the event node set can be expressed as:

[0029] in: The value representing the influence assessment of related nodes. The weight representing the relationship type of the connecting edges is defined by a system-preset mapping table. It represents the strength of temporal evidence that is directly or indirectly related to the associated node, such as the clarity of the temporal attributes of the associated node itself or the depth of association with existing temporal nodes.

[0030] Example 3: See Figure 3The regional heat calculation module reads the development trajectory chain of industrial projects, analyzes the metadata attributes of each knowledge node in the chain, and extracts the geographical location description information explicitly recorded or implicit through text analysis, such as the registered address of the institution, the location of the project application, and the area of ​​technology application. Using geocoding services, the acquired geographical location description information is converted into standard geographic grid codes, enabling geographical location information from different sources and with different precisions to be uniformly mapped to a standard geospatial grid system. A sliding time window is set to count the number of knowledge nodes falling within the same geographic grid code in each time window, and different weight coefficients are pre-assigned according to the type of knowledge node. The steps to calculate the total number of weighted nodes include: assigning a positive real number as a weight coefficient to each type of knowledge node, where node types representing the core progress of industrial projects have higher weight coefficients, for example, the weight of the "major achievement release" node is greater than that of the "general cooperation signing" node. For each knowledge node falling within a specific geographic grid and a specific time window, the corresponding weight coefficient is queried according to its type. The weight coefficients of all knowledge nodes within a specific geographic grid and a specific time window are accumulated, and the resulting sum is the original heat value of that grid within that time window. The original heat values ​​of the same geographic grid in different time windows are standardized to eliminate the dimensional effects caused by differences in the size of the time window or the total number of nodes, generate a standard heat value sequence, and use it to draw a dynamically changing regional industrial heat distribution map.

[0031] In practical implementation, the regional heat calculation module reads one or more industrial project development trajectory chains. For example, it reads the industrial project development trajectory chain of "2GWh annual production power battery project". This industrial project development trajectory chain includes knowledge nodes such as "2GWh annual production power battery project", "Project establishment approval document (approval number: 2023-001)", "Angel round financing agreement (signing party: XX Capital)" and "First generation prototype battery test report". The module analyzes the metadata attributes of each knowledge node in the industrial project development trajectory chain and extracts the geographical location description information that is explicitly recorded or implicit through text analysis. It extracts "No. 1, Keji Road, High-tech Zone, City A" from the "Registered Address" attribute of the "C New Energy Technology Co., Ltd." node, extracts "Science and Technology Bureau of City A" from the "Issuing Authority" attribute of the "Project Establishment Approval Document" node, and associates it with "No. 18, Donghai Road, City A" through the address database. It also extracts "City A" from the "Signing Place" clause of the "Angel Round Financing Agreement" node. Using geocoding services, the acquired geographic location description information is converted into standard geographic grid codes. For example, "No. 1, Keji Road, High-tech Zone, City A" is converted into "G-101" grid code, "No. 18, Donghai Road, City A" is converted into "G-102" grid code, and "City A" is converted into a city-level grid code "CA" that covers a wider area. In an optional scheme, the system sets up a multi-level grid system, with city-level codes used for macro analysis and fine grids such as "G-101" used for micro analysis.

[0032] A sliding time window is set, for example, the window length is set to "1 quarter" and the sliding step is "1 month". The number of knowledge nodes falling into the same geographic grid code within each time window is counted. In the time window of "Second Quarter of 2023", the number of knowledge nodes falling into the "G-101" grid is 2: "C New Energy Technology Co., Ltd." and "First Generation Prototype Battery Test Report". Different weight coefficients are pre-assigned according to the type of knowledge node. The steps to calculate the total number of weighted nodes include: assigning a positive real number as a weight coefficient for each type of knowledge node. In the system configuration, the weight coefficient for "Production Enterprise" type nodes is 1.0, the weight coefficient for "Research and Development Achievements" type nodes is 1.5, and the weight coefficient for "Policy Documents" type nodes is 0.8. Node types representing the core progress of industrial projects have higher weight coefficients. For each knowledge node falling within a specific geographic grid and time window, the corresponding weight coefficient is queried based on its type. For the "C New Energy Technology Co., Ltd." node in the "Second Quarter of 2023" window of the "G-101" grid, the weight coefficient is 1.0. For the "First Generation Prototype Battery Test Report" node in the same window and grid, the weight coefficient is 1.5. The weight coefficients of all knowledge nodes within the specific geographic grid and time window are summed, and the resulting sum is the original heat value. For the "G-101" grid in the "Second Quarter of 2023", the original heat value is calculated as 1.0 + 1.5 = 2.5. It can be understood that the calculation of the original heat value can be extended to a weighted sum. The formula shows a weighted summation calculation method:

[0033] in: This represents the raw heat value of a specific geographic grid within a specific time window. This represents the number of knowledge nodes that fall within the grid and window. Representing the Each knowledge node is assigned a basic weight coefficient based on its type. Representing the The local influence correction factor for a knowledge node, for example, a "production enterprise" node associated with multiple major projects has a factor greater than 1.0. Value, in order to amplify its contribution.

[0034] The original heat values ​​of the same geographic grid in different time windows are standardized to eliminate the dimensional effects caused by differences in time window size or total number of nodes. In some embodiments, the min-max normalization method is used to normalize the original heat value sequence [2.5, 3.8, 4.2, 5.0] of the "G-101" grid in four consecutive quarters to the interval [0,1], resulting in the standard heat value sequence [0.0, 0.433, 0.567, 1.0]. An optional standardization process uses the Z-score method. The regional heat calculation module uses the time window as the horizontal axis and the geographic grid as the spatial unit, mapping the standard heat value of each grid in each window to color depth or height, generating a dynamically changing regional industry heat distribution map. It can be understood that the regional industry heat distribution map can be represented as a series of heat maps arranged in chronological order, or presented in the form of an animation.

[0035] Example 4: The steps for simulating the next stage state of an industrial project in the core module of intelligent deduction are executed based on machine learning methods. The terminal state node of the current industrial project development trajectory chain is used as the model input. In the initial industrial association graph, knowledge nodes of all other industrial projects that have historically been connected to the current terminal state node are searched, such as historical projects connected through "technology origin" or "team association." The subsequent development trajectories of these historical industrial projects are used as a reference sample set. Using sequence pattern mining methods in machine learning, such as Hidden Markov Models or Recurrent Neural Networks, the potential patterns of industrial project state transitions are learned from the reference sample set, constructing a state transition probability model. The features of the terminal state node, including its type, key attributes, current trajectory length, and associated resource status, are input into the trained state transition probability model. The model calculates the probability distribution of the current state transitioning to the next state node. This distribution reflects the various possibilities and likelihoods of the project's future development under historical patterns.

[0036] In practical implementation, the intelligent simulation core module simulates the next stage of an industrial project's state using the terminal state node of the current industrial project's development trajectory chain as input. For example, for the development trajectory chain of the "2GWh annual production power battery project," its terminal state node is the knowledge node "First-generation prototype battery test report." The metadata attributes of this knowledge node include information such as "state description: energy density meets standards, cycle life passes initial testing." In the initial industrial association graph, the intelligent simulation core module searches for all other industrial project knowledge nodes that have historically been connected to the terminal state node "First-generation prototype battery test report." The query logic is to search for all records in the initial industrial association graph of type "industrial project" whose development trajectory chain contains a state node connected to the current terminal state node through a specific relationship edge. For example, it finds a "laboratory sample test report" node in the trajectory chain of the historical industrial project "high energy density battery pilot project," which is connected to the current "first-generation prototype battery test report" node through a "technology homology" relationship edge with a confidence weight of 0.75. Using the subsequent development trajectories of these historical industrial projects as reference samples, for example, after the "high energy density battery pilot project" reached the "laboratory sample test report" stage, its trajectory chain subsequently showed the "launch of Series A financing" and "construction of pilot production line" stages.

[0037] By utilizing sequence pattern mining methods in machine learning, the potential patterns of state transitions in industrial projects are learned from an acquired set of reference samples. In some embodiments, Hidden Markov Models (HMMs) are used to model the state sequences. In the core module of intelligent inference, a specific implementation of HMM modeling for state sequences involves using historical industrial project development trajectory chains as reference samples. Each trajectory chain consists of a series of state nodes arranged chronologically, representing events at different lifecycle stages of the industrial project, such as the R&D stage or financing events. The model training process uses these state node sequences as input and learns the transition probabilities between states through machine learning methods, thereby capturing the potential patterns of industrial project development. After training, the model can receive the feature vectors of the current industrial project's final state nodes, including type and key attributes, and output the probability distribution of the project transitioning to the next state node based on the learned state transition probabilities. In other embodiments, variants of recurrent neural networks, such as LSTM, are used to capture long-range dependencies between states. Through model training, a state transition probability model is constructed. This model can receive the feature vector of a state node and output its probability distribution of transitioning to the next state node. The features of the terminal state nodes are input into the trained state transition probability model. The feature vector of the terminal state node is composed of the encoding of key attributes in its metadata, such as technology field encoding, current funding stage encoding, team size quantification, number of existing patents, etc. The model calculates the probability distribution of the current state transitioning to the next state node. An optional core similarity calculation used in the state transition probability model to evaluate the probability of transition between two state nodes is shown in the formula:

[0038] in: Represents the state node Transfer to state node The predicted probability, and Representing state nodes respectively and eigenvectors, functions and The dot product operation represents a nonlinear transformation that maps the original feature vectors to the model's latent space. Used to calculate state With candidate subsequent states The fitness in the latent space, where the denominator is all candidate states. The fit indexes are summed to normalize the probabilities. After the model runs, the probability of the output "Initiating Series A Funding" is 0.45, the probability of the output "Applying for Major Technology Project" is 0.30, and the probability of the output "Seeking Strategic Partners" is 0.25 (see Table 1).

[0039] Table 1: Reference Sample Table for Status Transition of Historical Industrial Projects

[0040] In practice, the process of finding historically relevant nodes relies on the richness and accuracy of the connecting edges in the initial industry association graph. It's understandable that the relationship type and confidence weight of the connecting edges will affect the selection of reference samples. One possible approach is to consider not only directly connected nodes but also nodes connected via paths within a few hops, where the path semantics conform to "technology-derived" or "market-related." The intelligent inference core module passes the calculated probability distribution results, such as "launching Series A financing: 0.45," to subsequent steps for further analysis. The probability distribution results are stored in memory as structured data for later module access.

[0041] See Figure 4 In the initial construction of the industrial association network, the confidence level and sample size distribution of association types are the core indicators for evaluating the quality of the association edges. Specifically, the graph uses "association type" as the horizontal axis and simultaneously displays the "average confidence level" (green bars) and "sample size" (orange broken line) for each type: the average confidence level of policy association is the highest, reaching 0.84, but the corresponding sample size is only 9, reflecting that the reliability of this type of association is strong but the sample coverage is low; the number of samples for technology similarity association is the largest (18), but the average confidence level is only 0.72, reflecting that the sample accumulation for this type of association is sufficient but the confidence level is relatively weak; the average confidence level of capital cooperation association is 0.76 and the sample size is 14, while technology homology (0.78 / 15), team association (0.81 / 12), and industrial chain association (0.80 / 10) show different equilibrium states of confidence level and sample size. These data can serve as the basis for selecting reference samples in the core module of subsequent intelligent inference (such as prioritizing historical projects with high confidence correlation), and also provide direction for optimizing the rule template of the relationship network construction module (such as supplementing judgment rules for association types with low confidence but high sample size).

[0042] Example 5: After obtaining the state transition probability distribution, the core module of the intelligent simulation performs simulations of the implementation probability and resource requirements. From the probability distribution output by the state transition probability model, several candidate next states with the highest probabilities are selected. For each candidate next state, based on its attribute characteristics, the most suitable geographical grid is matched in the regional industry heat map. Matching criteria may include industry type fit and resource endowment fit. The current standard heat value of the matching grid is used as an environmental adaptation factor. This environmental adaptation factor is fused with the state transition probability to calculate the comprehensive implementation probability of the industry project in the specific geographical grid for that candidate next state. Simultaneously, based on the average value of various resources consumed by industry projects with similar states in similar heat areas in historical data, a multi-dimensional resource requirement vector is generated. The dimensions of this resource requirement vector include funding, talent, policy, and technology. The steps for outputting the decision simulation report include: summarizing all candidate next states for an industry project and their corresponding comprehensive implementation probabilities, target geographical grids, and resource requirement vectors. The summarized results are sorted from high to low according to the comprehensive implementation probability to form a clear simulation result list. This list of projection results, along with the original industrial project development trajectory chain and related regional industrial heat map segments, are packaged and integrated into a complete structured document that includes current status review, future projection, spatial suggestions, and resource prediction—namely, the decision projection report.

[0043] In practical implementation, the intelligent simulation core module receives the probability distribution output from the state transition probability model in the step of simulating the implementation probability and resource demand vector. For example, the state transition probability model output for the "2GWh annual production power battery project" is as follows: the probability of the "launching Series A financing" state is 0.45, the probability of the "applying for major technology projects" state is 0.30, and the probability of the "seeking strategic partners" state is 0.25. The intelligent simulation core module selects several candidate next states with the highest probabilities from the probability distribution output by the state transition probability model. For example, the top two states with the highest probabilities, "launching Series A financing" and "applying for major technology projects", are selected as candidates. For each candidate next state, the module matches the most suitable geographical grid in the regional industry heat map based on its attribute characteristics. For example, for the "Start Series A Funding" state, its attribute characteristics include "funding stage," "technology field," and "type of capital required." The module will perform matching calculations based on these characteristics and indicators such as industry tags and capital activity of each grid. This matching process can be understood as a similarity calculation based on feature vectors. In one optional scheme, the module matches the "Start Series A Funding" state with grid "G-201" in the regional industry heat map, which is marked as a "venture capital active area" and whose leading industry is "new energy vehicles." The current standard heat value of the matching grid is used as the environmental adaptation factor. For example, if the standard heat value of grid "G-201" in the latest time window is 0.85, this value of 0.85 is used as the environmental adaptation factor for the "Start Series A Funding" state in grid "G-201."

[0044] By fusing environmental adaptation factors and transition probabilities, a comprehensive probability of an industrial project achieving the candidate next state within the geographical grid is obtained. In some embodiments, the fusing calculation employs weighted multiplication or weighted addition. For example, the formula illustrates one such fusing calculation method:

[0045] in: Represents the overall probability of landing. This represents the original transition probability of the candidate state obtained from the state transition probability model. This represents the environmental adaptability factor obtained from the regional industrial heat map. It is a fusion coefficient between 0 and 1, used to adjust the contribution weights of the transfer probability and the environmental adaptation factor in the final overall implementation probability. Assuming it's for the "launching Series A funding" state, its... Its matching grid's environmental adaptation factor ,set up The calculated overall landing probability Simultaneously, based on the average resource consumption of similar industrial projects in similar heat zones in historical data, a multi-dimensional resource demand vector is generated. The intelligent inference core module queries the historical database for all industrial projects in the "launching Series A financing" state and located in the heat zone between 0.8 and 0.9, and calculates the average resource consumption of these projects in four dimensions: "funding," "talent," "policy," and "technology." The dimensions of the resource demand vector include funding, talent, policy, and technology. For example, the generated resource demand vector might be {funding: 5000 (unit: 10,000 yuan), talent: {senior R&D: 5 people, market expansion: 3 people}, policy: {high-tech enterprise certification, R&D expense deduction}, and technology: {BMS matching technology, fast charging technology}}. It can be understood that the specific content and units of measurement of each dimension of the resource demand vector can be defined and expanded according to actual management needs.

[0046] The steps for outputting the decision projection report include: summarizing all candidate next states for an industry project and their corresponding comprehensive implementation probabilities, target geographic grids, and resource demand vectors. The summarized results are sorted from high to low comprehensive implementation probabilities to form a projection result list, with the recommended path at the top of the list having the highest comprehensive implementation probability. The projection result list, along with the original industry project development trajectory chain and relevant regional industry heat map segments, are encapsulated. In some embodiments, the encapsulation format is a structured JSON or XML document, integrating them into a complete structured document containing current status retrospective, future projection, spatial suggestions, and resource predictions—the decision projection report. The current status retrospective section references the original industry project development trajectory chain data; the future projection section is the sorted projection result list; the spatial suggestions section includes screenshots of the recommended grids on the regional industry heat map; and the resource prediction section details the resource demand vectors.

[0047] See Figure 5 In the intelligent simulation core module of the industrial project auxiliary decision support system, during the execution of the implementation probability simulation, the distribution of standard heat values ​​for each candidate geographic grid in the grid matching stage is presented. Specifically, the figure shows the standard heat values ​​of five geographic grids: G-201, G-108, G-305, G-402, and G-509, with the geographic grid code as the horizontal axis and the standard heat value as the vertical axis. Among them, the standard heat value of grid G-201 is the highest (0.85), and the standard heat value of grid G-509 is the lowest (0.48). These heat values ​​are generated by the regional heat calculation module through aggregation, weighted statistics, and standardization of the geographic location labels of nodes in the industrial project development trajectory chain. In the intelligent simulation stage, these heat values ​​will be used as environmental adaptation factors and integrated with the state transition probability to calculate the comprehensive implementation probability of the industrial project in the corresponding grid. This is the core data support for the spatial suggestions and implementation probability assessment in the decision simulation report.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A decision support system for industrial projects based on government, industry, academia, research, finance, and application, characterized in that, include: The relational network construction module is used to receive raw multi-source data from government, industry, academia, research, finance, and application. By parsing the entity and event relationships between data, it generates a knowledge node with a unique identifier for each data point and constructs an initial industry association graph containing multi-dimensional connection edges based on the semantic and logical dependencies between nodes. The time-series trajectory restoration module is used to receive the initial industry association map, extract the status markers and time points related to the life cycle of the industry project from the knowledge nodes, connect different status nodes of the same industry project in chronological order, and generate an industry project development trajectory chain with continuous time attributes. The regional heat map calculation module is used to receive the development trajectory chain of the industrial projects, aggregate the nodes into the corresponding geographic grids according to the geographical location tags contained in the nodes in the trajectory chain, calculate the number and weight of new nodes added in each grid in different time windows, and dynamically generate a regional industrial heat map. The intelligent simulation core module is used to simultaneously receive the development trajectory chain of the industrial project and the regional industrial heat map. Based on machine learning methods, under the structural constraints of the industrial association map, it simulates the next stage state of the industrial project along the development trajectory chain, and combines the heat value of the regional industrial heat map to infer the probability of the industrial project's implementation and resource demand vector within a specified geographical grid, and outputs a structured decision simulation report.

2. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 1, characterized in that, The relational network construction module performs the following steps: Entity identification and relationship extraction are performed on the original multi-source data. Each identified government, industry, academia, research, finance and service institution, policy document, technology patent, financial product and service project is defined as an entity. A knowledge node is created for each entity, and each knowledge node is given metadata containing type, source and key attributes. Between the knowledge nodes, based on event co-occurrence, semantic similarity, and preset rule templates, connection edges representing cooperation, membership, reference, support, and conflict relationships are established, and each connection edge has a relationship type and confidence weight. Integrate all knowledge nodes and their connecting edges to form a network structure with multiple relational dimensions, which is the initial industry association graph. The initial industry association graph is stored in the form of a node list and an adjacency matrix.

3. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 1, characterized in that, The time-series trajectory restoration module performs the following steps: From the initial industry association map, knowledge nodes of type industry project are selected as the starting point of the trajectory; Starting from the knowledge node of the industry project, a breadth-first traversal is performed along the connecting edges in the initial industry association graph to find other knowledge nodes that have event associations with the industry project node. The event associations include project application, project approval, R&D stage, financing events, and results release. For each associated knowledge node found, the time point recorded in its attributes is extracted. The starting node of the industry project is linearly connected with each associated node in the order of time from early to late, forming a node sequence sorted by time. In the node sequence, the time interval and stage name are labeled for each adjacent node, and the development trajectory chain of the industry project is finally generated.

4. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 1, characterized in that, The regional heat calculation module performs the following steps: Read the development trajectory chain of the industry project, analyze the attributes of each knowledge node in the chain, and extract the geographical location description information that is explicitly recorded or implicit. The geographic location description information is converted into a standard geographic grid code using a geocoding service. Set a sliding time window, count the number of knowledge nodes that fall into the same geographic grid code in each time window, and pre-assign different weight coefficients according to the type of knowledge node. Calculate the total weighted number of nodes of the geographic grid in the time window as the original heat value. The original heat values ​​of the same geographic grid at different time windows are standardized to eliminate dimensional differences and generate a standard heat value sequence. Based on this, a dynamically changing regional industrial heat distribution map is drawn.

5. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 2, characterized in that, The steps for assigning metadata to each knowledge node in the relational network construction module include: Assign a globally unique string identifier to each identified entity; Based on the data source and inherent attributes of the entity, determine the type of knowledge node corresponding to the entity. The types include government departments, research institutions, production enterprises, financial institutions, service platforms, policy provisions, technical topics, and financial instruments. Key attributes of the entity are extracted from the raw data in a structured manner or predicted through machine learning models. These key attributes include establishment time, industry, scale level, effective period, and technological maturity. The globally unique string identifier, the type, and the key attributes are encapsulated to form the complete metadata of the knowledge node.

6. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 3, is characterized in that, The breadth-first traversal along the connecting edges in the time-series trajectory reconstruction module includes: Initialize a queue to be accessed, add the industry project knowledge nodes to the queue, and mark them as accessed; Take a node from the queue and search in the adjacency matrix of the initial industry association graph for all neighboring nodes that are directly connected to the industry project knowledge node; Check the relationship type of the connection edge between each neighbor node and the current node. If the relationship type belongs to the preset industry project development related event type, add the neighbor node to the queue to be visited and record its event relationship with the current node. Repeat the process of retrieving nodes from the queue to be visited, finding neighboring nodes, checking relationship types, and updating the queue until the queue to be visited is empty or the preset traversal depth is reached, and finally obtain the set of all event nodes related to the starting industry project node.

7. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 4, characterized in that, The steps for calculating the total number of weighted nodes in the regional heat calculation module include: A positive real number is assigned as a weighting coefficient for each type of knowledge node, with node types that represent the core progress of industrial projects having a higher weighting coefficient. For each knowledge node that falls within a specific geographic grid and a specific time window, query the corresponding weight coefficient based on its type; The weight coefficients of all knowledge nodes within the specific geographic grid and the specific window are summed, and the resulting sum is the original heat value.

8. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 1, characterized in that, The steps in the core module of the intelligent simulation to simulate the next stage of an industrial project include: The current state node of the development trajectory chain of the industry project is used as the input; In the initial industry association map, find the knowledge nodes of all other industry projects that have historically been connected to the terminal state node, and use the subsequent development trajectories of these industry projects as reference samples; By utilizing sequence pattern mining methods in machine learning, the potential patterns of state transitions in industrial projects are learned from the reference samples, and a state transition probability model is constructed. The features of the terminal state node are input into the state transition probability model to calculate the probability distribution of possible transitions to the next state node.

9. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 8, characterized in that, The steps in the core module of the intelligent simulation to deduce the probability of implementation and the vector of resource requirements include: From the probability distribution output by the state transition probability model, select the candidate next states with the highest probabilities; For each candidate next state, based on its attribute characteristics, the most suitable geographical grid is matched in the regional industrial heat distribution map, and the current standard heat value of the matching grid is used as the environmental adaptation factor. The environmental adaptation factor and the transfer probability are fused together to calculate the comprehensive probability of the industrial project achieving the candidate next state in the geographic grid. Meanwhile, based on the average value of various resources consumed by industrial projects in similar states in similar hot areas in historical data, a multi-dimensional resource demand vector is generated. The dimensions of the resource demand vector include funding, talent, policy, and technology.

10. The industrial project auxiliary decision support system based on government, industry, academia, research, finance, and application as described in claim 9, characterized in that, The steps for the intelligent deduction core module to output a decision deduction report include: This will summarize all candidate next states for an industrial project and their corresponding comprehensive implementation probability, target geographic grid, and resource demand vector; The simulation results are sorted from highest to lowest based on the overall probability of implementation, forming a list of projection results; The list of projection results, along with the original industrial project development trajectory chain and related regional industrial heat map segments, are packaged and integrated into a complete structured document containing current status retrospective, future projection, spatial suggestions, and resource prediction, namely the decision projection report.

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