Industrial chain atlas construction method and device based on multi-source data fusion

Abstract

The invention discloses an industrial chain graph construction method and device based on multi-source data fusion. The method comprises the following steps: constructing an enterprise association graph according to an enterprise association data set; executing a community division algorithm on the enterprise association graph, determining enterprise nodes divided to the same community as enterprises in the same industrial chain according to a division result of the community division algorithm, and determining each community as a corresponding industrial chain; and for each industrial chain, extracting text information of business scope or main business description of enterprises in the industrial chain, generating a label for representing the industry to which the industrial chain belongs based on the text information, and associating the label with the corresponding industrial chain. According to the invention, the efficiency and consistency of industrial chain construction and classification are improved, and a user can know the position of each enterprise in the industrial chain and the industry to which the enterprise belongs more clearly and visually.

Description

Technical Field

[0001] This invention relates to the field of knowledge graph construction technology, and more specifically, to a method and apparatus for constructing an industry chain graph based on multi-source data fusion. Background Technology

[0002] Currently, when banks and other institutions conduct supply chain finance and industrial chain finance business, they typically need to first construct the industrial chain structure of the target customer group to identify core enterprises and their upstream and downstream enterprises, and assess risk transmission. Existing practices mostly rely on manually compiling publicly available data, internal reports, and survey information, with business personnel using their experience to sort out the cooperative relationships and affiliations between enterprises. This is not only labor-intensive and untimely, but also results in inconsistent standards among different personnel, making it difficult to guarantee the accuracy and stability of the constructed industrial chain.

[0003] At the same time, due to the lack of a unified graphical representation method, existing industry chains are often presented in the form of lists, reports, etc., making it difficult for users to intuitively see the position of each enterprise in the industry chain and their relationships, which is not conducive to quickly understanding the overall industry structure and the connections between enterprises.

[0004] Therefore, how to construct an industry chain map that can intuitively display the relationships and industry chain structure between enterprises based on existing enterprise-related data, so that users can easily understand the position of each enterprise in the industry chain and the industry to which it belongs, thereby improving the efficiency and accuracy of industry chain construction and display, has become a technical problem to be solved in this field. Summary of the Invention

[0005] In order to solve at least one of the technical problems in the background art, the present invention proposes a method and apparatus for constructing an industrial chain map based on multi-source data fusion.

[0006] One aspect of the present invention provides a method for constructing an industry chain map based on multi-source data fusion, the method comprising:

[0007] The bank acquires corporate customer data and external business registration data, uses the unified social credit code as the association identifier, and matches and associates the corporate customer data and the external business registration data to obtain a corporate association dataset representing the relationship between enterprises. The relationship includes at least one of the following: transaction relationship, equity relationship, employment relationship and guarantee relationship.

[0008] An enterprise association graph is constructed based on the enterprise association dataset, wherein each enterprise is constructed as a node in the enterprise association graph, and the association relationships between enterprises are constructed as edges connecting nodes in the enterprise association graph;

[0009] A community segmentation algorithm is executed on the enterprise association graph. Based on the segmentation result of the community segmentation algorithm, enterprise nodes that are assigned to the same community are identified as enterprises in the same industry chain, and each community is identified as a corresponding industry chain.

[0010] For each of the aforementioned industry chains, text information describing the business scope or main business of enterprises within the industry chain is extracted. Based on the text information, tags are generated to characterize the industry to which the industry chain belongs, and the tags are associated with the corresponding industry chain.

[0011] Optionally, the method for constructing an industry chain map based on multi-source data fusion further includes:

[0012] For each of the aforementioned industry chains, a target enterprise node within the industry chain is selected as the starting node in the enterprise association graph. The graph is traversed using a depth-first search algorithm or a breadth-first search algorithm along the edges representing transaction relationships and guarantee relationships to obtain multiple paths from the starting node to other enterprise nodes within the industry chain.

[0013] The fund flow path is determined based on the transaction amount on each path's transaction relationship edge, and the risk transmission path is determined based on the guarantee amount on each path's guarantee relationship edge, in order to characterize the multi-level fund flow relationship and risk transmission relationship among enterprises within the industry chain.

[0014] Optionally, the method for constructing an industry chain map based on multi-source data fusion further includes:

[0015] Obtain the bank's internal corporate customer data and external business registration data corresponding to the newly added enterprise, perform data association processing using the unified social credit code as the association identifier, add the association results to the enterprise association dataset, and add enterprise nodes representing the newly added enterprise and edges representing the association relationship between the newly added enterprise and other enterprises to the enterprise association graph;

[0016] For a new enterprise that is connected to an existing enterprise node through at least one edge in the enterprise association graph, the association strength between the new enterprise and each connected enterprise node is calculated based on the edge attributes. When the association strength of a certain connected enterprise node reaches a preset classification threshold, the new enterprise is classified into the industrial chain corresponding to that connected enterprise node.

[0017] For a newly added enterprise that is not connected to any existing enterprise node in the enterprise association graph through any edge, extract the text information describing the business scope or main business of the newly added enterprise, calculate the similarity between the text information and each industry chain label, and classify the newly added enterprise into the industry chain with the highest similarity and not less than the preset similarity threshold; when the similarity of each industry chain is less than the preset similarity threshold, create a new industry chain for the newly added enterprise and mark the new industry chain as an industry chain to be further classified.

[0018] Optionally, generating a label based on the text information to characterize the industry to which the industrial chain belongs includes:

[0019] Extract keywords from the text information and count the frequency of each keyword within the industry chain;

[0020] Keywords that appear more frequently than a preset frequency threshold and can reflect specific industry attributes are identified as candidate industry keywords.

[0021] The candidate industry terms are matched with the industry names and sub-industry names in the preset industry classification system, and the main industry label of each industry chain is determined based on the matching results.

[0022] When multiple candidate industry terms exist within the same industry chain and correspond to different sub-industries, at least one auxiliary industry label is determined for the industry chain to characterize the sub-sectors involved in the industry chain.

[0023] Optionally, the method for constructing an industry chain map based on multi-source data fusion further includes:

[0024] In the enterprise relationship graph, for each of the industry chains, the multi-level relationships between enterprises within the industry chain are mined. Specifically: based on the edges representing transaction relationships, paths from the core enterprise node to the first-tier supplier node and from the first-tier supplier node to the second-tier supplier node are obtained, forming supply chain paths; based on the edges representing equity relationships, paths from the actual controller node to multiple controlled enterprise nodes are obtained, forming equity control paths.

[0025] Optionally, the method for constructing an industry chain map based on multi-source data fusion further includes:

[0026] In the enterprise association graph, enterprise nodes with risk records are marked with risk. Based on the position of the risk-marked enterprise node in the corresponding industry chain, the downstream enterprise node connected to the risk-marked enterprise node through the guarantee relationship edge is determined by the path query method, thus forming a risk transmission path.

[0027] The industrial chain correlation analysis results are generated based on the aforementioned industrial chains, supply chain paths, equity control paths, and risk transmission paths. The industrial chain correlation analysis results include an industrial chain list, an industrial chain hierarchical path diagram, and a list of enterprises under risk warning.

[0028] Optionally, performing a community segmentation algorithm on the enterprise association graph includes:

[0029] The Louvain algorithm is used to divide the enterprise association graph into communities. The modularity threshold is set to 0.3, and the minimum community size is set to 5 nodes. The set of nodes with a modularity not lower than the modularity threshold and a number of nodes not less than the minimum community size are identified as the same community.

[0030] Another aspect of the present invention provides an apparatus for constructing an industry chain map based on multi-source data fusion, the apparatus comprising:

[0031] The enterprise association dataset establishment unit is used to acquire enterprise customer data within the bank and external business registration data, and uses the unified social credit code as the association identifier to match and associate the enterprise customer data within the bank with the external business registration data to obtain an enterprise association dataset representing the relationship between enterprises. The relationship includes at least one of the following: transaction relationship, equity relationship, employment relationship and guarantee relationship.

[0032] The enterprise association graph construction unit is used to construct an enterprise association graph based on the enterprise association dataset, wherein each enterprise is constructed as a node in the enterprise association graph, and the association relationships between enterprises are constructed as edges connecting the nodes in the enterprise association graph;

[0033] The industry chain segmentation unit is used to execute the community segmentation algorithm on the enterprise association graph. According to the segmentation result of the community segmentation algorithm, the enterprise nodes that are segmented into the same community are identified as enterprises in the same industry chain, and each community is identified as the corresponding industry chain.

[0034] The industry label determination unit is used to extract text information describing the business scope or main business of enterprises within each industry chain, generate labels representing the industry to which the industry chain belongs based on the text information, and associate the labels with the corresponding industry chain.

[0035] To achieve the above objectives, according to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for constructing an industry chain map based on multi-source data fusion.

[0036] To achieve the above objectives, according to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, which, when executed by a processor, implements the steps of the above-described method for constructing an industry chain map based on multi-source data fusion.

[0037] To achieve the above objectives, according to another aspect of the present invention, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method for constructing an industry chain map based on multi-source data fusion.

[0038] The beneficial effects of this invention are as follows:

[0039] This invention uses the unified social credit code as the association identifier to match and associate bank corporate customer data with external business registration data, constructing a corporate association dataset that includes transaction relationships, equity relationships, employment relationships, and guarantee relationships. Based on this, enterprises are abstracted as nodes, and the relationships between enterprises are abstracted as edges to form a corporate association graph. A community partitioning algorithm is executed on the corporate association graph, identifying enterprise nodes assigned to the same community as enterprises in the same industry chain. Furthermore, labels representing the industry to which the industry chain belongs are generated based on textual information describing the business scope or main business of enterprises within the industry chain. This allows for unified modeling and aggregation of various relationships between enterprises, automatically identifying and labeling the affiliation of each enterprise in different industry chains and the corresponding industry attributes of each industry chain. Compared to existing solutions that rely on manual sorting and experience-based judgment, this improves the efficiency and consistency of industry chain construction and classification, and enables users to more clearly and intuitively understand the position of each enterprise in the industry chain and the industry to which it belongs. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0041] Figure 1 This is the first flowchart of the industry chain map construction method based on multi-source data fusion in this embodiment of the invention;

[0042] Figure 2 This is the second flowchart of the industry chain map construction method based on multi-source data fusion in this embodiment of the invention;

[0043] Figure 3 This is the third flowchart of the industry chain map construction method based on multi-source data fusion in this embodiment of the invention;

[0044] Figure 4 This is a flowchart illustrating how an embodiment of the present invention generates labels used to characterize the industry to which the industrial chain belongs;

[0045] Figure 5 This is a structural block diagram of the industrial chain map construction device based on multi-source data fusion according to an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0047] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0048] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0052] It should be noted that the technical solution of this application provides users with corresponding operation entry points for users to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0053] It should be noted that the method and apparatus for constructing industry chain maps based on multi-source data fusion of the present invention can be used in the financial field, or in any field other than the financial field. The application fields of the method and apparatus for constructing industry chain maps based on multi-source data fusion of the present invention are not limited.

[0054] This invention addresses the problems of existing industry chain construction, which relies on manual sorting, single data sources, difficulty in timely updates, and lack of intuitive representation of enterprise relationships. It proposes an industry chain mining scheme that utilizes multi-source data fusion verification, community discovery and path mining, and labeling and dynamic classification based on natural language processing. Specifically, this invention collects enterprise customer data from within banks through data interfaces, such as transaction records, basic customer information, and credit records. This data is then cleaned and integrated with external business registration data, such as equity structure, senior management appointments, guarantee relationships, and business scope texts. Cross-validation rules are used to remove redundant records and correct contradictory data, ensuring that data from different dimensions, such as transaction relationships and equity relationships, mutually corroborate each other. This constructs a complete enterprise relationship dataset representing inter-enterprise relationships. Enterprises are abstracted as points, and transaction relationships, equity relationships, appointment relationships, and guarantee relationships are abstracted as edges, forming a "enterprise-relationship" point-edge data structure. Enterprise nodes with risk records are marked.

[0055] Based on the aforementioned data, this invention constructs an enterprise association graph using a point-edge dataset. A community discovery algorithm is employed to divide the graph into multiple industry chain communities. Path queries are used to mine the hierarchical relationships between core enterprises and their upstream and downstream partners within each industry chain community, such as the relationships between core enterprises, first-tier suppliers, and second-tier suppliers. Furthermore, natural language processing technology is used to analyze the textual information of enterprises within each industry chain community, including their business scope and business descriptions, extracting industry-specific keywords. These keywords are then matched with a pre-defined standard industry classification system to generate industry labels identifying the industry sectors to which each industry chain community belongs. For new enterprises or new relationships, this invention integrates the new data into the enterprise association graph. By combining the strength of the relationship between the new enterprise and existing enterprises, or based on the similarity between the new enterprise's business scope text and existing industry chain labels, rapid community classification of the new enterprise is achieved. This enables dynamic updates to the industry chain graph, supporting subsequent industry chain association mining and risk assessment.

[0056] From the perspective of implementation process, the method of this invention can be summarized as follows: Step 1, multi-source data fusion and cross-validation to construct an enterprise association dataset; Step 2, constructing an enterprise association graph based on the enterprise association dataset; Step 3, using a community discovery algorithm to segment communities and mine industry chain paths; Step 4, using natural language processing to generate industry tags at the community level; Step 5, dynamically classifying newly added enterprises or newly added relationships and updating the industry chain graph; Step 6, conducting industry chain association mining and risk auxiliary judgment based on the constructed industry chain graph.

[0057] Figure 1 This is the first flowchart of the industry chain map construction method based on multi-source data fusion according to an embodiment of the present invention, as shown below. Figure 1 As shown, in one embodiment of the present invention, the method for constructing an industry chain map based on multi-source data fusion includes steps S101 to S104.

[0058] Step S101: Obtain corporate customer data within the bank and external business registration data. Using the unified social credit code as the association identifier, match and associate the corporate customer data within the bank with the external business registration data to obtain a corporate association dataset representing the relationship between enterprises. The relationship includes at least one of the following: transaction relationship, equity relationship, employment relationship, and guarantee relationship.

[0059] In one embodiment of the present invention, the bank's internal corporate customer data may include transaction records, basic customer information, credit records, and repayment records. External business registration data may include the company's equity structure, senior management appointments, guarantee filing information, and business scope documents. By linking these two types of data using the unified social credit code, records about the same company from different data sources can be automatically merged. The merged data undergoes consistency checks and missing data repair, allowing the associated information such as transactions, equity, appointments, and guarantees to be used simultaneously in subsequent modeling.

[0060] During the data association process, duplicate records are deduplicated, and records with abnormal transaction amounts, inconsistent legal entity information, or contradictory equity ratios are cross-checked and adjusted or removed according to predefined rules. Through these processes, a clear and accurate enterprise association dataset is obtained, laying the foundation for subsequent mapping and community segmentation.

[0061] Step S102: Construct an enterprise association graph based on the enterprise association dataset, wherein each enterprise is constructed as a node in the enterprise association graph, and the association relationship between enterprises is constructed as an edge connecting the nodes in the enterprise association graph.

[0062] In one embodiment of the present invention, the enterprise relationship graph can be stored using a node-edge structure. Each enterprise is a node, and each type of relationship forms a type of edge in the graph. Transaction relationships can include attributes such as the number of historical transactions, cumulative transaction amount, and transaction direction. Equity relationships can include attributes such as shareholding ratio and position in the control chain. Appointment relationships can represent the overlap of personnel among senior executives. Guarantee relationships can include attributes such as guarantee amount and guarantee period.

[0063] By incorporating these various relationships as edges into the graph, the transaction relationship network, equity control network, personnel affiliation network, and guarantee risk network of enterprises can be simultaneously presented within the same graph structure. This graph structure provides a unified data foundation for subsequent automatic identification of industry chains and industry labels, and also helps users understand the relationships between enterprises through visualization.

[0064] Step S103: Execute a community segmentation algorithm on the enterprise association graph. Based on the segmentation result of the community segmentation algorithm, enterprise nodes that are assigned to the same community are identified as enterprises in the same industry chain, and each community is identified as the corresponding industry chain.

[0065] In one embodiment of the present invention, a community partitioning algorithm based on modularity optimization can be used to process the enterprise relationship graph, aggregating closely related enterprise nodes into the same community. In practical applications, enterprises with frequent transactions or obvious equity connections often have upstream and downstream cooperation, supply relationships, or control relationships, and are therefore represented in the graph by higher edge weights or a larger number of edges. Through the community partitioning algorithm, structurally stable enterprise groups can be formed based on these characteristics.

[0066] Each community is considered a supply chain. The number of businesses within a community is generally no less than a predetermined minimum size to avoid overly fragmented clusters of businesses. After the communities are divided, multiple independent supply chains can be obtained, each containing several interconnected business nodes, thus laying the foundation for subsequent industry analysis.

[0067] Step S104: For each of the aforementioned industrial chains, extract text information describing the business scope or main business of enterprises within the industrial chain, generate labels based on the text information to characterize the industry to which the industrial chain belongs, and associate the labels with the corresponding industrial chains.

[0068] In one embodiment of the present invention, common industry-related terms, such as equipment manufacturing, raw material processing, and software services, can be extracted from the enterprise's business scope text and main business description. The industry specificity of these terms can then be determined by combining their frequency of occurrence. General terms without industry specificity, such as technical consulting or trade services, can be removed during the processing stage to avoid interfering with industry identification.

[0069] The extracted keywords can be matched with a preset industry classification system, such as national standard industry classifications or industry-defined classifications. Based on the matching results, a primary industry tag is generated for each industry chain. If multiple categories of words with industry characteristics exist within the same industry chain, one or more secondary industry tags can be generated to make the industry attributes of the industry chain more complete. The final generated industry tags are stored together with the industry chain structure to provide a basis for subsequent display and analysis.

[0070] In one embodiment of the present invention, step S101 specifically includes two parts: data acquisition and cross-validation and fusion.

[0071] During the data collection process, the system gathers relevant data from corporate clients within the bank, including corporate transaction records such as counterparties, transaction amounts, and transaction frequency; basic corporate information such as company name, registered address, unified social credit code, and credit limit; and loan repayment records. Simultaneously, it collects corporate business registration information from external business data sources, including equity structure such as shareholder names, shareholding ratios, and share pledge status; senior management information such as the companies where the legal representative, directors, and supervisors hold positions; and guarantee relationship information such as guarantors, guaranteed parties, guarantee amounts, and guarantee periods. Furthermore, it obtains the company's business scope document and business registration change records, such as changes in legal representatives and adjustments to registered capital. This information collectively forms the data foundation for various relationships that may exist between companies.

[0072] During the cross-validation and fusion process, the unified social credit code is used as the unique association key to integrate corporate customer data within the bank with external business registration data. In the fusion process, cross-validation between different data items improves data consistency. For example, when internal bank data shows that company A transferred large sums of money to company B multiple times within a month, and external business registration data shows that company A holds a significant percentage of shares in company B, both control and transaction relationships between company A and company B can be confirmed based on these two types of information, thereby improving the credibility of the association results. When there are differences between the counterparty name and the business registration name, text name similarity calculation can be used for auxiliary matching, and a similarity threshold can be set to determine whether they are the same company. After the above processing, a fused dataset containing basic enterprise information, inter-enterprise relationships, and quantitative indicators of these relationships can be generated for subsequent construction of enterprise relationship graphs.

[0073] In one embodiment of the present invention, step S102 specifically includes three parts: node definition, edge definition, and association graph storage.

[0074] During node definition, enterprises from the merged dataset are used as nodes in the enterprise association graph. Each node can contain two types of information: basic attributes and association attributes. Basic attributes can include enterprise name, unified social credit code, enterprise registration time, and the region where the enterprise is located, reflecting the basic information of the enterprise. Association attributes can include the enterprise's cumulative transaction amount, the percentage of the largest single trading partner, the number of industrial and commercial shareholding enterprises, and the amount of guarantees involved in the enterprise, describing the business scale and association strength of the enterprise under different association relationships. Through the above node attribute settings, the basic status and association characteristics of enterprises can be presented relatively comprehensively in the graph structure.

[0075] During edge definition, different categories of edges are constructed based on the type of relationship between enterprises. Transaction-related edges connect enterprises with existing transactions; edge attributes can include the cumulative transaction amount over the past twelve months and the average monthly transaction frequency, reflecting transaction activity. Equity-related edges connect enterprises with shareholding relationships; edge attributes can include shareholding percentage and share pledge status. Appointment-related edges connect enterprises with overlapping senior management positions; edge attributes can include job title and tenure. Guarantee-related edges connect enterprises with guarantee relationships; edge attributes can include guarantee amount, guarantee period, and guarantee type. By setting different types of edges, different dimensions of relationships between enterprises can be represented in the same relationship graph.

[0076] During the storage of the relational graph, enterprise nodes and their corresponding edges are imported into a graph database for unified management. The graph database supports querying node attributes and filtering edge relationships, such as filtering transactional relationships between enterprises based on large transaction amounts, thereby helping users quickly identify important business partnerships. Through the visualization capabilities of the graph database, various types of relationships between enterprises can be graphically displayed, providing data support for subsequent community segmentation and industry chain identification.

[0077] In one embodiment of the present invention, the step S103 above, which involves performing a community segmentation algorithm on the enterprise association graph, specifically includes:

[0078] The Louvain algorithm is used to divide the enterprise association graph into communities. The modularity threshold is set to 0.3, and the minimum community size is set to 5 nodes. The set of nodes with a modularity not lower than the modularity threshold and a number of nodes not less than the minimum community size are identified as the same community.

[0079] In one embodiment of the present invention, a community discovery algorithm can be executed on the constructed enterprise association graph to automatically identify sets of enterprises with strong business association characteristics. To achieve this, the Louvain algorithm based on modularity optimization can be used to partition the enterprise association graph into communities. Before the algorithm runs, a modularity threshold of 0.3 is set to indicate that the connections within a community are relatively close, which is consistent with the characteristic of stable business dealings between enterprises in an industry chain structure. Simultaneously, a minimum community size of 5 nodes is set to avoid misclassifying excessively small sets of enterprises as independent industry chains, thereby improving the effectiveness of the partitioning results.

[0080] During the community partitioning process, the Louvain algorithm iteratively optimizes the community structure. In the initial stage, each enterprise node in the enterprise association graph is treated as an independent community. Based on the adjacency relationships between nodes, the algorithm calculates the change in modularity of several adjacent communities after merging, and determines whether the merging operation can improve the overall modularity. Merging operations that improve modularity are retained and the next iteration continues. As the iteration progresses, the relationships between enterprise nodes are gradually merged into several relatively stable community structures. When the overall modularity no longer improves, the algorithm stops and outputs multiple final communities.

[0081] Through the community segmentation process described above, closely related enterprise nodes can be automatically grouped into the same community. Each community can serve as a collection of enterprises within an industry chain, which can be used for subsequent operations such as industry tag generation, relationship analysis, and industry chain display.

[0082] Figure 2 This is the second flowchart of the industry chain map construction method based on multi-source data fusion in this embodiment of the invention, as shown below. Figure 2 As shown, in one embodiment of the present invention, the industrial chain map construction method based on multi-source data fusion of the present invention further includes steps S201 and S202.

[0083] Step S201: For each of the aforementioned industry chains, select the target enterprise node within the industry chain as the starting node in the enterprise association graph, and perform graph traversal along the edges representing transaction relationships and the edges representing guarantee relationships using a depth-first search algorithm or a breadth-first search algorithm to obtain multiple paths from the starting node to other enterprise nodes within the industry chain.

[0084] In this embodiment of the invention, for each industry chain obtained through community segmentation, a graph traversal operation can be performed starting from the core enterprise node in the community. The core enterprise node can be determined by indicators such as transaction amount, number of suppliers, or guarantee scale. During the graph traversal, depth-first search can continuously expand downstream along a certain direction, suitable for identifying long, multi-level supply chain structures. Breadth-first search prioritizes traversing nearby nodes, suitable for identifying the set of first-tier enterprises surrounding the core enterprise. Using these two algorithms, a path covering different levels of enterprises can be obtained from the starting node.

[0085] For example, in supply chains with dense transaction relationships, depth-first search can identify the path structure from core enterprises to multi-tiered suppliers, reflecting the hierarchical supply relationships within the supply chain. In supply chains with significant guarantee relationships, breadth-first search can identify several downstream enterprises with guarantee connections to high-risk enterprises, providing a foundation for subsequent risk analysis.

[0086] By using the above traversal method, multiple paths from a single enterprise node to other enterprise nodes within the same industry chain can be collected, and these paths can be classified and processed in subsequent steps.

[0087] Step S202: Determine the fund flow path based on the transaction amount of the transaction relationship edge on each path, and determine the risk transmission path based on the guarantee amount of the guarantee relationship edge on each path, so as to characterize the multi-level fund flow relationship and risk transmission relationship between enterprises in the industry chain.

[0088] In this embodiment of the invention, the multiple paths obtained in step S201 can be classified according to the attribute values ​​of different types of edges on the paths. For paths containing transaction relationship edges, the transaction amounts between nodes at each level can be counted, and the paths can be sorted according to the transaction amount to form a fund flow path. The fund flow path can reflect the order of business transactions between the core enterprise and first-tier suppliers, second-tier suppliers, and further downstream enterprises.

[0089] For example, path extraction can reveal the direction and amount of funds flowing between core enterprise nodes and first-tier suppliers, as well as between first-tier suppliers and second-tier suppliers, allowing users to intuitively understand the fund flow relationships within the industry chain.

[0090] For paths containing guarantee relationships, the paths can be sorted according to the size of the guarantee amount to form risk transmission paths. Risk transmission paths can be used to characterize the potential risk diffusion from a high-risk enterprise to other enterprises within the chain. For example, when an enterprise in a community has overdue or default records, its downstream enterprises can be identified through guarantee relationships, thereby determining the potential direction of risk transmission.

[0091] By classifying and processing the above paths, we can obtain the multi-level capital flow structure and risk transmission structure within the industrial chain, which will provide support for subsequent risk analysis, credit management and industrial chain monitoring.

[0092] Figure 3 This is the third flowchart of the industry chain map construction method based on multi-source data fusion in this embodiment of the invention, as shown below. Figure 3 As shown, in one embodiment of the present invention, the industrial chain map construction method based on multi-source data fusion of the present invention further includes steps S301 to S303.

[0093] Step S301: Obtain the bank's internal corporate customer data and external business registration data corresponding to the newly added enterprise, perform association processing on the data using the unified social credit code as the association identifier, add the association results to the enterprise association dataset, and add enterprise nodes representing the newly added enterprise and edges representing the association relationship between the newly added enterprise and other enterprises to the enterprise association graph.

[0094] In this embodiment of the invention, newly added enterprises may include enterprises that have opened new corporate accounts, enterprises that have applied for supply chain loans, and enterprises that have made their first large transaction or provided a guarantee for the first time in the course of business. For the aforementioned newly added enterprises, the system collects and merges their corporate transaction records, basic customer information, credit status, industrial and commercial equity, senior management appointments, guarantee records, and business scope text, etc., according to the data cleaning and fusion rules adopted in step S101, and verifies them using the unified social credit code as a unique identifier. The verified data is added to the original enterprise association dataset, and corresponding enterprise nodes are added to the enterprise association graph.

[0095] Simultaneously, based on the transaction relationships, equity relationships, employment relationships, or guarantee relationships of newly added enterprises, edges connecting the new enterprises to other enterprise nodes are added to the enterprise relationship graph, allowing the relationship information between new and existing enterprises to be fully presented in the graph structure. Through the above processing, the enterprise relationship graph can be dynamically updated as business changes occur.

[0096] Step S302: For a new enterprise that is connected to an existing enterprise node through at least one edge in the enterprise association graph, calculate the association strength between the new enterprise and each connected enterprise node based on the edge attributes. When the association strength corresponding to a certain connected enterprise node reaches a preset classification threshold, classify the new enterprise into the industrial chain corresponding to that connected enterprise node.

[0097] In this embodiment of the invention, when classifying newly added enterprises, the first step is to determine whether they have any connection with existing enterprise nodes in the industrial chain. For example, when a newly added enterprise has transactions or guarantee relationships with a core enterprise in a certain industrial chain, a connection strength value can be constructed based on the transaction amount or guarantee amount. The connection strength can be calculated as the ratio of the cumulative transaction amount between the newly added enterprise and the target enterprise to the total transaction amount of the target enterprise, or as the ratio of the guarantee amount to the outstanding guarantee balance of the target enterprise.

[0098] When the correlation strength reaches a set classification threshold, such as a correlation strength of no less than 10%, the newly added enterprise can be classified into the industry chain with the highest correlation. Through this method, new enterprises can be accurately assigned to existing industry chains based on actual business relationships, enabling the industry chain structure to promptly reflect new business connections between enterprises.

[0099] Step S303: For a newly added enterprise that is not connected to any existing enterprise node in the enterprise association graph through any edge, extract the text information of the business scope or main business description of the newly added enterprise, calculate the similarity between the text information and each industry chain label, and classify the newly added enterprise into the industry chain with the highest similarity and not less than the preset similarity threshold; when the similarity of each industry chain is less than the preset similarity threshold, create a new industry chain for the newly added enterprise and mark the new industry chain as an industry chain to be further classified.

[0100] In this embodiment of the invention, newly added enterprises that have not formed any transaction, equity, employment, or guarantee relationships with any existing enterprise nodes can be considered as isolated nodes. The system extracts key business terms from the business scope text of the newly added enterprise and matches them with industry tags in the existing industry chain to calculate the similarity between the texts. The similarity can be calculated based on keyword overlap, text vector distance, etc.

[0101] When a new enterprise's similarity to a certain industry chain label reaches a set threshold, such as a similarity of no less than 0.6, the new enterprise is classified into that industry chain to maintain consistency and interpretability in industry classification. When a new enterprise's similarity to all industry chain labels is below the set threshold, a new industry chain can be created for the new enterprise and designated as an industry chain for further subdivision. Adjustments will be made after more transaction or guarantee information is collected.

[0102] Through the above methods, the system can still reasonably classify newly added enterprises even when there are insufficient connections, and maintain the continuous updating of the industrial chain structure.

[0103] like Figure 4 As shown, in one embodiment of the present invention, the step S104 above, which generates a label based on the text information to represent the industry to which the industrial chain belongs, specifically includes steps S401 to S404.

[0104] Step S401: Extract keywords from the text information and count the frequency of each keyword in the industry chain.

[0105] In this embodiment of the invention, terms describing the main business of enterprises can be extracted from the business scope texts of all enterprises within the industry chain. Simultaneously, descriptions of the main business can also be extracted from annual business reports to supplement the content of the business scope texts. Terms that may appear in the text include business-related terms such as equipment manufacturing, material processing, and parts sales. To reduce interference, generic terms that are not closely related to the industry can be filtered during the extraction process, such as technical consulting and import / export of goods. The extracted keywords will be statistically analyzed according to their frequency of occurrence within the industry chain, providing a basis for subsequent industry trend judgments.

[0106] Step S402: Keywords that appear more frequently than a preset frequency threshold and can reflect specific industry attributes are identified as candidate industry keywords.

[0107] In this embodiment of the invention, a frequency threshold can be set to filter out representative keywords within the industry chain. For example, in a certain industry chain, if most companies include terms such as "engine," "parts," and "welding equipment" in their business scope, these terms can be identified as industry-specific keywords. When the frequency of a keyword exceeds the set frequency threshold and reflects the main business characteristics of a company, that term can be identified as a candidate industry term. Candidate industry terms can initially reflect the industry sectors involved in the industry chain.

[0108] Step S403: Match the candidate industry terms with the industry names and sub-industry names in the preset industry classification system, and determine the main industry label for each of the industrial chains based on the matching results.

[0109] In this embodiment of the invention, a standard industry classification system, such as the National Economic Industry Classification Standard, can be used as the matching basis. By matching candidate industry terms one by one with industry names or sub-industry names in the standard industry classification, the main industry corresponding to the industrial chain can be determined. For example, when candidate industry terms include words such as engine manufacturing and parts processing, the industrial chain can be matched to the machinery manufacturing industry. More detailed content in the candidate industry terms can also be used to match smaller industry classification levels, thereby generating a main industry label that represents the core industry direction of the industrial chain.

[0110] Step S404: In the case of multiple candidate industry terms within the same industry chain and corresponding to different sub-industries, at least one auxiliary industry label is determined for the industry chain to characterize the sub-sectors involved in the industry chain.

[0111] In this embodiment of the invention, when different categories of industry keywords appear in the business scope of enterprises within the industrial chain, multiple industry tags can be assigned to them. For example, when most enterprises in the industrial chain focus on parts manufacturing, while some enterprises are involved in new energy material processing, the main industry tag for the industrial chain can be determined as the machinery manufacturing industry based on the keyword matching results, while supplementing it with auxiliary industry tags related to new energy. These auxiliary industry tags can reflect multiple sub-sectors involved in the industrial chain, making the industry classification of the industrial chain more accurate.

[0112] After the tags are generated, they can be re-validated based on the overall tag distribution of companies within the industry chain. For example, if the keywords of most companies within the industry chain fall within a certain industry category, the accuracy of the main industry tag can be further confirmed. When cross-industry keywords exist, corresponding auxiliary industry tags can be added to enhance the applicability and explanatory power of the tags.

[0113] In one embodiment of the present invention, the method for constructing an industry chain map based on multi-source data fusion further includes:

[0114] In the enterprise relationship graph, for each of the industry chains, the multi-level relationships between enterprises within the industry chain are mined. Specifically: based on the edges representing transaction relationships, paths from the core enterprise node to the first-tier supplier node and from the first-tier supplier node to the second-tier supplier node are obtained, forming supply chain paths; based on the edges representing equity relationships, paths from the actual controller node to multiple controlled enterprise nodes are obtained, forming equity control paths.

[0115] In one embodiment of the present invention, the method for constructing a supply chain map based on multi-source data fusion further includes mining multi-level relationships within the supply chain in the enterprise relationship graph. This process can construct supply chain paths and equity control paths based on edges of transaction relationships and equity relationships.

[0116] In the process of supply chain path mining, the transaction paths between core enterprises and their first-tier suppliers can be identified based on the direction and amount of the transaction relationships, and further extended downstream to second-tier supplier nodes. For example, in a certain industrial chain, the core enterprise is the main producer of the main products, and its upstream enterprises may include suppliers of major components, while the upstream of these suppliers may include raw material companies. Through the step-by-step extension of the path, a multi-level supply chain structure can be formed between the core enterprise, first-tier suppliers, and second-tier suppliers, making the business hierarchy within the industrial chain intuitively apparent.

[0117] In the process of mining equity control paths, the control transfer relationship between the actual controller and the companies in which they hold shares can be identified based on the equity relationship edges between enterprises. The actual controller may control multiple companies through direct or indirect shareholding. By performing path queries along the equity direction in the enterprise relationship graph, paths extending from the actual controller node to multiple controlled company nodes can be identified, forming equity control paths. Equity control paths can reflect the interconnectedness of enterprises in the industry chain in terms of governance structure, providing a foundation for subsequent assessment of potential influence relationships between enterprises.

[0118] In one embodiment of the present invention, the method for constructing an industry chain map based on multi-source data fusion further includes:

[0119] In the enterprise association graph, enterprise nodes with risk records are marked with risk. Based on the position of the risk-marked enterprise node in the corresponding industry chain, the downstream enterprise node connected to the risk-marked enterprise node through the guarantee relationship edge is determined by the path query method, thus forming a risk transmission path.

[0120] The industrial chain correlation analysis results are generated based on the aforementioned industrial chains, supply chain paths, equity control paths, and risk transmission paths. The industrial chain correlation analysis results include an industrial chain list, an industrial chain hierarchical path diagram, and a list of enterprises under risk warning.

[0121] In one embodiment of the present invention, the method for constructing an industry chain map based on multi-source data fusion further includes constructing a risk transmission path based on guarantee relationships.

[0122] In processing risk transmission paths, enterprise nodes with risk records can be marked first in the enterprise relationship diagram. Risk records can include overdue payments, defaults, or other adverse events. When an enterprise node is marked as a risk node, the path can be extended downstream through guarantee relationship edges. If the risk node is a guarantor, its guaranteed party may bear potential liability for compensation; if the risk node is a guaranteed party, the guarantor enterprise may face pressure to fulfill its guarantee obligations.

[0123] For example, in a certain industry chain, company X is a core supplier, and there is a guarantee relationship between company X and company Y, with company X guaranteeing company Y's loan. If company Y defaults, a risk marker will be attached to the company Y node in the graph, and the guarantee relationship edge can indicate that company X may face the risk of guarantee default. In this case, the operational stability of company X may be affected, potentially impacting its upstream suppliers. Through the path mining described above, the possible transmission directions of risk within the industry chain can be identified, providing a basis for risk control.

[0124] In one embodiment of the present invention, after constructing the aforementioned supply chain path, equity control path, and risk transmission path, industrial chain correlation analysis results can be generated based on these paths. The analysis results may include an industrial chain list, an industrial chain hierarchical path diagram, and a list of enterprises subject to risk warnings.

[0125] The industry chain list can display the collection of enterprises within each industry chain, their corresponding industry tags, and core enterprises according to the industry chain segmentation results. The industry chain hierarchy path diagram can be drawn based on supply chain paths, equity control paths, or risk transmission paths, making the hierarchical structure within the industry chain visible. The risk warning enterprise list can identify enterprises that may be affected by risks based on risk transmission paths, providing a basis for credit approval and post-loan risk monitoring.

[0126] This analysis helps users understand the relationships between companies from two dimensions: the overall structure of the industry chain and the upstream and downstream relationships. It can also identify company nodes that may be affected by potential risks, thus improving the comprehensiveness and accuracy of industry chain analysis.

[0127] As can be seen from the above embodiments, the industry chain graph construction method based on multi-source data fusion provided by this invention can integrate internal bank corporate customer data and external business registration data under a unified data framework. This enables centralized modeling of transaction relationships, equity relationships, employment relationships, and guarantee relationships among enterprises, and constructs a clearly structured enterprise relationship graph. Based on this, a community segmentation algorithm automatically identifies closely related enterprise groups, forming industry chain-level enterprise groups. This transforms the industry chain construction process from relying on manual judgment to automatic analysis based on graph models, improving the stability and accuracy of industry chain identification.

[0128] This invention utilizes path traversal to uncover multi-level supply relationships, equity control relationships, and risk transmission relationships within the industry chain, enabling users to intuitively understand a company's hierarchical position and scope of influence within the industry chain. By analyzing the business scope text, it automatically generates industry tags for the industry chain, standardizing and interpreting the identification of industry chain attributes.

[0129] This invention can also dynamically identify and classify newly added enterprises and new related relationships, enabling the industry chain map to be updated in real time as business changes occur, maintaining the integrity and timeliness of the industry chain structure. Furthermore, by constructing supply chain paths, equity control paths, and risk transmission paths, an industry chain list, hierarchical path diagram, and risk warning enterprise list can be generated, providing support for credit approval, post-loan management, and risk monitoring.

[0130] In summary, the method of this invention automates, labels, and visualizes the construction of the industrial chain, improves the efficiency and accuracy of industrial chain analysis, and provides effective technical support for the industrial chain financial service system.

[0131] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0132] Based on the same inventive concept, embodiments of the present invention also provide an apparatus for constructing an industry chain map based on multi-source data fusion, which can be used to implement the industry chain map construction method based on multi-source data fusion described in the above embodiments, as described in the following embodiments. Since the principle of the problem-solving principle of the industry chain map construction apparatus based on multi-source data fusion is similar to that of the industry chain map construction method based on multi-source data fusion, embodiments of the industry chain map construction apparatus based on multi-source data fusion can refer to embodiments of the industry chain map construction method based on multi-source data fusion, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0133] Figure 5 This is a structural block diagram of the industry chain map construction device based on multi-source data fusion according to an embodiment of the present invention, as shown below. Figure 5 As shown, in one embodiment of the present invention, the industry chain map construction device based on multi-source data fusion of the present invention includes:

[0134] Enterprise association dataset establishment unit 1 is used to acquire enterprise customer data within the bank and external business registration data, and use the unified social credit code as the association identifier to match and associate the enterprise customer data within the bank and the external business registration data to obtain an enterprise association dataset representing the relationship between enterprises. The relationship includes at least one of the following: transaction relationship, equity relationship, employment relationship and guarantee relationship.

[0135] Enterprise association graph construction unit 2 is used to construct an enterprise association graph based on the enterprise association dataset, wherein each enterprise is constructed as a node in the enterprise association graph, and the association relationship between enterprises is constructed as an edge connecting the nodes in the enterprise association graph;

[0136] The industry chain segmentation unit 3 is used to execute the community segmentation algorithm on the enterprise association graph. According to the segmentation result of the community segmentation algorithm, the enterprise nodes that are segmented into the same community are identified as enterprises in the same industry chain, and each community is identified as the corresponding industry chain.

[0137] The industry label determination unit 4 is used to extract text information describing the business scope or main business of enterprises within each industry chain, generate a label representing the industry to which the industry chain belongs based on the text information, and associate the label with the corresponding industry chain.

[0138] In one embodiment of the present invention, the industrial chain map construction device based on multi-source data fusion of the present invention further includes:

[0139] The graph traversal processing unit is used to select a target enterprise node within the enterprise association graph as the starting node for each of the aforementioned industry chains, and perform graph traversal along the edges representing transaction relationships and the edges representing guarantee relationships using a depth-first search algorithm or a breadth-first search algorithm to obtain multiple paths from the starting node to other enterprise nodes within the industry chain.

[0140] The path determination unit is used to determine the fund flow path based on the transaction amount of the transaction relationship edge on each path, and to determine the risk transmission path based on the guarantee amount of the guarantee relationship edge on each path, so as to characterize the multi-level fund flow relationship and risk transmission relationship among enterprises in the industry chain.

[0141] In one embodiment of the present invention, the industrial chain map construction device based on multi-source data fusion of the present invention further includes:

[0142] A new data processing unit is added to acquire the bank's internal corporate customer data and external business registration data corresponding to the new enterprise, perform association processing on the data using the unified social credit code as the association identifier, add the association results to the enterprise association dataset, and add enterprise nodes representing the new enterprise and edges representing the association relationship between the new enterprise and other enterprises to the enterprise association graph.

[0143] A new enterprise industry chain determination unit is added. For new enterprises connected to existing enterprise nodes via at least one edge in the enterprise association graph, the unit calculates the association strength between the new enterprise and each connected enterprise node based on edge attributes. When the association strength corresponding to a connected enterprise node reaches a preset classification threshold, the new enterprise is assigned to the industry chain corresponding to that connected enterprise node. For new enterprises not connected to existing enterprise nodes in the enterprise association graph via any edge, the unit extracts text information describing the new enterprise's business scope or main business, calculates the similarity between this text information and each industry chain label, and assigns the new enterprise to the industry chain with the highest similarity and not less than a preset similarity threshold. When the similarity of each industry chain is less than the preset similarity threshold, a new industry chain is created for the new enterprise, and this new industry chain is marked as an industry chain to be further classified.

[0144] In one embodiment of the present invention, the industry label determination unit 4 includes:

[0145] The keyword extraction module is used to extract keywords from the text information and count the frequency of each keyword in the industry chain.

[0146] The candidate industry keyword determination module is used to identify keywords that appear more frequently than a preset frequency threshold and can reflect specific industry attributes as candidate industry keywords.

[0147] The main industry label determination module is used to match the candidate industry terms with the industry names and sub-industry names in the preset industry classification system, and determine the main industry label of each industry chain based on the matching results;

[0148] The auxiliary industry label determination module is used to determine at least one auxiliary industry label for a given industry chain when there are multiple candidate industry terms corresponding to different sub-industries within the same industry chain. This label is used to characterize the sub-sectors involved in the industry chain.

[0149] In one embodiment of the present invention, the industrial chain map construction device based on multi-source data fusion of the present invention further includes:

[0150] The multi-level relationship mining unit is used to mine the multi-level relationships between enterprises within each of the industrial chains in the enterprise relationship graph. Specifically, based on the edges representing transaction relationships, it obtains paths from the core enterprise node within the industrial chain to the first-tier supplier node and from the first-tier supplier node to the second-tier supplier node, forming a supply chain path; and based on the edges representing equity relationships, it obtains paths from the actual controller node to multiple controlled enterprise nodes, forming an equity control path.

[0151] In one embodiment of the present invention, the industrial chain map construction device based on multi-source data fusion of the present invention further includes:

[0152] The risk transmission path determination unit is used to mark the enterprise nodes with risk records in the enterprise association diagram, and based on the position of the risk-marked enterprise nodes in the corresponding industry chain, use a path query method to determine the downstream enterprise nodes connected to the risk-marked enterprise nodes through the guarantee relationship edge, thus forming a risk transmission path.

[0153] The industrial chain correlation analysis result determination unit is used to generate industrial chain correlation analysis results based on each of the industrial chains, the supply chain paths, the equity control paths, and the risk transmission paths. The industrial chain correlation analysis results include an industrial chain list, an industrial chain hierarchical path diagram, and a list of enterprises under risk warning.

[0154] In one embodiment of the present invention, the industry chain segmentation unit 3 is specifically used to perform community segmentation on the enterprise association graph using the Louvain algorithm, setting the modularity threshold to 0.3 and setting the minimum community size to 5 nodes, so as to determine the set of nodes with a modularity not lower than the modularity threshold and a number of nodes not less than the minimum community size as the same community.

[0155] To achieve the above objectives, according to another aspect of this application, a computer device is also provided. For example... Figure 6As shown, the computer device includes a memory, a processor, a communication interface, and a communication bus. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps in the method of the above embodiments.

[0156] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0157] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program units corresponding to the above-described method embodiments of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods described in the above-described method embodiments.

[0158] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0159] The one or more units are stored in the memory and, when executed by the processor, perform the methods described in the above embodiments.

[0160] The specific details of the aforementioned computer equipment can be understood by referring to the relevant descriptions and effects in the above embodiments, and will not be repeated here.

[0161] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is also provided. This computer-readable storage medium stores a computer program, which, when executed in a computer processor, implements the steps in the above-described method for constructing an industry chain map based on multi-source data fusion. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0162] To achieve the above objectives, according to another aspect of this application, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method for constructing an industry chain map based on multi-source data fusion.

[0163] Obviously, those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps as a single integrated circuit module. Thus, the present invention is not limited to any particular hardware and software combination.

[0164] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing an industry chain map based on multi-source data fusion, characterized in that, include: The bank acquires corporate customer data and external business registration data, uses the unified social credit code as the association identifier, and matches and associates the corporate customer data and the external business registration data to obtain a corporate association dataset representing the relationship between enterprises. The relationship includes at least one of the following: transaction relationship, equity relationship, employment relationship and guarantee relationship. An enterprise association graph is constructed based on the enterprise association dataset, wherein each enterprise is constructed as a node in the enterprise association graph, and the association relationships between enterprises are constructed as edges connecting nodes in the enterprise association graph; A community segmentation algorithm is executed on the enterprise association graph. Based on the segmentation result of the community segmentation algorithm, enterprise nodes that are assigned to the same community are identified as enterprises in the same industry chain, and each community is identified as a corresponding industry chain. For each of the aforementioned industry chains, text information describing the business scope or main business of enterprises within the industry chain is extracted. Based on the text information, tags are generated to characterize the industry to which the industry chain belongs, and the tags are associated with the corresponding industry chain.

2. The method for constructing an industry chain map based on multi-source data fusion according to claim 1, characterized in that, Also includes: For each of the aforementioned industry chains, a target enterprise node within the industry chain is selected as the starting node in the enterprise association graph. The graph is traversed using a depth-first search algorithm or a breadth-first search algorithm along the edges representing transaction relationships and guarantee relationships to obtain multiple paths from the starting node to other enterprise nodes within the industry chain. The fund flow path is determined based on the transaction amount on each path's transaction relationship edge, and the risk transmission path is determined based on the guarantee amount on each path's guarantee relationship edge, in order to characterize the multi-level fund flow relationship and risk transmission relationship among enterprises within the industry chain.

3. The method for constructing an industry chain map based on multi-source data fusion according to claim 1, characterized in that, Also includes: Obtain the bank's internal corporate customer data and external business registration data corresponding to the newly added enterprise, perform data association processing using the unified social credit code as the association identifier, add the association results to the enterprise association dataset, and add enterprise nodes representing the newly added enterprise and edges representing the association relationship between the newly added enterprise and other enterprises to the enterprise association graph; For a new enterprise that is connected to an existing enterprise node through at least one edge in the enterprise association graph, the association strength between the new enterprise and each connected enterprise node is calculated based on the edge attributes. When the association strength of a certain connected enterprise node reaches a preset classification threshold, the new enterprise is classified into the industrial chain corresponding to that connected enterprise node. For a newly added enterprise that is not connected to any existing enterprise node in the enterprise association graph through any edge, extract the text information describing the business scope or main business of the newly added enterprise, calculate the similarity between the text information and each industry chain label, and classify the newly added enterprise into the industry chain with the highest similarity and not less than the preset similarity threshold; when the similarity of each industry chain is less than the preset similarity threshold, create a new industry chain for the newly added enterprise and mark the new industry chain as an industry chain to be further classified.

4. The method for constructing an industry chain map based on multi-source data fusion according to claim 1, characterized in that, The process of generating labels based on the text information to characterize the industry to which the industrial chain belongs includes: Extract keywords from the text information and count the frequency of each keyword within the industry chain; Keywords that appear more frequently than a preset frequency threshold and can reflect specific industry attributes are identified as candidate industry keywords. The candidate industry terms are matched with the industry names and sub-industry names in the preset industry classification system, and the main industry label of each industry chain is determined based on the matching results. When multiple candidate industry terms exist within the same industry chain and correspond to different sub-industries, at least one auxiliary industry label is determined for the industry chain to characterize the sub-sectors involved in the industry chain.

5. The method for constructing an industry chain map based on multi-source data fusion according to claim 1, characterized in that, Also includes: In the enterprise relationship graph, for each of the industry chains, the multi-level relationships between enterprises within the industry chain are mined. Specifically: based on the edges representing transaction relationships, paths from the core enterprise node to the first-tier supplier node and from the first-tier supplier node to the second-tier supplier node are obtained, forming supply chain paths; based on the edges representing equity relationships, paths from the actual controller node to multiple controlled enterprise nodes are obtained, forming equity control paths.

6. The method for constructing an industry chain map based on multi-source data fusion according to claim 5, characterized in that, Also includes: In the enterprise association graph, enterprise nodes with risk records are marked with risk. Based on the position of the risk-marked enterprise node in the corresponding industry chain, the downstream enterprise node connected to the risk-marked enterprise node through the guarantee relationship edge is determined by the path query method, thus forming a risk transmission path. The industrial chain correlation analysis results are generated based on the aforementioned industrial chains, supply chain paths, equity control paths, and risk transmission paths. The industrial chain correlation analysis results include an industrial chain list, an industrial chain hierarchical path diagram, and a list of enterprises under risk warning.

7. The method for constructing an industry chain map based on multi-source data fusion according to claim 1, characterized in that, The step of performing a community segmentation algorithm on the enterprise association graph includes: The Louvain algorithm is used to divide the enterprise association graph into communities. The modularity threshold is set to 0.3, and the minimum community size is set to 5 nodes. The set of nodes with a modularity not lower than the modularity threshold and a number of nodes not less than the minimum community size are identified as the same community.

8. A device for constructing an industrial chain map based on multi-source data fusion, characterized in that, include: The enterprise association dataset establishment unit is used to acquire enterprise customer data within the bank and external business registration data, and uses the unified social credit code as the association identifier to match and associate the enterprise customer data within the bank with the external business registration data to obtain an enterprise association dataset representing the relationship between enterprises. The relationship includes at least one of the following: transaction relationship, equity relationship, employment relationship and guarantee relationship. The enterprise association graph construction unit is used to construct an enterprise association graph based on the enterprise association dataset, wherein each enterprise is constructed as a node in the enterprise association graph, and the association relationships between enterprises are constructed as edges connecting nodes in the enterprise association graph; The industry chain segmentation unit is used to execute a community segmentation algorithm on the enterprise association graph. Based on the segmentation result of the community segmentation algorithm, enterprise nodes that are segmented into the same community are identified as enterprises in the same industry chain, and each community is identified as the corresponding industry chain. The industry label determination unit is used to extract text information describing the business scope or main business of enterprises within each industry chain, generate labels representing the industry to which the industry chain belongs based on the text information, and associate the labels with the corresponding industry chain.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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