Business relation chain reaching method and system based on relation graph

Abstract

The invention relates to a business relation chain reaching method and system based on a relation graph. The method comprises the following steps: receiving identification information of a target enterprise; based on the identification information, collecting multi-dimensional relation data of the target enterprise; according to the multi-dimensional relation data, constructing a graph data structure with enterprises as nodes and relations between the enterprises as edges; by taking the target enterprise as a starting point, carrying out multi-level relation expansion in a graph data structure, and discovering a reachable enterprise; for a reachable enterprise, calculating a relation path from the target enterprise to the reachable enterprise; based on the calculation result of the relation path, generating an analysis result containing a relation link list, a path analysis report and a reachable enterprise relation graph; and outputting a visual result of the analysis result. The method has the effects of comprehensive integration of multi-dimensional relations, intelligent discovery of multi-level accessible resources, efficient relation path analysis, visual and interactive graph visualization and real-time dynamic updating of data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise relationship network analysis, in particular to a business relationship chain reach method and system based on a relationship graph. BACKGROUND

[0002] With the deepening of digital transformation, the association between enterprises is increasingly complex and diverse, and the demand for comprehensive and accurate enterprise relationship network analysis is increasingly urgent in the fields of business intelligence, marketing, risk control, etc. By effectively identifying the reachable resources and relationship paths of enterprises, it can provide key support for enterprises to explore business opportunities and avoid operational risks.

[0003] Currently, traditional business relationship analysis relies on single-dimensional data, such as only focusing on direct investment or simple supplier relationships, which cannot fully reflect the complex multi-level and multi-dimensional associations between enterprises. In recent years, the development of graph database technologies such as Neo4j and NebulaGraph has provided new technical support for complex relationship network analysis, but how to effectively apply these technologies to enterprise relationship network analysis, build a complete multi-dimensional relationship model, achieve efficient multi-hop relationship query, and intuitive path analysis, is still a problem that has not been completely solved.

[0004] The existing technology has obvious defects: the relationship dimension is single, important association dimensions such as business cooperation and industry associations are ignored, leading to one-sided analysis results; it lacks multi-level analysis capability and cannot effectively identify reachable resources through multi-hop indirect relationships, limiting the scope of business opportunity exploration; path discovery efficiency is low, traditional relational databases have a sharp decline in performance when processing multi-hop relationship queries, making it difficult to handle large-scale enterprise relationship networks; visualization capability is insufficient, lacking intuitive interactive graph display, users have difficulty understanding and utilizing complex relationship network data, and these defects can be effectively solved by the technical solution of the present application. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application aims to provide a business relationship chain reach method and system based on a relationship graph, which has the technical effects of comprehensive integration of multi-dimensional relationships, intelligent mining of multi-level resources, efficient analysis of relationship paths, and visual and intuitive interaction.

[0006] The above invention objectives of the present application are achieved by the following technical solutions: A business relationship chain reach method based on a relationship graph, comprising the following steps: receiving identification information of a target enterprise; based on the identification information, collecting multi-dimensional relationship data of the target enterprise; according to the multi-dimensional relationship data, constructing a graph data structure with enterprises as nodes and relationships between enterprises as edges; Starting from the target enterprise, multi-level relationship expansion is performed in the graph data structure, and reachable enterprises are discovered; For the reachable enterprises, a relationship path from the target enterprise to the reachable enterprises is calculated; Based on the calculation result of the relationship path, an analysis result including a relationship link list, a path analysis report and a reachable enterprise relationship graph is generated; Output the visualization result of the analysis result.

[0007] Through the above technical solution, the whole process from target enterprise information receiving, multi-dimensional relationship data collection to graph construction, relationship expansion, path calculation, result generation and visualization output is completely covered, the standardization and intelligentization of enterprise relationship chain reach are realized, and the problem of single relationship dimension and missing multi-level analysis in traditional methods is effectively solved, providing comprehensive technical support for business opportunity mining.

[0008] As a further technical solution of the present application: the multi-dimensional relationship data includes business cooperation relationship data, investment holding relationship data, industry association relationship data and supply chain relationship data.

[0009] Through the above technical solution, the specific types of multi-dimensional relationship data are clearly defined, the comprehensive integration of business cooperation, investment holding, industry association and supply chain relationship is realized, and the one-sidedness of relationship analysis caused by single-dimensional data is avoided, laying a rich and reliable data foundation for subsequent construction of complete enterprise relationship network graph.

[0010] As a further technical solution of the present application: before the graph data structure with enterprises as nodes and relationships between enterprises as edges is constructed, it further includes: The multi-dimensional relationship data is preprocessed, and the preprocessing includes data cleaning, data standardization and data deduplication.

[0011] Through the above technical solution, the data is cleaned, standardized and deduplicated before graph construction, effectively eliminating redundant and incorrect data and unifying the data format, improving the quality of the original data, avoiding the interference of poor data on the accuracy of subsequent relationship expansion and path calculation, and ensuring the precision and reliability of the whole analysis process.

[0012] As a further technical solution of the present application: the step of constructing the graph data structure with enterprises as nodes and relationships between enterprises as edges specifically includes: The graph data structure is constructed with enterprises as nodes and multi-dimensional relationships between enterprises as edges; Attribute information is set for each enterprise node; The relationship type identifier and weight coefficient are set for each relationship edge; The constructed graph data structure is stored in a graph database.

[0013] Through the technical solution, the construction and storage logic of the graph data structure are refined, the type identification and weight coefficient setting of the node attribute and the relationship edge are refined, which provides a key basis for the relationship path importance evaluation, the storage mode of the graph database significantly improves the query and call efficiency of the relationship data, and supports efficient processing of large-scale enterprise relationship networks.

[0014] As a further technical solution of the application, the step of performing multi-level relationship expansion includes: Starting from the target enterprise, a first-level expansion is performed to identify first-level reachable enterprises; Starting from the first-level reachable enterprises, a second-level expansion is performed to identify second-level reachable enterprises; According to a preset expansion depth N, the second-level expansion is repeatedly performed to sequentially identify up to N-level reachable enterprises; Wherein, while performing each level of expansion, the relationship path and path hop count from the target enterprise to each reachable enterprise identified at this level are recorded.

[0015] Through the technical solution, hierarchical relationship expansion according to a preset depth is realized, which can identify first-level reachable enterprises directly associated and mine multi-level reachable enterprises indirectly associated, and simultaneously record path and hop count information, which not only expands the discovery range of business opportunities, but also clearly defines the relationship distance between enterprises, avoiding excessive diffusion of the relationship network.

[0016] As a further technical solution of the application, the step of calculating the relationship path from the target enterprise to the reachable enterprise includes: Calculating at least one shortest relationship path from the target enterprise to each reachable enterprise; Based on the preset weight coefficient of the relationship edge, the importance of multiple relationship paths is evaluated; According to the evaluation result of the relationship path, key node enterprises and bridge enterprises in the relationship path are identified.

[0017] Through the technical solution, the shortest relationship path can be accurately calculated, the path importance can be evaluated in combination with the weight coefficient, and the key nodes and bridge enterprises can be identified, which solves the problem of limited path discovery capability and low efficiency of traditional methods, provides targeted breakthroughs for enterprises, and improves the efficiency and success rate of business access.

[0018] As a further technical solution of the present application: the analysis result includes: a node sequence recording a relationship path and a relationship chain list recording a relationship type; a path analysis report containing the number of hops of the relationship path, the weight evaluation result, and the explanation of the key node enterprise and the bridge enterprise; and a reachable enterprise relationship graph integrating the relationship chain list and the path analysis report information.

[0019] Through the above technical solution, the composition and core content of the analysis result are clearly defined, the relationship chain list facilitates quick access to specific path details, the path analysis report provides in-depth value interpretation, and the reachable enterprise relationship graph realizes intuitive presentation of complex relationships, and the three complement each other to meet the use requirements in different scenarios, thereby improving the practicality and ease of use of the analysis result.

[0020] The present application also discloses a business relationship chain reach system based on a relationship graph, which is applied to a business relationship chain reach method based on a relationship graph, and specifically includes: A data acquisition module is configured to acquire multi-dimensional relationship data associated with a target enterprise. A graph construction and storage module is configured to construct a graph data structure with enterprises as nodes and relationships between enterprises as edges based on the multi-dimensional relationship data, and store the graph data structure in a graph database. A relationship analysis engine is configured to perform multi-level relationship expansion in the graph data structure stored in the graph database with the target enterprise as a starting point, and discover reachable enterprises. A path searching module is configured to calculate a relationship path from the target enterprise to the reachable enterprises. A result generation and visualization module is configured to display a relationship graph and a relationship path. An application programming interface gateway is configured to receive external requests and route the requests to the relationship analysis engine or the path searching module.

[0021] Through the above technical solution, the business relationship chain reach method is implemented as a modular system architecture, and each module has clear division of labor and collaborative cooperation, the API gateway ensures the orderliness and security of external request processing, realizes the automated and systematic operation of relationship chain analysis, and improves the engineering application ability and scalability of the technical solution.

[0022] The present application also discloses a computer device including a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the computer program is executed by the processor to implement the above-mentioned business relationship chain reach method based on a relationship graph.

[0023] The above technical solution provides hardware support for the business relationship chain outreach method. By leveraging the collaborative work of the processor and memory, the method can be executed efficiently and stably in a real hardware environment, solving the problem of "difficulty in implementation" of the technical solution and improving the feasibility and practical application value of the method.

[0024] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described business relationship chain outreach method based on relationship graphs.

[0025] The above technical solution enables the storage and reuse of the business relationship chain reach method, facilitating the dissemination, installation and operation of the program. It provides storage support for the widespread application of the technical solution, lowers the threshold for enterprises to adopt the technology, and helps it to be applied on a large scale in business intelligence, marketing and other fields.

[0026] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention discloses a business relationship chain reach method based on relationship graphs. By integrating multi-dimensional enterprise relationship data such as business cooperation, investment and shareholding, industry associations, and supply chains, it constructs a relationship graph and realizes multi-level relationship expansion and path calculation, enabling intelligent discovery and accurate path analysis of enterprise reachable resources, breaking through the limitations of traditional methods with single relationship dimensions and insufficient analysis depth.

[0027] 2. This invention discloses a business relationship chain outreach system based on relationship graphs. Through modular architecture design, it integrates data collection, graph construction, relationship analysis, path calculation and visualization functions to achieve automated and systematic operation of the business relationship chain outreach method, and improve the processing efficiency and intuitive presentation capability of complex relationship networks.

[0028] 3. This invention discloses a device and computer-readable storage medium for reaching business relationship chains based on relationship graphs. By providing a hardware operating platform and a program storage reuse mechanism, it enables the stable execution and widespread promotion of the business relationship chain reaching method based on relationship graphs, lowers the technical implementation threshold, and helps its large-scale application in business intelligence, marketing and other fields. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating a business relationship chain outreach method based on a relationship graph according to the present invention.

[0030] Figure 2 for Figure 1 A schematic diagram of S2.

[0031] Figure 3 for Figure 1A flowchart of the S3 process.

[0032] Figure 4 for Figure 1 A flowchart of the S4 process.

[0033] Figure 5 for Figure 1 A flowchart of the S5 process.

[0034] Figure 6 This is an architecture diagram of a business relationship chain outreach system based on a relationship graph, according to the present invention. Detailed Implementation

[0035] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0036] In the description of this application, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0037] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed," "equipped with," "sleeved / connected," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Example

[0038] Reference Figure 1This invention discloses a business relationship chain outreach method based on relationship graphs. This method follows an ordered logic of "identifier reception → data collection and preprocessing → graph construction → relationship expansion → path calculation → result generation → visualization output". Specifically, it includes seven core steps executed in sequence: S1: receiving target enterprise identification information, S2: multi-dimensional relationship data collection and preprocessing, S3: graph data structure construction and storage, S4: multi-level relationship expansion, S5: relationship path calculation, S6: analysis result generation, and S7: visualization result output. The steps are closely connected and the data flow is clear, which together constitute a complete business relationship chain outreach and analysis link.

[0039] Reference Figure 1 S1: Receiving target enterprise identification information. As the starting point of the entire method, the core is to obtain accurate information that can uniquely identify the target enterprise. It receives unique identification data such as the enterprise name and unified social credit code input by the user, and verifies the validity and completeness of the information through a format verification mechanism to ensure that subsequent data collection can accurately locate the target enterprise and avoid collection deviations due to ambiguous or incorrect identification.

[0040] Reference Figure 2 S2: Multi-dimensional Relationship Data Collection and Preprocessing aims to obtain comprehensive and high-quality enterprise relationship data. This step first configures access strategies for multiple heterogeneous data sources, such as business registration systems, industry association platforms, supply chain systems, and news announcement platforms, establishing a real-time data update mechanism and access control. Then, it collects four types of core relationship data through technologies such as API calls and direct database connections. Specifically, these include cooperation agreements and joint bidding information related to business cooperation; equity investment and M&A matters related to investment and shareholding; membership and association position information related to industry associations; and supplier and customer resources related to the supply chain. After collection, the data is cleaned, standardized, and deduplicated to remove erroneous and invalid data, unify data formats and statistical standards, and delete duplicate and redundant records to ensure that the data quality meets the requirements for subsequent graph construction.

[0041] Reference Figure 3 S3: Graph data structure construction and storage, a key step in transforming preprocessed data into a structured graph and storing it securely. The output structured graph data directly provides core data support for the multi-level relationship expansion in S4, and is specifically divided into four sub-steps: S31: Core construction of graph structure, with enterprises as nodes and multi-dimensional relationships as edges, to build a basic graph data structure and clarify the relationships between enterprises.

[0042] S32: Node attribute configuration, setting characteristic attributes such as enterprise size, industry classification, and location for each enterprise node, enriching the dimensions of node description.

[0043] S33: Configure relationship edge attributes, set relationship type identifier (such as "business cooperation" or "equity investment") and weight coefficient (set according to the closeness and stability of the relationship) for each relationship edge, and provide a basis for subsequent path evaluation.

[0044] S34: Graph Data Storage and Optimization. The completed graph data structure (including enterprise nodes and relationship edges) is stored in the Neo4j graph database, and nodes, relationships, and their attributes are created using the Cypher query language. To achieve efficient data management and access, a multi-database collaborative storage architecture is adopted: metadata such as attribute definitions and relationship type rules are stored in a MySQL relational database; frequently queried enterprise paths and other hot data are cached in Redis. Based on this, indexes are created for key attributes in the graph database to improve query performance, and regular backups and disaster recovery strategies are implemented to ensure data security and recoverability. This storage solution balances graph relationship traversal efficiency, metadata management standardization, and access speed for hot data, supporting the efficient and stable operation of subsequent relationship expansion and path analysis from a data perspective.

[0045] Reference Figure 4 S4: Multi-level Relationship Expansion, focuses on mining multi-level reachable enterprises to expand business opportunities. The list of reachable enterprises and path hop records it mines directly provide reachable enterprise objects and initial path data for the relationship path calculation in S5. It is specifically divided into three sub-steps: S41: Extend parameter configuration, preset extension depth N (can be flexibly adjusted according to business needs, such as N=3 to reach the third level of enterprises), and specify the extension termination condition to avoid excessive spread of the relationship network.

[0046] S42: Hierarchical expansion execution, using a multi-hop relationship expansion algorithm, starts with the target enterprise and performs the first-level expansion to identify directly related first-level reachable enterprises. Then, starting with the first-level reachable enterprises, it performs the second-level expansion to identify indirectly related second-level reachable enterprises. Subsequently, it expands level by level according to the logic of 'starting from the previous first-level reachable enterprise', repeating this expansion method until the preset depth N is reached, and successively identifying up to the Nth level reachable enterprises, gradually building a complete multi-level relationship network.

[0047] S43: Path and hop count records. During each level of expansion, the relationship path (through nodes and relationship edges) from the target enterprise to each reachable enterprise at that level and the number of hops are recorded synchronously, providing basic data for subsequent path analysis.

[0048] Reference Figure 5 S5: Relationship path calculation aims to filter high-quality paths and identify key nodes, specifically consisting of three sub-steps: S5:1 Shortest path calculation: The shortest path calculation algorithm is used to calculate at least one shortest relationship path from the target enterprise for each reachable enterprise, thereby shortening the reach path length.

[0049] S52: Path importance assessment. Based on the relationship edge weight coefficients preset in S33, the importance of multiple paths to the same reachable enterprise is assessed. The higher the sum of the weight coefficients, the higher the importance level of the path, and the higher it is given priority as the recommended reachable path.

[0050] S53: Identification of key node and bridge enterprises. Based on the path assessment results, key node enterprises that play a core supporting role in path connectivity, as well as bridge enterprises that connect different relationship subnets and help reach new networks, are selected to identify core cooperation or communication partners for users.

[0051] S6: Analysis Results Generation. This stage integrates path calculation results into diverse outputs, including a relationship chain list, a path analysis report, and a reachable enterprise relationship graph. The relationship chain list records the node sequence and relationship type for each path, facilitating quick access. The path analysis report integrates path hop counts, weight assessment results, and descriptions of key nodes and bridge enterprises, providing in-depth decision support. The reachable enterprise relationship graph integrates the above information to form a complete relationship network view, intuitively presenting the connections between the target enterprise and reachable enterprises at all levels.

[0052] Reference Figure 1 S7: Visualized output, presenting analysis results in an intuitive way, reducing the understanding threshold of complex relationship networks. It uses ECharts.js or D3.js to implement graph visualization, and uses force-directed layout algorithm to support multiple layout methods such as circular and hierarchical layouts. It develops interactive functions such as node click to display enterprise details, edge click to display relationship information, zoom and pan, filter query, and path highlighting. It optimizes performance through virtual rendering, data layering, asynchronous loading and other means to ensure the smoothness of large-scale node rendering and operation. At the same time, it supports users to re-enter target enterprise identification information based on the visualization results, forming a closed-loop interactive logic of "visual feedback → re-initiating query".

[0053] Reference Figure 6The business relationship chain outreach system based on relationship graphs of this invention is applied to the above-mentioned method. It adopts a microservice architecture, with modules distributed in a hierarchical structure of "data support layer – business logic layer – presentation layer," including a data acquisition module, a graph construction and storage module, a relationship analysis engine, a path lookup module, a result generation and visualization module, and an API gateway. As the underlying data support layer, the data acquisition module is responsible for multi-source data acquisition, preprocessing, and real-time updates. Receiving the preprocessed data output by the data acquisition module, the graph construction and storage module completes graph data structure construction, attribute configuration, and Neo4j storage optimization according to step S3. The relationship analysis engine, located in the business logic layer and relying on the graph data structure provided by the graph construction and storage module for computation, implements multi-level relationship expansion and path hop count recording in step S4 through a multi-hop algorithm. The reachable enterprise data mined by the engine is transmitted to the path lookup module, providing object basis for its path calculation. The path lookup module then performs shortest path calculation, path evaluation, and key node identification in step S5. This module serves as the system's presentation layer, receiving business results from the relationship analysis engine and path lookup module, and generating and visualizing them. It completes the generation of analysis results in S6 and the implementation of interactive visualization in S7. It also acts as an API gateway connected to the path lookup module, supporting the routing of external requests to business modules such as the relationship analysis engine. Implemented using Spring Cloud Gateway or Kong, it handles request reception, routing, authentication and authorization, rate limiting, and circuit breaking. Finally, it forms the system's supporting data storage layer, centered on Neo4j and associated with the graph construction and storage modules. It also utilizes MySQL to store metadata and Redis to cache hot data, providing data-driven support for the efficient and stable operation of each module.

[0054] The computer device of the present invention includes a processor, a memory, and a computer program stored in the memory. When the processor executes the program, it implements the above-described method, providing hardware support for the method. The computer-readable storage medium of the present invention stores the above-described computer program. When the program is executed by the processor, it implements the corresponding method, realizing the storage and reuse of the program, and providing support for the widespread promotion of the technical solution.

[0055] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0056] Application example of this invention: Taking a car manufacturing company A as the target company, the user first inputs the name and unified social credit code of company A through the system's visual interface. After the format is verified, the system loads the preset configuration—expansion depth N=2, and the relationship edge weight coefficients are set according to "business cooperation (0.8) > investment and shareholding (0.7) > industry association (0.5) > supply chain (0.4)". The system calls a multi-source platform through API to collect four types of core relationship data: business cooperation, investment and shareholding, industry association, and supply chain. After cleaning, standardization, and deduplication, 18 valid relationship data are formed. The graph construction and storage module uses company A as the core node, related companies as secondary nodes, and the 18 valid relationships as edges to build a graph data structure. It supplements the node attributes and relationship edge identifiers and weights, stores them in the Neo4j database using Cypher language, and establishes a dual index. The relationship analysis engine performs expansion according to N=2: the first-level expansion identifies 12 directly related first-level reachable companies, the second-level expansion mines 35 indirectly related second-level reachable companies, and simultaneously records 58 complete relationship paths and hop counts. The path finding module optimizes 58 paths, matching the shortest path for secondary reachable enterprises and selecting three optimal paths (with total weights of 1.6, 1.1, and 1.0 respectively). It also identifies key node enterprises and bridge enterprises. The system generates a relationship chain list, a path analysis report, and a visual graph. The visual graph supports interactive operations such as viewing node details, zooming, and panning. The report recommends prioritizing reaching target enterprises via high-weight paths.

[0057] The implementation principle of this invention is as follows: it revolves around the core logic of "multi-dimensional data integration - map construction - relationship expansion - path analysis - visualization output", and systematically solves existing defects through precise adaptation of technical means and technical problems. At the data level, multi-source heterogeneous data collection and preprocessing not only compensate for the shortcomings of traditional single-dimensional relationships but also ensure data quality, laying the foundation for subsequent analysis. In the graph construction stage, structured modeling of "node-relationship-attribute" and graph database storage leverage the inherent advantages of graph databases in complex relationship queries, solving the problem of low efficiency in multi-hop queries of traditional databases. Relationship expansion employs multi-hop algorithms for layer-by-layer mining, achieving comprehensive coverage of "direct + indirect" reachable resources, breaking the limitations of traditional technologies lacking multi-level analysis. Path calculation, through shortest path filtering and weight evaluation combined with key node identification, provides "efficient + reliable" precise guidance for business outreach, addressing the limited path discovery capabilities of traditional technologies. In the visualization stage, intuitive interactive design lowers the user's understanding threshold, compensating for the shortcomings of traditional visualization technologies. At the system level, microservice architecture and multi-database collaborative storage ensure the automated and efficient operation of the entire process, ultimately achieving a comprehensive characterization, efficient discovery, and intuitive presentation of the enterprise relationship chain.

[0058] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for reaching business relationship chains based on relationship graphs, characterized in that, Includes the following steps: Receive the target company's identification information; Based on the identification information, multi-dimensional relationship data of the target enterprise is collected; Based on the multi-dimensional relationship data, a graph data structure is constructed with enterprises as nodes and relationships between enterprises as edges; Starting with the target company, multi-level relationship expansion is performed in the graph data structure to discover reachable companies; For the reachable enterprises, calculate the relationship path from the target enterprise to the reachable enterprises; Based on the calculation results of the relationship paths, an analysis result is generated that includes a list of relationship links, a path analysis report, and a relationship graph of reachable enterprises. Output a visualization of the analysis results.

2. The business relationship chain outreach method based on relationship graphs according to claim 1, characterized in that, The multi-dimensional relationship data includes business cooperation relationship data, investment and shareholding relationship data, industry association relationship data, and supply chain relationship data.

3. The method according to claim 1 or 2, characterized in that, Before constructing the graph data structure with enterprises as nodes and inter-enterprise relationships as edges, the following is also included: The multi-dimensional relationship data is preprocessed, including data cleaning, data standardization, and data deduplication.

4. The business relationship chain outreach method based on relationship graphs according to claim 1, characterized in that, The steps for constructing a graph data structure with enterprises as nodes and inter-enterprise relationships as edges specifically include: A graph data structure is constructed using enterprises as nodes and multi-dimensional relationships between enterprises as edges. Set attribute information for each enterprise node; Set a relation type identifier and weight coefficient for each relation edge; The completed graph data structure is stored in the graph database.

5. The business relationship chain outreach method based on relationship graphs according to claim 1, characterized in that, The steps for expanding multi-level relationships include: Starting with the target company, perform the first level of expansion to identify first-level reachable companies; Starting with the first-level reachable enterprises, the second-level expansion is performed to identify second-level reachable enterprises; Based on the preset expansion depth N, the second level of expansion is repeatedly executed to identify enterprises up to the Nth level of reachable enterprises. During each level of expansion execution, the relationship path and path hop count from the target enterprise to each reachable enterprise identified at that level are recorded.

6. The business relationship chain outreach method based on relationship graphs according to claim 1, characterized in that, The steps for calculating the relationship path from the target enterprise to the reachable enterprises include: Calculate at least one shortest relationship path from the target enterprise to each reachable enterprise; The importance of multiple relationship paths is evaluated based on the pre-defined weight coefficients for the relationship edges; Based on the evaluation results of the relationship path, key node enterprises and bridge enterprises in the relationship path are identified.

7. The business relationship chain outreach method based on relationship graphs according to claim 1, characterized in that, The analysis results include: A list of relational chains containing the node sequence of relational paths and the relational types; A path analysis report that includes the number of hops in the relationship path, weight assessment results, and descriptions of key node companies and bridge companies; In addition, an accessible enterprise relationship graph that integrates the relationship chain list and path analysis report information.

8. A business relationship chain outreach system based on relationship graphs, characterized in that, The business relationship chain outreach method based on relationship graphs, applied to any one of claims 1-7, specifically includes: The data acquisition module is used to collect multi-dimensional relationship data related to the target enterprise; The graph construction and storage module is used to construct a graph data structure with enterprises as nodes and inter-enterprise relationships as edges based on the multi-dimensional relationship data, and store the graph data structure in a graph database. The relationship analysis engine is used to expand multi-level relationships within the graph data structure stored in the graph database, starting from the target enterprise, to discover reachable enterprises; The path lookup module is used to calculate the relationship path from the target enterprise to the reachable enterprise for the reachable enterprise; The results generation and visualization module is used to display relationship graphs and relationship paths; An application programming interface gateway is used to receive external requests and route them to the relationship analysis engine or path lookup module.

9. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the business relationship chain outreach method based on a relationship graph as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the business relationship chain outreach method based on relationship graphs as described in any one of claims 1-7.

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