Method and system for obtaining enterprise association relationship

Abstract

The present invention provides a method and system for obtaining enterprise association relationships, including: a host system front-end module: configuring association parameters according to dimensions, configuring association thresholds according to enterprise association indicators; submitting an enterprise directory, obtaining enterprise relationship information data packets from an external data source in real time; providing display and download functions; a host system back-end module: parsing the data in the enterprise information relationship data packet according to rules and storing it in a database to obtain structured data; preprocessing the structured data, and obtaining the association relationship values ​​between enterprises according to the set association parameter dimensions for normal data; classifying data; a database module: storing classified data. The present invention can realize risk identification for a single enterprise based on enterprise identification information, and can also realize risk identification of associated enterprises and automatic review and identification of the strength of association relationships between enterprises through identification and data analysis of association information such as equity and legal persons between multiple enterprises.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for acquiring enterprise association relationships. Background Art

[0002] With the rapid development of the Internet, a large amount of data is made public on the Internet, including a large amount of data related to enterprises. Data about enterprises can include a large number of different types of data such as enterprise official website data, related enterprise data, enterprise APP data, and enterprise recruitment data. Obtaining and identifying enterprise relationships is particularly important for cooperation between companies and between companies and customers. If you don’t know the corporate relationships of the partners you are working with, your work will be very passive, and investment risks and cooperation risks will easily occur. Enterprise risks involve all aspects of the enterprise’s business management, asset allocation, capital utilization, profit distribution, information disclosure, etc. If there are major risks in an enterprise, it will cause significant losses to the enterprise, investors, job seekers, the country and the collective. Therefore, it is particularly important to obtain corporate relationships based on data publicly available on the Internet. In order to adapt to the changes in the business operations of commercial banks in the era of Internet finance and strengthen the substantive and effective control of business operation risks, financial institutions such as commercial banks need to establish automatic risk identification methods to replace the traditional identification mode of risk information of legal person users and improve the accuracy of risk identification.

[0003] Patent document CN113435762A (application number: CN202110757269.4) discloses an enterprise risk identification method, device and equipment, which includes: obtaining standardized recruitment data, using a recruitment subject enterprise identification model to identify the recruitment subject enterprise corresponding to the recruitment data, and generating a first enterprise recruitment information portrait of the recruitment subject enterprise based on the recruitment data; then determining a second enterprise recruitment information portrait of an analogous enterprise of the recruitment subject enterprise based on the obtained registration data of the recruitment subject enterprise; calculating the difference between the first enterprise recruitment information portrait and the second enterprise recruitment information portrait; when the difference is greater than a first preset threshold, determining that the recruitment subject enterprise is at risk.

[0004] At present, when risk control departments in various industries conduct risk screening and assessment inspections, they frequently query the information of target enterprises from the industrial and commercial information network or third-party websites through the query method of the above-mentioned patent documents to evaluate their risks. The above-mentioned patent documents can only judge whether the target enterprise has risks based on the information of the target enterprise itself and the information of analogous enterprises. When the target enterprise is normal but its affiliated enterprises have risks, it is impossible to identify them in time. For industries with strict risk control such as the financial industry, the risks of affiliated enterprises to the target enterprise are also crucial, and it is not enough to just check the information of the target enterprise.

[0005] Therefore, it is usually necessary to manually query the information of the target company's shareholders and affiliated companies. For affiliated companies, it is necessary to further query their shareholders and related information until all affiliated companies are queried and the relationships between all affiliated companies are sorted out, so as to comprehensively assess the risks of the company in the future. This manual query method of corporate affiliation is very time-consuming and prone to errors. In addition, since the corporate affiliation often changes, even for the same target company, it is necessary to query again in a timely manner every once in a while to avoid information failure to update in time and a large amount of repetitive work. Summary of the invention

[0006] In view of the defects in the prior art, the object of the present invention is to provide a

[0007] A system for obtaining enterprise association relationships provided by the present invention includes:

[0008] Host system front-end module: configure association parameters according to dimensions, configure association thresholds according to enterprise association indicators; submit enterprise directories, obtain enterprise relationship information data packages from external data sources in real time; provide display and download functions;

[0009] Host system backend module: parse the data in the enterprise information relationship data package according to the rules and store it in the database to obtain structured data; pre-process the structured data, and for normal data, obtain the correlation relationship value between enterprises according to the set correlation parameter dimension; and classify the data;

[0010] Database module: stores classified data.

[0011] Preferably, in the host system front-end module:

[0012] Configure association parameters based on dimensions, including equity relations, executive relations, corporate credit, and corporate industrial and commercial penalties. Obtain data from the bank's external data integration platform through HTTP requests. The external data integration platform aggregates legitimate data source information from sources including the People's Bank of China, courts, public security, telecommunications operators, local provident fund data, FICO, Bairong, and Beijing Zhongshuzhihui.

[0013] Configure strong, medium and weak relationship thresholds based on the enterprise relationship indicators; the thresholds set here are mainly used as the criteria for determining the strength of the relationship between the affiliated companies;

[0014] Provides display functions, including dynamic family tree diagrams of enterprise relationships and result tables, and provides download functions for diagrams and tables.

[0015] Preferably, in the host system backend module:

[0016] After obtaining the data packet, the data is parsed according to the rules and stored in the database to obtain structured data; the data in the interface provided by the external data integration platform is returned in the form of an HTTP response message, which contains message header information and message body data, wherein the message body contains data information related to the enterprise, including a node array and a relationship array, wherein the node array contains node ID, node name, whether to enter the node, node type, registered capital, name, establishment time, and registered currency;

[0017] Preprocess the structured data, use error tags to mark the error node data, use no-association tags to mark the no-association data, and do not mark the normal data. Use error tags and no-association tags to mark the association between any two enterprises, so as to filter out the no-association and association enterprise groups through the front-end page of the system;

[0018] For normal data, the relationship groups in the data graph are traversed in turn to obtain the executives and equity quantitative values ​​in the enterprise data, and the association relationship values ​​between the enterprises are analyzed according to the set parameter dimensions; because the acquired data is returned in the form of node array and relationship array, the source node id and target node id in the relationship array identify the directionality of the relationship between the two enterprise nodes, and the shareholding ratio between the two enterprises is obtained according to the association direction of the source node id and the target node id, the relationship type, and the investment ratio.

[0019] Preferably, in the database module:

[0020] After analyzing the relationship data between all enterprises, the data is classified and stored in the database according to enterprise name A, enterprise name B, strong and weak relationship, association category, indicator value, and time dimension.

[0021] According to a method for obtaining enterprise association relationships provided by the present invention, a system for identifying enterprise association relationships is used to perform the following steps:

[0022] Step S1: configure association parameters according to dimensions, and configure association thresholds according to enterprise association indicators;

[0023] Step S2: Submit the enterprise directory and obtain enterprise relationship information data package from external data source in real time;

[0024] Step S3: Parse the data in the enterprise information relationship data package according to the rules and store it in the database to obtain structured data;

[0025] Step S4: pre-process the structured data, and obtain the association relationship values ​​between enterprises according to the set association parameter dimensions for normal data;

[0026] Step S5: classify the data and store them in a database;

[0027] Step S6: Display the enterprise relationship.

[0028] Preferably, in step S1:

[0029] On the host system front end, configure the parameters:

[0030] Configure the judgment dimension parameters through the parameter configuration page, including equity relations, senior management relations, corporate credit, and corporate industrial and commercial penalties.

[0031] On the host system front end, configure the enterprise relationship threshold parameters through the parameter configuration page, including weak relationship threshold, medium relationship threshold, and strong relationship threshold;

[0032] On the host system front end, configure the time parameters for scheduled batch processing of scheduled tasks for existing enterprises through the parameter configuration page.

[0033] Preferably, in step S2:

[0034] On the host system front end, batch import enterprise information:

[0035] On the front end of the host system, in the enterprise information import module on the enterprise relationship information query page, batch upload the list of enterprises to be analyzed according to the data template format. The data template includes two fields: enterprise name and enterprise unified credit code;

[0036] After the upload is completed, the enterprise information currently uploaded in batches and the relevant information of the current operator can be viewed through the enterprise relationship information query page. The field information includes operator, time, enterprise collection, analysis progress, and analysis results.

[0037] At the back end of the system, the system obtains enterprise-related data from the outside in two ways:

[0038] The first method is the timed trigger method. The system backend automatically initiates data query requests to the external data integration platform for all current enterprise sets through the configured timed tasks.

[0039] The second method is the real-time trigger method, where the operator uses the real-time query button on the page to notify the system backend to initiate a data query request instruction to the external data integration platform.

[0040] Preferably, in step S3:

[0041] After the request is initiated, the system backend obtains the response data message from the external data integration platform. The system parses the data according to the interface specification and stores the data in the database.

[0042] Among them, the response message includes two parts: a message header and a message body. The message body includes a node array and a relationship array. The node array contains information including node id, node name, node type, enterprise status, whether to enter the node, registered capital, unified social credit code, name, and registration number. The relationship array contains information including node id, source node id, target node id, relationship type, investment ratio, control path relationship, position, whether it is a legal person, and subscribed capital.

[0043] Preferably, in step S4:

[0044] The system backend analyzes and processes structured data:

[0045] The system backend traverses the data stored in the database according to the node array, traverses the node IDs in the node array in turn, finds the relationship array information associated with the node ID, finds all the target node IDs of enterprises associated with the current enterprise through the source node ID in the relationship array, and constructs the equity relationship and legal person relationship between the source node ID enterprise and the target node ID enterprise;

[0046] Based on the traversal processing method, the system backend constructs the equity relationship and legal person relationship between each enterprise as the source node id and all target enterprises in turn, and stores the relationship information in the database with the enterprise ID as the index.

[0047] If the enterprise in the enterprise set uploaded by the operator is not the target node id enterprise of the enterprise with the source node id, the relationship between the two enterprises is marked as unrelated, and the relationship between other related enterprises is marked as related;

[0048] The back-end system calculates the strength of the relationship between related companies:

[0049] For enterprises marked as related, the related enterprises of each enterprise are traversed to obtain the strength of the relationship between the two enterprises. The direct shareholding of the enterprise is calculated by addition, and the indirect shareholding is calculated by multiplication;

[0050] The calculated actual shareholding ratio of enterprises is compared with the parameter threshold, and the relationship between enterprises is classified into weakly associated, medium associated, and strongly associated enterprises, and the association results are stored in the database.

[0051] Preferably, in step S6:

[0052] Display data results on the front end:

[0053] The front-end system provides a display page for enterprise association data, which allows users to filter and view enterprise relationship results by conditions including enterprise name, operator, and time. The information includes the strength of the association, shareholding ratio, and legal person relationship.

[0054] The front-end system provides a report download function to download and export the operator's search results in an encrypted Excel file format;

[0055] The front-end system provides a relationship diagram display of the relationship between enterprises, showing all related enterprises related to the main enterprise in the form of a node diagram. Directed lines with arrows are used between nodes to show the directionality of the relationship. The lines carry the specific relationship between the enterprises, including legal persons and shareholdings.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention is based on a method of traversing data according to a node array, and sequentially constructs the equity relationship and legal person relationship between each enterprise and all target enterprises, thereby obtaining the relationship between multiple enterprises. It can identify and analyze the equity, legal person and other related information between multiple enterprises to realize the risk identification of related enterprises and realize the automatic review and identification of the strength of the relationship between enterprises.

[0058] The present invention dynamically tracks enterprise association information. When the association relationship between enterprises changes, the enterprise information exceeding the warning threshold in the indicator window is recorded in detail and provided to the risk department for subsequent processing.

[0059] The present invention has advantages in the following scenarios by improving the recognition of related information such as equity and legal persons between multiple enterprises: quickly identifying all related enterprises of a known risk enterprise and the strength of their association; identifying all the strong and weak relationships between known enterprises in scenarios such as project bidding and enterprise review; and quickly warning of risky enterprises and changes in the strength of enterprise associations.

[0060] After acquiring the data packet, the present invention parses the data according to the rules and stores it to obtain structured data, and marks the structured data. Error node data is marked with an error mark, and unrelated data is marked with an unrelated mark. Normal data is not marked, so that whether there is a relationship between two enterprises can be quickly screened out, which solves the problem of quickly querying all related enterprises of a risky enterprise and the strength of their relationship, and also solves the problem of quickly querying whether a target enterprise has risky related enterprises and the strength of their relationship. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0062] Figure 1 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0064] Embodiment 1:

[0065] According to a system for obtaining enterprise association relationships provided by the present invention, Figure 1 As shown, including:

[0066] Host system front-end module: configure association parameters according to dimensions, configure association thresholds according to enterprise association indicators; submit enterprise directories, obtain enterprise relationship information data packages from external data sources in real time; provide display and download functions;

[0067] Host system backend module: parse the data in the enterprise information relationship data package according to the rules and store it in the database to obtain structured data; pre-process the structured data, and for normal data, obtain the correlation relationship value between enterprises according to the set correlation parameter dimension; and classify the data;

[0068] Database module: stores classified data.

[0069] Specifically, in the host system front-end module:

[0070] Configure association parameters based on dimensions, including equity relations, executive relations, corporate credit, and corporate industrial and commercial penalties. Obtain data from the bank's external data integration platform through HTTP requests. The external data integration platform aggregates legitimate data source information from sources including the People's Bank of China, courts, public security, telecommunications operators, local provident fund data, FICO, Bairong, and Beijing Zhongshuzhihui.

[0071] Configure strong, medium and weak relationship thresholds based on the enterprise relationship indicators; the thresholds set here are mainly used as the criteria for determining the strength of the relationship between the affiliated companies;

[0072] Provides display functions, including dynamic family tree diagrams of enterprise relationships and result tables, and provides download functions for diagrams and tables.

[0073] Specifically, in the host system backend module:

[0074] After obtaining the data packet, the data is parsed according to the rules and stored in the database to obtain structured data; the data in the interface provided by the external data integration platform is returned in the form of an HTTP response message, which contains message header information and message body data, wherein the message body contains data information related to the enterprise, including a node array and a relationship array, wherein the node array contains node ID, node name, whether to enter the node, node type, registered capital, name, establishment time, and registered currency;

[0075] Preprocess the structured data, use error tags to mark the error node data, use no-association tags to mark the no-association data, and do not mark the normal data. Use error tags and no-association tags to mark the association between any two enterprises, so as to filter out the no-association and association enterprise groups through the front-end page of the system;

[0076] For normal data, the relationship groups in the data graph are traversed in turn to obtain the executives and equity quantitative values ​​in the enterprise data, and the association relationship values ​​between the enterprises are analyzed according to the set parameter dimensions; because the acquired data is returned in the form of node array and relationship array, the source node id and target node id in the relationship array identify the directionality of the relationship between the two enterprise nodes, and the shareholding ratio between the two enterprises is obtained according to the association direction of the source node id and the target node id, the relationship type, and the investment ratio.

[0077] Specifically, in the database module:

[0078] After analyzing the relationship data between all enterprises, the data is classified and stored in the database according to enterprise name A, enterprise name B, strong and weak relationship, association category, indicator value, and time dimension.

[0079] Embodiment 2:

[0080] Embodiment 2 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0081] Those skilled in the art may understand the method for obtaining enterprise association relationships provided by the present invention as a specific implementation of a system for obtaining enterprise association relationships, that is, the system for obtaining enterprise association relationships may be implemented by executing the steps of the method for obtaining enterprise association relationships.

[0082] According to a method for obtaining enterprise association relationships provided by the present invention, a system for identifying enterprise association relationships is used to perform the following steps:

[0083] Step S1: configure association parameters according to dimensions, and configure association thresholds according to enterprise association indicators;

[0084] Step S2: Submit the enterprise directory and obtain enterprise relationship information data package from external data source in real time;

[0085] Step S3: Parse the data in the enterprise information relationship data package according to the rules and store it in the database to obtain structured data;

[0086] Step S4: pre-process the structured data, and obtain the association relationship values ​​between enterprises according to the set association parameter dimensions for normal data;

[0087] Step S5: classify the data and store them in a database;

[0088] Step S6: Display the enterprise relationship.

[0089] Specifically, in step S1:

[0090] On the host system front end, configure the parameters:

[0091] Configure the judgment dimension parameters through the parameter configuration page, including equity relations, senior management relations, corporate credit, and corporate industrial and commercial penalties.

[0092] On the host system front end, configure the enterprise relationship threshold parameters through the parameter configuration page, including weak relationship threshold, medium relationship threshold, and strong relationship threshold;

[0093] On the host system front end, configure the time parameters for scheduled batch processing of scheduled tasks for existing enterprises through the parameter configuration page.

[0094] Specifically, in step S2:

[0095] On the host system front end, batch import enterprise information:

[0096] On the front end of the host system, in the enterprise information import module on the enterprise relationship information query page, batch upload the list of enterprises to be analyzed according to the data template format. The data template includes two fields: enterprise name and enterprise unified credit code;

[0097] After the upload is completed, the enterprise information currently uploaded in batches and the relevant information of the current operator can be viewed through the enterprise relationship information query page. The field information includes operator, time, enterprise collection, analysis progress, and analysis results.

[0098] At the back end of the system, the system obtains enterprise-related data from the outside in two ways:

[0099] The first method is the timed trigger method. The system backend automatically initiates data query requests to the external data integration platform for all current enterprise sets through the configured timed tasks.

[0100] The second method is the real-time trigger method, where the operator uses the real-time query button on the page to notify the system backend to initiate a data query request instruction to the external data integration platform.

[0101] Specifically, in step S3:

[0102] After the request is initiated, the system backend obtains the response data message from the external data integration platform. The system parses the data according to the interface specification and stores the data in the database.

[0103] Among them, the response message includes two parts: a message header and a message body. The message body includes a node array and a relationship array. The node array contains information including node id, node name, node type, enterprise status, whether to enter the node, registered capital, unified social credit code, name, and registration number. The relationship array contains information including node id, source node id, target node id, relationship type, investment ratio, control path relationship, position, whether it is a legal person, and subscribed capital.

[0104] Specifically, in step S4:

[0105] The system backend analyzes and processes structured data:

[0106] The system backend traverses the data stored in the database according to the node array, traverses the node IDs in the node array in turn, finds the relationship array information associated with the node ID, finds all the target node IDs of enterprises associated with the current enterprise through the source node ID in the relationship array, and constructs the equity relationship and legal person relationship between the source node ID enterprise and the target node ID enterprise;

[0107] Based on the traversal processing method, the system backend constructs the equity relationship and legal person relationship between each enterprise as the source node id and all target enterprises in turn, and stores the relationship information in the database with the enterprise ID as the index.

[0108] If the enterprise in the enterprise set uploaded by the operator is not the target node id enterprise of the enterprise with the source node id, the relationship between the two enterprises is marked as unrelated, and the relationship between other related enterprises is marked as related;

[0109] The back-end system calculates the strength of the relationship between related companies:

[0110] For enterprises marked as related, the related enterprises of each enterprise are traversed to obtain the strength of the relationship between the two enterprises. The direct shareholding of the enterprise is calculated by addition, and the indirect shareholding is calculated by multiplication;

[0111] The calculated actual shareholding ratio of enterprises is compared with the parameter threshold, and the relationship between enterprises is classified into weakly associated, medium associated, and strongly associated enterprises, and the association results are stored in the database.

[0112] Specifically, in step S6:

[0113] Display data results on the front end:

[0114] The front-end system provides a display page for enterprise association data, which allows users to filter and view enterprise relationship results by conditions including enterprise name, operator, and time. The information includes the strength of the association, shareholding ratio, and legal person relationship.

[0115] The front-end system provides a report download function to download and export the operator's search results in an encrypted Excel file format;

[0116] The front-end system provides a relationship diagram display of the relationship between enterprises, showing all related enterprises related to the main enterprise in the form of a node diagram. Directed lines with arrows are used between nodes to show the directionality of the relationship. The lines carry the specific relationship between the enterprises, including legal persons and shareholdings.

[0117] Embodiment 3:

[0118] Embodiment 3 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0119] A solution to quickly identify corporate relationships is implemented through the following steps:

[0120] Step 1: On the front end of the host system, configure related parameters according to dimensions such as equity relations, executive relations, corporate credit, and corporate industrial and commercial penalties, and obtain data from the bank's external data integration platform through HTTP requests. The external data integration platform aggregates information from legitimate data sources such as the People's Bank of China, courts, public security, telecommunications operators, local provident fund data, FICO, Bairong, and Beijing Zhongshuzhihui.

[0121] At the same time, strong, medium and weak relationship thresholds are configured according to the enterprise relationship indicators; the Company Law of the People's Republic of China has clear provisions for the relationship between enterprises. For example, if Company A holds more than 25% of the shares of Company B, then Company AB is an affiliated company. The thresholds set here are mainly used as the judgment conditions for the strength of the relationship between affiliated companies. For example, if the shareholding relationship threshold is set to 25%, then when A holds less than 25% of the shares of Company B, it is a weak relationship, and when it is greater than or equal to 25%, it is a strong relationship.

[0122] Step 2: At the front end of the host system, submit a list of all enterprises that need to be dynamically tracked, and obtain enterprise relationship information data packets from external data sources in real time;

[0123] Step 3: After the system backend obtains the data packet, it parses the data according to the rules and stores it in the database to obtain structured data; the data in the interface provided by the external data integration platform is returned in the form of an HTTP response message, which contains message header information and message body data. The message body contains data information related to the enterprise, including a node array (nodes) and a relationship array (links). The node array contains node ID, node name, whether to enter the node, node type, registered capital, name, establishment time, and registered currency;

[0124] Step 4: The system backend pre-processes the structured data, using ErrMks (ErrorMarks) to mark the error node data and NoRelationMks (NoRelationMarks) to mark the unrelated relationship data. Normal data is not marked, and the relationship between any two enterprises is marked by ErrMks and NoRelationMks, so that the enterprise groups with "no relationship" and "related relationship" can be quickly screened out through the front-end page of the system;

[0125] Step 5: For normal data, the system backend traverses the links relationship groups in the data graph in turn, obtains the executives and equity quantitative values ​​in the enterprise data according to the directionality of From-To, and analyzes the relationship values ​​between the enterprises according to the parameter dimensions set in step 1; because the acquired data is returned in the form of node arrays (nodes) and relationship arrays (links), the source node id (LINK_FROM) and target node id (LINK_TO) in the relationship array identify the directionality of the relationship between the two enterprise nodes. According to the association direction of LINK_FROM and LINK_TO, the relationship type (LINK_TYPE) and the investment ratio (CONPROP), the shareholding ratio between the two enterprises can be obtained.

[0126] Step 6: After analyzing the relationship data between all enterprises in the system backend, the data is classified and stored in the database according to enterprise name A, enterprise name B, strong or weak relationship, association category, indicator value, and time dimension;

[0127] Step 7: The system front end provides display functions such as enterprise relationship dynamic family tree diagram and result table, and provides download functions for diagrams and tables;

[0128] Embodiment 4:

[0129] Embodiment 4 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0130] Step 1: Configure parameters on the host system front end.

[0131] 1.1 On the host system front end, configure the judgment dimension parameters through the parameter configuration page, involving parameters such as equity relations, executive relations, corporate credit, and corporate industrial and commercial penalties.

[0132] 1.2 On the host system front end, configure the enterprise relationship threshold parameters through the parameter configuration page, involving weak relationship threshold, medium relationship threshold, and strong relationship threshold.

[0133] 1.3 On the host system front end, configure the time parameters for scheduled batch processing of scheduled tasks for existing enterprises through the parameter configuration page.

[0134] Step 2: Import enterprise information in batches on the host system front end.

[0135] 2.1 On the front end of the host system, in the enterprise information import module of the enterprise relationship information query page, batch upload the list of enterprises to be analyzed according to the data template format. The data template includes two fields: "Enterprise Name" and "Enterprise Unified Credit Code".

[0136] 2.2 After the upload is completed, you can view the current batch uploaded enterprise information and related information of the current operator through the enterprise relationship information query page, including "operator", "time", "enterprise collection", "analysis progress", "analysis results" and other field information.

[0137] Step 3: At the system backend, the system obtains enterprise-related data from the outside in two ways.

[0138] 3.1 The first method is the timed trigger method. The system backend automatically initiates data query requests to the external data integration platform for all current enterprise sets through the timed task configured in step 1.3.

[0139] 3.2 The second method is the real-time trigger method. The operator notifies the system backend to initiate a data query request instruction to the external data integration platform through the "Real-time Query" button on the page.

[0140] 3.3 After initiating a request according to 3.1 or 3.2, the system backend obtains the response data message from the external data integration platform, and the system parses the data according to the interface specification and stores the data in the database. Among them, the response message contains two parts: the message header and the message body. The message body contains a node array (nodes) and a relationship array (links). The node array contains information such as node id, node name, node type, enterprise status, whether to enter the node, registered capital, unified social credit code, name, registration number, etc. The relationship array contains information such as node id, source node id, target node id, relationship type, investment ratio, control path relationship, position, whether legal person, and subscribed capital contribution.

[0141] Step 4: The system backend analyzes and processes the structured data.

[0142] 4.1 The system backend traverses the data stored in the database according to the node array (nodes), traverses the node ids in the node array one by one, finds the relationship array (links) information associated with the node id, finds all enterprises related to the current enterprise (target node id) through the source node id in the relationship array (links), and constructs the equity relationship and legal person relationship between the source node id enterprise and the target node id enterprise.

[0143] 4.2 The system backend, based on the traversal processing method of 4.1, sequentially constructs the equity relationship and legal person relationship between each enterprise as the source node id and all target enterprises, and stores the relationship information in the database using the enterprise ID as an index.

[0144] 4.3 If the enterprise in the enterprise set uploaded by the operator is not the target node id enterprise of the source node id, the relationship between the two enterprises is marked as "no relationship (NoRelationMks)", and the relationship between other related enterprises is marked as "related relationship (RelatrionMks)".

[0145] Step 5: The back-end system calculates the strength of the relationship between related companies.

[0146] 5.1 Based on the enterprises marked as related in 4.3, traverse the related enterprises of each enterprise to obtain the strength of the relationship between the two enterprises. The direct shareholding of the enterprise is calculated by addition, and the indirect shareholding is calculated by multiplication. Taking the calculation of equity relationship as an example, assuming that enterprise A holds 20% of the shares of enterprise B, enterprise C, and enterprise D respectively, and enterprise B holds 50% of the shares of enterprise C, then the actual shareholding ratio of enterprise A to enterprise B is 20%, the actual shareholding ratio of enterprise A to enterprise C is 20% + 20% * 50% = 30%, and the actual shareholding ratio of enterprise A to enterprise D is 20%.

[0147] 5.2 Compare the actual shareholding ratio of enterprises calculated in 5.1 with the parameter threshold in 1.2, classify the relationship between enterprises into weakly associated, medium associated, and strongly associated enterprises, and store the association results in the database.

[0148] Step 6: Display data results on the front end.

[0149] 6.1 The front-end system provides a display page for enterprise-related data. Enterprise relationship results can be screened and viewed by enterprise name, operator, time and other conditions, including the strength of the relationship, shareholding ratio, legal person relationship and other information.

[0150] 6.2 The front-end system provides a report download function to download and export the operator's search results in an encrypted Excel file format.

[0151] 6.3 The front-end system provides a relationship diagram display of the relationship between enterprises, showing all related enterprises related to the main enterprise in the form of a node diagram. The directed nature of the relationship is shown in the form of directed lines with arrows between nodes, and the lines carry the specific relationship between the enterprises, such as legal persons, shareholdings, etc.

[0152] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0153] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A system for obtaining enterprise association relationships, characterized in that: include: Host system front-end module: configure association parameters according to dimensions, and configure association thresholds according to enterprise association indicators; Submit the company directory and obtain the company relationship information data package from the external data source in real time; provide display and download functions; Host system back-end module: parses the data in the enterprise information relationship data package according to the rules and stores it in the database to obtain structured data; Pre-process structured data, and for normal data, derive the correlation values ​​between enterprises according to the set correlation parameter dimensions; perform data classification; Database module: stores classified data; In the host system front-end module: Configure association parameters based on dimensions, including equity relations, executive relations, corporate credit, and corporate industrial and commercial penalties. Obtain data from the bank's external data integration platform through HTTP requests. The external data integration platform aggregates legitimate data source information from sources including the People's Bank of China, courts, public security, telecommunications operators, local provident fund data, FICO, Bairong, and Beijing Zhongshuzhihui. Configure strong, medium and weak relationship thresholds based on the enterprise relationship indicators; the thresholds set here are mainly used as the criteria for determining the strength of the relationship between the affiliated companies; Provide display functions, including enterprise relationship dynamic family tree diagrams, result tables, and download functions of diagrams and tables; In the host system backend module: After obtaining the data packet, the data is parsed according to the rules and stored in the database to obtain structured data; the data in the interface provided by the external data integration platform is returned in the form of an HTTP response message, which contains message header information and message body data, wherein the message body contains data information related to the enterprise, including a node array and a relationship array, wherein the node array contains node ID, node name, whether to enter the node, node type, registered capital, name, establishment time, and registered currency; Preprocess the structured data, use error tags to mark the error node data, use no-association tags to mark the no-association data, and do not mark the normal data. Use error tags and no-association tags to mark the association between any two enterprises, so as to filter out the no-association and association enterprise groups through the front-end page of the system; For normal data, the relationship groups in the data graph are traversed in turn to obtain the executives and equity quantitative values ​​in the enterprise data, and the correlation relationship values ​​between the enterprises are analyzed according to the set parameter dimensions; because the acquired data is returned in the form of node array and relationship array, the source node id and target node id in the relationship array identify the directionality of the relationship between the two enterprise nodes, and the shareholding ratio between the two enterprises is obtained according to the correlation direction of the source node id and the target node id, the relationship type, and the investment ratio; In the database module: After analyzing the relationship data between all enterprises, the data is classified and stored in the database according to enterprise name A, enterprise name B, strong and weak relationship, association category, indicator value, and time dimension.

2. A method for obtaining enterprise association relationships, characterized in that: The system for obtaining enterprise association relationships according to claim 1 is used to perform the following steps: Step S1: configure association parameters according to dimensions, and configure association thresholds according to enterprise association indicators; Step S2: Submit the enterprise directory and obtain enterprise relationship information data package from external data source in real time; Step S3: Parse the data in the enterprise information relationship data package according to the rules and store it in the database to obtain structured data; Step S4: pre-process the structured data, and obtain the association relationship values ​​between enterprises according to the set association parameter dimensions for normal data; Step S5: classify the data and store them in a database; Step S6: displaying the enterprise relationship; In step S1: On the host system front end, configure the parameters: Configure the judgment dimension parameters on the parameter configuration page, including equity relationship, senior management relationship, corporate credit, and corporate industrial and commercial penalties; On the host system front end, configure the enterprise relationship threshold parameters through the parameter configuration page, including weak relationship threshold, medium relationship threshold, and strong relationship threshold; On the host system front end, configure the time parameters for scheduled batch processing of scheduled tasks for existing enterprises through the parameter configuration page; In step S2: On the host system front end, batch import enterprise information: On the front end of the host system, in the enterprise information import module on the enterprise relationship information query page, batch upload the list of enterprises to be analyzed according to the data template format. The data template includes two fields: enterprise name and enterprise unified credit code; After the upload is completed, the enterprise information currently uploaded in batches and the relevant information of the current operator can be viewed through the enterprise relationship information query page. The field information includes operator, time, enterprise collection, analysis progress, and analysis results. At the back end of the system, the system obtains enterprise-related data from the outside in two ways: The first method is the timed trigger method. The system backend automatically initiates data query requests to the external data integration platform for all current enterprise sets through the configured timed tasks. The second method is real-time triggering. The operator uses the real-time query button on the page to notify the system backend to initiate a data query request instruction to the external data integration platform. In step S3: After the request is initiated, the system backend obtains the response data message from the external data integration platform. The system parses the data according to the interface specification and stores the data in the database. The response message includes two parts: a message header and a message body. The message body includes a node array and a relationship array. The node array contains information including node id, node name, node type, enterprise status, whether to enter the node, registered capital, unified social credit code, name, and registration number. The relationship array contains information including node id, source node id, target node id, relationship type, investment ratio, control path relationship, position, whether legal person, and subscribed capital. In step S4: The system backend analyzes and processes structured data: The system backend traverses the data stored in the database according to the node array, traverses the node IDs in the node array in turn, finds the relationship array information associated with the node ID, finds all the target node IDs of enterprises associated with the current enterprise through the source node ID in the relationship array, and constructs the equity relationship and legal person relationship between the source node ID enterprise and the target node ID enterprise; Based on the traversal processing method, the system backend sequentially constructs the equity relationship and legal person relationship between each enterprise as the source node ID and all target enterprises, and stores the relationship information in the database using the enterprise ID as an index; If the enterprise in the enterprise set uploaded by the operator is not the target node id enterprise of the enterprise with the source node id, the relationship between the two enterprises is marked as unrelated, and the relationship between other related enterprises is marked as related; The back-end system calculates the strength of the relationship for related companies: For enterprises marked as related, the related enterprises of each enterprise are traversed to obtain the strength of the relationship between the two enterprises. The direct shareholding of the enterprise is calculated by addition, and the indirect shareholding is calculated by multiplication; The calculated actual shareholding ratio of enterprises is compared with the parameter threshold, and the relationship between enterprises is classified into weakly related, medium-related, and strongly related enterprises, and the association results are stored in the database; In step S6: Display data results on the front end: The front-end system provides a display page for enterprise association data, which allows users to filter and view enterprise relationship results by conditions including enterprise name, operator, and time. The information includes the strength of the association, shareholding ratio, and legal person relationship. The front-end system provides a report download function to download and export the operator's search results in an encrypted Excel file format; The front-end system provides a relationship diagram display of the relationship between enterprises, showing all related enterprises related to the main enterprise in the form of a node diagram. Directed lines with arrows are used between nodes to show the directionality of the relationship. The lines carry the specific relationship between the enterprises, including legal persons and shareholdings.

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