Data processing method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring enterprise related data and business opportunity information; identifying enterprise entities in the enterprise related data, and establishing an enterprise entity index database; constructing an enterprise relation graph according to the enterprise entity index database; obtaining geographical location information of the target equipment, receiving a screening range input by the target equipment, and determining a target area according to the geographical location information and the screening range; and screening target business opportunity information of the position of the enterprise entity in the target area from the business opportunity information, displaying the target business opportunity information and displaying an enterprise relation graph of a target enterprise entity in the target business opportunity information. The technical problem that the labor cost in the operation process is relatively high due to the fact that the enterprise entity and business opportunity information in the target area cannot be automatically identified and displayed by obtaining the geographic position information of the target equipment and combining the screening range defined by the user is solved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and more specifically, to a data processing method and device, a non-volatile storage medium, and an electronic device. Background Art

[0002] In traditional financial institution operations and customer relationship management, operators need to rely on manual methods to collect and analyze corporate customers' geographic location data, and then identify and formulate business opportunities based on basic corporate information, transaction history, and market intelligence. This process is not only inefficient, but also has obvious limitations in accuracy, real-time, and geographic coverage. Specifically, the relevant technology cannot automatically identify corporate entities and business opportunity information in the target area by obtaining the geographic location information of the target device and combining it with the user-defined screening range, resulting in low work efficiency for financial institution operators.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The present application provides a data processing method and device, a non-volatile storage medium, and an electronic device to at least solve the technical problem of high labor costs in the business process caused by the inability to automatically identify and display corporate entities and business opportunity information in the target area by obtaining the geographic location information of the target device in combination with a user-defined screening range.

[0005] According to one aspect of the present application, a data processing method is provided, including: collecting enterprise-related data and business opportunity information; identifying enterprise entities in the enterprise-related data, and establishing an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; constructing an enterprise relationship map based on the enterprise entity index library, wherein the nodes in the enterprise relationship map are enterprise entities and the edges are associations between enterprise entities; obtaining geographic location information of a target device, receiving a filtering range input by the target device, and determining a target area based on the geographic location information and the filtering range; filtering target business opportunity information whose location of the enterprise entity is within the target area in the business opportunity information, displaying the target business opportunity information and displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information.

[0006] Optionally, collecting enterprise-related data includes: collecting enterprise-related data from different data sources, wherein the data sources include: enterprise registration information database, enterprise financial statement database, enterprise official website information, and enterprise-related data include: enterprise name, enterprise registration address, enterprise business scope, enterprise equity structure information, and enterprise cooperation agreement information; identifying enterprise entities in enterprise-related data includes: performing data cleaning on the collected enterprise-related data, wherein data cleaning includes: removing duplicate data, erroneous data, and incomplete data; for missing values ​​in the enterprise-related data after data cleaning, filling in the missing values ​​with a filling algorithm based on data distribution characteristics to obtain target data; using natural language processing technology and a pre-trained enterprise name recognition model to identify enterprise entities from the target data.

[0007] Optionally, an enterprise relationship map is constructed based on the enterprise entity index library, including: determining the association relationships between enterprise entities in the enterprise entity index library, wherein the association relationships include: equity relationships, investment relationships, cooperative relationships, upstream and downstream relationships in the supply chain, and competitive relationships; constructing an enterprise relationship map based on enterprise entities and the association relationships between enterprise entities, wherein the node attributes in the enterprise relationship map include: enterprise scale attributes, enterprise industry attributes, and enterprise regional attributes, and the edge attributes include: relationship type attributes, relationship strength attributes, and relationship time attributes.

[0008] Optionally, target business opportunity information whose corporate entity is located within a target area is filtered out from the business opportunity information, including: receiving enterprise scale information and enterprise business scope information input by a target device; determining first business opportunity information whose semantic matching degree with the enterprise scale information and the enterprise business scope information is greater than a preset threshold; and filtering, from the first business opportunity information, target business opportunity information whose corporate entity is located within the target area.

[0009] Optionally, displaying the target business opportunity information includes: representing the target business entities with different color marks according to the different geographical locations of the target business entities in the target business opportunity information, wherein the size of the RGB value corresponding to the target business entity is proportional to the expected return in the target business opportunity information; mapping the investment amount in the target business opportunity information to a bar chart in an industry classification view, wherein the investment amount is proportional to the height of the bar chart; in the timeline view, mapping the time nodes in the target business opportunity information to line segments or node positions on the timeline, and indicating the duration or importance of the business opportunity with the length of the line segment or the size of the node.

[0010] Optionally, a corporate relationship map of the target corporate entity in the target business opportunity information is displayed, including: representing the nodes in the corporate relationship map of the target corporate entity with preset shapes, and the size of the nodes is proportional to the corporate scale attribute; representing different corporate industry attributes corresponding to the nodes with different colors; representing equity relationships with solid straight lines, representing investment relationships with dashed straight lines, representing cooperative relationships with solid curved lines, representing upstream and downstream relationships in the supply chain with dashed curved lines, and representing competitive relationships with broken lines, wherein the thickness of the lines is proportional to the relationship strength attribute.

[0011] Optionally, after displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information, the method also includes: detecting the input behavior of the target device on the node in the enterprise relationship map of the target enterprise entity; when it is detected that the target device has generated a first input behavior on the node, displaying a target information box, wherein the target information box includes: enterprise scale attributes, enterprise industry attributes, enterprise regional attributes, and the first input behavior includes: single-clicking the node; when it is detected that the target device has generated a second input behavior on the node, displaying the relationship type attribute, relationship strength attribute, and relationship time attribute between the node and other nodes in the target area, wherein the second input behavior includes: double-clicking the node.

[0012] Optionally, before displaying the target business opportunity information, the method also includes: classifying the target business opportunity information according to the enterprise types of multiple target enterprise entities in the target business opportunity information to obtain a first classification result; determining the difference between the geographical locations of multiple target enterprise entities in the target business opportunity information and the geographical location of the target device to obtain multiple target differences, and determining a preset distance interval for each target difference to obtain a second classification result; after displaying the target business opportunity information, the method also includes: receiving a target enterprise type input by the target device, and searching for target business opportunity information corresponding to the target enterprise type in the first classification result; receiving a target distance interval input by the target device, and searching for target business opportunity information corresponding to the target distance interval in the second classification result.

[0013] According to another aspect of the present application, a data processing device is also provided, including: a collection module for collecting enterprise-related data and business opportunity information; an establishment module for identifying enterprise entities in the enterprise-related data and establishing an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; a construction module for constructing an enterprise relationship map based on the enterprise entity index library, wherein the nodes in the enterprise relationship map are enterprise entities and the edges are associations between enterprise entities; a determination module for obtaining geographic location information of a target device, receiving a filtering range input by the target device, and determining a target area based on the geographic location information and the filtering range; a display module for filtering target business opportunity information whose location of the enterprise entity is within the target area in the business opportunity information, displaying the target business opportunity information, and displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information.

[0014] According to another aspect of the present application, a non-volatile storage medium is provided, the storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above data processing method.

[0015] According to another aspect of the present application, an electronic device is provided, including: a memory and a processor, the processor being used to run a program stored in the memory, wherein the above data processing method is executed when the program is run.

[0016] According to yet another aspect of the present application, a computer program is provided, wherein the above data processing method is implemented when the computer program is executed by a processor.

[0017] According to another aspect of the present application, a computer program product is provided, which includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above data processing method is implemented.

[0018] In the present application, the following methods are adopted: collecting enterprise-related data and business opportunity information; identifying enterprise entities in the enterprise-related data, and establishing an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; constructing an enterprise relationship map based on the enterprise entity index library, wherein the nodes in the enterprise relationship map are enterprise entities and the edges are associations between enterprise entities; obtaining the geographic location information of the target device, receiving the filtering range input by the target device, and determining the target area based on the geographic location information and the filtering range; filtering the target business opportunity information whose location of the enterprise entity is within the target area in the business opportunity information, displaying the target business opportunity information and displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information, so as to achieve the purpose of automatically identifying and displaying the enterprise entities and business opportunity information within the target area by obtaining the geographic location information of the target device and combining the user-defined filtering range, thereby achieving the technical effect of reducing the labor cost in the operation process, and further solving the technical problem of high labor cost in the operation process caused by the inability to automatically identify and display the enterprise entities and business opportunity information within the target area by obtaining the geographic location information of the target device and combining the user-defined filtering range. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application;

[0021] Figure 2 It is an architecture diagram of a knowledge graph according to an embodiment of the present application;

[0022] Figure 3 is a structural diagram of a data processing device according to an embodiment of the present application;

[0023] Figure 4 It is a hardware structure block diagram of a computer terminal according to a data processing method of an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present application, a method embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0028] Step S102, collecting enterprise-related data and business opportunity information.

[0029] Use data integration interfaces or crawler technology to collect corporate customer data from the bank's internal system, including basic information, transaction flow, account status, etc. Obtain corporate geographic location, credit assessment, legal person information, intellectual property rights and other data from external data suppliers such as China Data Intelligence, Qixinbao, Wind Financial Terminal, etc. through API interfaces or data subscription services. Design a multi-source data collection module to capture business opportunity information from news, corporate announcements, and other channels in real time.

[0030] Step S104, identifying enterprise entities in the enterprise-related data and establishing an enterprise entity index library, wherein the enterprise entity index library stores the enterprise entities and identification information corresponding to the enterprise entities.

[0031] Apply natural language processing and entity recognition technology to extract enterprise entity information, such as enterprise name, legal representative, registered address, etc., from the collected enterprise data. Implement entity deduplication logic to exclude duplicate enterprise entity records by comparing key fields such as enterprise name, enterprise ID, and registered address to ensure the uniqueness of each entity in the index library. Assign a unique identifier, such as enterprise ID or UUID, to each enterprise entity as the primary key of the enterprise entity to ensure that each entity can be uniquely identified in the database. Select appropriate data storage technology, such as relational databases (such as MySQL, PostgreSQL), NoSQL databases (such as MongoDB), or graph databases (such as Neo4j, JanusGraph) to support the storage and query of enterprise entity information. Store enterprise entity information and unique identifiers in the index library. For each enterprise entity, its basic information (such as name, type, address, contact information, etc.) and identifier association information should be stored. Implement data indexing strategies, such as full-text indexing, geospatial indexing, inverted indexing, etc., to improve the efficiency and speed of data retrieval.

[0032] Step S106, constructing an enterprise relationship graph based on the enterprise entity index library, wherein the nodes in the enterprise relationship graph are enterprise entities and the edges are the association relationships between enterprise entities.

[0033] By combining rule matching and machine learning models, we extract inter-enterprise relationships, such as equity relationships, cooperative relationships, competitive relationships, etc., from the enterprise entity index library. We define clear rules and models for each relationship type to ensure the accuracy and comprehensiveness of relationship extraction. We use graph database technology (such as Neo4j) to build an enterprise relationship graph, taking enterprise entities as nodes and relationships as edges, and assigning attributes to nodes and edges. The use of graph databases ensures efficient data storage and fast query, and supports complex relationship query and analysis.

[0034] Step S108, obtaining geographic location information of the target device, receiving a screening range input by the target device, and determining a target area according to the geographic location information and the screening range.

[0035] Cooperate with geographic information services to obtain real-time geographic location information (latitude and longitude) of target devices (such as account managers' mobile devices) through APIs; ensure the accuracy and timeliness of geographic location information to quickly respond to changes in geographic location.

[0036] A filter range input function is designed in the user interface of Jingketupu, allowing users to customize the filter range, such as 5km, 20km, >20km, etc.; based on the filter range entered by the user and the obtained geographic location information, a circular target area is determined, whose radius is defined by the user and the center is the geographic location of the target device.

[0037] Step S110 , screening target business opportunity information whose business entities are located in a target area from the business opportunity information, displaying the target business opportunity information and displaying a business relationship map of the target business entities in the target business opportunity information.

[0038] According to the definition of the target area, business opportunity information with corporate entity locations within the target area is filtered out from the business opportunity information database, supporting multi-dimensional filtering, such as enterprise type (listed companies, state-owned enterprises, etc.), business opportunity category (deposit opportunities, loan opportunities, account opening opportunities, etc.) to meet different business needs; the filtered target business opportunity information is displayed on the user interface, including key information such as opportunity details, enterprise name, distance, etc., to facilitate users to quickly understand and make decisions, and provide detailed business opportunity query functions, users can view detailed information of specific business opportunities, such as project name, demand details, budget range, time plan, etc.; while displaying the target business opportunity information, the enterprise relationship map of the target enterprise entity is displayed, using an intuitive graphical interface to display enterprise nodes and relationship edges, users can click on the enterprise node to view specific enterprise information, such as name, registered capital, listing information, etc., as well as the map relationship related to the enterprise, such as equity structure, partners, competitors, etc., and provide interactive functions of the enterprise relationship map, such as zooming, panning, and node information pop-up windows, to help users deeply understand and analyze the complex relationships between enterprises.

[0039] According to the above steps, by building a comprehensive digital operation and customer management platform, through efficient data collection and integration, intelligent enterprise entity identification, in-depth enterprise relationship analysis, location-based business opportunity screening and optimized user interface design, centralized and unified management of data assets is achieved, providing financial institutions with a comprehensive understanding of customer network structure, significantly improving the accuracy and efficiency of business activities, providing all-round operational support for bank account managers, improving business management level and risk management capabilities, and achieving the technical effect of refined business operations and intelligent services.

[0040] The following Figure 1 The steps shown are exemplary and explanatory.

[0041] According to some optional embodiments of the present application, the collection of enterprise-related data can be achieved through the following methods: collecting enterprise-related data from different data sources, wherein the data sources include: enterprise registration information database, enterprise financial statement database, enterprise official website information, and the enterprise-related data include: enterprise name, enterprise registration address, enterprise business scope, enterprise equity structure information, and enterprise cooperation agreement information.

[0042] First, it is necessary to establish connections with various data sources and design corresponding data interfaces, including but not limited to data sources such as enterprise registration information database, enterprise financial statement database, and enterprise official website information. The interface design should consider data security, transmission efficiency, and data format compatibility. Through the designed data interface, basic information such as enterprise name, registered address, and business scope can be obtained regularly or in real time from the enterprise registration information database; the financial status data of the enterprise, including key financial indicators such as total assets, net profit, and debt-to-asset ratio, can be extracted from the enterprise financial statement database to analyze the economic strength and operating conditions of the enterprise. After data collection, data cleaning is performed to remove duplicate, erroneous, or incomplete information. Standardize the data and unify the data format, such as unifying the date format and standardizing the currency unit to ensure data consistency and comparability. Use natural language processing technology to perform entity recognition on the collected text data to ensure that the information of each enterprise can be accurately classified, integrate information from different data sources, and construct enterprise information cards, including comprehensive information such as enterprise name, registered address, business scope, financial status, equity structure, and cooperation agreement.

[0043] Through the above-mentioned specific implementation methods, it is possible to comprehensively collect enterprise-related data from different data sources, integrate and construct enterprise relationship maps, provide financial institutions with in-depth market insights, support precise operations and risk control, and ensure data security and compliance.

[0044] According to some optional embodiments of the present application, identifying corporate entities in corporate-related data can be achieved through the following methods: performing data cleaning on the collected corporate-related data, wherein data cleaning includes: removing duplicate data, erroneous data, and incomplete data; filling missing values ​​in the corporate-related data after data cleaning with a filling algorithm based on data distribution characteristics to obtain target data; and using natural language processing technology and a pre-trained corporate name recognition model to identify corporate entities from the target data.

[0045] First, the collected raw data is preliminarily cleaned to remove obvious duplicate records, outliers and incomplete data items, including: by comparing key fields such as company name and registration number, identify and delete duplicate company records; check the format and range of the data, such as whether the date format is correct and whether the value is within a reasonable range, and remove obviously erroneous data; filter out company information containing a large number of missing fields to ensure that the data processed later has a certain degree of integrity. Then, for the situation where there are still missing values ​​after data cleaning, a filling algorithm based on data distribution characteristics is used to process the data, and statistical analysis is performed on the data to understand the data distribution of different fields, such as the mean, median, and mode of financial data. For missing numerical data, the mean, median, or mode can be used to fill in the missing data. For missing categorical data, the most likely category is selected according to the distribution characteristics for filling. For missing specific information (such as company address), a prediction algorithm based on geographic location can be used to infer its possible location information based on the company name and other known information. Finally, the pre-trained company name recognition model is used to perform entity recognition on the pre-processed text data to accurately identify the company name. The model can be a sequence annotation model based on deep learning, such as BiLSTM-CRF, BERT, etc., or a rule-based model that combines the characteristics of the company name and a common named entity dictionary. The identified company name is matched with the company information in the company registration information database to further improve the details of the company entity, such as company size, industry category, financial status, etc.

[0046] Through the above steps, we can effectively identify corporate entities and build complete corporate information cards, providing accurate and comprehensive basic data for subsequent corporate relationship map construction and business opportunity analysis. At the same time, data cleaning and preprocessing ensure the quality of data, while corporate entity identification uses advanced natural language processing technology to improve the intelligence level of data processing.

[0047] According to some other optional embodiments of the present application, constructing an enterprise relationship map based on the enterprise entity index library can be achieved by the following method: determining the association relationships between enterprise entities in the enterprise entity index library, wherein the association relationships include: equity relationships, investment relationships, cooperative relationships, upstream and downstream relationships in the supply chain, and competitive relationships; constructing an enterprise relationship map based on enterprise entities and the association relationships between enterprise entities, wherein the node attributes in the enterprise relationship map include: enterprise scale attributes, enterprise industry attributes, and enterprise regional attributes, and the edge attributes include: relationship type attributes, relationship strength attributes, and relationship time attributes.

[0048] Specifically, the relationships between corporate entities are determined in the corporate entity index library, including: from the corporate equity structure information, using the graph construction algorithm to identify the shareholding ratio, shareholder type (direct shareholder, indirect shareholder), etc., to determine the controlling and equity relationships between enterprises; based on the corporate financial statements and investment records, identify the direct and indirect investment relationships between enterprises, including information such as investment amount and investment time, and quantify the investment intensity between enterprises; through data such as corporate cooperation agreement information, records of jointly participated projects, and publicly released joint statements, identify the cooperation relationships between enterprises, such as long-term cooperation agreements, project cooperation, etc., and evaluate the stability and depth of the cooperation relationships; use corporate transaction flow data and supply chain information to identify suppliers and customers, build a supply chain network, and evaluate the upstream and downstream supply chain relationships between enterprises, including indicators such as transaction frequency and transaction amount; based on industry analysis reports, market intelligence, comparisons of similar products and other data, identify potential competitive relationships between enterprises, and evaluate the intensity and potential threats of competitive relationships.

[0049] According to the enterprise entities and the relationship between enterprise entities, an enterprise relationship map is constructed. A node is created for each enterprise entity in the graph database, and node attributes are assigned, including enterprise scale (such as operating income, number of employees), industry (such as finance, manufacturing), and regional attributes (such as city, province); edges representing the relationship between enterprise entities are created, and attributes are assigned to the edges, such as relationship type (equity, cooperation, competition, etc.), relationship strength (quantified by equity ratio, investment amount, transaction amount, etc.), and relationship time (the time when the relationship is established and lasts); enterprise entity information and relationship data are stored in the graph database to ensure that the attribute information of edges and nodes is complete and the relationship is correctly associated. Use a layout algorithm suitable for the scale and complexity of the enterprise relationship map, such as force-directed layout or multi-level layout algorithm, optimize the layout of the map, reduce the intersection of edges, clearly display the relationship structure between enterprises, design a user-friendly visual interface, and display the enterprise relationship map. The styles of nodes and edges should intuitively reflect their attributes, such as the size of the node is proportional to the size of the enterprise, the thickness of the edge is related to the strength of the relationship, and the color or style of the edge indicates the type of relationship.

[0050] Through the above-mentioned specific implementation methods, a comprehensive and in-depth enterprise relationship map can be constructed. This map not only reflects the multi-dimensional connections between enterprises, but also provides rich market insight information through the attribute design of nodes and edges, providing strong data support for financial institutions' customer relationship management, market analysis and risk assessment.

[0051] In some optional embodiments of the present application, filtering target business opportunity information whose corporate entity is located within a target area in business opportunity information can be achieved by the following method: receiving enterprise scale information and enterprise business scope information input by a target device; determining first business opportunity information whose semantic matching degree with the enterprise scale information and the enterprise business scope information is greater than a preset threshold; and filtering, in the first business opportunity information, target business opportunity information whose corporate entity is located within the target area.

[0052] Receive enterprise scale information input by the target device (such as the account manager's mobile terminal or PC), including specific values ​​such as the number of employees, annual turnover, and total assets of the enterprise, and receive enterprise business scope information, which describes the enterprise's main business areas, such as financial services, manufacturing, and information technology. Develop or call a semantic matching algorithm to compare the similarity between the enterprise scale information and business scope information input by the target device and the corresponding information stored in the business opportunity database. Traverse the business opportunity database, calculate the semantic matching degree for each business opportunity information, compare its similarity with the input enterprise scale information and business scope information, and set a preset threshold for the semantic matching degree, such as 0.8, according to the characteristics of the specific matching algorithm and business needs, and filter out the first business opportunity information with a matching degree greater than the threshold.

[0053] The geographic location information of the target device, including the latitude and longitude coordinates of the device, is obtained through positioning services (such as GPS and base station positioning), and the scope of the target area is set as a circular area based on the location of the target device, and its radius is set according to business needs, such as 5km, 20km or more. Using geographic information processing technology (such as geocoding and Haversine distance calculation), the distance between the geographic location of the enterprise entity in the business opportunity information and the location of the target device is calculated, and the business opportunity information in which the location of the enterprise entity in the first business opportunity information is within the target area is screened out, that is, the target business opportunity information, which can be further subdivided into multi-dimensional screening according to enterprise type (such as listed companies, state-owned enterprises, etc.) and distance to provide more customized search results.

[0054] Through the above-mentioned specific implementation steps, it is possible to automatically screen out business opportunity information with a high matching degree and located in the target area based on the enterprise size information and business scope information input by the target device and combined with the geographic location information, thereby providing accurate data support for the business activities and customer development of financial institutions.

[0055] As some optional embodiments of the present application, displaying the target business opportunity information can be achieved by the following methods: representing the target business entity with different color markers according to the different geographical locations of the target business entity in the target business opportunity information, wherein the size of the RGB value corresponding to the target business entity is proportional to the expected return in the target business opportunity information; mapping the investment amount in the target business opportunity information to a bar chart in an industry classification view, wherein the investment amount is proportional to the height of the bar chart; in the timeline view, mapping the time nodes in the target business opportunity information to line segments or node positions on the timeline, and using the line segment length or node size to represent the duration or importance of the business opportunity.

[0056] Define the color marking rules according to the geographical location of the target business entity. For example, divide the target area into several geographical areas, each area corresponds to one or a group of specific colors. For each target business entity in the target business opportunity information, use the geocoding service (such as Amap API) to obtain its precise geographical location information, including longitude and latitude coordinates. According to the obtained geographical location information, determine the geographical area where the target business entity is located, and calculate the corresponding RGB value according to the color marking rules of the area. The size of the RGB value is proportional to the expected profit in the target business opportunity information, that is, the higher the expected profit, the higher the brightness or saturation of the corresponding color mark.

[0057] Extract investment amount data from target business opportunity information as the mapping basis for the height of the bar chart in the industry classification view. In the industry classification view, create a bar chart for each industry category, and the height of the bar chart is proportional to the average or total investment amount of all target business opportunity information under that category. If the investment amount is particularly important, you can also consider using a logarithmic scale to avoid the influence of extreme values.

[0058] Design the layout of the timeline view based on the time nodes in the target business opportunity information, such as the start time, end time, key event time, etc. of the business opportunity. The length of the timeline should cover the time range of all business opportunity information. On the timeline, map the time nodes in the target business opportunity information to the corresponding positions. They can be represented by line segments or nodes. The length of the line segment or the size of the node represents the duration or importance of the business opportunity. For example, the longer the business opportunity duration, the longer the line segment; the higher the importance of the business opportunity, the larger the node or the darker the color.

[0059] Through the above-mentioned specific implementation methods, the geographical location, investment amount and time node of the target business opportunity information can be visualized. It not only provides an intuitive geographical distribution view, but also intuitively displays the investment situation of different industries through bar charts, and the timeline view clearly displays the time structure of business opportunities, which helps decision makers of financial institutions to quickly understand and analyze business opportunity information and formulate more effective operating and investment strategies.

[0060] In some optional embodiments of the present application, displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information can be achieved by the following method: representing the nodes in the enterprise relationship map of the target enterprise entity with preset shapes, and the size of the nodes is proportional to the enterprise scale attribute; representing the different enterprise industry attributes corresponding to the nodes with different colors; representing the equity relationship with a solid straight line, representing the investment relationship with a dotted straight line, representing the cooperative relationship with a solid curved line, representing the upstream and downstream relationship of the supply chain with a dotted curved line, and representing the competitive relationship with a broken line, wherein the thickness of the line is proportional to the relationship strength attribute.

[0061] In the enterprise relationship map construction tool, create a node of a preset shape, such as a circle or hexagon, for each target enterprise entity as a representation of the enterprise entity in the map. Set the mapping rules between the node size and the enterprise scale attribute. For example, the larger the enterprise scale, the larger the diameter or area of ​​the node. Adjust the size of the node according to the actual value of the enterprise scale attribute to ensure that different enterprise sizes are intuitively reflected in the map.

[0062] Define a specific color for each industry attribute. You can use industry standard color coding or custom color schemes to distinguish enterprise nodes in different industries. According to the industry attributes of the target enterprise entity, assign corresponding colors to its nodes.

[0063] The relationship type is associated with the line style, as follows: equity relationship, use solid straight line, the line thickness is proportional to the relationship strength attribute; investment relationship, use dashed straight line, the line thickness is also related to the relationship strength attribute; cooperative relationship, use solid curve, the line thickness is proportional to the relationship strength attribute; upstream and downstream supply chain relationship, use dashed curve, the line thickness is proportional to the relationship strength attribute; competitive relationship, use broken line, the line thickness also reflects the strength of the relationship. Obtain the relationship information between the target enterprise entities from the enterprise relationship map, including the relationship type and relationship strength attribute; according to the above line style design and relationship strength attribute, draw lines connecting the enterprise entity nodes to reflect the relationship between enterprises, and intuitively show the strength of the relationship through the thickness of the line.

[0064] Through the above specific implementation methods, a comprehensive and intuitive enterprise relationship map can be constructed, in which the size and color of the nodes reflect the scale of the enterprise and the attributes of the industry to which it belongs, and the style and thickness of the lines show the complex relationship between enterprises, including the strength of equity, investment, cooperation, supply chain and competition. This provides in-depth market insights for corporate decision makers and helps to formulate precise business strategies and risk control measures.

[0065] As other optional embodiments of the present application, after displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information, the following steps can also be performed: detecting the input behavior of the target device on the node in the enterprise relationship map of the target enterprise entity; when it is detected that the target device has generated a first input behavior on the node, displaying the target information box, wherein the target information box includes: enterprise scale attributes, enterprise industry attributes, enterprise regional attributes, and the first input behavior includes: single-clicking the node; when it is detected that the target device has generated a second input behavior on the node, displaying the relationship type attribute, relationship strength attribute, and relationship time attribute between the node and other nodes in the target area, wherein the second input behavior includes: double-clicking the node.

[0066] By using front-end interactive technologies (such as JavaScript, HTML5, etc.), real-time monitoring of user input behaviors is achieved on the display page of the enterprise relationship map. The specific monitored target behaviors include single-clicking (first input behavior) and double-clicking (second input behavior) on the node.

[0067] Response to click behavior (first input behavior): When it is detected that the target device has generated the first input behavior (single click) on the node of the target enterprise entity, the event handler for displaying the target information box is immediately triggered, and the detailed information of the enterprise entity corresponding to the clicked node is obtained from the enterprise entity index library, including enterprise scale attributes, enterprise industry attributes, enterprise geographical attributes, etc. An information box pops up near the target enterprise entity node or at a fixed position in the map interface to display the detailed attribute information of the above enterprise entity, ensuring that the style and layout of the information box are clear for easy reading by users.

[0068] Double-click behavior (second input behavior) response: When it is detected that the target device has generated a second input behavior (double-click) on the node, the event handler for displaying the node relationship is immediately triggered; from the enterprise relationship map database, the association relationship information between the double-clicked node and other nodes in the target area is obtained, including relationship type attributes, relationship strength attributes, relationship time attributes, etc. In the map interface, the relationship line between the double-clicked node and other nodes in the target area is highlighted, and the relationship type is intuitively displayed in a graphical way on one side of the map interface or in a pop-up window, and the detailed relationship attribute information between the double-clicked node and its associated nodes is displayed, such as equity ratio, investment amount, name of cooperation project, transaction frequency and amount of upstream and downstream relationships in the supply chain, market share comparison of competitive relationships, etc.

[0069] Through the above-mentioned specific implementation methods, refined interaction with nodes in the enterprise relationship map can be achieved. Users can quickly obtain the attribute information of the target enterprise entity and the complex relationship details between it and other enterprise entities in the target area through simple single-click and double-click operations, providing instant and intuitive data insights for decision makers of financial institutions, and supporting more efficient and accurate operations and risk management activities.

[0070] In some optional embodiments, before displaying the target business opportunity information, the following steps may also be performed: classify the target business opportunity information according to the enterprise types of multiple target enterprise entities in the target business opportunity information to obtain a first classification result; determine the difference between the geographical locations of multiple target enterprise entities in the target business opportunity information and the geographical location of the target device to obtain multiple target differences, and determine the preset distance interval within which each target difference lies to obtain a second classification result.

[0071] After displaying the target business opportunity information, the following steps can also be performed: receiving the target enterprise type input by the target device, and searching for the target business opportunity information corresponding to the target enterprise type in the first classification result; receiving the target distance interval input by the target device, and searching for the target business opportunity information corresponding to the target distance interval in the second classification result.

[0072] According to the corporate types of multiple target corporate entities involved in the target business opportunity information, such as listed companies, state-owned enterprises, qualified enterprises, etc., the target business opportunity information is preliminarily classified to obtain the first classification result. Determine the geographic location information of multiple target corporate entities, and use geocoding services (such as Amap API) to convert addresses into longitude and latitude coordinates; obtain the real-time geographic location information of the target device and convert it into longitude and latitude coordinates. Calculate the difference between the geographic location of the target corporate entity and the geographic location of the target device, and use the Haversine formula or other geographic distance calculation methods; based on the calculated target difference, compare it with the preset distance interval, and further classify the target business opportunity information into the corresponding distance interval to obtain the second classification result.

[0073] After completing the above classification, the target business opportunity information is displayed in the user interface, and a classification and filtering function is provided. On the user interface, the classified target business opportunity information is displayed in the form of a list or card. Each business opportunity information contains key information such as the company name, company type, expected income, investment amount, etc., and the user is allowed to enter the target company type through a drop-down menu, search box or preset button. The system automatically filters out the target business opportunity information that matches the input company type based on the first classification result, and updates the display list in real time. Users can enter the target distance interval through the slider, numeric input box or predefined distance interval button on the interface. The system filters out the target business opportunity information that matches the input distance interval based on the second classification result, and updates the display list to reflect the business opportunity information under the user's filtering conditions.

[0074] Through the above-mentioned specific implementation methods, multi-dimensional classification of target business opportunity information and dynamic screening functions based on target enterprise types and target distance ranges can be achieved, providing financial institution operators with accurate and efficient means of querying business opportunity information, supporting them to quickly locate business opportunities that meet specific enterprise types and geographical location conditions, thereby improving the pertinence and success rate of business activities.

[0075] Figure 2 is an architecture diagram of a knowledge graph according to an embodiment of the present application, wherein: Figure 2 yes Figure 1 The architecture diagram of the knowledge graph of the program applied by the data processing method shown in FIG. Figure 2 As shown in the figure, the architecture diagram consists of three parts: data dimension, intent recognition model, and customer portrait function. The data dimension includes internal data and external data. The internal data includes basic customer information, transaction flow information, product information, and jurisdictional longitude and latitude information; the external data includes data sources such as China Data Intelligence, Qixinbao, AutoNavi Map, and Industry and Commerce. The intent model recognition model introduces similarity calculation models, such as cosine similarity, to intelligently identify and optimize the user's question scenarios, and then continuously enriches the model's training corpus, introduces new questions and answers, improves the model's generalization ability, and performs keyword expansion and similar word recognition. Through the expansion of synonyms and industry professional terms, it enhances the understanding of user questions and ensures the comprehensiveness and depth of information. The customer portrait function includes customer information, business opportunities, relationship maps, and Jingke maps. The customer information module is broken down into basic information, business information, listing information, intellectual property and other information fields. The business opportunity module includes business opportunity screening tools, business opportunity information query, enterprise type screening, etc. The relationship map module displays information such as enterprise maps, group structures, control paths, and substantive relationship maps, emphasizing the visualization and in-depth analysis of relationships between enterprises. Jingke Map performs distance screening and enterprise type screening through geographic information applications and user location services.

[0076] The architecture of this knowledge graph emphasizes core functions such as data integration, customer profile construction, business opportunity identification, geographic information application and model optimization, as well as the bidirectionality and connectivity of data flow, reflecting the system's powerful comprehensive processing capabilities and user experience optimization after iterative upgrades.

[0077] Figure 3 is a structural diagram of a data processing device according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0078] The collection module 31 is used to collect enterprise-related data and business opportunity information.

[0079] The establishment module 32 is used to identify enterprise entities in enterprise-related data and establish an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities.

[0080] The construction module 33 is used to construct an enterprise relationship map based on the enterprise entity index library, wherein the nodes in the enterprise relationship map are enterprise entities and the edges are the association relationships between enterprise entities.

[0081] The determination module 34 is used to obtain the geographic location information of the target device, receive the screening range input by the target device, and determine the target area according to the geographic location information and the screening range.

[0082] The display module 35 is used to filter the target business opportunity information whose business entity is located in the target area from the business opportunity information, display the target business opportunity information and display the business relationship map of the target business entity in the target business opportunity information.

[0083] Optionally, the acquisition module 31 is further used to perform the following steps: collect enterprise-related data from different data sources, wherein the data sources include: enterprise registration information database, enterprise financial statement database, enterprise official website information, and enterprise-related data include: enterprise name, enterprise registration address, enterprise business scope, enterprise equity structure information, and enterprise cooperation agreement information. The establishment module 32 is also used to perform the following steps: perform data cleaning on the collected enterprise-related data, wherein data cleaning includes: removing duplicate data, erroneous data, and incomplete data; for the missing values ​​in the enterprise-related data after data cleaning, fill in the missing values ​​using a filling algorithm based on data distribution characteristics to obtain target data; and use natural language processing technology and a pre-trained enterprise name recognition model to identify enterprise entities from the target data.

[0084] Optionally, construction module 33 is also used to perform the following steps: determining the association relationships between enterprise entities in the enterprise entity index library, wherein the association relationships include: equity relationships, investment relationships, cooperative relationships, upstream and downstream relationships in the supply chain, and competitive relationships; constructing an enterprise relationship map based on enterprise entities and the association relationships between enterprise entities, wherein the node attributes in the enterprise relationship map include: enterprise scale attributes, enterprise industry attributes, and enterprise region attributes, and the edge attributes include: relationship type attributes, relationship strength attributes, and relationship time attributes.

[0085] Optionally, the display module 35 is also used to perform the following steps: receiving enterprise scale information and enterprise business scope information input by the target device; determining first business opportunity information whose semantic matching degree with the enterprise scale information and the enterprise business scope information is greater than a preset threshold; and in the first business opportunity information, screening target business opportunity information whose enterprise entity is located within the target area.

[0086] Optionally, the display module 35 is also used to perform the following steps: representing the target business entity with different color marks according to the different geographical locations of the target business entity in the target business opportunity information, wherein the size of the RGB value corresponding to the target business entity is proportional to the expected return in the target business opportunity information; mapping the investment amount in the target business opportunity information to a bar chart in the industry classification view, wherein the investment amount is proportional to the height of the bar chart; in the timeline view, mapping the time nodes in the target business opportunity information to the line segments or node positions on the timeline, and using the line segment length or node size to represent the duration or importance of the business opportunity.

[0087] Optionally, the display module 35 is also used to perform the following steps: representing the nodes in the enterprise relationship map of the target enterprise entity with preset shapes, and the size of the nodes is proportional to the enterprise scale attribute; representing the different enterprise industry attributes corresponding to the nodes with different colors; representing the equity relationship with a solid straight line, representing the investment relationship with a dotted straight line, representing the cooperative relationship with a solid curve, representing the upstream and downstream relationship of the supply chain with a dotted curve, and representing the competitive relationship with a broken line, wherein the thickness of the line is proportional to the relationship strength attribute.

[0088] Optionally, the data processing device is also used to perform the following steps after displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information: detecting the input behavior of the target device on the node in the enterprise relationship map of the target enterprise entity; when it is detected that the target device has generated a first input behavior on the node, displaying the target information box, wherein the target information box includes: enterprise scale attributes, enterprise industry attributes, enterprise regional attributes, and the first input behavior includes: single-clicking the node; when it is detected that the target device has generated a second input behavior on the node, displaying the relationship type attribute, relationship strength attribute, and relationship time attribute between the node and other nodes in the target area, wherein the second input behavior includes: double-clicking the node.

[0089] Optionally, the data processing device is also used to perform the following steps before displaying the target business opportunity information: classify the target business opportunity information according to the enterprise types of multiple target enterprise entities in the target business opportunity information to obtain a first classification result; determine the difference between the geographical locations of multiple target enterprise entities in the target business opportunity information and the geographical location of the target device to obtain multiple target differences, and determine a preset distance interval for each target difference to obtain a second classification result; after displaying the target business opportunity information, the method also includes: receiving a target enterprise type input by the target device, and searching for target business opportunity information corresponding to the target enterprise type in the first classification result; receiving a target distance interval input by the target device, and searching for target business opportunity information corresponding to the target distance interval in the second classification result.

[0090] It should be noted that the above Figure 3 Each module in the can be a program module (for example, a set of program instructions that implement a specific function) or a hardware module. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above modules is a processor, or the functions of each of the above modules are implemented by a processor.

[0091] It should be noted that Figure 3 The preferred implementation of the illustrated embodiment can be found in Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0092] Figure 4 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a data processing method. Figure 4As shown, the computer terminal 40 may include one or more (402a, 402b, ..., 402n are used to illustrate) processors 402 (the processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 404 for storing data, and a transmission module 406 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown.

[0093] It should be noted that the one or more processors 402 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 40. As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0094] The memory 404 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in the embodiment of the present application. The processor 402 executes various functional applications and data processing by running the software programs and modules stored in the memory 404, that is, realizing the above-mentioned data processing method. The memory 404 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 404 may further include a memory remotely arranged relative to the processor 402, and these remote memories may be connected to the computer terminal 40 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0095] The transmission module 406 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 40. In one example, the transmission module 406 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0096] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 40 .

[0097] It should be noted that, in some optional embodiments, the above Figure 4 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 4 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0098] It should be noted that Figure 4 The computer terminal shown is used to execute Figure 1 The data processing method shown, therefore the relevant explanations in the execution method of the above command are also applicable to the electronic device and will not be repeated here.

[0099] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above data processing method.

[0100] A program for performing the following functions on a non-volatile storage medium: collecting enterprise-related data and business opportunity information; identifying enterprise entities in the enterprise-related data and establishing an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; constructing an enterprise relationship map based on the enterprise entity index library, wherein nodes in the enterprise relationship map are enterprise entities and edges are associations between enterprise entities; obtaining geographic location information of a target device, receiving a filtering range input by the target device, and determining a target area based on the geographic location information and the filtering range; filtering target business opportunity information whose location of the enterprise entity is within the target area in the business opportunity information, displaying the target business opportunity information, and displaying an enterprise relationship map of the target enterprise entity in the target business opportunity information.

[0101] An embodiment of the present application further provides an electronic device, including: a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the above data processing method is executed when the program is run.

[0102] The processor is used to run a program that performs the following functions: collect enterprise-related data and business opportunity information; identify enterprise entities in the enterprise-related data and establish an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; construct an enterprise relationship map based on the enterprise entity index library, wherein the nodes in the enterprise relationship map are enterprise entities and the edges are associations between enterprise entities; obtain geographic location information of a target device, receive a filtering range input by the target device, and determine a target area based on the geographic location information and the filtering range; filter target business opportunity information whose location of the enterprise entity is within the target area in the business opportunity information, display the target business opportunity information, and display the enterprise relationship map of the target enterprise entity in the target business opportunity information.

[0103] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0104] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0105] In the above-mentioned embodiments of the present application, the collected information is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary protection measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0110] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: include: Collect enterprise-related data and business opportunity information; Identifying enterprise entities in the enterprise-related data and establishing an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; According to the enterprise entity index library, an enterprise relationship map is constructed, wherein the nodes in the enterprise relationship map are the enterprise entities, and the edges are the association relationships between the enterprise entities; Acquire geographic location information of a target device, receive a screening range input by the target device, and determine a target area according to the geographic location information and the screening range; Target business opportunity information whose business entity is located in the target area is filtered from the business opportunity information, and the target business opportunity information and a business relationship map of the target business entity in the target business opportunity information are displayed.

2. The method according to claim 1, characterized in that Collect enterprise-related data, including: Collect the enterprise-related data from different data sources, wherein the data sources include: enterprise registration information database, enterprise financial statement database, enterprise official website information, and the enterprise-related data include: enterprise name, enterprise registration address, enterprise business scope, enterprise equity structure information, and enterprise cooperation agreement information; Identify the business entity in the business-related data, including: Performing data cleaning on the collected enterprise-related data, wherein the data cleaning includes: removing duplicate data, erroneous data, and incomplete data; and filling missing values ​​in the enterprise-related data after data cleaning with a filling algorithm based on data distribution characteristics to obtain target data; Business entities are identified from the target data using natural language processing techniques and a pre-trained business name recognition model.

3. The method according to claim 1, characterized in that According to the enterprise entity index library, an enterprise relationship map is constructed, including: Determining the association relationships between the enterprise entities in the enterprise entity index library, wherein the association relationships include: equity relationships, investment relationships, cooperative relationships, upstream and downstream relationships in the supply chain, and competitive relationships; According to the enterprise entities and the association relationships between the enterprise entities, an enterprise relationship map is constructed, wherein the node attributes in the enterprise relationship map include: enterprise scale attributes, enterprise industry attributes, enterprise region attributes, and the edge attributes include: relationship type attributes, relationship strength attributes, and relationship time attributes.

4. The method according to claim 1, characterized in that: Filtering the target business opportunity information in which the location of the enterprise entity is within the target area from the business opportunity information includes: Receiving enterprise scale information and enterprise business scope information input by the target device; Determine first business opportunity information whose semantic matching degree with the enterprise scale information and the enterprise business scope information is greater than a preset threshold; In the first business opportunity information, the target business opportunity information whose business entity is located within the target area is screened.

5. The method according to claim 1, characterized in that Display the target business opportunity information, including: According to different geographical locations of the target enterprise entities in the target business opportunity information, the target enterprise entities are represented by different color marks, wherein the size of the RGB value corresponding to the target enterprise entity is proportional to the expected profit in the target business opportunity information; Mapping the investment amount in the target business opportunity information to a bar chart in the industry classification view, wherein the investment amount is proportional to the height of the bar chart; In the timeline view, the time nodes in the target business opportunity information are mapped to line segments or node positions on the timeline, and the duration or importance of the business opportunity is represented by the line segment length or node size.

6. The method according to claim 3, characterized in that Displaying a corporate relationship map of the target corporate entity in the target business opportunity information, including: Representing a node in the enterprise relationship graph of the target enterprise entity with a preset shape, wherein the size of the node is proportional to the enterprise scale attribute; Different colors are used to represent different industry attributes of the enterprise corresponding to the nodes; The equity relationship is represented by a solid straight line, the investment relationship is represented by a dotted straight line, the cooperative relationship is represented by a solid curved line, the upstream and downstream relationship of the supply chain is represented by a dotted curved line, and the competitive relationship is represented by a broken line, wherein the thickness of the line is proportional to the relationship strength attribute.

7. The method according to claim 6, characterized in that After displaying the enterprise relationship map of the target enterprise entity in the target business opportunity information, the method further includes: Detecting input behavior of the target device to a node in an enterprise relationship graph of the target enterprise entity; In the case of detecting that the target device generates a first input behavior on the node, displaying a target information box, wherein the target information box includes: the enterprise scale attribute, the enterprise industry attribute, and the enterprise region attribute, and the first input behavior includes: clicking the node; When it is detected that the target device generates a second input behavior on the node, the relationship type attribute, the relationship strength attribute, and the relationship time attribute between the node and other nodes in the target area are displayed, wherein the second input behavior includes: double-clicking the node.

8. The method according to claim 6, characterized in that Before displaying the target business opportunity information, the method further includes: Classifying the target business opportunity information according to the enterprise types of multiple target enterprise entities in the target business opportunity information to obtain a first classification result; Determine the difference between the geographical locations of multiple target business entities in the target business opportunity information and the geographical location of the target device to obtain multiple target differences, and determine the preset distance interval where each target difference is located to obtain a second classification result; After displaying the target business opportunity information, the method further includes: receiving a target enterprise type input by the target device, and searching for target business opportunity information corresponding to the target enterprise type in the first classification result; A target distance interval input by the target device is received, and target business opportunity information corresponding to the target distance interval is searched in the second classification result.

9. A data processing device, characterized in that: include: The collection module is used to collect enterprise-related data and business opportunity information; An establishment module is used to identify enterprise entities in the enterprise-related data and establish an enterprise entity index library, wherein the enterprise entity index library stores enterprise entities and identification information corresponding to the enterprise entities; A construction module, used to construct an enterprise relationship map according to the enterprise entity index library, wherein the nodes in the enterprise relationship map are the enterprise entities and the edges are the association relationships between the enterprise entities; A determination module, configured to obtain geographic location information of a target device, receive a screening range input by the target device, and determine a target area according to the geographic location information and the screening range; The display module is used to filter target business opportunity information whose business entity is located in the target area from the business opportunity information, display the target business opportunity information, and display a corporate relationship map of the target corporate entity in the target business opportunity information.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 8.

11. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the program executes the data processing method according to any one of claims 1 to 8 when running.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 8 is implemented.

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