Business object query method and device and electronic equipment

Abstract

The invention discloses a business object query method and device and electronic equipment. The method comprises the following steps: determining a target business object from P preset business objects according to a target business type in a query request in a target relationship graph; determining business objects having a connection relationship with the target business object in the target relationship graph to obtain H first business objects; determining an intensity value of a connection relationship between each first business object and the target business object to obtain H target intensity values, and selecting Q third business objects according to the H target intensity values and the target business type; and determining the Q third business objects as a query result of the business object query request. Through the method and the device, the problem that the business promotion efficiency is low due to the fact that the accuracy rate of querying the business object is low and the query range is small in the related technology is solved.

Description

Technical Field

[0001] This application relates to the field of big data, and more specifically, to a method, apparatus, and electronic device for querying business objects. Background Technology

[0002] When financial institutions expand their business, they first need to determine which business targets have a high degree of matching with the business to be expanded in order to increase the success rate of business expansion.

[0003] However, currently, when identifying business targets for promotion, the database is typically used to query these targets. The query results determine which targets to target. However, the current method only allows querying business targets stored within the financial institution, and the matching degree between the retrieved targets and the business to be expanded cannot be determined. This results in an incomplete and inaccurate query of business targets, leading to low efficiency in subsequent business promotion.

[0004] There is currently no effective solution to the problem that the accuracy of querying business objects in related technologies is low and the query scope is small, which leads to low efficiency in business promotion. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, and electronic device for querying business objects, in order to solve the problems of low accuracy and limited query scope in related technologies, which leads to low efficiency in business promotion.

[0006] To achieve the above objectives, according to one aspect of this application, a method for querying business objects is provided. The method includes: receiving a query request sent by a financial institution; determining a target business object from P preset business objects in a target relationship graph based on the target business type in the query request; wherein the target relationship graph includes N initial business objects, and the N initial business objects include P preset business objects, where P and N are positive integers; determining business objects with connection relationships to the target business object in the target relationship graph, obtaining H first business objects, where each first business object has a direct or indirect connection relationship with the target business, where H is a positive integer; determining the strength value of the connection relationship between each first business object and the target business object, obtaining H target strength values; selecting T second business objects from the H first business objects based on the H target strength values; and selecting Q third business objects from the T second business objects based on the target business type, where T and Q are positive integers; and determining the Q third business objects as the query results of the business object query request.

[0007] Optionally, the target relationship graph is constructed as follows: First public attribute information of M internal business objects in a financial institution is obtained, and external related objects of each internal business object are determined based on the first public attribute information, resulting in M ​​groups of external business objects, where M is a positive integer; each of the M internal business objects and each of the M groups of external business objects is determined as an initial business object, resulting in N initial business objects, and the relationship between the N initial business objects is determined based on the first and second public attribute information, where the second public attribute information is the attribute information of each of the M groups of external business objects; an initial relationship graph of the N initial business objects is generated based on the relationship, and the N initial business objects in the initial relationship graph are grouped to obtain P groups, and preset business objects in each group are determined to obtain P preset business objects; the initial relationship graph that completes the determination of the P groups and P preset business objects is determined as the target relationship graph.

[0008] Optionally, grouping the N initial business objects in the initial relationship graph to obtain P groups includes: determining the association strength value between each initial business object and its adjacent initial business objects, and determining the initial division method for the N initial business objects based on the association strength value; calculating the modularity of the target relationship graph under the initial division method, obtaining the initial modularity, and iteratively updating the initial division method to obtain the initial modularity of the initial division method after each iteration; calculating the difference between the initial modularity obtained from two adjacent iterations, and stopping the iterative update operation if the difference is less than the difference threshold, and determining the initial division method obtained after the last iteration update as the target division method, and using the target division method to group the N initial business objects in the target relationship graph to obtain P groups.

[0009] Optionally, determining the preset business objects in each group to obtain P preset business objects includes: for any target group, configuring initial weight values ​​for each business object in the target group, and obtaining the sum of the node index values ​​of the upstream nodes of each business object to obtain the total node index value of each business object, wherein the node index value is the value obtained by dividing the upstream weight value of any upstream node by the total number of outgoing edges of the upstream node, and the upstream weight value is obtained by multiplying the initial weight value of the upstream node by the edge weight; updating the initial weight value of each business object according to the total node index value and damping factor of each business object to obtain the updated initial weight value, and repeating the update operation of the initial weight value; if the weight change rate between two adjacent weight update operations is less than a preset threshold, the initial weight value obtained after the last execution of the initial weight value update operation is determined as the target weight value, and the business object with the largest target weight value is determined as the preset business object in the target group.

[0010] Optionally, determining the strength value of the connection relationship between each first business object and the target business object to obtain H target strength values ​​includes: for any first business object, obtaining the number of edges between the first business object and the target business object to obtain a first value; calculating the total weight value of the edges between the first business object and the target business object to obtain a second value; and dividing the second value by the first value to obtain the target strength value of the first business object.

[0011] Optionally, selecting T second business objects from H first business objects based on H target intensity values ​​includes: determining the first business objects whose target intensity values ​​are greater than the intensity value threshold as second business objects, thus obtaining T second business objects.

[0012] Optionally, selecting Q third business objects from T second business objects based on the target business type includes: obtaining the business content of each second business object and determining the initial business type of each second business object based on the business content; determining the second business objects with the same initial business type as the target business type as the third business objects, thus obtaining Q third business objects.

[0013] To achieve the above objectives, according to another aspect of this application, a business object query device is provided. The device includes: a first determining unit, configured to receive a query request sent by a financial institution and determine a target business object from P preset business objects in a target relationship graph based on the target business type in the query request, wherein the target relationship graph includes N initial business objects, and the N initial business objects include P preset business objects, where P and N are positive integers; a second determining unit, configured to determine business objects with connection relationships to the target business object in the target relationship graph, obtaining H first business objects, wherein each first business object has a direct or indirect connection relationship with the target business, where H is a positive integer; a third determining unit, configured to determine the strength value of the connection relationship between each first business object and the target business object, obtaining H target strength values, and selecting T second business objects from the H first business objects based on the H target strength values, and selecting Q third business objects from the T second business objects based on the target business type, where T and Q are positive integers; and a fourth determining unit, configured to determine the Q third business objects as the query results of the business object query request.

[0014] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described query method for business objects during runtime.

[0015] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the above-described business object query method.

[0016] In this embodiment, a query request sent by a financial institution is received, and a target business object is determined from P preset business objects in the target relationship graph based on the target business type in the query request. The target relationship graph includes N initial business objects, and the N initial business objects include P preset business objects. Business objects with connection relationships to the target business object are determined in the target relationship graph, resulting in H first business objects, each of which has a direct or indirect connection relationship with the target business. The strength value of the connection relationship between each first business object and the target business object is determined, resulting in H target strength values. Based on the H target strength values, T second business objects are selected from the H first business objects, and then selected from the T second business objects based on the target business type. The method involves selecting Q third-party business objects and determining these Q third-party business objects as the query results for business object query requests. This is achieved by querying target business objects under the target business type in the target relationship graph, querying business objects with connection relationships to the target business objects, and then selecting third-party business objects based on the strength of the connection relationships. This method identifies business objects of the same type as the target business objects and with strong association relationships as the query results for business object query requests. This improves the query scope and accuracy of business objects, thereby enhancing the efficiency of business promotion operations based on the query results. It also solves the technical problems of low accuracy and limited query scope in related technologies, which lead to low efficiency in business promotion. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a query method for business objects is shown.

[0019] Figure 2 This is a flowchart of a method for querying business objects according to Embodiment 1 of this application;

[0020] Figure 3 This is a schematic diagram of the association extraction results provided in Embodiment 1 of this application. Figure 1 ;

[0021] Figure 4 This is a schematic diagram of the association extraction results provided in Embodiment 1 of this application. Figure 2 ;

[0022] Figure 5This is a schematic diagram of the grouping results provided in Embodiment 1 of this application;

[0023] Figure 6 This is a schematic diagram of a business object query device provided according to Embodiment 2 of this application;

[0024] Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It should be noted that the query methods, devices, and electronic devices for the business objects identified in this disclosure can be used in the field of big data, or in any field other than big data. The application fields of the query methods, devices, and electronic devices for the business objects identified in this disclosure are not limited.

[0029] It should be noted that all information, user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) used in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse use. If the user chooses to refuse, the process will proceed to the expert decision-making process. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface. After receiving consent from the aforementioned user or organization, the relevant information is obtained. Users can view the purpose of data use in real time through the authorization interface and have the right to withdraw authorization or delete data at any time. After the authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0030] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0031] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0032] LightGCN algorithm: Lightweight Graph Convolutional Network Algorithm.

[0033] LLM: Large Language Model.

[0034] MindMap Algorithm: Mind Mapping Algorithm, a knowledge graph mind mapping algorithm.

[0035] Leiden Algorithm: Leiden Community Detection Algorithm.

[0036] Example 1

[0037] According to an embodiment of this application, an embodiment of a method for querying business objects is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a query method for business objects is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, processing devices such as microprocessors or programmable logic devices), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface, a universal serial bus port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0039] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0040] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the business object query method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned business object query method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0041] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0042] The display may be, for example, a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0043] Under the aforementioned operating environment, this application provides the following: Figure 2 The query method for the business object shown. Figure 2 This is a flowchart of the query method for business objects provided in Embodiment 1 of this application, such as... Figure 2 As shown, the method includes:

[0044] Step S201: Receive the query request sent by the financial institution, and determine the target business object from P preset business objects in the target relationship graph according to the target business type in the query request. The target relationship graph includes N initial business objects, and the N initial business objects include P preset business objects, where P and N are positive integers.

[0045] It should be noted that the execution entity in this embodiment can be a business object query system. This system can query the target business object under the target business type in the target relationship graph, and query the business objects that have a connection relationship with the target business object. Then, it selects a third business object from them according to the strength value of the connection relationship, and determines the business objects of the same type as the target business object and with a high-strength association relationship as the query result of the business object query request.

[0046] It should be noted that the target relationship graph is a comprehensive network constructed from knowledge graphs and algorithms, including N initial business objects, which cover internal and external customers of financial institutions and their related entities. P preset business objects are key objects with high influence selected from the N initial business objects.

[0047] For example, a query request can be a request to query business objects with a high success rate when promoting a certain business product. When querying business objects, the system first uses the front-end interface to receive the query request submitted by the financial institution. The request content includes the name of the business product and the target business type (such as corporate loans, credit card services, etc.).

[0048] Upon receiving a request, the system can use a search algorithm based on the target relationship graph, taking the target business type specified in the query request as a clue, to determine at least one target business object closely related to that business type from P preset business objects in the target relationship graph. Simultaneously, the system automatically checks the current status and historical data of these business objects, improving the accuracy and timeliness of selecting target business objects.

[0049] Step S202: In the target relationship graph, identify the business objects that have a connection relationship with the target business object to obtain H first business objects, where each first business object has a direct connection relationship or an indirect connection relationship with the target business, and H is a positive integer.

[0050] It should be noted that a direct connection refers to a direct transaction, collaboration, or other association record between two entities. An indirect connection is a link between two entities indirectly through multiple paths in the graph. The first business object is all business entities that the system selects from the target relationship graph that have direct or indirect connections with the target business object.

[0051] For example, after identifying the target business object, the system initiates a deep relationship mining algorithm. This algorithm performs a depth-first traversal of the target relationship graph, centering on the target business object, to find all entities that have direct or indirect connections with it, forming a list of H first business objects. In other words, by identifying the first business objects that are associated with the target business object, potential customer objects are identified. Furthermore, by leveraging the influential target business objects, potential customers are developed into clients of the financial institution.

[0052] It's worth noting that when searching for all primary business objects, the MindMap algorithm can be used to extract path relationships. The MindMap algorithm enhances the reasoning ability and transparency of large language models by leveraging relationship graphs, extracting information such as path relationships from the target relationship graph. Based on LLM reasoning, the LLM integrates the constructed reasoning graph with its own implicit knowledge to generate the final answer. During this process, a mind map is constructed to reveal the LLM's knowledge ontology-based reasoning path, thereby enabling the extraction and display of path relationships.

[0053] Figure 3 This is a schematic diagram of the association extraction results provided in Embodiment 1 of this application. Figure 1 ,like Figure 3 As shown, business objects A1 and A2 are connected through intermediate business object B1, and business objects A2 and A3 are connected through intermediate business object B2. Thus, when the target business object is A2, B1, B2, A1, and A3 are determined as the first business object.

[0054] Furthermore, the LightGCN algorithm can be used to mine latent relationships in relation graphs. LightGCN is an effective method for mining latent relationships in relation graphs. By simplifying the graph convolutional network structure, it exhibits good performance in learning node embeddings and predicting latent relationships. The latent relationship mining process can be summarized as follows: In the target relation graph, LightGCN captures information about each node's neighboring nodes and updates the node's feature representation layer by layer, thereby effectively learning complex relationships between nodes. As the number of network layers increases, nodes can capture more global information through their embeddings, thus enabling the discovery of latent relationships in the target relation graph. For example, in link prediction tasks, LightGCN can predict whether a potential relationship exists between two nodes based on the generated node embedding representations, either by calculating embedding similarity or through a scoring function. The LightGCN model has a simple structure, is easier to implement and train, and has high computational efficiency.

[0055] For example, Figure 4 This is a schematic diagram of the association extraction results provided in Embodiment 1 of this application. Figure 2 ,like Figure 4As shown, the LightGCN algorithm revealed a potential connection between business object A1 and business objects A3, A3, and A4 belonging to the same segment B.

[0056] Step S203: Determine the strength value of the connection relationship between each first business object and the target business object to obtain H target strength values. Then, select T second business objects from the H first business objects based on the H target strength values, and select Q third business objects from the T second business objects based on the target business type, where T and Q are positive integers.

[0057] It should be noted that the strength value is an indicator used in algorithmic quantitative analysis to evaluate the closeness and similarity of the relationship between the first business object and the target business object. The second business object is the T entities with the highest potential value selected from the H first business objects based on their connection strength values. The third business object is the Q most matching objects selected from the second business objects based on the target business type.

[0058] For example, the system performs in-depth analysis on the connection relationship between each first business object and the target business object. It can use methods such as transaction frequency analysis and relationship link evaluation to calculate the connection strength value, and then select the second business object based on the strength value.

[0059] Furthermore, based on the specific attributes of the target business type, such as business risk preference and target customer group characteristics, the system further filters the second business objects and selects Q third business objects that are highly matched with the target business type from the second business objects.

[0060] Step S204: Determine Q third business objects as the query results of the business object query request.

[0061] For example, after completing all filtering and analysis, the system summarizes the information of Q third-party business objects to form a structured and actionable query result. The query result includes not only basic information about the business objects but also correlation analysis reports, market potential assessments, reasons for recommendation, and possible business expansion paths. The system can present the query result to financial institutions through intuitive charts and detailed data reports, facilitating the subsequent development of corresponding business promotion plans.

[0062] It should be noted that in the above steps S201-S204, when processing and analyzing business objects, the data used are all content that can be used and shared and is publicly displayed on public media (such as the Internet, newspapers, etc.) for each business object. If a certain business object is not allowed to be used, then the above process will not analyze or use that business object.

[0063] It should be noted that the business object query method provided in this embodiment allows users to query information about business objects using natural language, such as whether they are bank loan customers or affiliated companies, and display this information visually. At the same time, users can also use natural language to query the relationships between potential business objects, thereby uncovering customer acquisition leads and providing auxiliary information for product promotion operations.

[0064] The business object query method provided in this application embodiment involves receiving a query request sent by a financial institution and determining a target business object from P preset business objects in a target relationship graph based on the target business type in the query request. The target relationship graph includes N initial business objects, and the N initial business objects include P preset business objects. Business objects with connection relationships to the target business object are determined in the target relationship graph, resulting in H first business objects, each of which has a direct or indirect connection relationship with the target business. The strength value of the connection relationship between each first business object and the target business object is determined, resulting in H target strength values. Based on the H target strength values, T second business objects are selected from the H first business objects, and based on the target business type, T second business objects are selected from the T first business objects. The method of selecting Q third business objects from two business objects and determining these Q third business objects as the query results of the business object query request involves querying the target business objects under the target business type in the target relationship graph, querying business objects with connection relationships to the target business objects, and then selecting third business objects from them based on the strength value of the connection relationship. This method aims to improve the query scope and accuracy of business objects, thereby increasing the efficiency of business promotion operations based on the query results. It also solves the technical problem in related technologies where the accuracy and scope of business object queries are low, leading to low efficiency in business promotion.

[0065] To obtain a more accurate target relationship graph, optionally, in the business object query method provided in this application embodiment, the target relationship graph is constructed in the following manner: First public attribute information of M internal business objects in a financial institution is obtained, and external related objects of each internal business object are determined based on the first public attribute information to obtain M groups of external business objects, where M is a positive integer; each of the M internal business objects and each of the external business objects in the M groups of external business objects is determined as an initial business object to obtain N initial business objects, and the association relationship between the N initial business objects is determined based on the first public attribute information and the second public attribute information, where the second public attribute information is the attribute information of each external business object in the M groups of external business objects; an initial relationship graph of the N initial business objects is generated based on the association relationship, and the N initial business objects in the initial relationship graph are divided into groups to obtain P groups, and preset business objects in each group are determined to obtain P preset business objects; the initial relationship graph that has completed the determination of the P groups and the P preset business objects is determined as the target relationship graph.

[0066] It should be noted that the first public attribute information refers to the public attributes of internal business objects within the financial institution, such as business type, transaction history, and customer base. External related objects are business entities found outside the financial institution that are associated with these internal business objects, based on their attribute information and through data mining and association analysis techniques. The second public attribute information is the public attribute information of external business objects. The initial business objects include both internal business objects and external related objects. The preset business objects can be the most influential business objects in each group.

[0067] For example, when constructing the target relationship graph, the system first extracts the first public attribute information of M internal business objects from the financial institution's database, including but not limited to business category, transaction amount, customer group characteristics, and geographical location. Then, the system initiates an external data collection process, which can collect external information related to the attributes of the internal business objects, such as supplier information, customer reviews, market dynamics, and industry reports, through methods such as online data collection and public data interface calls. Based on the collected external information, the system uses natural language processing and association rule learning techniques to identify external business entities potentially related to the internal business objects, forming M groups of external business objects.

[0068] By collecting and analyzing the first publicly available attribute information, the system can accurately identify the external related entities of internal business objects of financial institutions, build a more comprehensive network of business objects, and provide rich data support for subsequent graph construction.

[0069] Furthermore, based on the first and second public attribute information, the system employs knowledge graph construction technology to analyze the relationships between business objects, including transaction cooperation, upstream and downstream supply chains, and jointly owned customer groups. The system uses graph databases and graph construction tools to build an initial relationship graph of these business objects and their relationships, in the form of nodes and edges. The system also needs to regularly update and maintain the data to improve the accuracy and timeliness of the graph.

[0070] It should be noted that, for grouping, the system can use community detection algorithms, such as the Leiden algorithm, to perform cluster analysis on the nodes in the initial relationship graph, identifying groups of closely related business entities, forming P business groups. Each group contains business objects related to a specific business domain or market area. Furthermore, the system can run node influence algorithms, such as PageRank, within each group to evaluate the status and influence of each business object in the network, selecting the business entity with the highest influence or the highest match with the target business type as the preset business objects. Thus, by grouping and determining the preset business objects, the efficiency of mining business objects using the target relationship graph is improved.

[0071] This embodiment identifies each business object by disclosing attribute information, determines the relationships between business objects based on the disclosing attribute information, and then constructs a target relationship graph, thereby improving the accuracy of the target relationship graph.

[0072] To perform accurate grouping operations, optionally, in the business object query method provided in this application embodiment, grouping N initial business objects in the initial relationship graph to obtain P groups includes: determining the association strength value between each initial business object and its adjacent initial business objects, and determining a preliminary grouping method for the N initial business objects based on the association strength value, thus obtaining an initial grouping method; calculating the modularity of the target relationship graph under the initial grouping method, thus obtaining an initial modularity, and iteratively updating the initial grouping method to obtain the initial modularity of the initial grouping method after each iteration; calculating the difference between the initial modularities obtained from two adjacent iterations, and stopping the iterative update operation if the difference is less than a difference threshold, and determining the initial grouping method obtained after the last iteration update as the target grouping method, and using the target grouping method to group the N initial business objects in the target relationship graph to obtain P groups.

[0073] It should be noted that the association strength value refers to a quantitative value calculated based on multiple indicators such as historical transaction frequency and the number of common customers between any two initial business objects in the initial relationship graph, used to measure the closeness of their relationship. Modularity is an evaluation metric in community detection algorithms, used to assess whether a certain partitioning scheme in the graph can reasonably separate densely connected node groups (communities). The higher the modularity, the tighter the internal connections and the sparser the external connections of the partitioned communities.

[0074] For example, when grouping business objects, the system performs in-depth analysis on each initial business object in the initial relationship graph and its directly connected neighboring objects. This process involves the comprehensive calculation of multiple indicators, such as transaction frequency, average transaction amount, and number of common customers. Through weighted averaging or other statistical methods, the system assigns a correlation strength value to each pair of adjacent initial business objects, which serves as the basis for preliminary grouping. Based on the calculated correlation strength values, the system employs community detection algorithms, such as hierarchical clustering, spectral clustering, and label propagation, to attempt preliminary grouping of the N initial business objects.

[0075] Furthermore, the system calculates the modularity of the graph using the Leiden algorithm. Modularity is an indicator that measures the rationality of node grouping and is used to evaluate whether the current partitioning method effectively aggregates densely connected nodes into communities. The initial modularity is calculated by comparing the internal connections of each initial business object under the current partitioning scheme with the randomly expected external connections, thus quantifying the merits of the current partitioning scheme.

[0076] Furthermore, the system continuously runs the Leiden algorithm for community detection. In each iteration, the algorithm attempts to optimize the existing partitioning method through an iterative process of local movement-aggregation-partitioning. That is, the system tries to change the community to which each node belongs to to observe whether this can improve the modularity value. After each iteration, the system recalculates the modularity of the target relation graph to assess the impact of changes in community partitioning on the modularity. The system records the difference in modularity before and after each iteration. When the difference first falls below a set threshold, it indicates that the update benefits of further iterative optimization are very limited, and the system stops the iteration process.

[0077] After the final iteration, the resulting partitioning method is determined as the target partitioning method. This means the community partitioning scheme has reached a relatively ideal optimization state, effectively reflecting the inherent connections and community attributes between business objects. At this point, the system will partition the N initial business objects in the target relationship graph into P business groups according to this partitioning scheme, ensuring that the business objects within each group have high correlation strength and similarity.

[0078] Figure 5 This is a schematic diagram of the grouping results provided in Embodiment 1 of this application, as shown below. Figure 5 As shown, A, B, and C are the three groups obtained after the division.

[0079] This embodiment, based on N initial business objects in the initial relationship graph, achieves reasonable business group division by determining the association strength value, calculating the modularity, and iterating optimization, thereby improving the accuracy of business group division.

[0080] To accurately determine the preset business objects, optionally, in the query method for business objects provided in this application embodiment, determining the preset business objects in each group to obtain P preset business objects includes: for any target group, configuring initial weight values ​​for each business object in the target group, and obtaining the sum of the node index values ​​of the upstream nodes of each business object to obtain the total node index value of each business object, wherein the node index value is the value obtained by dividing the upstream weight value of any upstream node by the total number of outgoing edges of the upstream node, and the upstream weight value is obtained by multiplying the initial weight value of the upstream node by the edge weight; updating the initial weight value of each business object according to the total node index value and damping factor of each business object to obtain the updated initial weight value, and repeating the update operation of the initial weight value; if the weight change rate between two adjacent weight update operations is less than a preset threshold, determining the initial weight value obtained after the last execution of the initial weight value update operation as the target weight value, and determining the business object with the largest target weight value as the preset business object in the target group.

[0081] It should be noted that the initial weight value represents the standard weight assigned to each business object at the beginning of the weight calculation process, used to measure its relative importance in the group. The node indicator value is calculated based on the weight of the upstream node and the number of outgoing edges, reflecting the degree to which the business object receives influence from other objects within the group. The upstream weight value is calculated by multiplying the initial weight value of the upstream node by its edge weight with the downstream node, reflecting the strength of the direct influence of the upstream node on the downstream node. The damping factor plays a regulatory role in the weight update algorithm, used to control the dependence on historical weights during the weight value update process and prevent excessive fluctuations in weight values. The weight change rate is an indicator that measures the difference before and after the weight update; when the weight change rate drops below a preset threshold, it indicates that the weight allocation has approached a stable state.

[0082] For example, for each pre-defined target group, the system first configures an initial weight value for each business object within it. This weight value can be based on the business object's basic attributes, such as company size and historical transaction records, or it can be obtained through historical data analysis. Further, the system traverses the graph, calculating the sum of the node indicator values ​​of each business object's upstream nodes to obtain the total node indicator value for each business object. The calculation method for the node indicator value of an upstream node is as follows: the upstream weight value of each upstream node is divided by the total number of outgoing edges of the upstream node. The upstream weight value is the value obtained by multiplying the initial weight value of the upstream node by the edge weight of that edge, and the total number of outgoing edges is the number of edges from the upstream node to other nodes.

[0083] Furthermore, the system updates the initial weight value of each business object based on its total node index value and damping factor. The damping factor is used to balance the dependence on global and local information in the weight update. The formula for weight update is: New weight value = (1 - damping factor) / D + damping factor × (sum of node index values ​​of all upstream nodes), where D represents the total number of business objects in the group. The system will continue to perform weight update operations until the weight change rate between two adjacent weight update operations is less than a preset threshold (e.g., 0.001), that is, the weight iteration process has converged, the weight allocation tends to be stable, and no further adjustment is needed.

[0084] For example, if the weight iteration process converges, the system determines the weight value after the last update as the target weight value, which reflects the actual influence of each business object in the group. The system then selects the business object with the largest target weight value and identifies it as the preset business object of the target group, thus completing the selection process for the preset business objects.

[0085] This embodiment achieves the technical effect of improving the accuracy of weight value calculation by iteratively calculating the weight values ​​of each business object, thereby improving the accuracy of selecting preset business objects.

[0086] To accurately determine the strength value, optionally, in the query method for business objects provided in this application embodiment, determining the strength value of the connection relationship between each first business object and the target business object to obtain H target strength values ​​includes: for any first business object, obtaining the number of edges between the first business object and the target business object to obtain a first value; calculating the total weight value of the edges between the first business object and the target business object to obtain a second value; dividing the second value by the first value to obtain the target strength value of the first business object.

[0087] For example, when calculating the strength value between a first business object and a target business object, the system first traverses the target relationship graph. For each first business object, it determines all direct or indirect connection paths between the entity and the target business object. These paths are represented by edges in the graph, representing various types of business relationships, such as mutual customers, transaction records, and upstream / downstream supply chains. The system records information about each edge, including its type, direction, and weight. The number of edges (the first value) reflects the frequency or quantity of connections between the first and target business objects, while the total weight of the edges (the second value) comprehensively considers the quality of the connections.

[0088] For each first business object, the system divides the calculated second value (total weight of edges) by the first value (number of edges) to obtain the target strength value of the object. This averages the connection relationships so that both high-frequency but relatively low-quality connections and low-frequency but high-quality connections can be reflected in the target strength value.

[0089] This embodiment determines the strength values ​​of the first business object and the target business object based on the weight and number of edges, achieving the technical effect of accurately determining the strength values ​​of the first business object and the target business object.

[0090] Optionally, in the query method for business objects provided in the embodiments of this application, selecting T second business objects from H first business objects based on H target intensity values ​​includes: determining the first business objects among the H first business objects whose target intensity values ​​are greater than the intensity value threshold as second business objects, thereby obtaining T second business objects.

[0091] For example, when selecting the second business object, the target strength values ​​of H first business objects can be compared and analyzed. The first business objects with target strength values ​​higher than the strength value threshold are identified as the second business objects, so that only business objects with strong correlation with the target business object can be filtered out, thereby improving the effectiveness of the query results of business objects.

[0092] This embodiment filters business objects by setting an intensity threshold, thereby improving the effectiveness of the query results for business objects.

[0093] Optionally, in the query method for business objects provided in this application embodiment, selecting Q third business objects from T second business objects according to the target business type includes: obtaining the business content of each second business object, and determining the initial business type of each second business object according to the business content; determining the second business objects with the same initial business type as the target business type as third business objects, thereby obtaining Q third business objects.

[0094] For example, when selecting a third business object, the system extracts a description of the business content from the basic information and transaction records of the second business object, including but not limited to information such as service scope, product type, customer group, and industry affiliation. The system uses natural language processing technology and pattern recognition algorithms to perform semantic analysis on the business content, matches it with a preset business type classification system, and determines the initial business type of each second business object.

[0095] Furthermore, the system matches the initial business type of each previously determined second business object with the target business type of the business to be promoted, and then identifies all second business objects with the same initial business type as the target business type as third business objects, thereby improving the effectiveness of the query results for business objects.

[0096] This embodiment achieves the technical effect of improving the effectiveness of query results for business objects by filtering the second business object through the target business type.

[0097] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0098] Example 2

[0099] This application also provides a business object query device. It should be noted that the business object query device of this application can be used to execute the business object query method provided in the above embodiments. The business object query device provided in this application is described below.

[0100] According to an embodiment of this application, an apparatus for implementing the above-described query method for business objects is also provided. Figure 6 This is a schematic diagram of a business object query device provided according to Embodiment 2 of this application, such as... Figure 6 As shown, the device includes:

[0101] The first determining unit 61 is used to receive a query request sent by a financial institution and determine a target business object from P preset business objects in the target relationship graph according to the target business type in the query request. The target relationship graph includes N initial business objects, and the N initial business objects include P preset business objects, where P and N are positive integers.

[0102] The second determining unit 62 is used to determine the business objects that have a connection relationship with the target business object in the target relationship graph, and obtain H first business objects, wherein each first business object has a direct connection relationship or an indirect connection relationship with the target business, and H is a positive integer.

[0103] The third determining unit 63 is used to determine the strength value of the connection relationship between each first business object and the target business object, obtain H target strength values, select T second business objects from the H first business objects according to the H target strength values, and select Q third business objects from the T second business objects according to the target business type, where T and Q are positive integers.

[0104] The fourth determining unit 64 is used to determine Q third business objects as the query results of the business object query request.

[0105] The business object query device provided in this application queryes target business objects under target business types in a target relationship graph, and queries business objects that have a connection relationship with the target business objects. Then, it selects a third business object based on the strength value of the connection relationship. In this way, business objects of the same type as the target business objects and with a high degree of correlation are determined as the query results of the business object query request. This achieves the purpose of improving the query scope and query accuracy of business objects, thereby realizing the technical effect of improving the efficiency of business promotion operations based on query results. It also solves the technical problem in related technologies that the accuracy of querying business objects is low and the query scope is small, which leads to low efficiency of business promotion.

[0106] Optionally, in the business object query device provided in this application embodiment, the target relationship graph is constructed by the following device: an acquisition unit, used to acquire first public attribute information of M internal business objects in a financial institution, and determine the external related objects of each internal business object according to the first public attribute information to obtain M groups of external business objects, where M is a positive integer; a fifth determination unit, used to determine each external business object in the M internal business objects and M groups of external business objects as initial business objects to obtain N initial business objects, and determine the relationship between the N initial business objects according to the first public attribute information and the second public attribute information, where the second public attribute information is the attribute information of each external business object in the M groups of external business objects; a generation unit, used to generate an initial relationship graph of N initial business objects according to the relationship, and divide the N initial business objects in the initial relationship graph into groups to obtain P groups, and determine the preset business objects in each group to obtain P preset business objects; a sixth determination unit, used to determine the initial relationship graph that has completed the determination operation of P groups and P preset business objects as the target relationship graph.

[0107] Optionally, in the business object query device provided in this application embodiment, the generation unit includes: a first determining module, used to determine the association strength value between each initial business object and adjacent initial business objects, and determine a preliminary division method for N initial business objects based on the association strength value, to obtain an initial division method; a first updating module, used to calculate the modularity of the target relationship graph under the initial division method, to obtain the initial modularity, and iteratively update the initial division method to obtain the initial modularity of the initial division method obtained after each iteration; a second determining module, used to calculate the difference between the initial modularity obtained from two adjacent iteration operations, and stop the iterative update operation when the difference is less than the difference threshold, and determine the initial division method obtained after the last iteration update as the target division method, and use the target division method to group the N initial business objects in the target relationship graph to obtain P groups.

[0108] Optionally, in the business object query device provided in this application embodiment, the generation unit includes: a first acquisition module, configured to configure initial weight values ​​for each business object in any target group, and acquire the sum of the node index values ​​of the upstream nodes of each business object to obtain the total node index value of each business object, wherein the node index value is the value obtained by dividing the upstream weight value of any upstream node by the total number of outgoing edges of the upstream node, and the upstream weight value is obtained by multiplying the initial weight value of the upstream node and the edge weight; a second update module, configured to update the initial weight value of each business object according to the total node index value and the damping factor of each business object to obtain the updated initial weight value, and repeatedly execute the update operation of the initial weight value; a third determination module, configured to determine the initial weight value obtained after the last execution of the initial weight value update operation as the target weight value when the weight change rate between two adjacent weight update operations is less than a preset threshold, and determine the business object with the largest target weight value as the preset business object in the target group.

[0109] Optionally, in the query device for business objects provided in this application embodiment, the third determining unit 63, which obtains H target strength values, includes: a second obtaining module, used to obtain the number of edges between the first business object and the target business object for any first business object, and obtain a first value; a first calculating module, used to calculate the total weight value of the edges between the first business object and the target business object, and obtain a second value; and a second calculating module, used to divide the second value by the first value, and obtain the target strength value of the first business object.

[0110] Optionally, in the query device for business objects provided in the embodiments of this application, the third determining unit 63 includes: a fourth determining module, used to determine the first business objects with target intensity values ​​greater than the intensity value threshold among H first business objects as second business objects, thereby obtaining T second business objects.

[0111] Optionally, in the query device for business objects provided in the embodiments of this application, the third determining unit 63 includes: a third obtaining module, used to obtain the business content of each second business object and determine the initial business type of each second business object according to the business content; and a fifth determining module, used to determine the second business objects with the same initial business type and target business type as third business objects, thereby obtaining Q third business objects.

[0112] It should be noted that the first determining unit 61, the second determining unit 62, the third determining unit 63, and the fourth determining unit 64 mentioned above correspond to steps S201 to S204 in Embodiment 1. The instances and application scenarios implemented by each of the above units and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware components or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and can run in the computer terminal 10 provided in Embodiment 1.

[0113] Example 3

[0114] Embodiments of this application may provide an electronic device. Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0115] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 7 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 7 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 7 The different configurations shown.

[0117] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0118] Example 4

[0119] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the query method for the business object provided in Embodiment 1.

[0120] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0121] Embodiments of this application also provide a computer program product, which, when executed on a data processing device, is a program adapted to perform the steps of a query method for a business object.

[0122] Embodiments of this application also provide a computer-readable storage medium, which includes a stored executable program, wherein, when the executable program runs, it controls the device where the computer-readable storage medium is located to execute the above-described method for querying business objects.

[0123] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0124] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0128] If the integrated unit is implemented as 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 this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0129] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for querying business objects, characterized in that, include: The system receives a query request from a financial institution and determines a target business object from P preset business objects in the target relationship graph based on the target business type in the query request. The target relationship graph includes N initial business objects, and the N initial business objects include the P preset business objects, where P and N are positive integers. In the target relationship graph, business objects that have a connection relationship with the target business object are identified, resulting in H first business objects, where each first business object has a direct or indirect connection relationship with the target business, and H is a positive integer; Determine the strength value of the connection relationship between each first business object and the target business object to obtain H target strength values, and select T second business objects from the H first business objects according to the H target strength values, and select Q third business objects from the T second business objects according to the target business type, where T and Q are positive integers; The Q third business objects are identified as the query results of the business object query request.

2. The method according to claim 1, characterized in that, The target relationship graph is constructed in the following manner: Obtain the first public attribute information of M internal business objects in the financial institution, and determine the external related objects of each internal business object based on the first public attribute information to obtain M groups of external business objects, where M is a positive integer; The M internal business objects and each external business object in the M groups of external business objects are determined as initial business objects to obtain N initial business objects. The association relationship between the N initial business objects is determined according to the first public attribute information and the second public attribute information, wherein the second public attribute information is the attribute information of each external business object in the M groups of external business objects. An initial relationship graph of the N initial business objects is generated based on the association relationship, and the N initial business objects in the initial relationship graph are divided into groups to obtain P groups. The preset business objects in each group are determined to obtain P preset business objects. The initial relationship graph obtained by completing the determination of the P groups and the P preset business objects is determined as the target relationship graph.

3. The method according to claim 2, characterized in that, The N initial business objects in the initial relationship graph are divided into P groups, including: Determine the association strength value between each initial business object and its adjacent initial business objects, and determine the initial partitioning method for the N initial business objects based on the association strength value, thus obtaining the initial partitioning method; Calculate the modularity of the target relation graph under the initial partitioning method to obtain the initial modularity, and iteratively update the initial partitioning method to obtain the initial modularity of the initial partitioning method after each iteration; Calculate the difference between the initial modularity obtained from two adjacent iterations, and stop the iterative update operation if the difference is less than the difference threshold. Determine the initial partitioning method obtained after the last iteration update as the target partitioning method, and use the target partitioning method to group the N initial business objects in the target relationship graph to obtain P groups.

4. The method according to claim 2, characterized in that, The preset business objects in each group are identified, resulting in P preset business objects, including: For any target group, configure an initial weight value for each business object in the target group, and obtain the sum of the node index values ​​of the upstream nodes of each business object to obtain the total node index value of each business object. The node index value is the value obtained by dividing the upstream weight value of any upstream node by the total number of outgoing edges of the upstream node. The upstream weight value is obtained by multiplying the initial weight value of the upstream node and the edge weight. The initial weight value of each business object is updated based on the total node index value and damping factor of each business object to obtain the updated initial weight value, and the update operation of the initial weight value is repeated. If the rate of change of weight between two adjacent weight update operations is less than a preset threshold, the initial weight value obtained after the last execution of the initial weight value update operation is determined as the target weight value, and the business object with the largest target weight value is determined as the preset business object in the target group.

5. The method according to claim 1, characterized in that, Determine the strength value of the connection relationship between each first business object and the target business object to obtain H target strength values, including: For any first business object, obtain the number of edges between the first business object and the target business object to get a first value; Calculate the total weight of the edges between the first business object and the target business object to obtain the second value; Divide the second value by the first value to obtain the target strength value of the first business object.

6. The method according to claim 1, characterized in that, Selecting T second business objects from the H first business objects based on the H target intensity values ​​includes: The first business object whose target intensity value is greater than the intensity value threshold among the H first business objects is determined as the second business object, thus obtaining the T second business objects.

7. The method according to claim 1, characterized in that, Selecting Q third business objects from the T second business objects according to the target business type includes: Obtain the business content of each second business object, and determine the initial business type of each second business object based on the business content; The second business object whose initial business type is the same as the target business type is determined as the third business object, thus obtaining the Q third business objects.

8. A query device for business objects, characterized in that, include: The first determining unit is used to receive a query request sent by a financial institution and determine a target business object from P preset business objects in the target relationship graph according to the target business type in the query request. The target relationship graph includes N initial business objects, and the N initial business objects include the P preset business objects, where P and N are positive integers. The second determining unit is used to determine the business objects that have a connection relationship with the target business object in the target relationship graph, and obtain H first business objects, wherein each first business object has a direct connection relationship or an indirect connection relationship with the target business, and H is a positive integer; The third determining unit is used to determine the strength value of the connection relationship between each first business object and the target business object, obtain H target strength values, select T second business objects from the H first business objects according to the H target strength values, and select Q third business objects from the T second business objects according to the target business type, where T and Q are positive integers; The fourth determining unit is used to determine the Q third business objects as the query results of the business object query request.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the query method for the business object as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the query method for the business object as described in any one of claims 1 to 7.

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