Data processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring multi-source business data sent by different business systems by using a target intelligent model; using the target intelligent model to determine a target knowledge graph according to the multi-source business data; in response to the service request, using the target intelligent model to call an intelligent tool library to determine portrait data and service evaluation data associated with the target object, the intelligent tool library comprising a plurality of intelligent tools, different intelligent tools adopting a unified call interface, and each object comprising the target object; and executing the target task based on the portrait data and the business evaluation data by using the target intelligent model. The technical problems of slow data processing flow and low efficiency caused by complex interaction between business systems are solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a data processing method and device, a storage medium, and an electronic device. Background Art

[0002] At present, in the relevant technology, business systems exchange data through traditional interfaces. Due to the low degree of interface standardization and diverse data formats, the data processing process is lengthy and inefficient. In addition, the business system lacks a unified information integration and real-time update mechanism, making it difficult to quickly obtain comprehensive business information, further reducing the efficiency of business processing. Therefore, there are technical problems in the relevant technology that the data processing process is slow and inefficient due to the complex interaction between business systems.

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

[0004] The embodiments of the present application provide a data processing method and device, a storage medium, and an electronic device to at least solve the technical problem of slow data processing flow and low efficiency due to complex interactions between business systems.

[0005] According to one aspect of an embodiment of the present application, a data processing method is provided, comprising: using a target intelligent model to obtain multi-source business data sent by different business systems, wherein the multi-source business data includes business resources related to each object, and the different business systems include internal business systems and external business systems that are isolated from each other; using the target intelligent model to determine a target knowledge graph based on the multi-source business data, wherein each node in the target knowledge graph corresponds to each object, and the connecting edges between nodes represent the business relationship between the corresponding objects; in response to a business request, using the target intelligent model to call an intelligent tool library to determine portrait data and business evaluation data associated with the target object, wherein the intelligent tool library includes multiple intelligent tools, different intelligent tools use a unified calling interface, and the various objects include the target object; using the target intelligent model to perform a target task based on the portrait data and the business evaluation data.

[0006] According to another aspect of an embodiment of the present application, a data processing device is also provided, including: an acquisition module, used to use a target intelligent model to acquire multi-source business data sent by different business systems, wherein the multi-source business data includes business resources related to each object, and the different business systems include internal business systems and external business systems that are isolated from each other; a determination module, used to use the target intelligent model to determine a target knowledge graph based on the multi-source business data, wherein each node in the target knowledge graph corresponds to each object, and the connecting edges between nodes represent the business relationship between the corresponding objects; a calling module, used to respond to a business request, use the target intelligent model to call an intelligent tool library to determine portrait data and business evaluation data associated with the target object, wherein the intelligent tool library includes multiple intelligent tools, different intelligent tools use a unified calling interface, and the various objects include the target object; an execution module, used to use the target intelligent model to execute a target task based on the portrait data and the business evaluation data.

[0007] Optionally, the device is used to use the target intelligent model to call the intelligent tool library to determine the portrait data and business evaluation data associated with the target object in the following manner: parse the business request to obtain the object field and the business type field; determine the target object according to the value of the object field, and determine the target business type according to the value of the business type field; determine the portrait data corresponding to the target object according to the target knowledge graph; use the intelligent tool library to determine the target analysis tool corresponding to the target business type; use the target analysis tool to process the target business data to obtain business evaluation data, wherein the target business data includes multi-source business data.

[0008] Optionally, the device is used to determine the portrait data corresponding to the target object according to the target knowledge graph in the following manner: query the target knowledge graph according to the target object to obtain a first node connected to the target object, wherein the first node represents an object that has a direct business association with the target object; query the target knowledge graph according to the first node to obtain a second node connected to the first node, wherein the second node represents an object that has an indirect business association with the target object; determine the portrait data based on the first node, the second node, the connecting edge between the target object and the first node, and the connecting edge between the first node and the first node.

[0009] Optionally, the device is used to determine the target analysis tool corresponding to the target business type using the smart tool library in the following manner: obtaining the smart tool library, wherein the smart tool library includes a number of business analysis tools pre-set according to a number of business types, and the number of business analysis tools include the target analysis tool; determining a group of business analysis tools associated with the target business type from the smart tool library, wherein each business analysis tool in the group of business analysis tools is used to process businesses whose business type is the target business type in each of the business systems; determining the target analysis tool from the group of business analysis tools according to the business system corresponding to the portrait data.

[0010] Optionally, the device is also used to: obtain initial business data related to the target object and the target business type from the business system; generate the target business data based on the initial business data and the multi-source business data; send the target business data to the target analysis tool through a business interface, and the target analysis tool processes the target business data to obtain the business evaluation data.

[0011] Optionally, the device is used to use the target intelligent model to perform the target task based on the portrait data and the business evaluation data in the following manner: using the target intelligent model to determine the object basic information and business status information of the target object based on the portrait data and the business evaluation data; generating a business processing policy for the target object according to the object basic information and the business status information; using the object basic information, the business status information and the business processing policy to perform the target task.

[0012] Optionally, the device is also used to: identify the business to be executed using the target intelligent model; determine the business processing path based on the preset reinforcement learning results of the target intelligent model, wherein the preset reinforcement learning results are used to indicate the optimal business processing path based on the historical business processing data corresponding to the business to be executed and the business environment of the business to be executed; process the business to be executed according to the business processing path to obtain a business execution result; generate a prompt message based on the business execution result, wherein the prompt message is used to adjust the business processing path.

[0013] Optionally, the device is also used to: respond to a risk assessment request, use the target intelligent model to determine first data corresponding to the object to be analyzed from the business relationship map, determine a risk assessment model corresponding to the risk assessment type from the intelligent tool library, and use the risk assessment model to predict the business risk level and risk factors of the object to be analyzed to obtain second data corresponding to the target object; use the target intelligent model to generate a risk prediction result of the object to be analyzed based on the first data and the second data, wherein the risk prediction result is used to instruct the object to be analyzed to adjust its own business resources; and use the target intelligent model to generate a risk warning message when the risk prediction result meets a preset threshold condition.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned data processing method when running.

[0015] According to another aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above data processing method.

[0016] According to another aspect of the embodiments of the present application, there is further provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the data processing method through the computer program.

[0017] Through the embodiments of the present application, the above-mentioned target intelligent model can not only efficiently acquire and integrate multi-source business data, but also construct a target knowledge graph that reflects the business relationship between enterprise customers. During the business request processing process, the target intelligent model can call the tools in the intelligent tool library according to the needs to generate customer portraits and business evaluations, thereby improving the intelligence and flexibility of decision support. Finally, through autonomous learning and optimization algorithms, the target intelligent model can adjust strategies based on real-time feedback, accelerate business processes and strengthen risk control, and effectively solve the technical problems of slow data processing processes and low efficiency due to complex interactions between business systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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:

[0019] Figure 1 is a schematic diagram of an application environment of an optional data processing method according to an embodiment of the present application;

[0020] Figure 2 is a flowchart of an optional data processing method according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of an architecture of an optional data processing method according to an embodiment of the present application;

[0022] Figure 4 is a schematic structural diagram of an optional data processing device according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of the structure of an optional data processing product according to an embodiment of the present application;

[0024] Figure 6 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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 the present application.

[0026] 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.

[0027] The present application is described below in conjunction with embodiments:

[0028] According to one aspect of an embodiment of the present application, a data processing method is provided. Optionally, in this embodiment, the data processing method can be applied to Figure 1In the hardware environment composed of the server 101 and the terminal device 103 shown in FIG. Figure 1 As shown, the server 101 is connected to the terminal 103 via a network, and can be used to provide services for the terminal device or an application 107 installed on the terminal device. The application can be a video application, an instant messaging application, a browser application, an educational application, a game application, etc. A database 105 may be set up on the server or independently of the server to provide data storage services for the server 101, for example, a game data storage server. The above-mentioned network may include, but is not limited to, a wired network and a wireless network, wherein the wired network includes, for example, a local area network, a metropolitan area network, and a wide area network; the wireless network includes, for example, Bluetooth, WIFI, and other networks that implement wireless communication; the terminal device 103 may be a terminal configured with an application program, and may include, but is not limited to, at least one of the following: a mobile phone (such as an Android phone, an iOS phone, etc.), a laptop computer, a tablet computer, a PDA, a MID (Mobile Internet Devices), a PAD, a desktop computer, a smart TV, an intelligent voice interaction device, a smart home appliance, a vehicle-mounted terminal, an aircraft, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a mixed reality (MR) terminal, and other computer devices; the above-mentioned server may be a single server, a server cluster consisting of multiple servers, or a cloud server.

[0029] Combination Figure 1 As shown, the above data processing method can be executed by an electronic device, which can be a terminal device or a server. The above data processing method can be implemented by the terminal device or the server respectively, or by the terminal device and the server together.

[0030] The above is only an example and is not specifically limited in this embodiment.

[0031] Optionally, as an optional implementation, as Figure 2 As shown, the above data processing method includes:

[0032] S202, using the target intelligent model to obtain multi-source business data sent by different business systems, wherein the multi-source business data includes business resources related to each object, and the different business systems include internal business systems and external business systems that are isolated from each other;

[0033] S204, using the target intelligent model to determine the target knowledge graph according to the multi-source business data, wherein each node in the target knowledge graph corresponds to each object, and the connection edges between the nodes represent the business relationship between the corresponding objects;

[0034] S206, in response to the business request, using the target intelligent model to call the intelligent tool library to determine the portrait data and business evaluation data associated with the target object, wherein the intelligent tool library includes multiple intelligent tools, different intelligent tools use a unified calling interface, and each object includes the target object;

[0035] S208, use the target intelligent model to execute the target task based on the portrait data and business evaluation data.

[0036] Optionally, in an embodiment of the present application, the above-mentioned target intelligent model refers to a combination of a deep learning model and a reinforcement learning algorithm, including but not limited to a large language model (LLM), a deep neural network (DNN), reinforcement learning (RL), etc., which is used to efficiently process and analyze complex and diverse business data.

[0037] Optionally, in an embodiment of the present application, the above-mentioned target knowledge graph refers to a graphical database structure constructed after processing multi-source business data through a target intelligent model, wherein each node corresponds to each object in the business system, including but not limited to corporate customers, internal departments of the bank, external partners, industry standards, etc.; the connecting edges between nodes represent the business relationships between these objects, such as credit relationships, guarantee relationships, business dealings, industry competition, etc. The target knowledge graph can intuitively display the overall picture of corporate customers and their business environment, and provide comprehensive information support for subsequent business evaluations and decisions.

[0038] Optionally, in an embodiment of the present application, the above-mentioned response to a business request refers to the system receiving a specific task requirement signal issued by a business personnel or AI agent, including but not limited to business scenarios such as loan approval, risk assessment, and customer relationship management; using the target intelligent model to call the intelligent tool library to determine the portrait data and business assessment data associated with the target object means that the intelligent model can select and execute appropriate analysis tools or models from the intelligent tool library based on a unified calling interface to generate multi-dimensional portrait data and business risk assessment results for target corporate customers; each object includes a target object, which means that all entities in the system, such as corporate customers, business departments, external data sources, etc., can become the objects of analysis and decision-making by the intelligent model.

[0039] It should be noted that the sources of multi-source business data may be diverse, including but not limited to structured data from internal business systems, unstructured data from external business systems, public information on the Internet, third-party data services, etc. This application does not limit this, as long as the data can provide valuable information for business management.

[0040] Exemplarily, using the target intelligent model to obtain multi-source business data sent by different business systems means that the intelligent agent can autonomously identify and process data from different systems and channels, including basic information of corporate customers, financial data, market dynamics, risk events, etc., to build a comprehensive business resource library.

[0041] In an exemplary embodiment, taking the business scenario of corporate loan application as an example, the target intelligent model can collect multi-source business data related to corporate loan applications from the bank's internal credit system, financial system, external credit assessment agencies, industry news websites, etc. in the S02 stage; in the S204 stage, the intelligent agent constructs a target knowledge graph based on the integrated data to display the business relationship between the enterprise and the business system and other enterprises; in the S206 stage, in response to the business request for loan approval, the intelligent model calls the analysis tools in the intelligent tool library to generate a customer profile and business risk assessment of the target enterprise; finally, in the S208 stage, the intelligent agent automatically executes the loan approval process based on the portrait data and business assessment data, including credit checks, risk scoring, approval decisions, etc., to realize the intelligent processing of loan business.

[0042] Through the embodiments of the present application, a target intelligent model is used to autonomously acquire and integrate multi-source business data, determine the target knowledge graph, call the intelligent tool library for data analysis and business evaluation, and execute target tasks, thereby realizing intelligent and efficient business management in the financial field and achieving the purpose of optimizing business processes, improving decision-making support capabilities, and enhancing risk control effects.

[0043] As an optional solution, in response to a business request, the target intelligent model is used to call the intelligent tool library to determine the portrait data and business evaluation data associated with the target object, including: parsing the business request to obtain the object field and the business type field; determining the target object according to the value of the object field, and determining the target business type according to the value of the business type field; determining the portrait data corresponding to the target object according to the target knowledge graph; using the intelligent tool library to determine the target analysis tool corresponding to the target business type; using the target analysis tool to process the target business data to obtain the business evaluation data, wherein the target business data includes the multi-source business data.

[0044] Optionally, in an embodiment of the present application, the above-mentioned parsing of business requests refers to converting the request information issued by business personnel or intelligent agents into field data that can be processed by computers, including object fields and business type fields, which are used to indicate the objects that the intelligent model needs to analyze and the business types to be performed; the values ​​of the object fields and business type fields are used to determine the target objects and target business types, ensuring that the intelligent model calls the correct tools in the intelligent tool library for data analysis; the target knowledge graph is used to determine the portrait data corresponding to the target object, and provide the basic information required for business evaluation; the target analysis tools in the intelligent tool library are used to process target business data, generate business evaluation data, and support business decisions.

[0045] In an exemplary embodiment, taking the application scenario of enterprise loan approval as an example, when the system receives a business request for loan approval, the target intelligent model first parses the request information to obtain the target enterprise (object field) and loan approval (business type field); then, the model determines the target enterprise based on the value of the object field, and identifies the loan approval business based on the value of the business type field; next, by querying the target knowledge graph, the intelligent model extracts portrait data related to the target enterprise, including basic enterprise information, financial status, market performance, etc.; then, the intelligent model calls the credit assessment model and financial analysis tool from the intelligent tool library as the target analysis tool, processes multi-source business data related to loan approval, and generates business assessment data, such as credit score, financial risk index, etc.

[0046] Through the embodiments of the present application, the target intelligent model is used to parse business requests and the target analysis tool is called to process multi-source business data, thereby realizing the automation and intelligence of the enterprise loan approval process, achieving the purpose of improving business processing efficiency and enhancing risk control capabilities.

[0047] As an optional scheme, the above-mentioned determination of the above-mentioned portrait data corresponding to the above-mentioned target object according to the above-mentioned target knowledge graph includes: querying the above-mentioned target knowledge graph according to the above-mentioned target object to obtain a first node connected to the above-mentioned target object, wherein the above-mentioned first node represents an object that has a direct business association with the above-mentioned target object; querying the above-mentioned target knowledge graph according to the above-mentioned first node to obtain a second node connected to the above-mentioned first node, wherein the above-mentioned second node represents an object that has an indirect business association with the above-mentioned target object; determining the above-mentioned portrait data according to the above-mentioned first node, the above-mentioned second node, the connecting edge between the above-mentioned target object and the above-mentioned first node, and the connecting edge between the above-mentioned first node and the above-mentioned first node.

[0048] Optionally, in an embodiment of the present application, the above-mentioned query of the target knowledge graph according to the target object refers to extracting information directly related to the target enterprise from the graph database; the first node represents the object that has a direct business relationship with the target object, including but not limited to the credit department and fund management department within the bank, and the direct trading partners and guarantors of the corporate customers; the second node represents the object that has an indirect business relationship with the target object, which may cover a wider network of relationships, such as the company's industry competitors, suppliers of suppliers, other corporate customers in the customer base, etc. The connecting edge represents the type of relationship between objects, including direct business dealings, indirect influence relationships, credit guarantees, etc. By querying the first node and the second node, and the connecting edge between them, the system can generate multi-dimensional portrait data reflecting the business environment of the target enterprise.

[0049] In an exemplary embodiment, taking the application scenario of enterprise credit assessment as an example, the system receives a business request for credit assessment of a certain enterprise. The target intelligent model first queries the target knowledge graph according to the target object to obtain the first node connected to the enterprise, that is, the direct business-related object, such as the loan record of the enterprise's customers, the financial status of the trading partners, etc.; next, the model queries the target knowledge graph according to the first node, and further obtains the second node connected to the first node, that is, the indirect business-related object, including the enterprise's competitors, upstream and downstream enterprises in the supply chain, etc. Based on these nodes and the connecting edges between them, the system comprehensively considers direct and indirect business associations and generates customer portrait data of the target enterprise, including credit status, market position, supply chain stability and other information.

[0050] Through the embodiments of the present application, a target intelligent model is used to mine direct and indirect business-related objects from a target knowledge graph, thereby achieving comprehensiveness and depth in the generation of enterprise customer portraits and achieving the purpose of enhancing business decision-making support capabilities.

[0051] As an optional scheme, the above-mentioned use of the above-mentioned intelligent tool library to determine the target analysis tool corresponding to the above-mentioned target business type includes: obtaining the above-mentioned intelligent tool library, wherein the above-mentioned intelligent tool library includes a number of business analysis tools pre-set according to a number of business types, and the above-mentioned several business analysis tools include the above-mentioned target analysis tool; determining a group of business analysis tools associated with the above-mentioned target business type from the above-mentioned intelligent tool library, wherein each business analysis tool in the above-mentioned group of business analysis tools is used to process the business of the above-mentioned target business type in each of the above-mentioned business systems; determining the above-mentioned target analysis tool from the above-mentioned group of business analysis tools according to the above-mentioned business system corresponding to the above-mentioned portrait data.

[0052] Optionally, in an embodiment of the present application, the above-mentioned acquisition of the intelligent tool library refers to accessing a constructed intelligent tool collection, which contains pre-classified and set business analysis tools, each tool corresponding to a specific business type, including but not limited to credit risk assessment, customer portrait generation, market trend analysis, etc.; a group of business analysis tools covers all possible analysis tools that may be used under a specific business type, ensuring that comprehensive analysis support can be provided when processing various business scenarios; the target analysis tool refers to selecting the most appropriate tool from this group for in-depth analysis of business data based on the specific content of the business request and the source of the portrait data.

[0053] In an exemplary embodiment, taking the application scenario of corporate loan approval as an example, when the target intelligent model receives a loan approval business request, it will query the intelligent tool library and find a pre-set credit risk assessment tool group. This group of tools may include a credit scoring model, a financial risk analyzer, an industry environmental impact assessor, etc. Then, based on the target enterprise's profile data source, such as basic enterprise information, financial statements, and industry reports, the model selects the most suitable tool from this group of tools to process these data, such as a credit scoring model for processing basic enterprise information and financial statements, and an industry environmental impact assessor for analyzing industry reports, to generate comprehensive business assessment data to support loan decisions.

[0054] Through the embodiment of the present application, the target intelligent model is used to accurately locate the target analysis tool from the intelligent tool library, so as to achieve the efficiency and accuracy of business processing in the financial field and achieve the purpose of intelligent decision-making and risk control. This method ensures that when processing complex business, the most appropriate analysis tool can be quickly called to conduct in-depth analysis of business data.

[0055] As an optional solution, the above method also includes: obtaining initial business data related to the above target object and the above target business type from the above business system; generating the above target business data based on the above initial business data and the above multi-source business data; sending the above target business data to the above target analysis tool through the business interface, and the above target analysis tool processes the above target business data to obtain the above business evaluation data.

[0056] Optionally, in an embodiment of the present application, the above-mentioned acquisition of initial business data related to the target object and target business type from the business system refers to collecting first-hand data related to the target corporate customers (target objects) and their specific businesses (such as loan applications, credit assessments, target business types) from business systems such as the internal management system of the business system, which may include but is not limited to basic corporate information, historical transaction records, financial statements, business application forms, internal evaluation reports, etc.

[0057] Furthermore, sending the target business data to the target analysis tool through the business interface refers to using the standardized interface designed by the system to transmit the aforementioned generated target business data to the specified tools in the intelligent tool library, such as credit scoring models, financial risk analyzers, compliance checking tools, etc. These target analysis tools will perform in-depth processing and analysis on the data and produce business evaluation data, such as credit scores, risk indexes, compliance evaluation reports, etc., to provide support for business decisions.

[0058] Through the embodiments of the present application, the method of obtaining initial business data from the business system, combining multi-source business data to generate target business data, and sending it to the target analysis tool for in-depth analysis is adopted, thereby achieving comprehensive integration and efficient utilization of business data and achieving the purpose of enhancing the accuracy and intelligence of business decisions.

[0059] As an optional scheme, the above-mentioned use of the above-mentioned target intelligent model to perform the target task based on the above-mentioned portrait data and the above-mentioned business evaluation data includes: using the above-mentioned target intelligent model to determine the object basic information and business status information of the above-mentioned target object based on the above-mentioned portrait data and the above-mentioned business evaluation data; generating a business processing strategy for the above-mentioned target object according to the above-mentioned object basic information and the above-mentioned business status information; using the above-mentioned object basic information, the above-mentioned business status information and the above-mentioned business processing strategy to perform the above-mentioned target task.

[0060] In an exemplary embodiment, taking the application scenario of corporate customer credit risk monitoring as an example, the system first uses the target intelligent model to determine the target object (corporate customer)'s basic information (such as company name, industry type, registered address, etc.) and business status information (such as loan balance, overdue status, default risk, etc.) based on the corporate customer's portrait data (such as historical transaction records, financial status, market performance, etc.) and business evaluation data (such as credit score, financial risk index, etc.). Next, the model will generate a business processing strategy for the target enterprise based on these basic information and status information, such as adding guarantee conditions, adjusting credit limits, and issuing risk warnings. Finally, the system executes specific target tasks based on the target object's basic information, business status information, and formulated business processing strategies, such as automatically sending risk reminder emails to relevant business departments and generating detailed credit risk reports for management review.

[0061] Through the embodiments of the present application, a target intelligent model is used in combination with enterprise customer portrait data and business evaluation data to automatically generate business processing strategies and execute target tasks, thereby realizing the intelligence and automation of business processes, and achieving the purpose of efficiently monitoring enterprise credit risks, quickly responding to market changes, and improving customer relationship management levels.

[0062] As an optional solution, the above method also includes: using the above target intelligent model to identify the business to be executed; determining the business processing path based on the preset reinforcement learning results of the above target intelligent model, wherein the above preset reinforcement learning results are used to indicate the optimal business processing path based on the historical business processing data corresponding to the business to be executed and the business environment of the above business to be executed; processing the above business to be executed according to the above business processing path to obtain the business execution result; generating a prompt message based on the above business execution result, wherein the above prompt message is used to adjust the above business processing path.

[0063] Optionally, in an embodiment of the present application, the above-mentioned use of the target intelligent model to identify the business to be executed means that the system can automatically identify the specific business type and content that needs to be processed, such as corporate loan applications, credit limit adjustments, customer complaint processing, etc., by analyzing the current business requests and customer portrait data. Determining the business processing path based on the preset reinforcement learning results of the target intelligent model means that the system uses the model results obtained through reinforcement learning training to intelligently select the most effective and convenient process path based on the historical processing data of the business to be executed and the current business environment, so as to achieve the optimization of business processing. The preset reinforcement learning results contain the best decision-making strategies in different business scenarios, which can guide the system to make the best choice when facing a complex and changing business environment.

[0064] Through the embodiments of the present application, a target intelligent model is used to identify the business to be executed and the optimal business processing path is determined based on the reinforcement learning results, thereby realizing intelligent and automated optimization of business processes, achieving the purpose of improving business processing efficiency, reducing manual intervention, and enhancing risk control.

[0065] As an optional scheme, the above method also includes: in response to a risk assessment request, using the above target intelligent model to determine the first data corresponding to the object to be analyzed from the above target knowledge graph, determining the risk assessment model corresponding to the risk assessment type from the above intelligent tool library, and using the above risk assessment model to predict the business risk level and risk factors of the object to be analyzed to obtain the second data corresponding to the above target object; using the above target intelligent model to generate a risk prediction result of the object to be analyzed based on the above first data and the above second data, wherein the above risk prediction result is used to instruct the above object to be analyzed to adjust its own business resources; when the above risk prediction result meets the preset threshold condition, using the above target intelligent model to generate a risk warning message.

[0066] Optionally, in an embodiment of the present application, the above-mentioned target intelligent model determines the first data corresponding to the object to be analyzed from the target knowledge graph, which means that the system intelligently retrieves all business data related to the object to be analyzed (such as corporate customers) from the knowledge graph according to the risk assessment request, including but not limited to financial statements, transaction history, market performance, credit rating, etc. Determine the risk assessment model corresponding to the risk assessment type from the intelligent tool library, and use the risk assessment model to predict the business risk level and risk factors of the object to be analyzed to obtain the second data, which means that the system can automatically call the preset risk assessment model in the intelligent tool library according to the specific type of risk assessment (such as credit risk, market risk, operational risk, etc.). The model predicts the business risk level and possible risk factors of the object to be analyzed by deeply analyzing the first data.

[0067] Optionally, in an embodiment of the present application, using a target intelligent model to generate a risk prediction result for the object to be analyzed based on the first data and the second data means that the system comprehensively considers the business data extracted from the knowledge graph and the prediction results of the risk assessment model to generate a comprehensive analysis report on business risks, and the report will indicate how the object to be analyzed can adjust its own business resources to deal with potential risks. When the risk prediction results meet the preset threshold conditions, using the target intelligent model to generate a risk warning message means that when the predicted risk level exceeds the set threshold, the system will automatically trigger the risk warning mechanism, generate a warning message, and notify the relevant business departments and management so that timely measures can be taken.

[0068] Exemplarily, when the system receives a credit risk assessment request from a corporate customer, the target intelligent model extracts all relevant financial data, transaction records, industry status and other information of the enterprise from the target knowledge graph. At the same time, a model suitable for credit risk assessment is selected from the intelligent tool library, such as a credit scoring model based on deep learning. The model analyzes the first data, predicts the credit risk level of the enterprise, and identifies specific risk factors, such as tight cash flow, high debt ratio, and declining industry prosperity. Then, the system generates risk prediction results, indicating how the enterprise should adjust its financial strategy or business operations to reduce credit risk. If the predicted credit risk level is higher than the preset warning threshold, the system will automatically trigger a risk warning and send a warning message to corporate customers and internal business personnel to ensure that the risk is paid attention to and handled in a timely manner.

[0069] In an exemplary embodiment, the data input into the target analysis tool includes, but is not limited to, user-related data within the target knowledge graph: user basic information, credit rating, related enterprise data, risk event records, etc.; information stored in other business systems: historical transaction records, transaction details, account information, etc.; and related text knowledge bases, etc.

[0070] In an exemplary embodiment, taking risk assessment as the target business as an example, it may include but is not limited to the following:

[0071] S1, Input and Task Analysis: The user input instruction is “Assess the credit risk of customer A.”

[0072] AI agent (the above target intelligent model) parsing task: The agent reasoning task type is "risk assessment". According to the requirements, the following inputs are required for inference:

[0073] Customer A's basic information, historical transaction records, related enterprise data, industry environment data and public opinion trends.

[0074] S2, call tools to obtain data: According to task requirements, the AI ​​agent dynamically calls the following tools to obtain relevant data:

[0075] Call the knowledge graph structured search to obtain the core node data and association relationships of customer A, and obtain: basic information S of customer A (such as registration place, industry type, enterprise scale); transaction relationship between customer A and affiliated enterprise B and supplier C; risk event record S of customer A's affiliated enterprise B (such as business abnormalities, layoff news).

[0076] Call the business system interface to extract customer A's historical transaction and financial data, and obtain: CRM system: customer A's order quantity, transaction frequency, and amount owed; ERP system: customer A's accounts receivable balance and increase or decrease trend.

[0077] Call the text knowledge base to retrieve and obtain public opinion data and industry trends related to customer A. Get: negative news S of customer A determined by the news library, such as lawsuits and reports of illegal operations). Social media analysis determines that customer A's recent social exposure has declined, which may affect brand value.

[0078] S3, data analysis and reasoning: Based on the results of the called tools, the AI ​​agent enters the reasoning stage, comprehensively analyzes the knowledge graph data, and combines the node and relationship analysis of the knowledge graph to find that customer A has a significant transaction relationship with related company B, and that related company B has recently had a risk event. It infers that customer A's risk may be affected by company B; statistical analysis of business system data: historical transaction data analysis shows that customer A's transaction frequency has decreased, and the amount of accounts receivable has increased, and there may be pressure to recover funds.

[0079] In addition, text data-assisted reasoning can also be performed: customer A in the public opinion data has recent litigation information, which further increases the credit risk. Then the automated tool is called: the risk assessment model in the intelligent tool library is called to integrate basic customer information, financial data, and knowledge graph relationships to generate a risk score.

[0080] S4, result generation and output: According to the above-mentioned intelligent agent's observation, reasoning and tool calling process, the last step is for the intelligent agent to call the risk assessment model output example: Risk score: 68 / 100S medium-high risk), risk point analysis: Customer A has high accounts receivable, the operation of related company B is abnormal, and public opinion has potential negative impact.

[0081] Examples of further business suggestions: reduce the credit limit; strengthen the monitoring of the recovery of customer A's accounts receivable; continue to pay attention to the relationship dynamics between customer A and company B.

[0082] In an exemplary embodiment, the related technology has many deficiencies in existing management systems and methods in processing complex business scenarios, real-time information integration, customer relationship management, etc., and cannot fully meet the business system's needs for efficient and accurate management. Specific problems include: information island problem: the data sources in the existing system are diverse and scattered, which makes it difficult for business personnel to quickly and accurately obtain and integrate relevant information from multiple systems when processing business. The information island effect is serious, which reduces the processing efficiency of business; insufficient real-time update and maintenance: the traditional business management system cannot capture changes from external and internal data sources in real time, resulting in customer information and risk data that are relied on when making business decisions may not be accurate and timely, which in turn affects the effectiveness of business processes and customer service quality; lack of intelligent analysis and decision support: the existing system has limited data analysis and decision support capabilities, and it is difficult to conduct in-depth analysis through real-time and historical data, and it is impossible to effectively predict business trends, identify risk points, and provide intelligent customer service suggestions, which affects the business insight and risk control capabilities of the business system; risk control lags: in the risk management of the business, the existing system mostly uses preset rules, which is difficult to dynamically adjust and respond to new business situations and potential risks, resulting in a lag in risk warning and control mechanisms, and the inability to timely discover and avoid risks; insufficient customer portraits and personalized services: customer relationship management lacks in-depth mining and analysis of data, and cannot generate accurate customer portraits, which makes it difficult for the business system to provide personalized products and services based on the customer's historical behavior and actual needs, reducing customer satisfaction and business competitiveness.

[0083] To sum up, the existing business management systems have limitations in information integration, intelligence, tool integration and real-time performance, and cannot fully meet the needs of modern financial business for efficient, accurate and intelligent management.

[0084] The embodiments of the present application can solve the above-mentioned technical problems. Through the business tracking method and management system based on AI intelligent body, efficient integration and real-time updating of information can be achieved, intelligent decision-making support, risk warning and customer relationship management can be provided, and the management efficiency and customer service level of business system can be improved. The specific solutions include: autonomously integrating multi-source information through AI intelligent body to eliminate information islands; building and updating the knowledge graph of corporate customers in real time to ensure the accuracy and timeliness of information; providing flexible tool calls through intelligent tool library to support in-depth analysis, risk control and business decision-making; dynamically generating customer portraits and supporting personalized service recommendations to improve the accuracy and effectiveness of customer relationship management.

[0085] Specifically, Figure 3 This is an architectural diagram of an optional data processing method according to an embodiment of the present application. The embodiment of the present application proposes a financial business tracking method and management system based on an AI agent (LLM Agent). The business system utilizes the autonomous action capabilities of the AI ​​agent to integrate multi-source information, build and update the knowledge graph of corporate customers in real time. By designing and integrating an intelligent tool library, the system can flexibly call various tools to achieve operations such as decision support and customer relationship portrait generation. Combined with business functions such as process management, risk control, and customer relationship maintenance, it realizes real-time tracking and intelligent management of the entire life cycle of the business. The overall system architecture is as follows: Figure 3 As shown in the figure, it includes the following core components: AI agent (LLMAgent) autonomous information integration module, knowledge graph construction and real-time update module, intelligent tool library module, process management and visualization module, risk control and early warning module, decision support and customer portrait generation module, autonomous action and task execution module, human-machine collaboration and feedback mechanism module, system security and authority management module, specifically:

[0086] S1, AI agent (LLM Agent) autonomous information integration module can achieve the following functions:

[0087] Natural language processing and understanding: Use large language models (LLM) to give AI agents natural language processing and understanding capabilities, enabling them to understand and parse text information from different data sources, including business documents, news reports, industry reports, etc.

[0088] Autonomous learning and knowledge updating: Using deep learning and reinforcement learning algorithms, AI agents can learn from historical data and interactions, and continuously optimize their knowledge base and strategies.

[0089] Data source interface design: Build a unified data interface to connect internal business systems (such as CRM, ERP, etc.) and external data sources (such as the Internet, third-party data service providers). Use API, Web crawlers and other technologies to obtain structured, semi-structured and unstructured data.

[0090] Data parsing and standardization: Design a data parsing module and use regular expressions, natural language processing, and machine learning algorithms to convert data in different formats into a unified structured format for subsequent processing.

[0091] Exemplarily, data acquisition: AI agents acquire internal and external multi-source data through preset interfaces or autonomous discovery. Data parsing: Parse the acquired data to extract key information, such as company name, business type, transaction amount, risk events, etc. Data storage: Store the parsed data in a temporary storage area for further processing.

[0092] S2, the knowledge graph construction and real-time maintenance module can achieve the following functions:

[0093] Graph database technology: Use graph databases (such as Neo4j) to store and manage knowledge graphs, supporting efficient node and relationship queries.

[0094] Entity recognition and relationship extraction: Use natural language processing technology to identify entities (such as companies, people, events) and relationships (such as equity relationships, business dealings) in text.

[0095] Real-time update mechanism: Set up data listeners to detect changes in data sources and trigger updates to the knowledge graph.

[0096] Exemplarily, entity and relationship extraction: perform entity recognition and relationship extraction on the parsed data to generate triples (entity, relationship, entity). Knowledge graph construction: import triples into the graph database to build a knowledge graph. Real-time update: when there is new information in the data source, re-execute entity and relationship extraction and update the nodes and edges in the knowledge graph.

[0097] S3, the design and integration module of the intelligent tool library can achieve the following functions:

[0098] Tool library construction: Integrate multiple data analysis tools, risk assessment models, prediction algorithms, etc. to form a callable tool library.

[0099] Unified interface design: Design a unified calling interface for each tool, standardize the input and output formats, and facilitate AI agent calls.

[0100] Tool registration and management: Establish a tool registration mechanism to record tool functions, parameters, applicable scenarios and other information to facilitate tool management and expansion.

[0101] For example, tool collection: collect existing analysis tools and models, or develop new tools to enrich the tool library. Interface design: design API interfaces for each tool, define input parameters, output results, calling methods, etc. Tool integration: connect the tool library with the AI ​​agent so that it can call the corresponding tools as needed.

[0102] S4, process management and visualization module can achieve the following functions:

[0103] Business process modeling: Use business process management (BPM) technology to establish a standardized process model for the business, including business application, approval, execution and other links.

[0104] Process engine: Use process engines (such as Activiti and Flowable) to execute and manage business processes.

[0105] Visual interface: Develop a process visualization interface and use front-end frameworks (such as React and Vue.js) to display process status and progress.

[0106] For example, process definition: defines each node, process path, approval rules, etc. of the business process. Process execution: The process engine controls the execution of the business process according to the defined process, handles the assignment of tasks and status updates. Visual display: The front-end interface obtains the process status in real time and displays it to business personnel to support process monitoring and management.

[0107] S5, risk control and early warning module can achieve the following functions:

[0108] Risk assessment model: Build a risk assessment model based on machine learning, input customer information, business data, etc., and output a risk score.

[0109] Real-time monitoring: Combine knowledge graphs and real-time data to monitor key risk points in business processes.

[0110] Early warning mechanism: Set a risk threshold. When the risk score exceeds the threshold, an early warning notification is automatically triggered.

[0111] For example, model training: collect historical risk events and related data to train risk assessment models. Risk assessment: in the business process, call the risk model in real time to assess the risk level of the current business. Early warning notification: when the risk exceeds the set threshold, the system automatically sends an early warning message to the relevant personnel.

[0112] S6, the decision support and customer profile generation module can achieve the following functions:

[0113] Data analysis and visualization: Use BI tools (such as Tableau, PowerBI) and data analysis libraries (such as Pandas, NumPy) to perform data analysis and display results.

[0114] Machine learning algorithms: Use clustering, classification, regression and other algorithms to analyze customer behavior and predict business trends.

[0115] Customer portrait generation: Based on the knowledge graph and analysis results, a multi-dimensional portrait of the customer is generated, including basic attributes, behavioral characteristics, risk preferences, etc.

[0116] For example, data preparation: extracting required data from knowledge graphs and business databases. Model application: calling algorithms in the intelligent tool library to analyze data. Result presentation: presenting analysis results in the form of charts, reports, etc. for reference by business personnel.

[0117] S7, the autonomous action and task execution module can achieve the following functions:

[0118] Task planning: The AI ​​agent autonomously plans the tasks to be performed based on business rules and the current environment.

[0119] Reinforcement learning: Using reinforcement learning algorithms, AI agents can optimize strategies and improve efficiency and effectiveness during continuous task execution.

[0120] Automated execution: Execute repetitive or rule-based tasks through scripts or robotic process automation (RPA) technology.

[0121] For example, task identification: AI agents identify tasks that need to be performed, such as data update, risk monitoring, etc. Strategy selection: Select the best execution strategy based on historical experience and reinforcement learning results. Task execution: Automatically execute tasks and record execution results and feedback.

[0122] S8, the human-machine collaboration and feedback mechanism module can achieve the following functions:

[0123] Interactive interface design: Develop human-computer interactive interface to support multiple interactive methods such as text and voice.

[0124] Feedback collection and processing: Design a feedback mechanism so that business personnel can submit feedback, and the AI ​​agent analyzes and adjusts strategies.

[0125] Collaborative workflow: In the business process, it supports human-machine collaborative operation, and AI agents and business personnel complete tasks together.

[0126] For example, interface development: Use front-end technology to build an interactive interface to provide a friendly user experience. Feedback mechanism: Design feedback forms or interactive methods to collect opinions and suggestions from business personnel. Strategy adjustment: The AI ​​agent updates model parameters and action strategies based on feedback.

[0127] S9, the system security and rights management module can achieve the following functions:

[0128] Identity authentication: Use multi-factor authentication (MFA) mechanism to ensure the legitimacy of user identity.

[0129] Permission control: Role-based access control (RBAC) sets the permission scope for different users.

[0130] Data encryption and protection: Encrypt sensitive data for storage and transmission to prevent data leakage.

[0131] Log and Audit: Record system operation logs and support security audit and traceability.

[0132] For example, authentication system integration: integrating authentication services such as OAuth and LDAP to implement user identity authentication. Permission configuration: defining user roles and permissions, and configuring access control policies. Security monitoring: deploying security monitoring tools to detect and respond to security threats in real time.

[0133] In a specific embodiment, including but not limited to:

[0134] S1, data collection and preprocessing: AI agents obtain multi-source data from internal business systems and external data sources through preset interfaces. The acquired data is parsed and preprocessed, unified in format, and stored in temporary data storage.

[0135] S2, knowledge graph construction and update: extract entities and relationships from preprocessed data to generate triples. Import triples into the graph database to build or update the knowledge graph to ensure data integrity and real-time performance.

[0136] S3, Risk Assessment and Early Warning: In the business process, the risk assessment model is called in real time to conduct risk assessment on the current business. When the risk level exceeds the threshold, the system automatically triggers an early warning and notifies relevant personnel.

[0137] S4, Business process management and visualization: The process engine controls the execution of the process and the allocation of tasks according to the defined business process. Business personnel can view the process status and handle pending tasks through the visualization interface.

[0138] S5, decision support and customer portrait generation: AI agents call the analysis tools in the intelligent tool library according to business needs to generate decision support reports. Based on the knowledge graph and analysis results, customer portraits are generated to provide personalized service recommendations.

[0139] S6, Autonomous Action and Task Execution: The AI ​​agent autonomously monitors the business environment and identifies tasks that need to be performed. It automatically executes tasks such as data update, event monitoring, etc., and records the execution results.

[0140] S7, Human-machine collaboration and feedback: Business personnel can communicate with AI agents through interactive interfaces to obtain suggestions or submit feedback. AI agents adjust strategies and models based on feedback to improve system performance.

[0141] S8, Security and Rights Management: The system authenticates the user's identity and rights to ensure the legitimacy of the operation. Monitor the system's security status and respond to potential security threats in a timely manner.

[0142] It can be understood that the embodiments of the present application can improve information integration capabilities and eliminate information islands: use AI agents to autonomously acquire and integrate internal and external multi-source data, including unstructured and semi-structured data, to build a knowledge graph of corporate customers, achieve unified management and display of data, and facilitate business personnel to obtain comprehensive information; achieve real-time updates and maintenance, and improve information timeliness: AI agents continuously monitor data sources and update knowledge graphs in real time to ensure that business decisions are based on the latest data, improve the accuracy and timeliness of business processing, and enhance market responsiveness; provide intelligent analysis and decision support: through the design and integration of intelligent tool libraries, AI agents can flexibly call various data analysis tools, risk assessment models, and prediction algorithms to deeply explore data value, provide business trend forecasts, risk warnings, and opportunity identification. , assist management in making scientific decisions; enhance the flexibility and integration of tool calls: establish a unified intelligent tool library management mechanism to support flexible combination and calling of tools; the system has good scalability and can quickly integrate new tools according to business needs to improve the adaptability of the system; strengthen risk control and early warning mechanisms: utilize the autonomous learning ability of AI agents to dynamically adjust risk assessment models and promptly discover new risk factors; establish a complete early warning mechanism to notify relevant personnel in real time and enhance risk prevention and response capabilities; generate accurate customer portraits and improve personalized service levels: based on knowledge graphs and machine learning algorithms, deeply analyze customers' historical transactions and behavior patterns, generate accurate customer portraits, and provide personalized product and service recommendations to meet customers' actual needs and improve customer satisfaction and loyalty.

[0143] It should be noted that as far as multi-source information integration and knowledge graph construction driven by AI agents are concerned, based on the autonomous data collection, analysis and integration capabilities of AI agents (LLM Agents), the system can obtain multi-source data (structured, semi-structured and unstructured) from internal and external sources, and build a knowledge graph of corporate customers through a graph database, and update customer information, business relationships and business evaluation data in real time to ensure the comprehensiveness, accuracy and timeliness of the data, including autonomous information collection and processing mechanisms, and knowledge graph construction and real-time update methods.

[0144] It should be noted that in terms of the design and flexible calling of the intelligent tool library, an intelligent tool library has been designed and integrated, covering a variety of tools such as data analysis, risk assessment, and prediction models. The AI ​​agent can autonomously call different tools according to business needs to achieve decision support for complex business scenarios such as dynamic risk assessment and customer portrait generation, and improve business efficiency and the level of intelligent decision-making. This includes the integration of the intelligent tool library and the design of a unified calling interface, as well as the autonomous calling and application mechanism of the AI ​​agent for tools.

[0145] It should be noted that in terms of risk assessment and autonomous action decision-making, the system uses machine learning algorithms to conduct real-time risk assessment, set up early warning mechanisms, dynamically monitor risks in business processes and automatically trigger early warnings. AI agents have the ability to act autonomously, can perform tasks according to environmental changes and continuously optimize decision-making strategies, and support the automated execution of complex tasks, including dynamic risk assessment and early warning triggering mechanisms, and the autonomous decision-making and task execution capabilities of AI agents.

[0146] Through the embodiments of the present application, compared with the existing business management system, by introducing AI agents (LLM Agents) and intelligent tool libraries, the defects of information islands, insufficient intelligence, and limited decision support capabilities in traditional systems are overcome. The AI ​​agent of the present invention can autonomously integrate data from multiple sources (including structured, unstructured, and semi-structured data), and build a real-time updated enterprise customer knowledge map to achieve a global view of the business process. This not only improves the comprehensiveness and real-time nature of the data, but also ensures the accuracy of business decisions.

[0147] In addition, through the design of the intelligent tool library, this application realizes the flexible calling of multiple analysis tools. The AI ​​agent can independently select tools according to business needs to conduct intelligent risk assessment and customer profile generation. This function greatly enhances the decision-making support capabilities of the system and overcomes the problems of low tool integration and inability to adapt to dynamic business environments in traditional systems.

[0148] Combined with the autonomous action and task execution capabilities of AI agents, this application also has a high degree of automation, and can autonomously adjust strategies and perform complex tasks according to the real-time environment, such as risk monitoring, early warning triggering, etc. This makes the system significantly superior to existing technologies in terms of business processing speed, decision-making accuracy, and risk management.

[0149] It should also be noted that a rule-driven business management system can be used as an alternative to AI agents, processing information and making decisions through preset business rules and workflows; using traditional relational databases to manage corporate customer information and business data can be used as an alternative to knowledge graphs; integrating multiple independent tools or modular systems to replace the unified calling method of the intelligent tool library. For example, data analysis tools, risk assessment models, etc. are separately integrated into the system, and their respective functions are called separately; RPA technology is used to replace the autonomous action and task execution functions of AI agents. RPA can automatically perform tasks with clear rules and high repetition, such as data entry and form generation.

[0150] It is understandable that in the specific implementation of this application, related data such as user information is involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0151] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0152] According to another aspect of the embodiment of the present application, a data processing device for implementing the above data processing method is also provided. Figure 4 As shown, the device comprises:

[0153] An acquisition module 402 is used to acquire multi-source business data sent by different business systems using a target intelligent model, wherein the multi-source business data includes business resources related to each object, and different business systems include internal business systems and external business systems that are isolated from each other;

[0154] A determination module 404 is used to determine a target knowledge graph based on multi-source business data using a target intelligent model, wherein each node in the target knowledge graph corresponds to each object, and the connection edges between nodes represent business relationships between corresponding objects;

[0155] A calling module 406 is used to respond to the business request and use the target intelligent model to call the intelligent tool library to determine the portrait data and business evaluation data associated with the target object, wherein the intelligent tool library includes multiple intelligent tools, different intelligent tools use a unified calling interface, and each object includes the target object;

[0156] The execution module 408 is used to use the target intelligent model to execute the target task based on the portrait data and the business evaluation data.

[0157] As an optional solution, the above-mentioned device is used to use the target intelligent model to call the intelligent tool library to determine the portrait data and business evaluation data associated with the target object in the following manner: parse the business request to obtain the object field and the business type field; determine the target object according to the value of the object field, and determine the target business type according to the value of the business type field; determine the portrait data corresponding to the target object according to the target knowledge graph; use the intelligent tool library to determine the target analysis tool corresponding to the target business type; use the target analysis tool to process the target business data to obtain business evaluation data, wherein the target business data includes multi-source business data.

[0158] As an optional solution, the above-mentioned device is used to determine the portrait data corresponding to the target object according to the target knowledge graph in the following manner: query the target knowledge graph according to the target object to obtain a first node connected to the target object, wherein the first node represents an object that has a direct business association with the target object; query the target knowledge graph according to the first node to obtain a second node connected to the first node, wherein the second node represents an object that has an indirect business association with the target object; determine the portrait data based on the first node, the second node, the connecting edge between the target object and the first node, and the connecting edge between the first node and the first node.

[0159] As an optional solution, the above-mentioned device is used to determine the target analysis tool corresponding to the target business type using the intelligent tool library in the following manner: obtain the intelligent tool library, wherein the intelligent tool library includes a number of business analysis tools pre-set according to a number of business types, and the number of business analysis tools include a target analysis tool; determine a group of business analysis tools associated with the target business type from the intelligent tool library, wherein each business analysis tool in the group of business analysis tools is used to process businesses whose business type is the target business type in each business system; determine the target analysis tool from a group of business analysis tools according to the business system corresponding to the portrait data.

[0160] As an optional solution, the above-mentioned device is also used to: obtain initial business data related to the target object and target business type from the business system; generate target business data based on the initial business data and multi-source business data; send the target business data to the target analysis tool through the business interface, and the target analysis tool processes the target business data to obtain business evaluation data.

[0161] As an optional solution, the above-mentioned device is used to perform the target task based on the portrait data and the business evaluation data using the target intelligent model in the following manner: using the target intelligent model to determine the object basic information and business status information of the target object based on the portrait data and the business evaluation data; generating a business processing strategy for the target object according to the object basic information and the business status information; using the object basic information, the business status information and the business processing strategy to perform the target task.

[0162] As an optional solution, the above-mentioned device is also used to: use the target intelligent model to identify the business to be executed; determine the business processing path based on the preset reinforcement learning results of the target intelligent model, wherein the preset reinforcement learning results are used to indicate the optimal business processing path based on the historical business processing data corresponding to the business to be executed and the business environment of the business to be executed; process the business to be executed according to the business processing path to obtain the business execution result; generate a prompt message according to the business execution result, wherein the prompt message is used to adjust the business processing path.

[0163] As an optional solution, the above-mentioned device is also used to: respond to a risk assessment request, use the target intelligent model to determine the first data corresponding to the object to be analyzed from the business relationship map, determine the risk assessment model corresponding to the risk assessment type from the intelligent tool library, and use the risk assessment model to predict the business risk level and risk factors of the object to be analyzed to obtain the second data corresponding to the target object; use the target intelligent model to generate a risk prediction result for the object to be analyzed based on the first data and the second data, wherein the risk prediction result is used to instruct the object to be analyzed to adjust its own business resources; when the risk prediction result meets the preset threshold condition, use the target intelligent model to generate a risk warning message.

[0164] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0165] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0166] According to one aspect of the present application, a computer program product is provided. The computer program product includes a computer program.

[0167] 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.

[0168] Figure 5 The structure block diagram of a computer system for implementing an electronic device according to an embodiment of the present application is schematically shown.

[0169] It should be noted that Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0170] like Figure 5 As shown, the computer system 500 includes a central processing unit 501 (CPU), which can perform various appropriate actions and processes according to the program stored in the read-only memory 502 (ROM) or the program loaded from the storage part 508 to the random access memory 503 (RAM). Various programs and data required for system operation are also stored in the random access memory 503. The central processing unit 501, the read-only memory 502 and the random access memory 503 are connected to each other through a bus 504. The input / output interface 505 (Input / Output interface, i.e., I / O interface) is also connected to the bus 504.

[0171] The following components are connected to the input / output interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a local area network card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0172] In particular, according to an embodiment of the present application, the process described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program contains a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processor 501, various functions defined in the system of the present application are executed.

[0173] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processor 501, various functions provided by the embodiment of the present application are performed.

[0174] According to another aspect of the embodiment of the present application, an electronic device for implementing the above data processing method is also provided. The electronic device may be Figure 1 The terminal device or server shown in the figure. This embodiment is described by taking the electronic device as a terminal device as an example. Figure 6 As shown, the electronic device includes a memory 602 and a processor 604. The memory 602 stores a computer program, and the processor 604 is configured to execute the steps in any of the above method embodiments through the computer program.

[0175] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0176] Optionally, in this embodiment, the above-mentioned processor can be configured to execute the methods in each embodiment of the present application through a computer program.

[0177] Alternatively, a person skilled in the art may understand that: Figure 6 The structure shown is for illustration only. Figure 6 The structure of the electronic device is not limited. Figure 6 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 6 Different configurations are shown.

[0178] Among them, the memory 602 can be used to store software programs and modules, such as program instructions / modules corresponding to the data processing method and device in the embodiments of the present application. The processor 604 executes various functional applications and data processing by running the software programs and modules stored in the memory 602, that is, realizing the above-mentioned data processing method. The memory 602 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 instances, the memory 602 may further include a memory remotely located relative to the processor 604, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 602 can be specifically, but not limited to, used to store information such as business data. As an example, such as Figure 6 As shown, the memory 602 may include but is not limited to the acquisition module 402, determination module 404, call module 406 and execution module 408 in the data processing device. In addition, it may also include but is not limited to other module units in the data processing device, which will not be repeated in this example.

[0179] Optionally, the transmission device 606 is used to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one example, the transmission device 606 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 606 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0180] In addition, the electronic device further includes: a display 608 for displaying the processing flow of the target service; and a connection bus 610 for connecting various module components in the electronic device.

[0181] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. The nodes may form a peer-to-peer network, and any form of computing device, such as a server, terminal or other electronic device, may become a node in the blockchain system by joining the peer-to-peer network.

[0182] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the data processing method provided in various optional implementations of the above-mentioned data processing aspects.

[0183] Optionally, in this embodiment, the above-mentioned computer-readable storage medium can be configured to store data for executing the methods in various embodiments of the present application.

[0184] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0185] 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.

[0186] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, which is stored in a storage medium and includes several instructions for one or more electronic devices to execute all or part of the steps of the methods described in each embodiment of the present application.

[0187] 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.

[0188] In the several embodiments provided in the present application, it should be understood that the disclosed application can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only 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.

[0189] 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 network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] 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.

[0191] 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: Using the target intelligent model to obtain multi-source business data sent by different business systems, wherein the multi-source business data includes business resources related to each object, and the different business systems include internal business systems and external business systems that are isolated from each other; Determine a target knowledge graph based on the multi-source business data using the target intelligent model, wherein each node in the target knowledge graph corresponds to each object, and the connection edges between nodes represent the business relationship between the corresponding objects; In response to a business request, the target intelligent model is used to call an intelligent tool library to determine the portrait data and business evaluation data associated with the target object, wherein the intelligent tool library includes a plurality of intelligent tools, different intelligent tools use a unified calling interface, and the various objects include the target object; Use the target intelligent model to perform the target task based on the portrait data and the business evaluation data.

2. The method according to claim 1, characterized in that: In response to the business request, the target intelligent model is used to call the intelligent tool library to determine the portrait data and business evaluation data associated with the target object, including: Parsing the service request to obtain an object field and a service type field; Determine the target object according to the value of the object field, and determine the target service type according to the value of the service type field; Determine the portrait data corresponding to the target object according to the target knowledge graph; Determine a target analysis tool corresponding to the target business type using the intelligent tool library; The target business data is processed using the target analysis tool to obtain the business evaluation data, wherein the target business data includes the multi-source business data.

3. The method according to claim 2, characterized in that The determining the portrait data corresponding to the target object according to the target knowledge graph includes: Querying the target knowledge graph according to the target object to obtain a first node connected to the target object, wherein the first node represents an object that has a direct business association with the target object; Querying the target knowledge graph according to the first node to obtain a second node connected to the first node, wherein the second node represents an object that has an indirect business association with the target object; The portrait data is determined according to the first node, the second node, the connecting edge between the target object and the first node, and the connecting edge between the first node and the first node.

4. The method according to claim 2, characterized in that: The using the intelligent tool library to determine the target analysis tool corresponding to the target business type includes: Acquire the intelligent tool library, wherein the intelligent tool library includes a plurality of business analysis tools pre-set according to a plurality of business types, and the plurality of business analysis tools include the target analysis tool; Determining a group of business analysis tools associated with the target business type from the intelligent tool library, wherein each business analysis tool in the group of business analysis tools is used to process a business whose business type is the target business type in each of the business systems; The target analysis tool is determined from the group of business analysis tools according to the business system corresponding to the portrait data.

5. The method according to claim 2, characterized in that: The method further comprises: Acquire initial business data related to the target object and the target business type from the business system; Generate the target business data according to the initial business data and the multi-source business data; The target business data is sent to the target analysis tool through a business interface, and the target analysis tool processes the target business data to obtain the business evaluation data.

6. The method according to claim 1, characterized in that The using the target intelligent model to perform the target task based on the portrait data and the business evaluation data includes: Determine the object basic information and business status information of the target object based on the portrait data and the business evaluation data using the target intelligent model; Generate a business processing strategy for the target object according to the object basic information and the business status information; The target task is executed using the object basic information, the business status information and the business processing strategy.

7. The method according to claim 1, characterized in that The method further comprises: Using the target intelligent model to identify the business to be executed; Determine a business processing path based on a preset reinforcement learning result of the target intelligent model, wherein the preset reinforcement learning result is used to indicate that an optimal business processing path is determined according to historical business processing data corresponding to the business to be executed and a business environment of the business to be executed; Process the to-be-executed business according to the business processing path to obtain a business execution result; A prompt message is generated according to the service execution result, wherein the prompt message is used to adjust the service processing path.

8. The method according to claim 1, characterized in that The method further comprises: In response to a risk assessment request, the target intelligent model is used to determine first data corresponding to the object to be analyzed from the business relationship map, a risk assessment model corresponding to the risk assessment type is determined from the intelligent tool library, and the business risk level and risk factors of the object to be analyzed are predicted using the risk assessment model to obtain second data corresponding to the target object; Using the target intelligent model to generate a risk prediction result of the object to be analyzed based on the first data and the second data, wherein the risk prediction result is used to instruct the object to be analyzed to adjust its own business resources; When the risk prediction result meets the preset threshold condition, the target intelligent model is used to generate a risk warning message.

9. A data processing device, characterized in that: include: An acquisition module, used to acquire multi-source business data sent by different business systems using a target intelligent model, wherein the multi-source business data includes business resources related to each object, and the different business systems include internal business systems and external business systems that are isolated from each other; A determination module, configured to determine a target knowledge graph using the target intelligent model according to the multi-source business data, wherein each node in the target knowledge graph corresponds to each object, and the connection edges between nodes represent business relationships between corresponding objects; A calling module, configured to respond to a business request and use the target intelligent model to call an intelligent tool library to determine the portrait data and business evaluation data associated with the target object, wherein the intelligent tool library includes a plurality of intelligent tools, different intelligent tools use a unified calling interface, and the various objects include the target object; An execution module is used to use the target intelligent model to execute the target task based on the portrait data and the business evaluation data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein the computer program can be executed by an electronic device to perform the method described in any one of claims 1 to 8.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

12. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 8 through the computer program.

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