Method, apparatus, and computer program product for processing business data

Abstract

The application discloses a kind of processing method, device, electronic equipment and computer program product of business data.It relates to the field of financial technology, which comprises: receiving business processing instruction, determining business processing type according to business processing instruction, wherein the business processing instruction is initiated by different levels of business objects;According to the agricultural business data of M institutions obtained according to the business processing type, M groups of agricultural business data are obtained, and the business processing model is determined according to the business processing type, wherein the business processing model is used to process M groups of agricultural business data, and M is a positive integer;M groups of agricultural business data are input into business processing model, and business processing result is obtained by processing, and business risk report is generated according to business processing result.Through the present application, the technical problem of low recognition efficiency in the related art when identifying agricultural risks based on agricultural-related business data is solved.

Description

Technical Field

[0001] This application relates to the field of financial technology, and more specifically, to a method, apparatus, electronic device, and computer program product for processing business data. Background Technology

[0002] Agricultural guarantee business refers to financial services provided to agricultural operators to promote agricultural economic development. Currently, existing technological solutions in agricultural operations mainly rely on a combination of traditional business systems and manual operations. This involves collecting basic information about farmers, followed by preliminary data processing (such as credit scoring) by humans or simple rule engines. Then, business personnel conduct loan approvals and risk assessments based on established rules and experience. During this process, data is typically scattered across individual system databases, lacking a unified data management platform, and data sharing relies primarily on manual transmission or methods such as email and documents.

[0003] Despite attempts to incorporate technologies such as data platforms and access control to improve data processing efficiency and sharing capabilities, a systematic solution for the fusion and intelligent sharing of multi-source data in agricultural operations remains lacking. First, the lack of unified data standards and sharing mechanisms among systems results in data being scattered across multiple independent systems, making it difficult to form a unified data view. This severely impacts business collaboration efficiency, and the existence of duplicate data collection and entry between different organizations increases operational costs and data management complexity. Second, staff cannot obtain comprehensive and real-time data support in a timely manner, leading to delays in key processes and affecting the quality and accuracy of business decisions. Third, the lack of a unified data governance mechanism results in inconsistent data formats, untimely updates, and even duplicate and erroneous data, directly impacting data usability.

[0004] Finally, due to the lack of a unified architecture between systems, each time a new business function is added or a new data source is connected, it needs to be redeveloped and integrated, resulting in poor system scalability and high maintenance costs. At the same time, due to the low efficiency of data processing and sharing, the response speed of business personnel is limited, which affects user experience and overall business efficiency.

[0005] There is currently no effective solution to the technical problem of low identification efficiency when using agricultural-related business data for agricultural risk identification. Summary of the Invention

[0006] The main objective of this application is to provide a method, apparatus, electronic device, and computer program product for processing business data, in order to solve the technical problem of low identification efficiency when identifying agricultural risks based on agricultural-related business data in related technologies.

[0007] To achieve the above objectives, according to one aspect of this application, a method for processing business data is provided. The method includes: receiving a business processing instruction; determining a business processing type based on the business processing instruction, wherein the business processing instruction is initiated by business objects at different levels; acquiring agricultural business data from M institutions according to the business processing type to obtain M sets of agricultural business data; determining a business processing model based on the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, where M is a positive integer; inputting the M sets of agricultural business data into the business processing model, processing to obtain a business processing result, and generating a business risk report based on the business processing result.

[0008] Optionally, obtaining agricultural business data from M institutions based on business processing type to obtain M sets of agricultural business data includes: obtaining the institution types of the M institutions, wherein the institution types include at least financial system types and non-financial system types; determining the data acquisition strategy for each institution based on the institution types of the M institutions, and obtaining the agricultural business data of the M institutions based on the M data acquisition strategies to obtain M sets of initial agricultural business data; and preprocessing the M sets of initial agricultural business data to obtain M sets of agricultural business data.

[0009] Optionally, acquiring agricultural business data from M institutions according to M data acquisition strategies to obtain M sets of initial agricultural business data includes: in the case that N institutions are of the financial system type, connecting to the databases of N institutions based on a preset interface, where N is less than or equal to M and N is a positive integer; extracting agricultural business data from the databases of N institutions through a preset service to obtain N sets of initial agricultural business data; extracting agricultural business data from the databases of MN institutions based on a file transfer protocol to obtain MN sets of initial agricultural business data, and caching the MN sets of initial agricultural business data to a message queue; and extracting data from the message queue according to a preset time interval to obtain the MN sets of initial agricultural business data.

[0010] Optionally, after obtaining M sets of agricultural business data, the method further includes: encapsulating the M sets of agricultural business data to obtain M sets of processed business data; and performing the step of inputting the M sets of agricultural business data into the business processing model based on the M sets of processed business data.

[0011] Optionally, inputting M sets of agricultural business data into the business processing model, processing the data to obtain business processing results, and generating a business risk report based on the business processing results includes: classifying the M sets of agricultural business data into data types to obtain Y sets of business data, wherein the data types include at least: user type and item type, and Y is a positive integer; performing feature extraction processing on each set of business data to obtain Y sets of business features; constructing an agricultural data map based on the Y sets of business features, and generating a business risk report based on the agricultural data map.

[0012] Optionally, constructing an agricultural data map based on Y sets of business characteristics and generating a business risk report based on the agricultural data map includes: for a set of business characteristics, obtaining preset business labels, aggregating the business characteristics in the set of business characteristics according to the preset business labels to obtain an agricultural data map corresponding to the set of business characteristics; for a set of business data, standardizing the set of business data to obtain a set of standardized business data; obtaining filtering rules, filtering the set of standardized business data according to the filtering rules to obtain filtered business data, calculating a risk probability value based on the filtered business data; determining the risk level based on the risk probability value, and constructing a business risk report based on the risk level and Y agricultural data maps.

[0013] Optionally, after generating a business risk report based on the business processing results, the method further includes: extracting agricultural data maps from the business risk report and displaying the agricultural data maps in the form of a radar chart; generating risk warning information based on the business risk report and sending the risk warning information to the client of the risk warning personnel.

[0014] To achieve the above objectives, according to another aspect of this application, a business data processing apparatus is provided. The apparatus includes: a receiving unit, configured to receive a business processing instruction and determine a business processing type based on the instruction, wherein the business processing instruction is initiated by business objects at different levels; an acquisition unit, configured to acquire agricultural business data from M institutions according to the business processing type, obtaining M sets of agricultural business data, and determining a business processing model based on the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, where M is a positive integer; and an input unit, configured to input the M sets of agricultural business data into the business processing model, process the data to obtain a business processing result, and generate a business risk report based on the business processing result.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the processing method for any of the above-mentioned business data.

[0016] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory storing an executable program, and the processor for running the program, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-described methods for processing business data.

[0017] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program, wherein when the computer program is executed by a processor, it implements the processing method for any of the above-mentioned business data.

[0018] In this embodiment, a business data processing approach is adopted. This involves receiving business processing instructions, determining the business processing type based on these instructions (where the instructions are initiated by business objects at different levels), acquiring agricultural business data from M institutions based on the business processing type to obtain M sets of agricultural business data, and determining a business processing model based on the business processing type. This business processing model is used to process the M sets of agricultural business data, where M is a positive integer. The M sets of agricultural business data are input into the business processing model, processed to obtain business processing results, and a business risk report is generated based on these results. This approach solves the technical problem of low identification efficiency when identifying agricultural risks based on agricultural-related business data in related technologies. By acquiring agricultural business data from M institutions, determining the business processing model based on the business processing type, inputting the M sets of agricultural business data into the business processing model, and generating a business risk report based on the output business processing results, the technical effect of improving the identification efficiency of agricultural risk identification based on agricultural-related business data is achieved. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal (or mobile device) used to implement a method for processing business data;

[0021] Figure 2 This is a flowchart of a business data processing method provided according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of a business data processing apparatus according to an embodiment of this application;

[0023] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, this system has interfaces with relevant users or organizations to provide users with corresponding operation data for them to choose to agree to or refuse automated decision-making results. Before obtaining relevant information, a request for obtaining the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained after receiving consent from the aforementioned user or organization; if the user chooses to refuse, the expert decision-making process is initiated. Users can view the purpose of data use in real time through authorization decoding and have the right to withdraw authorization or delete data at any time. After the authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0027] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize use or refuse use.

[0028] Example 1

[0029] According to an embodiment of this application, a method embodiment for processing business data is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) used to implement a method for processing business data, such as... Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1 The processor 102 (which may include, but is not limited to, a microprocessor MCU (Microcontroller Unit) or a programmable gate array (FPGA)) is shown as 102a, 102b, ..., 102n. It also includes a memory 104 for storing data and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a keyboard, a cursor control device, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the business data processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned business data processing method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC) and a network interface, which can be connected to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a radio frequency (RF) module, used for wireless communication with the Internet.

[0034] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0035] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for processing business data is shown. Figure 2 This is a flowchart of a business data processing method provided according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:

[0036] Step S201: Receive a business processing instruction and determine the business processing type based on the business processing instruction. The business processing instruction is initiated by business objects at different levels.

[0037] Specifically, a business processing instruction is triggered by the initiator through a front-end interface or back-end system interface. This instruction may include: the request type (e.g., risk warning query) and the associated business entity (e.g., farmer's ID number). Upon receiving the business processing instruction, the business processing type (i.e., the aforementioned request type) can be determined based on the instruction. The business processing type may include credit assessment and comprehensive risk rating. Furthermore, different levels of business objects refer to roles or system nodes that initiate the request having different permission levels and business perspectives. Different levels of objects can determine the scope of subsequent data acquisition and the complexity of the processing model, avoiding a one-size-fits-all approach to data processing.

[0038] Step S202: Obtain agricultural business data from M institutions according to the business processing type to obtain M sets of agricultural business data, and determine the business processing model according to the business processing type. The business processing model is used to process the M sets of agricultural business data, where M is a positive integer.

[0039] Specifically, after determining the business processing type, the data source routing table can be queried according to the type to obtain agricultural business data from multiple institutions. Here, an institution refers to an external or internal data source entity that provides agricultural business-related data. Each institution has different data formats, update frequencies, and standards. Each group of data in the agricultural business data corresponds to a data source of an institution. For example, Data_Group_1 comes from a financial institution system, and Data_Group_2 comes from a meteorological bureau (time series numerical data).

[0040] At the same time, a business processing model can be determined based on the specific business processing type. This model can fuse, analyze, and evaluate multi-source data. Different models can be loaded depending on the business processing type. For example, if the type is farmer assessment, the model is a farmer profile generation model (including feature engineering, credit scoring cards, and machine learning classifiers); if the type is comprehensive risk, the model is a multi-factor risk fusion model (based on graph computing or knowledge graph-based risk transmission analysis).

[0041] Step S203: Input the agricultural business data of group M into the business processing model, process it to obtain the business processing results, and generate a business risk report based on the business processing results.

[0042] Specifically, after obtaining the corresponding business processing model and agricultural business data, the agricultural business data can be input into the business processing model. The model then performs data fusion operations: for example, multi-source data fusion, constructing a graph based on the multi-source data, and using machine learning models for risk scoring or value estimation, thereby outputting business processing results. These results, as structured data output by the model, can include quantitative indicators (such as risk levels), qualitative labels, and detailed data. Finally, a business risk report is generated based on the output business processing results. It should be noted that during report generation, different levels of business users must be able to see content only within their authorized scope.

[0043] The business data processing method provided in this application embodiment receives a business processing instruction, determines the business processing type based on the instruction (where the instruction is initiated by business objects at different levels), acquires agricultural business data from M institutions based on the business processing type to obtain M sets of agricultural business data, and determines a business processing model based on the model (where M is a positive integer) to process the M sets of agricultural business data. The M sets of agricultural business data are then input into the model to obtain a processing result, and a business risk report is generated based on the result. This method solves the technical problem of low identification efficiency in related technologies when identifying agricultural risks based on agricultural-related business data. By acquiring agricultural business data from M institutions, determining the business processing model based on the processing type, inputting the M sets of agricultural business data into the model, and generating a business risk report based on the output processing result, the method improves the identification efficiency of agricultural risk identification based on agricultural-related business data.

[0044] Optionally, in the business data processing method provided in this application embodiment, obtaining agricultural business data from M institutions according to the business processing type to obtain M sets of agricultural business data includes: obtaining the institution types of the M institutions, wherein the institution types include at least financial system types and non-financial system types; determining the data acquisition strategy for each institution according to the institution types of the M institutions, and obtaining the agricultural business data of the M institutions according to the M data acquisition strategies to obtain M sets of initial agricultural business data; and preprocessing the M sets of initial agricultural business data to obtain M sets of agricultural business data.

[0045] Specifically, to acquire agricultural business data, the first step is to obtain the classification attributes of each institution's external or internal data source entities, i.e., to determine each institution's type. Financial system types refer to financial institutions and other entities involved in funding, while non-financial system types refer to institutions that provide supplementary information such as operational background and external environment. Then, based on these institution types, a data acquisition strategy is determined for each institution. This strategy refers to the predefined technical means and protocol specifications for extracting data from different institution types. For example, for financial system types, the data acquisition strategy might involve API (Application Programming Interface) calls, direct database connections, or encrypted file exchange; for non-financial system types, the strategy might be batch file import or data platform subscription.

[0046] Furthermore, agricultural business data for each institution can be obtained according to the above data acquisition strategy, resulting in multiple sets of initial agricultural business data directly obtained from the original data sources of each institution without cleaning and standardization processing. Finally, the initial agricultural business data is preprocessed to obtain agricultural business data. For example, preprocessing methods may include removing duplicate or invalid records, converting heterogeneous data formats into a unified standard format (such as a unified date format and unified units), and performing preliminary desensitization of sensitive information.

[0047] This embodiment achieves differentiated access to data from financial and non-financial systems by acquiring agricultural business data, effectively solving the technical problems of inconsistent access standards and poor compatibility of multi-source heterogeneous data, and improving the efficiency of agricultural business data processing.

[0048] Optionally, in the business data processing method provided in this application embodiment, obtaining agricultural business data from M institutions according to M data acquisition strategies to obtain M sets of initial agricultural business data includes: when there are N institutions whose institution type is financial system, connecting to the databases of N institutions based on a preset interface, where N is less than or equal to M and N is a positive integer; extracting agricultural business data from the databases of N institutions through a preset service to obtain N sets of initial agricultural business data; extracting agricultural business data from the databases of MN institutions based on a file transfer protocol to obtain MN sets of initial agricultural business data, and caching the MN sets of initial agricultural business data to a message queue; and extracting data from the message queue according to a preset time interval to obtain MN sets of initial agricultural business data.

[0049] Specifically, when acquiring agricultural business data according to a data acquisition strategy, if multiple institutions are classified as financial systems, a stable communication link can be established between their databases and the databases of these institutions using a pre-defined interface to directly read data. This pre-defined interface can refer to a predefined application programming interface (API) used to achieve standardized communication between systems. Then, the corresponding agricultural business data is extracted from the databases of these institutions through a pre-defined service to obtain initial agricultural business data. This pre-defined service refers to a running program or service process responsible for executing specific data extraction tasks, such as API calls.

[0050] After obtaining the initial agricultural business data from institutions classified as financial institutions, the initial agricultural business data from the remaining institutions (i.e., institutions not classified as financial institutions) can be obtained. At this point, a file transfer protocol can be used to extract the corresponding initial agricultural business data from the databases of these remaining institutions and cache this data in a message queue. The file transfer protocol is a communication protocol used for transferring files over a network, capable of handling batch file or non-real-time data exchange. The message queue is used to temporarily store messages or data to decouple data producers and consumers and cope with data traffic fluctuations. Then, data is extracted from the message queue according to a pre-set fixed duration or frequency (i.e., a preset time interval) to obtain the corresponding initial agricultural business data. This means that non-financial institution data is asynchronously obtained from the message queue through polling, avoiding high-frequency access pressure on non-real-time data sources.

[0051] This embodiment effectively solves the problems of differences in real-time performance, security, and transmission format of multi-source heterogeneous data by using direct interface connection and file transfer protocol in conjunction with different data acquisition strategies of message queue. It realizes stable asynchronous access of non-real-time data and improves the compatibility and robustness of the entire data acquisition process.

[0052] Optionally, in the business data processing method provided in the embodiments of this application, after obtaining M sets of agricultural business data, the method further includes: encapsulating the M sets of agricultural business data to obtain M sets of processed business data; and performing the step of inputting the M sets of agricultural business data into a business processing model based on the M sets of processed business data.

[0053] Specifically, after acquiring agricultural business data, in order to utilize this data for business risk detection, the agricultural business data first needs to be encapsulated. This involves standardizing and packaging the agricultural business data according to the input format, data structure, and metadata required by the business processing model, thus obtaining processed business data. Subsequent steps are then performed based on this processed business data. This embodiment improves the accuracy of data input by adding a data encapsulation step, enabling the business processing model to efficiently and correctly receive and process multi-source agricultural business data.

[0054] Optionally, in the business data processing method provided in this application embodiment, inputting M sets of agricultural business data into a business processing model, processing to obtain business processing results, and generating a business risk report based on the business processing results includes: classifying the M sets of agricultural business data into data types to obtain Y sets of business data, wherein the data types include at least: user type and item type, and Y is a positive integer; performing feature extraction processing on each set of business data to obtain Y sets of business features; constructing an agricultural data map based on the Y sets of business features, and generating a business risk report based on the agricultural data map.

[0055] Specifically, after the business processing model receives agricultural business data, it first classifies the data into different logical groups based on the semantic attributes of the data, resulting in multiple sets of business data. The data types include at least: user type and item type. User type refers to data involving the identity and behavioral characteristics of the subject, such as farmers and credit records. Item type refers to data involving the attributes, value, and status of agricultural items, such as the mobility level of agricultural machinery and crops. Then, feature extraction processing is performed on each set of business data to obtain corresponding multiple sets of structured business features.

[0056] Furthermore, an agricultural data graph is constructed based on the extracted business characteristics, and a business risk report is generated based on the analysis results of the agricultural data graph (such as connectivity analysis, risk transmission path identification, and abnormal relationship detection). The agricultural data graph is a structured network model based on knowledge graph technology, which uses business characteristics of user type and item type as nodes and connects the relationships between nodes (such as ownership, association, etc.). This graph can intuitively display the complex relationships between multiple entities such as farmers and financial institutions. The business risk report includes risk level, risk cause analysis, and early warning information.

[0057] This embodiment first classifies the data by type, then extracts features and constructs an agricultural data map, thus achieving a leap from a single data point to a complex relationship network, significantly improving the depth and breadth of risk identification, and enhancing the comprehensiveness and accuracy of risk assessment.

[0058] Optionally, in the business data processing method provided in this application embodiment, constructing an agricultural data map based on Y groups of business features and generating a business risk report based on the agricultural data map includes: for a group of business features, obtaining preset business tags, aggregating the business features in the group of business features according to the preset business tags to obtain an agricultural data map corresponding to the group of business features; for a group of business data, standardizing the group of business data to obtain a group of standardized business data; obtaining filtering rules, filtering the group of standardized business data according to the filtering rules to obtain filtered business data, calculating a risk probability value based on the filtered business data; determining a risk level based on the risk probability value, and constructing a business risk report based on the risk level and Y agricultural data maps.

[0059] Specifically, in the process of constructing an agricultural data map and generating a business risk report, the first step is to obtain preset business tags. Based on the preset business tags, the business features in a set of business features are aggregated. Business feature data with the same tags or related data are classified and merged to obtain an agricultural data map corresponding to a set of business features. The preset business tags refer to predefined classification identifiers or attribute keys used to mark the category or entity type of business data. In this step, the agricultural data map refers to a sub-map or local relationship network constructed based on a single type of business feature, which shows the relationship structure between entities within the set of features.

[0060] Simultaneously, each set of business data is standardized to obtain corresponding standardized business data. Then, the standardized business data is filtered according to filtering rules to obtain filtered business data. A risk probability value is calculated based on the filtered business data. Here, filtering rules refer to pre-set screening conditions or threshold logic used to remove irrelevant, invalid, or low-confidence data; the risk probability value is the specific numerical probability of a risk event occurring, calculated based on the filtered data using a statistical model or machine learning algorithm. Finally, the risk level is determined based on the risk probability value, and a business risk report is constructed based on the risk level and agricultural data graph, including a risk level conclusion, risk probability details, and a risk relationship network displayed based on the graph. The risk level refers to a qualitative category based on the risk probability value, such as low risk, medium risk, high risk, or extremely high risk.

[0061] This embodiment effectively improves the accuracy of risk calculation and the depth of risk identification by generating business risk reports. The business risk reports not only have quantitative risk probability basis, but also intuitively display risk correlations through graphs, which significantly enhances the scientific nature of agricultural business risk control.

[0062] Optionally, in the business data processing method provided in the embodiments of this application, after generating a business risk report based on the business processing results, the method further includes: extracting agricultural data maps from the business risk report and displaying the agricultural data maps in the form of a radar chart; generating risk warning information based on the business risk report and sending the risk warning information to the client of the risk warning personnel.

[0063] Specifically, after generating the business risk report, an agricultural data map is first extracted from the report and displayed as a radar chart. This radar chart uses a central point as its starting point, with multiple axes representing different assessment dimensions. Connecting data points along these dimensions forms polygons, visually demonstrating the entity's overall performance and weaknesses across multiple dimensions. Then, based on the risk level, probability value, or abnormal correlations in the business risk report, specific warnings are generated, and risk alerts are sent to the client devices of risk warning personnel. This embodiment improves the readability and comprehension of risk information through the visual display of the agricultural data map and the proactive push of risk alerts. It effectively overcomes the shortcomings of traditional report formats, such as poor interactivity and delays, significantly enhancing the risk monitoring capabilities and response speed of agricultural operations.

[0064] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0065] Example 2

[0066] This application also provides a business data processing apparatus. It should be noted that the business data processing apparatus of this application can be used to execute the business data processing method provided in this application. The business data processing apparatus provided in this application will be described below.

[0067] According to an embodiment of this application, an apparatus for implementing the above-described business data processing method is also provided. Figure 3 This is a schematic diagram of a business data processing apparatus provided according to an embodiment of this application, such as... Figure 3 As shown, the device includes: a receiving unit 30, an acquisition unit 31, and an input unit 32.

[0068] The receiving unit 30 is used to receive business processing instructions and determine the business processing type according to the business processing instructions, wherein the business processing instructions are initiated by business objects at different levels.

[0069] The acquisition unit 31 is used to acquire agricultural business data from M institutions according to the business processing type, obtain M sets of agricultural business data, and determine the business processing model according to the business processing type. The business processing model is used to process the M sets of agricultural business data, where M is a positive integer.

[0070] Input unit 32 is used to input M sets of agricultural business data into the business processing model, process them to obtain business processing results, and generate a business risk report based on the business processing results.

[0071] The business data processing apparatus provided in this application embodiment receives business processing instructions through a receiving unit 30, determines the business processing type according to the business processing instructions, wherein the business processing instructions are initiated by business objects at different levels; an acquisition unit 31 acquires agricultural business data from M institutions according to the business processing type, obtains M sets of agricultural business data, and determines a business processing model according to the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, and M is a positive integer; an input unit 32 inputs the M sets of agricultural business data into the business processing model, processes the data to obtain business processing results, and generates a business risk report based on the business processing results. This solves the technical problem of low identification efficiency when identifying agricultural risks based on agricultural-related business data in related technologies. By acquiring agricultural business data from M institutions, determining the business processing model according to the business processing type, inputting the M sets of agricultural business data into the business processing model, and generating a business risk report based on the output business processing results, the technical effect of improving the identification efficiency of agricultural risk identification based on agricultural-related business data is achieved.

[0072] Optionally, in the business data processing apparatus provided in this application embodiment, the acquisition unit 31 includes: a first acquisition module, used to acquire the institution types of M institutions, wherein the institution types include at least financial system types and non-financial system types; a first determination module, used to determine the data acquisition strategy for each of the M institutions according to the institution types of the M institutions, and acquire the agricultural business data of the M institutions according to the M data acquisition strategies to obtain M sets of initial agricultural business data; and a first processing module, used to preprocess the M sets of initial agricultural business data to obtain M sets of agricultural business data.

[0073] Optionally, in the business data processing apparatus provided in this application embodiment, the acquisition unit 31 includes: a connection module, used to connect to the databases of N institutions based on a preset interface when there are N institutions of financial system type, wherein N is less than or equal to M and N is a positive integer; a first extraction module, used to extract agricultural business data of N institutions from the databases of N institutions through a preset service to obtain N sets of initial agricultural business data; a second extraction module, used to extract agricultural business data of MN institutions from the databases of MN institutions based on a file transfer protocol to obtain MN sets of initial agricultural business data, and cache the MN sets of initial agricultural business data in a message queue; and a third extraction module, used to extract data from the message queue according to a preset time interval to obtain MN sets of initial agricultural business data.

[0074] Optionally, in the business data processing apparatus provided in the embodiments of this application, the apparatus further includes: an encapsulation unit, used to encapsulate the M sets of agricultural business data after obtaining them, to obtain M sets of processed business data; and an execution unit, used to execute the step of inputting the M sets of agricultural business data into the business processing model based on the M sets of processed business data.

[0075] Optionally, in the business data processing apparatus provided in this application embodiment, the input unit 32 includes: a division module, used to divide M groups of agricultural business data into data types to obtain Y groups of business data, wherein the data types include at least: user type and item type, and Y is a positive integer; a second processing module, used to perform feature extraction processing on each group of business data to obtain Y groups of business features; and a construction module, used to construct an agricultural data map based on the Y groups of business features and generate a business risk report based on the agricultural data map.

[0076] Optionally, in the business data processing apparatus provided in this application embodiment, the input unit 32 includes: a second acquisition module, used to acquire preset business tags for a set of business features, and aggregate the business features in the set of business features according to the preset business tags to obtain an agricultural data map corresponding to the set of business features; a third processing module, used to standardize a set of business data to obtain a set of standardized business data; a third acquisition module, used to acquire filtering rules, filter the set of standardized business data according to the filtering rules to obtain filtered business data, and calculate a risk probability value based on the filtered business data; and a second determination module, used to determine the risk level based on the risk probability value, and construct a business risk report based on the risk level and Y agricultural data maps.

[0077] Optionally, in the business data processing apparatus provided in this application embodiment, the apparatus further includes: an extraction unit, used to extract agricultural data maps from the business risk report after generating a business risk report based on the business processing results, and display the agricultural data maps in the form of a radar chart; and a generation unit, used to generate risk warning information based on the business risk report, and send the risk warning information to the client of the risk warning personnel.

[0078] It should be noted that the receiving unit 30, acquiring unit 31, and input unit 32 mentioned above correspond to steps S201 to S203 in Embodiment 1. The instances and application scenarios implemented by the above units and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above units can also be part of a device and can run in the computer terminal 10 provided in Embodiment 1.

[0079] Example 3

[0080] Embodiments of this application may provide a computer terminal, which may be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the aforementioned computer terminal may also be replaced with a mobile terminal or an electronic device, etc.

[0081] Optionally, in this embodiment, the computer terminal may be located in at least one of a plurality of network devices in a computer network.

[0082] In this embodiment, the computer terminal described above can execute the program code for the following steps in the business data processing method: receiving a business processing instruction, determining the business processing type based on the business processing instruction, wherein the business processing instruction is initiated by business objects at different levels; obtaining agricultural business data from M institutions based on the business processing type to obtain M sets of agricultural business data, and determining a business processing model based on the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, and M is a positive integer; inputting the M sets of agricultural business data into the business processing model, processing to obtain a business processing result, and generating a business risk report based on the business processing result.

[0083] Optionally, the computer terminal described above can execute the program code for the following steps in the business data processing method: obtaining the institution types of M institutions, wherein the institution types include at least financial system types and non-financial system types; determining the data acquisition strategy for each institution based on the institution types of the M institutions, and obtaining the agricultural business data of the M institutions based on the M data acquisition strategies to obtain M sets of initial agricultural business data; preprocessing the M sets of initial agricultural business data to obtain M sets of agricultural business data.

[0084] Optionally, the computer terminal described above can execute the following steps in the business data processing method: In the case that there are N institutions of financial system type, connect to the databases of the N institutions based on a preset interface, where N is less than or equal to M and N is a positive integer; extract agricultural business data of the N institutions from their databases through a preset service to obtain N sets of initial agricultural business data; extract agricultural business data of the MN institutions from their databases based on a file transfer protocol to obtain MN sets of initial agricultural business data, and cache the MN sets of initial agricultural business data in a message queue; extract data from the message queue according to a preset time interval to obtain the MN sets of initial agricultural business data.

[0085] Optionally, the computer terminal described above can execute the program code for the following steps in the business data processing method: encapsulating M sets of agricultural business data to obtain M sets of processed business data; and executing the step of inputting the M sets of agricultural business data into the business processing model based on the M sets of processed business data.

[0086] Optionally, the computer terminal described above can execute the program code for the following steps in the business data processing method: classifying the M groups of agricultural business data into data types to obtain Y groups of business data, wherein the data types include at least: user type and item type, and Y is a positive integer; performing feature extraction processing on each group of business data to obtain Y groups of business features; constructing an agricultural data map based on the Y groups of business features, and generating a business risk report based on the agricultural data map.

[0087] Optionally, the aforementioned computer terminal can execute the program code for the following steps in the business data processing method: For a set of business features, obtain preset business labels, aggregate the business features in the set of business features according to the preset business labels, and obtain agricultural data maps corresponding to the set of business features; For a set of business data, standardize the set of business data to obtain a set of standardized business data; Obtain filtering rules, filter the set of standardized business data according to the filtering rules, and obtain filtered business data; Calculate the risk probability value based on the filtered business data; Determine the risk level based on the risk probability value, and construct a business risk report based on the risk level and Y agricultural data maps.

[0088] Optionally, the aforementioned computer terminal can execute program code for the following steps in the business data processing method: extracting agricultural data maps from the business risk report and displaying the agricultural data maps in the form of a radar chart; generating risk warning information based on the business risk report and sending the risk warning information to the client of the risk warning personnel.

[0089] Optionally, Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) Processor 402, memory 404, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0090] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the business data processing method and apparatus in this application embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned business data processing method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0091] The processor can access the information and application programs stored in the memory via the transmission device to execute the steps described above in the business data processing method.

[0092] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0093] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0094] Example 4

[0095] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the business data processing method provided in Embodiment 1.

[0096] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0097] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: receiving a business processing instruction; determining a business processing type based on the business processing instruction, wherein the business processing instruction is initiated by business objects at different levels; obtaining agricultural business data from M institutions based on the business processing type to obtain M sets of agricultural business data, and determining a business processing model based on the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, where M is a positive integer; inputting the M sets of agricultural business data into the business processing model, processing to obtain a business processing result, and generating a business risk report based on the business processing result.

[0098] This application also provides a computer program product, which, when executed on a data processing device, is suitable for performing steps of a business data processing method.

[0099] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0100] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0102] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0105] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for processing business data, characterized in that, include: Receive a business processing instruction, determine the business processing type based on the business processing instruction, wherein the business processing instruction is initiated by business objects at different levels; According to the business processing type, agricultural business data from M institutions are obtained to obtain M sets of agricultural business data. A business processing model is then determined according to the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, and M is a positive integer. The M sets of agricultural business data are input into the business processing model, processed to obtain business processing results, and a business risk report is generated based on the business processing results.

2. The method according to claim 1, characterized in that, Based on the aforementioned business processing type, agricultural business data from M institutions are obtained, resulting in M ​​sets of agricultural business data, including: Obtain the institution types of the M institutions, wherein the institution types include at least financial system types and non-financial system types; Based on the type of the M institutions, a data acquisition strategy is determined for each institution, and agricultural business data of the M institutions is acquired according to the M data acquisition strategies to obtain M sets of initial agricultural business data. The initial agricultural business data of the M groups are preprocessed to obtain the M groups of agricultural business data.

3. The method according to claim 2, characterized in that, Based on M data acquisition strategies, agricultural business data from the M institutions is obtained, resulting in M ​​sets of initial agricultural business data, including: In the case that there are N institutions whose institution type is the financial system type, the databases of the N institutions are connected based on a preset interface, where N is less than or equal to M and N is a positive integer; The agricultural business data of the N institutions is extracted from their databases using a preset service to obtain N sets of initial agricultural business data. Based on the file transfer protocol, the agricultural business data of the MN institutions are extracted from their databases to obtain the initial agricultural business data of the MN group, and the initial agricultural business data of the MN group is cached in the message queue. Data is extracted from the message queue according to a preset time interval to obtain the initial agricultural business data of the MN group.

4. The method according to claim 1, characterized in that, After obtaining M sets of agricultural business data, the method further includes: The M sets of agricultural business data are encapsulated to obtain M sets of processed business data; Based on the processed business data of the M groups, the step of inputting the agricultural business data of the M groups into the business processing model is performed.

5. The method according to claim 1, characterized in that, The M sets of agricultural business data are input into the business processing model, processed to obtain business processing results, and a business risk report is generated based on the business processing results, including: The M groups of agricultural business data are divided into data types to obtain Y groups of business data, wherein the data types include at least: user type and item type, and Y is a positive integer; Feature extraction processing is performed on each group of business data to obtain Y groups of business features; An agricultural data map is constructed based on the business characteristics of the Y group, and a business risk report is generated based on the agricultural data map.

6. The method according to claim 5, characterized in that, Constructing an agricultural data map based on the Y group of business characteristics, and generating the business risk report based on the agricultural data map, includes: For a set of business features, a preset business label is obtained, and the business features in the set of business features are aggregated according to the preset business label to obtain the agricultural data map corresponding to the set of business features. For a set of business data, the set of business data is standardized to obtain a set of standardized business data; Obtain filtering rules, filter the set of standardized business data according to the filtering rules to obtain filtered business data, and calculate the risk probability value based on the filtered business data; The risk level is determined based on the risk probability value, and the business risk report is constructed based on the risk level and Y agricultural data maps.

7. The method according to claim 1, characterized in that, After generating a business risk report based on the business processing results, the method further includes: Extract agricultural data maps from the business risk report and display the agricultural data maps in the form of radar charts; Risk alert information is generated based on the business risk report and sent to the client of the risk warning personnel.

8. A business data processing apparatus, characterized in that, include: A receiving unit is used to receive a service processing instruction and determine the service processing type based on the service processing instruction, wherein the service processing instruction is initiated by a service object at a different level. The acquisition unit is used to acquire agricultural business data from M institutions according to the business processing type, to obtain M sets of agricultural business data, and to determine a business processing model according to the business processing type, wherein the business processing model is used to process the M sets of agricultural business data, and M is a positive integer; The input unit is used to input the M sets of agricultural business data into the business processing model, process them to obtain business processing results, and generate a business risk report based on the business processing results.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the business data processing method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the business data processing method according to any one of claims 1 to 7.

Images