Personalized customization report generation method and device, equipment, medium and program product

Abstract

The invention provides a personalized customization report generation method and device, equipment, a storage medium and a program product, and can be applied to the technical field of financial science and technology. The method comprises the steps of obtaining a report generation request of a target object; according to the report generation request, providing a plurality of selectable check item components corresponding to the target preset service scene, the check item components being used for obtaining specific category data of the target object from an associated data source based on the basic identification information; receiving a selection operation for at least one of the plurality of inspection item components, and generating and executing an inspection task sequence based on the selected inspection item components and the basic identification information, the arrangement of the inspection task sequence being determined according to a data dependency relationship among the inspection item components; after the authorization of the target object on the inspection result data is obtained, obtaining the inspection result data; and generating a personalized customization report including the target object risk information at least based on the inspection result data.

Description

Technical Field

[0001] This application relates to the field of financial technology, specifically to a method, apparatus, device, medium, and program product for generating personalized reports. Background Technology

[0002] In the fields of finance, auditing, compliance, and risk management, generating comprehensive assessment reports for specific target groups (such as enterprises or individuals) based on multi-source data is a core and frequent requirement. Such reports typically need to aggregate information from heterogeneous data sources such as business registration, judiciary, credit reporting, and internal business systems, and conduct risk analysis and formatted presentation according to different business scenarios (such as customer due diligence, credit approval, supplier access, etc.).

[0003] Currently, the mainstream technical solutions for automating such reports typically rely on predefined static report templates. The technical implementation process can be summarized as follows: the system calls a fixed-structure template bound to the user-specified report type (i.e., business scenario); this template embeds all possible data query logic and fields. Subsequently, based on the input target object identifier (such as the company name), the system initiates batch query requests to all preset data source interfaces to obtain all data, and finally populates the data into fixed positions in the template to generate the report.

[0004] However, while the aforementioned automation solutions based on static templates reduce manual operations to some extent, their technical architecture itself has significant shortcomings in terms of computational resource efficiency, system storage overhead, and processing accuracy, specifically in the following aspects.

[0005] Excessive computational resource consumption and response latency issues: Static templates retrieve all related data items regardless of the actual needs of the current business scenario. This results in the system initiating a large number of redundant data query requests every time a report is generated, consuming network I / O and API call resources. Simultaneously, the background processor needs to process a large number of unnecessary computational tasks (such as data cleaning and transformation) in parallel or serially, leading to high CPU load, long report generation cycles, and an inability to meet the needs of high-concurrency or high-real-time business scenarios, thus reducing the overall system throughput and response performance.

[0006] Inefficient storage and caching: Because each report generation involves retrieving all data, the system struggles to effectively cache data differently. Frequent and repetitive full queries not only waste the quota and bandwidth of external data services but also result in a low cache hit rate within the system. This prevents the system from optimizing subsequent similar requests using historical query results, leading to low-value data occupying storage space and cache resources, resulting in overall low system storage efficiency.

[0007] The static template tightly couples data acquisition logic with report format, resulting in rigid business logic for report generation. When business rules change or the focus of inspections needs to be adjusted for specific objects, the entire template must be redeveloped and deployed, leading to high technical iteration costs and long cycles. Furthermore, the lack of intelligent orchestration of data dependencies between inspection items may cause tasks requiring preliminary data to fail or wait due to improper order, impacting the success rate and efficiency of task execution. Simultaneously, the excessive but unfocused accumulation of data makes the risk conclusions in the final report lack focus and accuracy, easily burying key information.

[0008] Therefore, how to intelligently orchestrate data acquisition tasks according to dynamic and personalized needs, while ensuring data security and compliance, and significantly reduce redundant computing and storage overhead, improve processing efficiency and the accuracy of report conclusions, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] In view of the above problems, this application provides a personalized report generation method, apparatus, device, medium and program product that can intelligently orchestrate data acquisition tasks according to dynamic and personalized needs, significantly reduce redundant computing and storage overhead of the system, and improve processing efficiency and accuracy of report conclusions while ensuring data security and compliance.

[0010] According to a first aspect of this application, a method for generating a personalized report is provided, comprising: obtaining a report generation request for a target object, wherein the report generation request includes basic identification information of the target object and a target preset business scenario; providing, according to the report generation request, a plurality of selectable inspection item components corresponding to the target preset business scenario, wherein the inspection item components are used to obtain specific category data of the target object from an associated data source based on the basic identification information; receiving a selection operation on at least one of the plurality of inspection item components, generating and executing an inspection task sequence based on the selected inspection item component and the basic identification information, wherein the arrangement of the inspection task sequence is determined according to the data dependency relationship between the inspection item components; obtaining authorization from the target object for inspection result data; after obtaining authorization from the target object for the inspection result data, invoking processing components corresponding to each inspection sub-task in the inspection task sequence to obtain the inspection result data of the target object from the corresponding data source; and generating a personalized report including risk information of the target object, at least based on the inspection result data.

[0011] According to an embodiment of this application, the step of providing multiple selectable inspection item components corresponding to the target preset business scenario based on the report generation request includes: querying a predefined business scenario-inspection item component mapping relationship based on the target preset business scenario in the report generation request, and determining a list of inspection item components supported under the target preset business scenario; when the target preset business scenario is used for the first time, using a preset default inspection item component as the initial selection state; when the target preset business scenario is not used for the first time, querying and applying the inspection item component previously selected by the user for the target preset business scenario as the initial selection state.

[0012] According to an embodiment of this application, the step of generating and executing an inspection task sequence based on the selected inspection item component and the basic identification information includes: parsing the selected inspection item component to generate multiple inspection sub-tasks; determining the execution order of the multiple inspection sub-tasks according to the data dependency relationship between the inspection item components, and generating an inspection task sequence, wherein inspection sub-tasks on which the output data is depended by subsequent inspection sub-tasks are determined as preceding tasks and executed first; and executing the inspection task sequence.

[0013] According to an embodiment of this application, the personalized report generation method further includes: after obtaining the inspection result data, performing a matching analysis between the inspection result data and preset risk identification rules; generating an intelligent risk conclusion for the target object based on the result of the matching analysis; and generating a personalized report including risk information of the target object based at least on the inspection result data, including: integrating the intelligent risk conclusion into the personalized report.

[0014] According to an embodiment of this application, the step of matching and analyzing the inspection result data with preset risk identification rules includes: constructing large language model prompt words according to the target preset business scenario and risk identification rules; inputting the large language model prompt words and the inspection result data into the large language model to obtain the matching analysis result of the large language model's intelligent risk identification and output.

[0015] According to an embodiment of this application, before providing multiple selectable inspection item components corresponding to the target preset business scenario, the method further includes: configuring differentiated preset business scenario templates according to different regions, wherein the preset business scenario template defines a set of inspection item components corresponding to a specific business scenario in the corresponding region; and when receiving the report generation request, preferentially matching the preset business scenario template corresponding to the region where the target object is located.

[0016] A second aspect of this application provides a personalized report generation device, comprising: a first acquisition module, configured to acquire a report generation request for a target object, wherein the report generation request includes basic identification information of the target object and a target preset business scenario; a providing module, configured to provide, according to the report generation request, a plurality of selectable inspection item components corresponding to the target preset business scenario, wherein the inspection item components are configured to acquire specific category data of the target object from an associated data source based on the basic identification information; and an execution module, configured to receive a selection operation on at least one of the plurality of inspection item components, and based on the selected... The system comprises: an inspection item component and the basic identification information; a generation and execution of an inspection task sequence, wherein the arrangement of the inspection task sequence is determined based on the data dependencies between the inspection item components; a second acquisition module, used to acquire the target object's authorization for the inspection result data; an invocation module, used to, after acquiring the target object's authorization for the inspection result data, invoke the processing components corresponding to each inspection sub-task in the inspection task sequence to acquire the target object's inspection result data from the corresponding data source; and a generation module, used to generate a personalized report including the target object's risk information, based at least on the inspection result data.

[0017] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0018] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0019] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0020] According to some embodiments of this application, the scope of data acquisition is constrained at the top level by pre-setting target business scenarios, allowing users to select only inspection item components related to the current business purpose. This reduces the possibility of initiating a large number of redundant data query requests to generate reports, and reduces unnecessary network I / O, external interface calls, and CPU computing load, thereby significantly reducing the system's computing resource consumption and shortening the report generation response time. Furthermore, the system intelligently orchestrates the sequence of inspection tasks based on the data dependencies between inspection item components, ensuring that logically dependent queries are executed in the optimal order. This avoids task failures or idle waiting due to incomplete data, which not only improves the first-time success rate and execution efficiency of the data processing flow, but also allows subsequent tasks to reuse their results due to the precise pre-positioning of dependent tasks, reducing duplicate queries and further optimizing cache utilization and overall storage efficiency. In addition, this application achieves dynamic configuration and precise execution of business logic through component-based on-demand invocation and dependency-driven intelligent task orchestration, avoiding data redundancy and task blocking, thereby significantly improving the flexibility, success rate, and accuracy of report generation. Attached Figure Description

[0021] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0022] Figure 1 The illustrations depict application scenarios of the personalized report generation method, apparatus, device, medium, and program products according to embodiments of this application.

[0023] Figure 2 A flowchart illustrating a personalized report generation method according to an embodiment of this application is shown schematically.

[0024] Figure 3 A flowchart illustrating a personalized report generation method according to an embodiment of this application is shown schematically.

[0025] Figure 4 This schematic diagram illustrates the structural block diagram of a personalized report generation apparatus according to an embodiment of the present application;

[0026] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing a personalized report generation method according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0029] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0030] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0031] In the technical solution of this application, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0032] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this application all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results; if the user chooses to reject, the process proceeds to expert decision-making. Here, "automated decision-making" refers to activities that automatically analyze and evaluate an individual's behavioral habits, interests, or economic and credit status through computer programs, and then make decisions. Here, "expert decision-making" refers to activities where individuals specializing in a particular field, possessing specialized experience, knowledge, and skills, and reaching a certain level of professional expertise make decisions.

[0033] Currently, mainstream technical solutions for automating the generation of comprehensive assessment reports for specific target objects (such as enterprises or individuals) based on multi-source data typically rely on predefined static report templates. The technical implementation process can be summarized as follows: the system calls a fixed-structure template bound to the user-specified report type (i.e., business scenario); this template embeds all possible data query logic and fields. Subsequently, based on the input target object identifier (such as enterprise name), the system initiates batch query requests to all preset data source interfaces to obtain all data, and finally fills the data into fixed positions in the template to generate the report. However, while the above-mentioned automated solutions based on static templates reduce manual operations to some extent, their technical architecture itself has significant shortcomings in terms of computational resource efficiency, system storage overhead, and processing accuracy. Therefore, how to intelligently orchestrate data acquisition tasks according to dynamic and personalized needs, while ensuring data security and compliance, significantly reducing redundant computation and storage overhead, and improving processing efficiency and the accuracy of report conclusions, has become a technical problem that urgently needs to be solved by those skilled in the art.

[0034] Embodiments of this application provide a method, apparatus, electronic device, computer-readable storage medium, and computer program product for generating personalized reports. The method includes: obtaining a report generation request from a target object, wherein the report generation request includes basic identification information of the target object and a target preset business scenario; providing multiple selectable inspection item components corresponding to the target preset business scenario based on the report generation request, wherein the inspection item components are used to obtain specific category data of the target object from an associated data source based on the basic identification information; receiving a selection operation on at least one of the multiple inspection item components; generating and executing an inspection task sequence based on the selected inspection item components and the basic identification information, wherein the arrangement of the inspection task sequence is determined according to the data dependencies between the inspection item components; obtaining authorization from the target object for the inspection result data; after obtaining authorization from the target object for the inspection result data, invoking processing components corresponding to each inspection sub-task in the inspection task sequence to obtain the inspection result data of the target object from the corresponding data source; and generating a personalized report including risk information of the target object, at least based on the inspection result data.

[0035] It should be noted that the personalized report generation method, apparatus, electronic device, computer-readable storage medium and computer program product of this application can be used in the field of financial technology technology, specifically in the field of financial technology report generation, or in any field other than financial technology technology. The field of this application is not limited here.

[0036] Figure 1 The illustrations depict application scenarios of the personalized report generation method, apparatus, device, medium, and program products according to embodiments of this application.

[0037] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0038] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0039] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0040] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0041] It should be noted that the personalized report generation method provided in this application embodiment can generally be executed by server 105. Correspondingly, the personalized report generation device provided in this application embodiment can generally be located in server 105. The personalized report generation method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the personalized report generation device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0042] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0043] The following will be based on Figure 1 The described scene, through Figure 2 and Figure 3 The method for generating personalized reports according to embodiments of this application will be described in detail.

[0044] Figure 2 A flowchart illustrating a personalized report generation method according to an embodiment of this application is shown schematically.

[0045] like Figure 2 As shown, the personalized report generation method of this embodiment includes operations S210 to S260.

[0046] In operation S210, a report generation request for the target object is obtained. The report generation request includes the basic identification information of the target object and the target preset business scenario.

[0047] In some examples, the target object can be understood as the entity that serves as the subject of personalized report generation and analysis, typically a legal person, non-legal person organization, or natural person, such as a business or individual user applying for the service.

[0048] In some examples, a report generation request can be understood as a structured digital instruction initiated by a user to trigger a personalized report generation process, which at least encapsulates the target preset business scenario identifier and basic identifier information.

[0049] In some examples, basic identification information can be understood as core data fields that can uniquely identify or accurately locate a target object, such as a company's unified social credit code or an individual's ID card number.

[0050] In some examples, the target preset business scenario can be understood as a pre-configured, optional template representing a specific business purpose and analytical paradigm. This template is associated with a set of checkpoint components that users can select, along with corresponding analytical rules. Target preset business scenarios could include, for example, due diligence on corporate clients, pre-loan review for enterprise credit, and supply chain supplier qualification assessments.

[0051] In some examples, users select a target preset business scenario (such as pre-loan review for corporate credit) through the front-end interface and enter the basic identification information of the target object (such as the unified social credit code). The front-end encapsulates the target preset business scenario identifier and basic identification information into a structured report to generate a request data packet, which is then transmitted to the server over the network. The server then parses and verifies the data packet, and after confirming that it conforms to the established protocol, it triggers the subsequent processing flow.

[0052] In operation S220, based on the report generation request, multiple selectable inspection item components corresponding to the target preset business scenario are provided. The inspection item components are used to obtain specific category data of the target object from the associated data source based on basic identification information.

[0053] In some examples, the inspection item component can be understood as the smallest configurable functional unit that encapsulates specific data acquisition and preliminary processing logic. Each inspection item component is responsible for performing queries and data extraction from a specified data source for a specific dimension of the target object (such as business registration information, litigation records, etc.).

[0054] In some examples, the data source can be understood as an external or internal data service entity that allows the inspection component to retrieve and extract data. Its forms include, but are not limited to: application programming interfaces (APIs) provided by external public institutions or third-party service providers, and controlled access interfaces to internal business system databases.

[0055] In some examples, the process of obtaining specific category data of a target object from an associated data source can be understood as the process by which the inspection item component retrieves and extracts raw data related to the target object from an associated, trusted data provider by calling an external application programming interface (API) or accessing an internal database.

[0056] In some examples, specific categories of data can be understood as structured or semi-structured data results belonging to a defined subject area, obtained from their associated data sources by performing a particular inspection component. The category is defined by the functionality of that component, such as shareholder and investment information, a list of legal cases, or credit delinquency records, etc.

[0057] In some examples, the report generation request is parsed, and the preset business scenario-check item component mapping library is queried based on the target preset business scenario to obtain the metadata (such as ID, name, and description) of all available check item components in that scenario. The front end dynamically renders a list of available check item components based on the metadata for display on the user interface. Each component is pre-bound to its corresponding specific data source API and data processing logic for subsequent targeted queries based on basic identification information.

[0058] In operation S230, a selection operation is received for at least one of a plurality of inspection item components. Based on the selected inspection item component and basic identification information, an inspection task sequence is generated and executed, wherein the arrangement of the inspection task sequence is determined according to the data dependency relationship between the inspection item components.

[0059] In some examples, a sequence of inspection tasks can be understood as an ordered set of computational tasks dynamically orchestrated by the system based on one or more inspection item components selected by the user, and the logical relationships between these components. This sequence specifies the startup order, parallel or serial relationship of each inspection subtask (each inspection subtask corresponds to an execution instance of an inspection item component).

[0060] In some examples, data dependencies can be understood as input-output constraints existing between multiple inspection components or inspection subtasks. Specifically, one inspection component (or inspection subtask) requires the output of another inspection component (or inspection subtask) as necessary input data for its execution, or can only obtain a complete and accurate data environment after its execution is complete.

[0061] In some examples, the system receives the final list of check item components submitted by the user and encapsulates it along with basic identification information. The background task orchestration engine parses the selected check item components and, based on a predefined data dependency graph between components (such as basic enterprise information being a prerequisite for equity penetration analysis), parallelizes the tasks of components without dependencies and orchestrates the tasks with dependencies into a serial workflow, thereby generating an optimal sequence of check tasks. This sequence is instantiated as a schedulable task object and pushed to the task queue to await sequential invocation by the execution engine.

[0062] In operation S240, obtain authorization from the target object for the inspection result data.

[0063] In operation S250, after obtaining authorization from the target object for the inspection result data, the processing components corresponding to each inspection subtask in the inspection task sequence are invoked to obtain the inspection result data of the target object from the corresponding data source.

[0064] In embodiments of this application, the consent or authorization of the target object can be obtained before acquiring information about the target object. For example, a request to acquire the inspection result data of the target object can be issued before operation S250. If the target object consents or authorizes the acquisition of the inspection result data, operation S250 is executed.

[0065] In the embodiments of this application, a corresponding operation entry can be provided to the target object, allowing them to choose to agree to or refuse the automated decision-making result. That is, before generating a personalized report including the target object's risk information based on the inspection result data, an instruction from the target object to agree or refuse to generate a personalized report including the target object's risk information can be obtained through the corresponding operation entry. If the target object agrees to generate a personalized report including the target object's risk information based on the inspection result data, then the user information is processed to generate a personalized report including the target object's risk information based on the inspection result data, i.e., step S260 is executed. If the target object refuses to generate a personalized report including the target object's risk information based on the inspection result data, then the expert decision-making process begins.

[0066] In some examples, an inspection subtask can be understood as a specific execution instance created by the system based on a particular inspection item component selected by the user. It is an atomic computational unit with explicit input (basic identification information), processing logic (calling the corresponding processing component), and expected output (data of a specific category).

[0067] In some examples, the processing component can be understood as a background execution module that corresponds one-to-one with the inspection item component, encapsulating the core code logic for accessing specific data sources, performing data queries, and carrying out format conversion and preliminary cleaning. When an inspection subtask is scheduled for execution, its associated processing component is invoked to complete the data acquisition operation.

[0068] In some examples, the inspection result data can be understood as standardized, specific category data related to the target object, successfully retrieved from the associated data source after the processing component has completed its execution. This data is a structured unit of information that can be directly used for subsequent risk analysis and report generation.

[0069] In some examples, the task execution engine retrieves inspection subtasks sequentially from the queue according to the order of the inspection task sequence. For each inspection subtask, the engine locates and invokes the corresponding processing component based on its bound inspection item component identifier. The processing component receives basic identifier information as input parameters and initiates an authorized query request to a specified internal or external data source through a pre-configured network interface (API) or database connection. After obtaining the raw response, the processing component parses, cleans, and formats it according to predefined rules to generate standardized inspection result data, and stores it in an intermediate result database associated with the task number for use in subsequent stages.

[0070] When operating S260, at least based on the inspection result data, a personalized report including risk information of the target object is generated.

[0071] In some examples, all standardized inspection result data associated with the task can be read from the intermediate results database and analyzed by the risk intelligence identification module. Based on the selected target's preset business scenario, the corresponding report template is retrieved, the inspection result data is populated according to the field mapping relationships defined in the template, and risk analysis conclusions (such as high-risk labels, compliance scores, etc.) are embedded into the designated module of the report. Finally, the fully populated structured data is rendered into a document file of the specified format to complete the generation of a personalized report.

[0072] According to the personalized report generation method of this application, the data acquisition scope is constrained at the top level by a target preset business scenario, allowing users to select only inspection item components related to the current business purpose. This reduces the possibility of initiating a large number of redundant data query requests for report generation, and reduces unnecessary network I / O, external interface calls, and CPU computing load, thereby significantly reducing the system's computing resource consumption and shortening the report generation response time. Furthermore, the system intelligently orchestrates the inspection task sequence based on the data dependencies between inspection item components, ensuring that logically dependent queries are executed in the optimal order, avoiding task failures or idle waiting due to incomplete data. This not only improves the first-time success rate and execution efficiency of the data processing flow, but also allows subsequent tasks to reuse their results due to the precise pre-positioning of dependent tasks, reducing duplicate queries and further optimizing cache utilization and overall storage efficiency. In addition, this application achieves dynamic configuration and precise execution of business logic through component-based on-demand invocation and dependency-driven intelligent task orchestration, avoiding data redundancy and task blocking, thereby significantly improving the flexibility, success rate, and accuracy of report generation.

[0073] According to some embodiments of this application, the step of providing multiple selectable inspection item components corresponding to a target preset business scenario based on a report generation request may include a first determination operation, a second determination operation, and a third determination operation.

[0074] The first step is to determine the target preset business scenario in the report generation request, query the predefined business scenario-inspection item component mapping relationship, and determine the list of inspection item components supported under the target preset business scenario.

[0075] The second operation is to determine the initial selection state when the target preset business scenario is used for the first time.

[0076] The third operation is to determine the initial selection state when the target preset business scenario is not being used for the first time. This involves querying and applying the check item component that the user previously selected for the target preset business scenario.

[0077] In some examples, this embodiment significantly improves system usability, personalization, and operational efficiency by specifically defining the intelligent logic of default selection. Specifically: the first determination operation, based on the preset business scenario-check item component mapping relationship, can accurately provide a list of check item components, ensuring from the source that the options are strongly related to the business purpose, avoiding users from filtering from a massive number of irrelevant components, and improving operational accuracy and interface simplicity. The second and third determination operations constitute an intelligent default selection mechanism. When a scenario is used for the first time, the system uses the preset default items (usually representing the most common or necessary check dimensions in the scenario) as a starting point, providing users with reasonable recommendations that conform to business practices, reducing the cognitive burden and selection difficulties for beginners. When a scenario is not used for the first time, the system automatically reuses the user's historical preferences as the default items. This design essentially realizes user behavior feedback-driven interface adaptation. It not only respects and continues the user's personalized work habits and reduces repetitive configuration steps, but also learns from the user's historical choices, enabling the system's interactive experience to be continuously optimized over time, thereby systematically improving human-computer interaction efficiency and user satisfaction in the long term.

[0078] According to some embodiments of this application, the steps of generating and executing an inspection task sequence based on selected inspection item components and basic identification information may include a first generation operation, a second generation operation, and an execution operation.

[0079] The first generation operation: parse the selected check item components and generate multiple check subtasks.

[0080] The second generation operation is to determine the execution order of multiple inspection subtasks based on the data dependencies between the inspection item components, and generate an inspection task sequence. Among them, the inspection subtasks whose output data is depended by subsequent inspection subtasks are determined as preceding tasks and executed first.

[0081] Execution: Perform the sequence of inspection tasks.

[0082] In some examples, this embodiment significantly improves the reliability and overall execution efficiency of the background data processing flow by explicitly decomposing tasks and using intelligent sorting based on data dependencies. Specifically, the first generation operation transforms the user-selected business logic (inspection item components) into independently schedulable computing units (inspection subtasks), achieving precise decoupling and instantiation of business intent from machine instructions, laying the foundation for subsequent automated scheduling. The second generation operation analyzes and applies data dependencies to dynamically orchestrate the task execution order, ensuring that all preceding data output tasks are executed first. This mechanism prevents subsequent tasks from failing or idling due to missing input data, significantly improving the one-time success rate of complex inspection processes. Simultaneously, by constructing optimal or near-optimal task sequences, it allows dependent tasks that can be executed in parallel to fully utilize multi-threaded or distributed computing resources, while serializing dependent tasks to ensure logical correctness. This intelligent orchestration optimizes the spatiotemporal allocation of system resources (such as CPU and I / O), reduces blocking latency between tasks, thereby shortening the overall processing cycle from task triggering to all data being in place, and improving system throughput and response performance. Ultimately, the execution of operations reliably drives the entire process based on this optimized sequence, ensuring efficient and accurate data processing.

[0083] According to some embodiments of this application, the personalized report generation method may further include an auxiliary matching operation and a first auxiliary generation operation.

[0084] Assisted matching operation: After obtaining the inspection result data, the inspection result data is matched and analyzed with the preset risk identification rules.

[0085] Assisted generation operation: Based on the results of matching analysis, generate intelligent risk conclusions for the target object.

[0086] The step of generating a personalized report that includes risk information of the target object based at least on the inspection result data may include a second auxiliary generation operation: integrating intelligent risk conclusions into the personalized report.

[0087] In some examples, this embodiment significantly enhances the report's risk insight depth and decision support value by introducing rule matching and intelligent risk conclusion generation, demonstrating a qualitative leap from data presentation to intelligent analysis. Specifically, the auxiliary matching operation does not simply list inspection results data, but rather performs automated matching analysis against pre-set risk identification rules that incorporate business expert knowledge. This process essentially transforms scattered data points into structured risk signals, systematically identifying risk patterns such as compliance deviations, credit defects, or operational anomalies hidden behind the data, greatly improving the report's risk focus and analytical depth. The first auxiliary generation operation further condenses the matching analysis results into highly summarized intelligent risk conclusions. These conclusions are not simply repetitions of data, but rather a comprehensive judgment of risk level, type, and correlation. The final second auxiliary generation operation integrates these intelligent conclusions into the report, enabling the report to not only provide raw data but also directly deliver processed core analytical opinions.

[0088] Therefore, this embodiment upgrades the final personalized report from a passive information record to a proactive decision-making support tool, reducing the workload of business personnel manually cross-analyzing massive amounts of data and the possibility of subjective misjudgment. By providing standardized intelligent risk conclusions, it effectively improves the consistency, objectivity, and accuracy of risk assessment, empowering business personnel to quickly grasp key risks and make more accurate decisions.

[0089] According to some embodiments of this application, the step of matching and analyzing inspection result data with preset risk identification rules may include a construction operation and a fourth determination operation.

[0090] Construction operation: Based on the target's preset business scenario and risk identification rules, construct prompt words for the large language model.

[0091] The fourth step is to input the prompt words and inspection result data from the large language model into the large language model to obtain the matching analysis results output by the large language model for risk intelligent identification.

[0092] In some examples, this embodiment, by specifically defining the application of the large language model, has achieved a leap in risk analysis from rigid matching based on hard-coded rules to intelligent recognition based on scene understanding and contextual reasoning, resulting in a significant technological synergy effect.

[0093] The construction operation does not directly use the original rules and data for matching. Instead, it combines the semantic context of the target's pre-defined business scenario to transform abstract risk identification rules into structured large language model prompts rich in business intent and assessment focus. This operation efficiently injects business domain knowledge into the model, guiding its focus. The fourth determination operation inputs the generated prompts along with the inspection result data into the large language model. Leveraging its powerful semantic understanding, information association, and logical reasoning capabilities, the model performs cross-field, in-depth comprehensive analysis of multi-dimensional inspection data within the given business scenario framework. It can identify complex risk patterns that are difficult for simple rules to capture (such as potential risks implied by multiple minor anomalies) and even generate interpretive judgment criteria.

[0094] Therefore, this embodiment achieves a dual effect: On the one hand, it greatly enhances the breadth of coverage and depth of reasoning in risk intelligence identification, making the analysis conclusions more comprehensive and insightful, and solving the problems of lagging updates to rigid rule bases and difficulty in handling complex cases. On the other hand, it efficiently combines business rules with general large-scale model capabilities through prompt word engineering, ensuring the professionalism of the analysis while significantly reducing the development and iteration costs of building and maintaining dedicated risk analysis models (or large rule bases), achieving cutting-edge analytical capabilities and a lightweight system architecture.

[0095] According to some embodiments of this application, such as Figure 3 As shown, before providing multiple selectable inspection item components corresponding to the target preset business scenario, the personalized report generation method may also include operation S001 and operation S002.

[0096] In operation S001, different preset business scenario templates are configured according to different regions. The preset business scenario templates define the set of inspection item components corresponding to specific business scenarios in the corresponding regions.

[0097] In operation S002, when a report generation request is received, the preset business scenario template corresponding to the region where the target object is located is matched first.

[0098] In some examples, this embodiment significantly improves the system's adaptability, compliance, and management efficiency in complex organizational structures and diverse regulatory environments by introducing a localized scenario template configuration and matching mechanism.

[0099] The configuration operation allows system administrators to customize differentiated preset business scenario templates for the same business scenario (such as open accounts) based on specific business requirements, local regulations, or regulatory priorities in different regions (such as different countries, provinces, or branches). Essentially, this embeds regional knowledge into the system configuration, ensuring that the set of inspection items associated with the template accurately covers the necessary inspection dimensions in the local area (for example, environmental compliance may be additionally checked in region A, while tax rating may be emphasized in region B), thus guaranteeing the localized compliance of the generated reports from the outset. The priority matching operation intelligently selects the most suitable scenario template based on the target object's region at the entry point of the report generation process. This automated routing mechanism eliminates the burden of users manually identifying applicable regional rules and avoids the risk of missing key localized inspection items in the report content due to incorrect template selection. It enables the same system to seamlessly support cross-regional business, flexibly responding to regional differences while meeting centralized management and a unified technical architecture.

[0100] Based on the above-described personalized report generation method, this application also provides a personalized report generation apparatus. The following will combine... Figure 4 The device is described in detail.

[0101] Figure 4 The diagram illustrates the structure of a personalized report generation apparatus according to an embodiment of this application.

[0102] like Figure 4 As shown, the personalized report generation device 10 includes a first acquisition module 1, a providing module 2, an execution module 3, a second acquisition module 4, a calling module 5, and a generation module 6.

[0103] The first acquisition module 1 is used to acquire the report generation request of the target object, wherein the report generation request includes the basic identification information of the target object and the target preset business scenario.

[0104] Module 2 provides multiple selectable inspection item components corresponding to the target preset business scenario based on the report generation request. The inspection item components are used to obtain specific category data of the target object from the associated data source based on basic identification information.

[0105] The execution module 3 is used to receive a selection operation for at least one of a plurality of inspection item components, and to generate and execute an inspection task sequence based on the selected inspection item component and basic identification information, wherein the arrangement of the inspection task sequence is determined according to the data dependency relationship between the inspection item components.

[0106] The second acquisition module 4 is used to obtain the target object's authorization for the inspection result data.

[0107] Module 5 is used to call the processing components corresponding to each inspection subtask in the inspection task sequence after obtaining authorization from the target object for the inspection result data, so as to obtain the inspection result data of the target object from the corresponding data source.

[0108] The generation module 6 is used to generate a personalized report that includes risk information of the target object, based at least on the inspection result data.

[0109] According to some embodiments of this application, the providing module may include a first determining unit, a second determining unit, and a third determining unit.

[0110] The first determining unit: Based on the target preset business scenario in the report generation request, queries the predefined business scenario-inspection item component mapping relationship to determine the list of inspection item components supported under the target preset business scenario.

[0111] The second determining unit: When the target preset business scenario is used for the first time, the preset default check item component is used as the initial selection state.

[0112] The third determining unit: When the target preset business scenario is not used for the first time, query and apply the inspection item component that the user selected last time for the target preset business scenario as the initial selection state.

[0113] According to some embodiments of this application, the execution module may include a first generation unit, a second generation unit, and an execution unit.

[0114] First generation unit: parses the selected check item component and generates multiple check subtasks.

[0115] The second generation unit determines the execution order of multiple inspection subtasks based on the data dependencies between the inspection item components, and generates an inspection task sequence. Among them, the inspection subtasks whose output data is depended by subsequent inspection subtasks are determined as preceding tasks and executed first.

[0116] Execution unit: Executes the sequence of inspection tasks.

[0117] According to some embodiments of this application, the personalized report generation device may further include an auxiliary matching module and a first auxiliary generation module.

[0118] Assisted matching module: After obtaining the inspection result data, the module matches and analyzes the inspection result data with the preset risk identification rules.

[0119] Assisted generation module: Based on the results of matching analysis, it generates intelligent risk conclusions for the target object.

[0120] The generation module may include a second auxiliary generation unit: integrating intelligent risk conclusions into personalized reports.

[0121] According to some embodiments of this application, the auxiliary matching module may include a construction unit and a fourth determination unit.

[0122] Construction Unit: Based on the target's preset business scenario and risk identification rules, construct prompt words for the large language model.

[0123] The fourth determination unit: Input the prompt words and inspection result data of the big language model into the big language model to obtain the matching analysis results of the big language model's risk intelligent identification and output.

[0124] According to some embodiments of this application, before providing multiple selectable inspection item components corresponding to a target preset business scenario, the personalized report generation device may further include a configuration module and a priority matching module.

[0125] Configuration module: Configures different preset business scenario templates according to different regions. The preset business scenario template defines the set of inspection item components corresponding to specific business scenarios in the corresponding region.

[0126] Priority matching module: When a report generation request is received, it prioritizes matching the preset business scenario template that corresponds to the region where the target object is located.

[0127] According to embodiments of this application, any multiple modules among the first acquisition module 1, the providing module 2, the execution module 3, the second acquisition module 4, the calling module 5, and the generation module 6 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this application, at least one of the first acquisition module 1, the providing module 2, the execution module 3, the second acquisition module 4, the calling module 5, and the generation module 6 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the first acquisition module 1, the providing module 2, the execution module 3, the second acquisition module 4, the calling module 5, and the generating module 6 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0128] Figure 5 A block diagram schematically illustrates an electronic device suitable for the described method according to an embodiment of this application.

[0129] like Figure 5 As shown, an electronic device 900 according to an embodiment of this application includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0130] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0131] According to embodiments of this application, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0132] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0133] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0134] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0135] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0136] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0137] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this application embodiment. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0138] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0140] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A method for generating personalized reports, characterized in that, include: Obtain a report generation request for the target object, wherein the report generation request includes the basic identification information of the target object and the target preset business scenario; Based on the report generation request, multiple selectable inspection item components corresponding to the target preset business scenario are provided, wherein the inspection item components are used to obtain specific category data of the target object from the associated data source based on the basic identification information; The system receives a selection operation for at least one of the plurality of inspection item components, and generates and executes an inspection task sequence based on the selected inspection item component and the basic identification information, wherein the arrangement of the inspection task sequence is determined according to the data dependency relationship between the inspection item components. Obtain the target object's authorization for the inspection result data; After obtaining authorization from the target object for the inspection result data, the processing components corresponding to each inspection subtask in the inspection task sequence are invoked to obtain the inspection result data of the target object from the corresponding data source. Based at least on the inspection results data, a personalized report including risk information of the target object is generated.

2. The personalized report generation method according to claim 1, characterized in that, The steps of providing multiple selectable inspection item components corresponding to the target preset business scenario based on the report generation request include: Based on the target preset business scenario in the report generation request, query the predefined business scenario-inspection item component mapping relationship to determine the list of inspection item components supported under the target preset business scenario; When the target preset business scenario is used for the first time, the preset default check item component is used as the initial selection state; When the target preset business scenario is not used for the first time, query and apply the inspection item component that the user last selected for the target preset business scenario as the initial selection state.

3. The personalized report generation method according to claim 1, characterized in that, The steps of generating and executing an inspection task sequence based on the selected inspection item components and the basic identification information include: Parse the selected inspection item components to generate multiple inspection subtasks; Based on the data dependencies between the inspection item components, the execution order of the multiple inspection subtasks is determined, and an inspection task sequence is generated. Among them, the inspection subtasks whose output data is depended by subsequent inspection subtasks are determined as preceding tasks and executed first. Execute the sequence of inspection tasks.

4. The personalized report generation method according to claim 1, characterized in that, Also includes: After obtaining the inspection result data, the inspection result data is matched and analyzed with preset risk identification rules; Based on the results of the matching analysis, intelligent risk conclusions are generated for the target object. The step of generating a personalized report including risk information of the target object based at least on the inspection result data includes: integrating the intelligent risk conclusion into the personalized report.

5. The personalized report generation method according to claim 4, characterized in that, The step of matching and analyzing the inspection result data with preset risk identification rules includes: Based on the target preset business scenario and risk identification rules, construct large language model prompt words; The prompt words of the large language model and the inspection result data are input into the large language model to obtain the matching analysis results of the risk intelligent identification and output of the large language model.

6. The personalized report generation method according to claim 1, characterized in that, Before providing multiple selectable inspection item components corresponding to the target preset business scenario, the method further includes: Different preset business scenario templates are configured according to different regions, wherein the preset business scenario template defines a set of inspection item components corresponding to a specific business scenario in the corresponding region; Upon receiving the report generation request, a preset business scenario template corresponding to the region where the target object is located is matched first.

7. A personalized report generation device, characterized in that, include: The first acquisition module is used to acquire a report generation request of a target object, wherein the report generation request includes the basic identification information of the target object and the target preset business scenario; A providing module is configured to provide multiple selectable inspection item components corresponding to the target preset business scenario according to the report generation request, wherein the inspection item components are configured to obtain specific category data of the target object from the associated data source based on the basic identification information; An execution module is configured to receive a selection operation on at least one of the plurality of inspection item components, and generate and execute an inspection task sequence based on the selected inspection item component and the basic identification information, wherein the arrangement of the inspection task sequence is determined according to the data dependency relationship between the inspection item components; The second acquisition module is used to acquire the target object's authorization for the inspection result data; The calling module is used to, after obtaining authorization from the target object for the inspection result data, call the processing component corresponding to each inspection subtask in the inspection task sequence to obtain the inspection result data of the target object from the corresponding data source; A generation module is used to generate a personalized report including risk information of the target object, based at least on the inspection result data.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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