Quota state linkage processing method and apparatus, electronic device, and storage medium

Abstract

The application relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a quota state linkage processing method and device, an electronic device and a storage medium. The method comprises the following steps: analyzing a quota processing request of a target quota object, determining a request type and an object identifier; acquiring quota basic data, business occupation data, quota parameter data, historical processing records, risk characteristic data and cooperation party synchronization data according to the object identifier; obtaining current quota state data through consistency verification and occupation state identification; predicting quota trend parameters and risk adjustment parameters based on the historical processing records and the risk characteristic data, linkage adjusting the quota parameter data, and obtaining target quota control parameters; performing a quota processing operation according to the request type, outputting a linkage processing result, and generating a quota processing traceability record. The method improves the quota state judgment accuracy, parameter adaptation capability and processing traceability capability, and enhances the quota management stability and risk control effect.

Description

Technical Field

[0001] This application relates to the field of data processing technology and can be applied to the field of financial technology, particularly to a method, device, electronic device and storage medium for linking credit limit status. Background Technology

[0002] In financial business data processing systems, credit limit data is typically used to characterize the available credit limit range, occupancy status, and processing constraints of a target object in a specific business scenario. With the increase in business types, data sources, and operational steps, credit limit data often needs to be correlated with multiple types of data, including basic object information, business occupancy data, parameter configuration data, status change data, and historical operation records. In traditional technologies, some credit limit management systems primarily focus on the static maintenance of credit limit data, enabling basic functions such as adding, modifying, deleting, and querying credit limits. However, in scenarios where multiple business data jointly influence the credit limit status, problems such as untimely data synchronization, inaccurate identification of occupancy status, and insufficient correlation between credit limit parameters and business data still exist, making it difficult for the system to reflect the actual status of the credit limit data in a timely and accurate manner. Furthermore, when credit limit data undergoes adjustments, occupancy, release, expiration, or abnormal changes, if the system only records the current processing result without a complete record of the processing process, status changes, associated data, and operational basis, it becomes difficult to trace and verify the changes in credit limit data subsequently, affecting the data consistency, traceability, and processing efficiency of the credit limit management process. Summary of the Invention

[0003] The main technical problem addressed by the implementation method of this application is that the accuracy and timeliness of credit limit status updates, occupancy judgments, parameter linkages, and historical tracing processing need to be improved in traditional high-financial business scenarios.

[0004] To address the aforementioned technical problems, the first technical solution adopted in this application is: providing a credit limit status linkage processing method, comprising: parsing a received credit limit processing request for a target credit limit object, determining the request type and object identifier corresponding to the credit limit processing request; obtaining, based on the object identifier, credit limit basic data, business usage data, credit limit parameter data, historical processing records, risk characteristic data, and collaborator synchronization data corresponding to the target credit limit object; performing consistency verification and usage status identification on the credit limit basic data, the business usage data, and the collaborator synchronization data to obtain current credit limit status data; and storing the historical processing records... The risk characteristic data is input into a preset credit limit prediction model to obtain the credit limit trend parameters and risk adjustment parameters corresponding to the target credit limit object; based on the credit limit trend parameters and the risk adjustment parameters, the credit limit parameter data is adjusted in a linked manner to obtain the target credit limit control parameters; according to the request type, the current credit limit status data, and the target credit limit control parameters, the corresponding credit limit processing operation is executed, and the credit limit status linkage processing result is output; the data source, parameter change information, collaborator synchronization information, and operation basis corresponding to the credit limit status linkage processing result are associated and stored to generate a credit limit processing traceability record corresponding to the target credit limit object.

[0005] To solve the above-mentioned technical problems, the second technical solution adopted in this application is: providing a credit limit status linkage processing device, including: a credit limit request parsing module, used to parse a received credit limit processing request for a target credit limit object, and determine the request type and object identifier corresponding to the credit limit processing request; a credit limit data acquisition module, used to acquire, based on the object identifier, the credit limit basic data, business occupancy data, credit limit parameter data, historical processing records, risk characteristic data, and collaborator synchronization data corresponding to the target credit limit object; a status verification and identification module, used to perform consistency verification and occupancy status identification on the credit limit basic data, the business occupancy data, and the collaborator synchronization data to obtain the current credit limit status data; and a credit limit model prediction module, used to... The historical processing records and the risk characteristic data are input into a preset credit limit prediction model to obtain the credit limit trend parameters and risk adjustment parameters corresponding to the target credit limit object; the parameter linkage adjustment module is used to adjust the credit limit parameter data in linkage according to the credit limit trend parameters and the risk adjustment parameters to obtain the target credit limit control parameters; the credit limit operation execution module is used to execute the corresponding credit limit processing operation according to the request type, the current credit limit status data and the target credit limit control parameters, and output the credit limit status linkage processing result; the traceability record generation module is used to associate and store the data source, parameter change information, collaborator synchronization information and operation basis corresponding to the credit limit status linkage processing result, and generate the credit limit processing traceability record corresponding to the target credit limit object.

[0006] To solve the above-mentioned technical problems, the third technical solution adopted in the embodiments of this application is: to provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the quota status linkage processing method as described above.

[0007] To solve the above-mentioned technical problems, the fourth technical solution adopted in the embodiments of this application is: to provide a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by an electronic device, the electronic device performs the limit status linkage processing method as described above.

[0008] Unlike related technologies, this application integrates credit limit status identification, trend prediction, parameter linkage, collaborator synchronization, and processing traceability into a single processing chain, freeing credit limit processing from static configuration and single-operation responses. Related technologies often suffer from delayed credit limit status judgment, insufficient parameter adaptation, and difficulty in tracing processing basis when multi-source business data changes, risk status changes, or collaborative information is updated. This application determines the current credit limit status based on multiple types of credit limit-related data and combines historical processing records and risk characteristic data to obtain credit limit trend parameters and risk adjustment parameters, enabling credit limit control parameters to adapt to credit limit changes and risk status. Simultaneously, by associating and storing data sources, parameter change information, collaborator synchronization information, and operational basis, key evidence in the credit limit processing result formation process can be preserved. Therefore, this application improves the accuracy of credit limit status judgment, the adaptability of credit limit parameter adjustments, and the traceability of the credit limit processing process. Attached Figure Description

[0009] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0010] Figure 1 This is a schematic diagram of the operating environment of the quota status linkage processing method provided in the embodiments of this application.

[0011] Figure 2 This is a schematic diagram of the execution flow of the quota status linkage processing method provided in the embodiments of this application.

[0012] Figure 3 This is a schematic diagram of the execution flow of the quota status linkage processing method provided in the embodiments of this application.

[0013] Figure 4 This is a schematic diagram of the execution flow of the quota status linkage processing method provided in the embodiments of this application.

[0014] Figure 5 This is a schematic diagram of the system structure of the quota status linkage processing device provided in the embodiments of this application.

[0015] Figure 6 This is a schematic diagram of the hardware structure of the electronic device for the execution quota status linkage processing method provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Software tools, components, or servers not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0017] It should be noted that, unless otherwise specified, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device schematic diagram or the order in the flowchart.

[0018] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0019] To facilitate understanding of this embodiment, a detailed description of the credit limit status linkage processing method disclosed in this application embodiment will be provided first. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a schematic diagram of the operating environment of the quota status linkage processing method provided in the embodiments of this application, such as... Figure 1 As shown, the execution subject of the credit limit status linkage processing method provided in this application embodiment is generally an electronic device with a certain computing power, such as a computer device. In some possible implementations, this credit limit status linkage processing method can be implemented by a processor calling computer-readable instructions stored in memory. Figure 1The computer equipment mentioned can be a server. A server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. This can be understood as... Figure 1 The number of computer devices shown is merely illustrative and can be expanded to any number as needed.

[0020] Please continue reading. Figure 2 , Figure 2 This is a schematic diagram of the execution flow of the quota status linkage processing method provided in the embodiments of this application, as shown below. Figure 2 As shown, it includes the following steps: S1. Parse the received quota processing request for the target quota object and determine the request type and object identifier corresponding to the quota processing request.

[0021] Step S1 serves as the unified entry point for the credit limit status linkage processing flow. Its function is to convert credit limit processing requests from different sources and operating scenarios into standardized processing information that the system can recognize. This ensures that subsequent data acquisition, status verification, model prediction, parameter adjustment, and result output all revolve around the same target object. In business scenarios such as credit limit queries, additions, modifications, deletions, credit limit parameter settings, and credit limit usage details queries, front-end pages, business interfaces, or external processing flows may submit different types of credit limit processing requests. If the request type and target object are not accurately identified at the entry stage, subsequent issues such as incorrect data query scope, incorrect credit limit status matching, or incorrect operation flow branches can easily occur. Taking a fintech business scenario as an example, when initiating a credit limit addition, credit limit adjustment, or usage details query for a specific counterparty, it is necessary to first identify the type of the request and extract the object identifier that can locate the counterparty. Then, in subsequent processes, this identifier is linked to basic credit limit data, business usage data, parameter configuration data, and historical processing records to ensure data consistency and process continuity for the same target credit limit object in different processing stages. Step S1 establishes a unified data index foundation for subsequent quota status linkage calculation and processing strategy selection, which helps reduce the risk of data mismatch under multiple operation entry points and improve the automation and accuracy of quota processing.

[0022] S2. Based on the object identifier, obtain the basic data of the target quota object, the data of business usage, the data of quota parameters, the historical processing records, the risk characteristic data and the data synchronized by the collaborator.

[0023] In step S2, after identifying the request entry point, a multi-dimensional credit limit data set is established around the same object identifier. This provides a data foundation for subsequent credit limit status judgment, trend prediction, parameter linkage, and processing result output. Basic credit limit data can be used to characterize the basic credit limit configuration and effective status of the target credit limit object. Business usage data can be used to reflect the actual usage of credit limits by different business processes. Credit limit parameter data can be used to limit control conditions during credit limit processing. Historical processing records can be used to reflect historical operations such as credit limit changes, queries, deletions, or parameter adjustments. Risk characteristic data can be used to describe the risk change characteristics of the target credit limit object in the current or historical business processes. Collaborator synchronization data can be used to characterize the credit limit usage status or related evaluation results provided by external collaborative nodes. Taking a fintech business scenario as an example, the credit limit status of the same counterparty may be influenced by basic credit information, different business usage results, historical credit limit adjustments, risk changes, and feedback information from collaborating institutions. If only a single data source is obtained, it can easily lead to a one-sided judgment of the subsequent credit limit status or inaccurate processing results. This step uses object identifiers to uniformly index and associate multiple types of data, enabling quota-related data scattered across different processing stages to enter the same processing chain, which helps improve the integrity and relevance of quota data and the accuracy of subsequent linked processing.

[0024] As an optional implementation, the execution process of step S2 may further include the following sub-steps S21 to S25.

[0025] S21. Query the quota management database based on the object identifier to obtain the basic quota data and quota parameter data corresponding to the target quota object.

[0026] Step S21 involves acquiring basic and control data of the target credit limit object from the credit limit management dimension, providing a foundational data source for subsequent credit limit status determination. Basic credit limit data may include the target credit limit object's identity attributes, credit limit range, credit limit status, validity period, etc., while credit limit parameter data may include control thresholds, verification rules, configuration parameters, etc., involved in credit limit processing. Querying through object identifiers ensures that the acquired data corresponds to the target credit limit object, preventing data commingling between different objects and providing a stable data foundation for subsequent occupancy identification, parameter adjustment, and credit limit processing operations.

[0027] S22. Call the data interface of at least one business processing system to obtain the business usage data corresponding to the target quota object.

[0028] Step S22 involves obtaining the credit limit usage status of the target credit limit object during actual business processing, enabling credit limit status determination to no longer rely solely on static credit limit configuration data. Business usage data typically originates from the actual business processing chain and reflects the target credit limit object's status in different business stages, including usage, release, outstanding balance, and completion. Obtaining business usage data through a data interface improves the data linkage between the credit limit processing flow and the business processing flow, ensuring that subsequent current credit limit status data more closely reflects the actual credit limit usage of the target credit limit object.

[0029] S23. Query the historical operation database based on the object identifier to obtain the historical processing records corresponding to the target quota object.

[0030] Step S23 involves acquiring the historical processing trajectory corresponding to the target credit limit object, providing historical evidence for subsequent credit limit trend analysis, risk adjustment, and traceability record generation. Historical processing records reflect the processing status of the target credit limit object during previous credit limit additions, changes, deletions, queries, or parameter adjustments. This data can be used to identify patterns in credit limit changes, operation frequency, stability of processing results, and historical anomalies. Querying the historical operation database using the object identifier allows for the association between historical processing behaviors and current credit limit processing requests, improving the reliability of subsequent predictive analysis and traceability processing.

[0031] S24. Obtain risk characteristic data and collaborator synchronization data associated with the target quota object based on the object identifier.

[0032] Step S24 supplements the target credit limit object with risk and collaboration dimension data, ensuring that subsequent credit limit processing is not limited to internal credit limit status and business usage results. Risk characteristic data reflects the target credit limit object's characteristics in terms of risk level, changing trends, or abnormal states, while collaborator synchronization data reflects the synchronization results of external collaborators regarding the target credit limit object's relevant credit limit status, usage status, or evaluation status. Introducing this type of data enhances the ability to perceive external changing factors and collaboration information during the credit limit status linkage processing, providing richer data input for subsequent credit limit prediction models and parameter linkage adjustments.

[0033] S25. Perform field matching and time identifier alignment on the basic data of the credit limit, business usage data, credit limit parameter data, historical processing records, risk characteristic data and collaborator synchronization data to obtain the credit limit dataset to be processed corresponding to the target credit limit object.

[0034] Step S25 involves formatting and integrating the aforementioned multi-source data in a time-series manner, enabling data from different sources, with different structures, and different update times to be incorporated into the same processing dataset. Field matching processing can be used to unify the correspondence between object fields, quota fields, status fields, and parameter fields from different data sources, while time stamp alignment processing can be used to determine the validity of various types of data under the same processing cycle or the same status judgment node. By generating a quota dataset to be processed, a data foundation with a unified structure and consistent time caliber can be provided for subsequent consistency verification, occupancy status identification, model prediction, and parameter linkage adjustment.

[0035] As an example, in a fintech business scenario, the target credit limit object can be a counterparty participating in the business processing. Upon receiving a credit limit processing request for that counterparty, the credit limit management database can be queried based on the object identifier to obtain the corresponding basic credit limit data and parameter data, such as the credit limit range, credit limit validity status, and credit limit control parameters. Further, data interfaces associated with the business processing flow are invoked to obtain the business usage data generated by the counterparty within the current business cycle, reflecting changes in its credit limit usage. Subsequently, the historical operation database is queried based on the same object identifier to obtain historical processing records of the counterparty in previous credit limit additions, adjustments, deletions, queries, or parameter settings. Simultaneously, risk characteristic data and collaborator synchronization data associated with the counterparty are obtained to supplement its risk change status and external collaborator feedback information. After obtaining the above-mentioned multiple types of data, the object field, quota field, status field, parameter field, and time field in the data from different sources are matched and aligned, so that different data can be integrated under the same object dimension and the same time caliber, thereby obtaining the quota dataset to be processed corresponding to the target quota object, providing a unified data foundation for subsequent quota status identification, predictive analysis, and parameter linkage processing.

[0036] Through steps S21 to S25, using object identifiers as a unified index, basic credit limit data, business usage data, credit limit parameter data, historical processing records, risk characteristic data, and collaborator synchronization data can be integrated into the same data processing chain, reducing data mismatch issues caused by inconsistencies in field definitions, timeframes, or object identifiers between different data sources. By separately acquiring basic configuration, business usage, historical operations, risk characteristics, and collaborative synchronization information, the completeness and coverage of the credit limit data to be processed can be improved, ensuring that subsequent credit limit status identification no longer relies on a single data source. Through field matching and time identifier alignment, the correlation and temporal consistency between multi-source data can be enhanced, providing a structurally unified and consistent data foundation for subsequent usage status judgment, credit limit trend prediction, parameter linkage adjustment, and traceability record generation. Therefore, this helps improve the accuracy of credit limit data acquisition, the reliability of credit limit status judgment, and the stability of subsequent linkage processing.

[0037] S3. Perform consistency verification and occupancy status identification on the basic data of the credit limit, the data occupied by the business, and the data synchronized by the collaborators to obtain the current credit limit status data.

[0038] In step S3, after acquiring various types of credit limit-related data, the consistency and occupancy status of the data corresponding to the target credit limit object are identified to form status data reflecting the current credit limit usage. Basic credit limit data typically characterizes the credit limit range, effective credit limit status, and basic configuration of the target credit limit object. Business occupancy data characterizes the credit limit occupancy, release, or incomplete status of the target credit limit object in different business processing stages. Collaborator synchronization data supplements the credit limit usage status or risk assessment status reported by external collaboration nodes. By performing consistency checks on the above data, it can be determined whether data from different sources points to the same target credit limit object, and whether there are inconsistencies, status conflicts, or synchronization anomalies between the data. Based on this, occupancy status identification of the business occupancy data can determine the target credit limit object's currently occupied credit limit, available credit limit, occupancy change status, and related business processing status. Taking a fintech business scenario as an example, the credit limit status of the same counterparty may be simultaneously affected by internal business occupancy and collaborator synchronization information. Without consistency checks and occupancy status identification, inconsistencies between the credit limit status and the actual business processing situation can easily occur. This step provides an accurate current status basis for subsequent quota trend prediction, quota parameter linkage adjustment, and quota processing operations, thereby improving the reliability and accuracy of quota status linkage processing.

[0039] As an optional implementation, the execution process of step S3 may further include the following sub-steps S31 to S35.

[0040] S31. Based on the object identifier, perform object consistency verification on the basic quota data, business usage data, and collaborator synchronization data.

[0041] Step S31 verifies whether data from multiple sources all correspond to the same target quota object, avoiding data attribution errors during subsequent quota status identification. Since basic quota data, business occupancy data, and collaborator-synchronized data may originate from different data tables, business interfaces, or collaborative nodes, there may be differences in field representations for object names, object codes, institution identifiers, or association numbers from different sources. Consistency verification through object identifiers confirms the object correspondence between various data types, eliminating anomalies such as object mismatches, incorrect field mappings, or erroneous associations of collaborative data, thereby ensuring that subsequent occupancy status identification is based on the same target quota object.

[0042] S32. Determine the data validity verification result based on the credit limit validity status corresponding to the basic credit limit data, the business processing status corresponding to the business usage data, and the synchronization status corresponding to the collaborator's synchronization data.

[0043] Step S32 determines whether the currently acquired data can participate in the credit limit status calculation from the perspectives of data availability and status validity. Credit limit validity status can be used to determine whether the basic credit limit data is in a state of availability, expiration, freeze, or invalidation; business processing status can be used to determine whether the business-occupied data is in a state of pending processing, occupied, released, revoked, or completed; and collaborator synchronization status can be used to determine whether external collaborative data has been successfully synchronized, whether it has timed out, or whether there is any abnormal feedback. By comprehensively determining the data validity verification result based on the above statuses, invalid data, abnormal data, or data that does not meet the processing conditions can be excluded before the credit limit status is generated, improving the accuracy of the current credit limit status data.

[0044] S33. When the object consistency check passes and the data validity check result meets the preset conditions, determine the occupied quota data and the available quota data based on the business occupied data.

[0045] In step S33, based on the consistency of objects and the validity of data, the actual usage of the target credit limit object is identified according to the business occupancy data. Occupied credit limit data can be determined based on business data in an occupied or pending settlement state, while available credit limit data can be determined by combining the credit limit range in the basic credit limit data with the occupied credit limit data. By setting object consistency verification and data validity verification as prerequisites, direct calculation of credit limit occupancy can be avoided when data ownership is unclear or the status is abnormal, reducing the risk of distorted credit limit calculation results and providing more reliable data for subsequent credit limit processing operations.

[0046] S34. Based on the occupied quota data, available quota data, and synchronized data from collaborators, generate the current quota status data corresponding to the target quota object.

[0047] Step S34 combines the occupied quota data, available quota data, and collaborator-synchronized data to form structured data that characterizes the current quota status of the target quota object. This current quota status data not only reflects the quota occupancy and remaining status of the target quota object but also integrates the external usage or risk status represented by the collaborator-synchronized data, giving the quota status result a more complete description. Generating this current quota status data provides a unified input for subsequent quota trend prediction, quota parameter adjustment, quota processing strategy selection, and quota status linkage processing output.

[0048] As an example, in a fintech business scenario, the target credit limit object can be a counterparty participating in the business processing. After obtaining the counterparty's basic credit limit data, business usage data, and collaborator synchronization data, a consistency check can be performed on the above data based on the object identifier to determine whether all types of data correspond to the same counterparty, avoiding deviations in credit limit status judgment due to inconsistent object codes, incorrect field mappings, or misassociation of collaborative data. Furthermore, based on the credit limit validity status in the basic credit limit data, the business processing status in the business usage data, and the synchronization status in the collaborator synchronization data, it can be determined whether the current data meets the conditions for participating in credit limit status identification. When the object consistency check passes and the data validity check result meets the preset conditions, the credit limit data currently used by the counterparty can be determined based on the business usage data, and the available credit limit data can be determined in conjunction with the basic credit limit data. Subsequently, the occupied quota data, available quota data, and synchronized data from collaborating parties are correlated to generate the current quota status data corresponding to the counterparty. This allows the current quota status data to simultaneously reflect the quota occupancy status, remaining quota status, and synchronized status feedback from collaborating parties, providing a status basis for subsequent quota prediction, parameter adjustment, and quota processing operations.

[0049] Through steps S31 to S34, object attribution and status validity checks are performed on basic credit limit data, business usage data, and collaborator synchronization data from different sources before generating the current credit limit status data. This reduces credit limit status identification errors caused by inconsistent object identifiers, abnormal business status, or abnormal synchronization status. After successful verification, the occupied credit limit data and available credit limit data are determined based on the business usage data, ensuring that the credit limit status results are based on valid and accurate data, avoiding invalid or abnormal data from participating in credit limit usage judgment. Furthermore, incorporating collaborator synchronization data into the generation process of the current credit limit status data allows the data to reflect not only internal business usage but also the synchronization or association status reported by collaborators. Therefore, this helps improve the accuracy, completeness, and consistency of the current credit limit status data, providing a reliable status basis for subsequent credit limit trend prediction, target credit limit control parameter adjustment, and credit limit processing operations.

[0050] S4. Input the historical processing records and risk characteristic data into the preset quota prediction model to obtain the quota trend parameters and risk adjustment parameters corresponding to the target quota object.

[0051] Step S4, based on the obtained current credit limit data, introduces historical processing and risk change dimensions to predict the potential future credit limit change trends of the target credit limit object, thereby forming the basis for credit limit control adjustments. Historical processing records reflect the processing trajectory of the target credit limit object during past credit limit additions, changes, deletions, queries, and parameter adjustments. Risk characteristic data reflects the target credit limit object's risk performance in terms of business status, performance stability, changes in the external environment, or changes in collaborative feedback. By inputting these two types of data into a preset credit limit prediction model, the correlation between historical credit limit change patterns and current risk characteristics can be analyzed, thereby obtaining credit limit trend parameters and risk adjustment parameters. The credit limit trend parameters characterize the upward, downward, or stable trend of the target credit limit object in subsequent processing cycles, while the risk adjustment parameters characterize the degree of influence of the current risk status on the intensity of credit limit control. Taking the fintech business scenario as an example, the credit limit status of the same counterparty may be affected by factors such as historical changes in usage, frequency of historical credit limit adjustments, current risk level, and feedback status from collaborating parties. This step allows credit limit processing to no longer be based solely on current static data, but rather to form a predictive processing basis by combining historical changes and risk characteristics, which helps to improve the adaptability of subsequent credit limit parameter linkage adjustments and the accuracy of risk identification.

[0052] As an alternative implementation method, please continue reading. Figure 3 , Figure 3This is a schematic diagram illustrating the execution flow of the credit limit prediction model processing data in the credit limit status linkage processing method provided in this application embodiment, as follows: Figure 3 As shown. The execution process of step S4 above may also specifically include the following steps S41 to S45.

[0053] S41. Sort the historical processing records according to the processing time identifier, and extract the quota change data, usage change data, processing type data and processing result data from the sorted historical processing records to obtain the historical quota feature sequence.

[0054] Step S41 converts discrete historical processing records into a time-ordered feature sequence, enabling the credit limit prediction model to identify the credit limit change patterns of the target credit limit object within different processing cycles. Sorting by processing time identifiers preserves the chronological relationship of credit limit processing actions, avoiding distortion of trend features caused by disordered input of historical records. Further extraction of credit limit change data, occupancy change data, processing type data, and processing result data allows for the acquisition of feature information directly related to credit limit status changes from historical records. This ensures that the obtained historical credit limit feature sequence reflects the magnitude of credit limit adjustments, the direction of occupancy changes, processing frequency, and the stability of processing results, providing a time-seriesd input basis for subsequent predictive analysis.

[0055] S42. Perform feature normalization and risk level coding on the risk feature data to obtain the risk feature vector.

[0056] Step S42 converts the risk feature data into a numerical feature representation suitable for model processing. Risk feature data may contain data with different dimensions, value ranges, or levels. Directly involving these data in model calculations could lead to some features having an excessively high or low impact on the prediction results. Feature normalization unifies the numerical scale of different risk features. Risk level coding converts risk levels, risk states, or risk categories into computable coded features, ensuring that the resulting risk feature vector accurately represents the risk state of the target asset, providing standardized input for subsequent feature fusion and model prediction.

[0057] S43. Perform feature fusion on the historical quota feature sequence and risk feature vector to obtain the quota prediction input data.

[0058] Step S43 integrates historical credit limit change characteristics with risk status characteristics, ensuring that the credit limit prediction input data simultaneously includes both time-dimensional patterns of credit limit changes and risk-dimensional influencing factors. The historical credit limit feature sequence reflects the credit limit change trend of the target credit limit object during past processing, while the risk feature vector reflects the impact of current or recent risk status on credit limit control. Through feature fusion, a correlation between historical credit limit behavior and risk characteristics can be established, avoiding reliance on single-dimensional data in the prediction process and thus improving the information completeness and expressive power of the credit limit prediction input data.

[0059] S44. Input the input data for the credit limit prediction model to obtain the probability of credit limit status change, credit limit occupancy trend value and risk matching coefficient output by the model.

[0060] In step S44, the fused quota prediction input data is processed using a pre-defined quota prediction model to obtain model output results that characterize the subsequent quota status changes of the target quota object. The quota status change probability can be used to characterize the likelihood of a change in quota status; the quota occupancy trend value can be used to characterize the direction and degree of change in quota occupancy during subsequent processing cycles; and the risk matching coefficient can be used to characterize the matching relationship between current risk characteristics and quota control requirements. Through the above model output, historical processing patterns and the impact of risk characteristics can be converted into quantitative results that can be used for subsequent quota control.

[0061] S45. Based on the probability of credit limit status changes, credit limit occupancy trend value, and risk matching coefficient, determine the credit limit trend parameter and risk adjustment parameter corresponding to the target credit limit object.

[0062] Step S45 involves using the model output to establish the control basis for subsequent linkage adjustments of credit limit parameters. The probability of credit limit status change reflects whether there is a risk of credit limit status change for the target credit limit object; the credit limit occupancy trend value reflects the pressure of credit limit usage or the trend of credit limit release; and the risk matching coefficient reflects the impact of the current risk level on the strength of credit limit control. By combining the above results, credit limit trend parameters and risk adjustment parameters can be determined, enabling subsequent credit limit parameter adjustments to no longer rely solely on static configuration but to dynamically adapt by combining historical change patterns and risk status, thereby improving the foresight and accuracy of credit limit control.

[0063] As an example, in a fintech business scenario, the target credit limit object can be the counterparty participating in the business processing. For records of credit limit additions, adjustments, deletions, changes in credit limit usage, and processing results generated by this counterparty within a historical period, they can first be sorted according to their processing time identifiers, forming a data sequence with a chronological relationship. Then, credit limit change data, usage change data, processing type data, and processing result data are extracted from the sorted historical processing records to obtain a historical credit limit feature sequence reflecting the credit limit change process of this counterparty. Simultaneously, the risk characteristic data corresponding to this counterparty can be normalized, and the risk level, risk status, or risk category can be encoded to obtain a risk feature vector. Finally, the historical credit limit feature sequence and the risk feature vector are fused to form the input data for credit limit prediction, ensuring that the input data simultaneously includes the historical patterns of credit limit changes and the impact of the current risk status. After inputting the quota prediction data into the preset quota prediction model, the probability of quota status change, quota occupancy trend value and risk matching coefficient can be obtained. Based on this, the quota trend parameters and risk adjustment parameters corresponding to the counterparty can be determined, providing a predictive basis for subsequent linkage adjustment of quota parameters.

[0064] Through steps S41 to S45, discrete historical processing records can be converted into a historical quota feature sequence with time order, and risk feature data of different dimensions and levels can be converted into standardized risk feature vectors, thereby improving the structure and computability of the quota prediction input data. By fusing the historical quota feature sequence and risk feature vector, the quota prediction process can simultaneously consider the historical change patterns of quotas and the influencing factors of risk status, avoiding judgments based solely on the current quota status or a single risk indicator. Furthermore, by outputting the probability of quota status changes, quota occupancy trend value, and risk matching coefficient through a preset quota prediction model, historical behavioral characteristics and risk characteristics can be converted into quantitative results that can be used for subsequent quota control. Therefore, the above steps help improve the foresight of quota trend judgment, the accuracy of risk adjustment parameter determination, and the adaptability of subsequent quota parameter linkage adjustments.

[0065] S5. Based on the credit limit trend parameters and risk adjustment parameters, adjust the credit limit parameter data accordingly to obtain the target credit limit control parameters.

[0066] Among them, step S5 converts the predicted quota change trend and risk impact degree into an executable quota control basis, enabling the quota parameter to no longer solely rely on the pre-set static configuration, but to be dynamically adapted according to the historical change pattern and risk status of the target quota object. The quota trend parameter can reflect the increase, decrease or stability of the quota occupancy of the target quota object in the subsequent processing cycle, and the risk adjustment parameter can reflect the influence degree of the current risk characteristics on the quota control intensity. By performing linkage processing on the above parameters and the quota parameter data, parameters such as the quota range, control threshold, expiration control condition, and early warning trigger condition can be adjusted, making the obtained target quota control parameters more in line with the current quota status and the subsequent risk change requirements. Taking the science and technology finance business scenario as an example, when the historical occupancy of a certain counterparty shows a continuous upward trend and the risk characteristics indicate an increase in its risk level, the quota control intensity can be increased or relevant thresholds can be tightened through this step. When the historical occupancy tends to be stable and the risk level is low, the corresponding control parameters can be maintained or moderately relaxed. This step enables the quota processing process to have the capabilities of trend perception and risk adaptation, providing a more accurate parameter basis for subsequent real-time quota calculation, quota processing operations, and hierarchical early warning judgment.

[0067] As an optional implementation manner, the execution process of the above step S5 may specifically include the following sub-steps S51 to S55.

[0068] S51. Extract the basic quota parameter, quota threshold parameter, and expiration control parameter from the quota parameter data.

[0069] Among them, step S51 performs parameter type splitting on the quota parameter data, enabling subsequent linkage adjustments to act on parameters with different functional attributes respectively. The basic quota parameter can be used to represent the quota range, initial quota or available quota benchmark of the target quota object. The quota threshold parameter can be used to represent trigger conditions such as quota occupancy ratio, control line, and early warning line. The expiration control parameter can be used to represent the quota expiration period, expiration processing rule or expiration status control condition. By extracting the above parameters from the quota parameter data, the originally mixed-stored configuration data can be converted into a parameter set with clear control meanings, providing a data basis for subsequent differential adjustments according to trends and risks.

[0070] S52. Determine the quota change direction and quota adjustment amplitude of the target quota object in the target processing cycle according to the quota trend parameter.

[0071] Step S52 identifies the credit limit change trend of the target credit limit object within the target processing cycle based on the credit limit trend parameter. The direction of credit limit change can be used to indicate whether the credit limit occupancy, available credit limit, or credit limit demand is increasing, decreasing, or remaining stable in subsequent cycles, and the credit limit adjustment magnitude can be used to indicate the adjustment intensity corresponding to this change trend. By determining the direction of credit limit change and the credit limit adjustment magnitude, the trend information output by the prediction model can be converted into control quantities that can be used for subsequent parameter adjustments, enabling credit limit parameter adjustments to have trend perception capabilities and avoiding processing based solely on the current static credit limit status.

[0072] S53. Determine the risk correction coefficient corresponding to the credit limit threshold parameter based on the risk adjustment parameter.

[0073] Step S53 converts the risk adjustment parameters into a correction basis for the credit limit threshold parameters. The risk adjustment parameters reflect the current risk level or degree of risk change of the target credit limit object, while the credit limit threshold parameters are typically used to control credit limit occupancy, credit limit criticality, and processing trigger conditions. By determining the risk correction coefficient, a mapping relationship between risk status and threshold control strength can be established, enabling the credit limit threshold parameters to adapt to changes in risk status, thereby improving the risk sensitivity of subsequent credit limit control.

[0074] S54. Based on the direction of quota change, the adjustment range of quota, and the risk correction coefficient, the basic quota parameters, quota threshold parameters, and maturity control parameters are adjusted in a coordinated manner to obtain candidate quota control parameters.

[0075] Step S54 involves applying both trend and risk factors to the credit limit control parameters, enabling coordinated adjustments among multiple parameters. The direction and magnitude of credit limit changes influence the direction and extent of adjustments to the basic credit limit parameters. The risk correction coefficient affects the tightening or loosening of the credit limit threshold parameters, and the maturity control parameters are updated synchronously based on credit limit changes and risk status. By coordinating the adjustments to the basic credit limit parameters, credit limit threshold parameters, and maturity control parameters, candidate credit limit control parameters that match the current trend prediction results and risk status can be obtained, improving the adaptability between credit limit parameter configuration and actual processing status.

[0076] S55. Perform parameter boundary verification and threshold relationship verification on the candidate quota control parameters, and determine the candidate quota control parameters that pass the verification as the target quota control parameters.

[0077] Step S55 imposes validity constraints on candidate quota control parameters to prevent parameters from exceeding reasonable ranges or threshold relationships from occurring due to linked adjustments. Parameter boundary verification can be used to determine whether candidate quota control parameters meet preset minimum, maximum, validity period, or value format requirements. Threshold relationship verification can be used to determine whether different control thresholds meet preset size relationships, hierarchical relationships, or triggering sequences. Only after a candidate quota control parameter passes verification is it determined as the target quota control parameter, which improves the rationality, stability, and executability of parameter adjustment results.

[0078] As an example, in a fintech business scenario, the target credit limit object can be the counterparty participating in the business processing. First, basic credit limit parameters, credit limit threshold parameters, and maturity control parameters can be extracted from the counterparty's credit limit parameter data. The basic credit limit parameters characterize the basis of its credit limit configuration, the credit limit threshold parameters characterize the criteria for judging when credit limit occupancy reaches different control conditions, and the maturity control parameters characterize the validity period of the credit limit and the rules for handling maturity. Subsequently, the direction of credit limit changes and the magnitude of credit limit adjustments for the counterparty within the target processing period can be determined based on the credit limit trend parameters, such as identifying whether its credit limit occupancy shows an upward, downward, or stable trend. Further, a risk correction coefficient is determined based on the risk adjustment parameters to apply to the credit limit threshold parameters, enabling threshold control to adapt to changes in risk status. On this basis, the basic credit limit parameters, credit limit threshold parameters, and maturity control parameters are adjusted in a linked manner according to the direction of credit limit changes, the magnitude of credit limit adjustments, and the risk correction coefficient, resulting in candidate credit limit control parameters. Finally, parameter boundary verification and threshold relationship verification are performed on the candidate quota control parameters. After confirming that the candidate quota control parameters meet the requirements of value range, hierarchical relationship and triggering order, they are determined as the target quota control parameters.

[0079] Through steps S51 to S55, the credit limit parameter data can be broken down into basic credit limit parameters, credit limit threshold parameters, and maturity control parameters. These parameters, combined with credit limit trend parameters and risk adjustment parameters, are then used to differentiate between different types of control parameters. This allows the target credit limit control parameters to simultaneously reflect credit limit change trends, changes in risk status, and maturity control requirements. By determining the direction of credit limit changes and the magnitude of credit limit adjustments, the adaptability of credit limit parameter adjustments to subsequent changes in credit limit status can be improved. By determining the risk correction coefficient, the responsiveness of the credit limit threshold parameters to risk changes can be enhanced. Furthermore, through parameter boundary verification and threshold relationship verification, it is possible to avoid parameters exceeding reasonable ranges or abnormal threshold hierarchy relationships after linkage adjustments. Therefore, the above steps help improve the accuracy, rationality, and stability of credit limit parameter adjustments, providing a reliable control parameter basis for subsequent credit limit processing operations and credit limit early warning judgments.

[0080] S6. Execute the corresponding credit limit processing operation according to the request type, current credit limit status data, and target credit limit control parameters, and output the credit limit status linkage processing result.

[0081] In step S6, after obtaining the current credit limit status data and target credit limit control parameters, the appropriate processing flow is selected based on the type of credit limit processing request. This ensures that different credit limit operations can be executed under a unified data foundation and control parameter constraints. The current credit limit status data reflects the target credit limit object's credit limit occupancy, available credit limit, effective status, and collaborator synchronization status. The target credit limit control parameters reflect the credit limit control requirements after trend prediction and risk adjustment. By combining the request type, current credit limit status data, and target credit limit control parameters, differentiated processing can be performed in different processing scenarios such as credit limit setting, credit limit change, credit limit deletion verification, and record query. This avoids fixed operations based solely on the request type while ignoring the current credit limit status and risk control requirements. Taking a fintech business scenario as an example, when initiating a credit limit adjustment request for a counterparty, the system can determine whether the adjusted credit limit meets the processing conditions by combining its currently occupied credit limit, available credit limit, and target credit limit control parameters. When initiating a credit limit deletion request, the system can determine whether deletion is allowed by combining the current occupancy status. When initiating a record query request, the system can output the corresponding historical record results based on the object identifier and processing status. This step can transform the aforementioned data acquisition, status recognition, model prediction, and parameter adjustment results into specific credit limit processing results, improving the accuracy, adaptability, and consistency of credit limit processing operations under different request scenarios.

[0082] As an optional implementation, the execution process of step S6 may further include the following sub-steps S61 to S65.

[0083] S61. Extract the occupied quota data, pending business data, pending release business data, and quota validity status from the current quota status data.

[0084] Step S61 extracts status elements directly related to real-time quota calculation from the current quota status data, enabling subsequent quota processing to proceed based on the latest status data. Occupied quota data reflects the quota occupancy status of the target quota object before the current processing node; pending quota data reflects business data about to be included in quota occupancy calculation; pending release data reflects data to be deducted from the occupied quota due to business completion, cancellation, or expiration; and quota validity status reflects whether the target quota object's current quota is available, expired, frozen, or invalid. By extracting the above data, status input can be provided for the subsequent calculation of the target occupied quota, target available quota, and quota occupancy ratio.

[0085] S62. Based on the occupied quota data, pending quota data, and pending release quota data, calculate in real time the target occupied quota, target available quota, and quota occupancy ratio corresponding to the target quota object.

[0086] In step S62, the credit status of the target credit object is calculated in real time based on the current credit limit occupancy and pending business changes. Occupied credit data provides the basis for current occupancy, pending business data reflects new or soon-to-be-effective occupancy changes, and pending business data reflects business changes that need to be released from the occupied credit limit. By comprehensively processing the above data, the target occupied credit limit, target available credit limit, and credit limit occupancy ratio can be obtained, ensuring that the credit limit status is updated promptly according to business occupancy and release, reducing credit limit judgment errors caused by data lag.

[0087] S63. Update the quota status corresponding to the target quota object based on the target quota occupied quota, target available quota, quota occupied ratio and quota validity status.

[0088] In step S63, the credit limit status of the target credit limit object is updated based on the real-time calculation results and the credit limit validity status. The target occupied credit limit reflects the current actual occupied level, the target available credit limit reflects the range of credit limit that can still be used, the credit limit occupancy ratio reflects the degree of credit limit usage, and the credit limit validity status determines whether the current credit limit meets the conditions for continued processing. By using the above data together to update the credit limit status, the updated credit limit status can simultaneously reflect the occupied scale, available space, usage ratio, and validity conditions, providing an accurate status basis for subsequent execution of different types of credit limit processing operations.

[0089] S64. Based on the request type, perform quota setting processing, quota change processing, quota deletion verification processing, or record query processing on the updated quota status.

[0090] In step S64, based on the request type, corresponding processing operations are performed on the updated credit limit status, enabling different credit limit requests to adapt to different processing logics. For credit limit setting processing, the setting result can be determined based on the updated credit limit status. For credit limit change processing, it can be determined whether the changed credit limit meets control requirements based on the updated credit limit status. For credit limit deletion verification processing, it can be determined whether the target credit limit object still has occupancy or restriction conditions based on the updated credit limit status. For record query processing, the corresponding processing record can be associated based on the updated credit limit status. By performing specific processing after updating the credit limit status, the accuracy and consistency of processing results under different request types can be improved.

[0091] S65. Output the target occupied quota, target available quota, quota occupancy ratio, and corresponding processing results as the quota status linkage processing results.

[0092] Step S65 outputs the real-time calculation results and corresponding processing results in a unified manner, forming result data that reflects the linkage processing of credit limit status. The target occupied credit limit, target available credit limit, and credit limit occupancy ratio can be used to characterize the credit limit usage of the target credit limit object under the current processing node. The corresponding processing results can be used to characterize the execution results of operations such as credit limit setting, credit limit change, credit limit deletion verification, or record query. By using the above data together as the linkage processing result of credit limit status, the output result not only includes the operation conclusion but also reflects the corresponding credit limit status basis, improving the interpretability and traceability of the processing result.

[0093] As an example, in a fintech business scenario, the target credit limit object can be the counterparty participating in the business processing. After obtaining the current credit limit status data of the counterparty, the data of occupied credit limit, pending business data, pending release business data, and credit limit validity status can be extracted first. Among them, the occupied credit limit data is used to represent the current credit limit occupancy, the pending business data is used to represent the occupancy changes that are about to be added or take effect, and the pending release business data is used to represent the credit limit changes that need to be released due to business completion, cancellation, or expiration. Subsequently, based on the occupied credit limit data, pending business data, and pending release business data, the target occupied credit limit, target available credit limit, and credit limit occupancy ratio corresponding to the counterparty can be calculated. Further, combined with the credit limit validity status, the credit limit status of the counterparty is updated so that the updated credit limit status can reflect the current occupancy level, available credit limit range, credit limit utilization, and credit limit validity. Subsequently, based on the request type of the quota processing request, the updated quota status is processed by quota setting, quota change, quota deletion verification, or record query. The target occupied quota, target available quota, quota occupancy ratio, and corresponding processing results are output as the quota status linkage processing result.

[0094] Through steps S61 to S65, data elements directly related to changes in credit limit occupancy can be extracted from the current credit limit status data. Combined with pending business data and pending release business data, the target occupied credit limit, target available credit limit, and credit limit occupancy ratio are calculated in real time. This ensures that the credit limit status is updated promptly as business changes occur, reducing status judgment bias caused by lagging credit limit data. By using the real-time calculation results and the valid credit limit status together for credit limit status updates, the updated credit limit status can simultaneously reflect the occupancy scale, available range, usage ratio, and validity conditions, improving the completeness of the credit limit status expression. Furthermore, by executing corresponding credit limit processing operations according to the request type and outputting the target occupied credit limit, target available credit limit, credit limit occupancy ratio, and corresponding processing results, the credit limit status linkage processing results not only reflect the operation results but also the credit limit status data upon which the operation results were based. Therefore, the above steps help improve the accuracy of real-time credit limit calculation, the timeliness of credit limit status updates, and the consistency and interpretability of credit limit processing results under different request types.

[0095] S7. Associate the data source, parameter change information, collaborator synchronization information, and operation basis corresponding to the linked storage of quota status processing results, and generate quota processing traceability records corresponding to the target quota object.

[0096] In step S7, after outputting the credit limit processing result, key data and operational basis involved in the formation of the processing result are linked and retained, providing a traceable data foundation for the credit limit processing process. Data sources can be used to identify the acquisition paths and data generation nodes of basic credit limit data, business usage data, risk characteristic data, and synchronized data from collaborating parties. Parameter change information can be used to record the differences in credit limit parameters before and after linkage adjustments. Collaborating party synchronization information can be used to reflect the synchronization status of external collaborating nodes in credit limit status judgment. Operational basis can be used to characterize the data conditions and control parameters upon which processing operations such as credit limit setting, credit limit change, credit limit deletion verification, or record query are based. By linking and storing the above information with the credit limit status linkage processing result, a complete processing chain around the target credit limit object can be formed, avoiding the problem of only saving the final processing result without being able to reconstruct the processing process. Taking a fintech business scenario as an example, when the credit limit status of a counterparty is adjusted or processing restrictions are triggered, the relevant data sources, parameter change process, collaborating party feedback status, and corresponding operational basis can be viewed through the credit limit processing traceability record, thereby improving the data transparency, verifiability, and compliance management capabilities of the credit limit processing process. This step provides a basis for subsequent historical queries, anomaly checks, responsibility identification, and credit limit strategy optimization, and helps to enhance the traceability and maintainability of the credit limit status linkage processing.

[0097] As an alternative implementation method, please continue reading. Figure 4 , Figure 4 This is a schematic diagram of the execution flow of the multi-level early warning mechanism in the quota status linkage processing method provided in this application embodiment, as shown below. Figure 4 As shown. The execution process of step S4 above may also specifically include the following steps S61 to S64.

[0098] S71. Extract the suggested control threshold, the first early warning threshold, and the second early warning threshold from the target quota control parameters.

[0099] Step S71 involves extracting different levels of credit limit control thresholds from the target credit limit control parameters, providing a parameter basis for subsequent credit limit early warning level judgments. It is suggested that the control thresholds can be used to characterize lower-intensity credit limit control conditions, while the first and second early warning thresholds can be used to characterize credit limit critical conditions under different risk levels. By extracting these thresholds from the target credit limit control parameters, the early warning judgment can be aligned with the aforementioned trend prediction and risk-adjusted control parameters, avoiding the early warning conditions remaining in a static configuration state, thereby improving the adaptability of the credit limit early warning judgment.

[0100] S72. Based on the target quota, target available quota, and quota occupancy ratio, match them with the suggested control threshold, the first warning threshold, and the second warning threshold to determine the quota warning level corresponding to the target quota object.

[0101] In step S72, the real-time calculated credit limit status is matched with control thresholds at different levels to determine the current credit limit warning level for the target credit limit object. The target occupied credit limit reflects the current scale of credit limit occupancy, the target available credit limit reflects the remaining credit limit space, and the credit limit occupancy ratio reflects the degree of credit limit usage. By matching the above data with the suggested control threshold, the first warning threshold, and the second warning threshold, it is possible to identify whether the target credit limit object is in a normal state, close to control conditions, has reached the first warning condition, or has reached the second warning condition, enabling the credit limit warning level to simultaneously reflect the occupancy scale, available space, and occupancy ratio.

[0102] S73. When the credit limit warning level meets the preset reminder conditions, a credit limit reminder message is generated based on the credit limit warning level, the target occupied credit limit, the target available credit limit, and the credit limit occupancy ratio.

[0103] In step S73, when the credit limit warning level reaches the alert trigger condition, a credit limit alert message matching the current credit limit status is generated. The credit limit alert message may include the credit limit warning level, target occupied credit limit, target available credit limit, credit limit occupancy ratio, and corresponding processing prompts, enabling relevant processing steps to promptly identify critical or abnormal credit limit states based on the alert message. By associating the generation conditions of the alert message with the credit limit warning level, invalid alerts can be avoided when the alert conditions are not met, and prompts can be generated promptly when the credit limit status reaches preset risk conditions, improving the timeliness of risk identification during the credit limit processing.

[0104] S74. Link the credit limit warning level and credit limit reminder information to the credit limit status linkage processing result.

[0105] Step S74 links the credit limit warning level and credit limit reminder information with the credit limit status linkage processing result, so that the final processing result not only includes credit limit occupancy, available credit limit, and processing result, but also synchronously reflects the corresponding warning status and reminder content. This linkage processing enables the credit limit status linkage processing result to have a more complete status expression capability, facilitating subsequent result display, historical query, and processing traceability. Simultaneously, incorporating the warning level and reminder information into the processing result also provides a basis for generating subsequent credit limit processing traceability records, improving data continuity between the credit limit warning process and the credit limit processing process.

[0106] As an example, in a fintech business scenario, the target credit limit object can be the counterparty participating in the business processing. After calculating the target occupied credit limit, target available credit limit, and credit limit occupancy ratio, suggested control thresholds, first warning thresholds, and second warning thresholds can be extracted from the target credit limit control parameters. Subsequently, the counterparty's target occupied credit limit, target available credit limit, and credit limit occupancy ratio are matched with the aforementioned thresholds at different levels to determine its current corresponding credit limit warning level. For example, when the credit limit occupancy level is close to the suggested control threshold, it can be determined to be in a suggested control state; when the credit limit occupancy level reaches the first or second warning threshold, it can be determined to be in a warning state of the corresponding level. When the credit limit warning level meets the preset reminder conditions, credit limit reminder information can be generated based on the credit limit warning level, target occupied credit limit, target available credit limit, and credit limit occupancy ratio, so that the reminder information can reflect the current credit limit usage level and corresponding risk level. Finally, the credit limit warning level and credit limit reminder information are linked to the credit limit status linkage processing result, so that the output result not only includes the credit limit occupancy status and processing result, but also synchronously reflects the corresponding warning status and reminder content.

[0107] Through steps S71 to S74, different threshold levels in the target credit limit control parameters can be matched with the real-time calculated target credit limit, target available credit limit, and credit limit occupancy ratio to determine the corresponding credit limit warning level for the target credit limit object, enabling the credit limit risk status to be expressed in a hierarchical manner. By setting suggested control thresholds, first warning thresholds, and second warning thresholds, the credit limit status judgment can move beyond a simple judgment of whether it exceeds the limit, and can identify different states such as the credit limit approaching control conditions, reaching lower warning conditions, or reaching higher warning conditions, thus improving the precision of credit limit risk identification. Generating credit limit reminder information when the credit limit warning level meets preset reminder conditions ensures that critical or abnormal credit limit states are output in a timely manner, reducing processing risks caused by untimely detection of changes in credit limit occupancy. Further linking the credit limit warning level and credit limit reminder information to the credit limit status linkage processing results can improve the completeness and traceability of the processing results, providing a basis for subsequent result display, historical query, anomaly verification, and credit limit strategy adjustment.

[0108] The quota status linkage processing method provided in this application integrates quota status identification, trend prediction, parameter adjustment, real-time calculation, hierarchical early warning, and processing traceability into a single processing chain. This allows the quota processing process to move beyond static data maintenance or single operation result output, instead forming a continuous and linkage-based status processing mechanism around the target quota object. On one hand, after unified association and verification of quota data from multiple sources, the impact of factors such as inconsistency of objects, inconsistent data standards, and delayed status updates on quota judgment results can be reduced, improving the accuracy and completeness of the current quota status data. On the other hand, by combining historical processing records and risk characteristic data for trend analysis and applying the analysis results to quota control parameters, the quota parameter configuration can be adapted to the actual changing trend and risk status of the target quota object, enhancing the foresight and dynamic adjustment capabilities of quota control. Simultaneously, real-time calculation and hierarchical early warning judgment based on the target occupied quota, target available quota, and quota occupancy ratio can more promptly identify quota critical states and risk change states, avoiding passive processing only after the quota is exceeded. Furthermore, by associating and storing data sources, parameter changes, collaborating party synchronization information, and operational basis, key evidence in the credit limit processing process can be fully preserved, providing a traceable data foundation for subsequent queries, verifications, and strategy optimization. Therefore, this method can improve the accuracy of credit limit status judgment, the adaptability of credit limit control parameter adjustments, the timeliness of early warning processing, and the traceability of the credit limit processing process, thereby enhancing the data processing efficiency and risk control capabilities of credit limit management in fintech business scenarios.

[0109] Please continue reading. Figure 5 , Figure 5This is a schematic diagram of the system structure of the quota status linkage processing device provided in the embodiments of this application, as shown below. Figure 5 As shown, the quota status linkage processing device 50 includes: a quota request parsing module 51, a quota data acquisition module 52, a status verification and identification module 53, a quota model prediction module 54, a parameter linkage adjustment module 55, a quota operation execution module 56, and a traceability record generation module 57.

[0110] The quota request parsing module 51 is specifically used to parse the received quota processing request for the target quota object and determine the request type and object identifier corresponding to the quota processing request; the quota data acquisition module 52 is specifically used to acquire the quota basic data, business usage data, quota parameter data, historical processing records, risk feature data, and collaborator synchronization data corresponding to the target quota object according to the object identifier; the status verification and identification module 53 is specifically used to perform consistency verification and usage status identification on the quota basic data, the business usage data, and the collaborator synchronization data to obtain the current quota status data; the quota model prediction module 54 is specifically used to combine the historical processing records and the risk feature data Inputting a preset credit limit prediction model, the system obtains the credit limit trend parameters and risk adjustment parameters corresponding to the target credit limit object. The parameter linkage adjustment module 55 is specifically used to perform linkage adjustment on the credit limit parameter data according to the credit limit trend parameters and the risk adjustment parameters to obtain the target credit limit control parameters. The credit limit operation execution module 56 is specifically used to execute the corresponding credit limit processing operation according to the request type, the current credit limit status data, and the target credit limit control parameters, and output the credit limit status linkage processing result. The traceability record generation module 57 is specifically used to associate and store the data source, parameter change information, collaborator synchronization information, and operation basis corresponding to the credit limit status linkage processing result, and generate the credit limit processing traceability record corresponding to the target credit limit object.

[0111] As an optional implementation, the quota data acquisition module 52 is further configured to: query the quota management database based on the object identifier to obtain the quota basic data and quota parameter data corresponding to the target quota object; call the data interface of at least one business processing system to obtain the business occupancy data corresponding to the target quota object; query the historical operation database based on the object identifier to obtain the historical processing records corresponding to the target quota object; obtain the risk characteristic data and collaborator synchronization data associated with the target quota object based on the object identifier; and perform field matching and time identifier alignment processing on the quota basic data, the business occupancy data, the quota parameter data, the historical processing records, the risk characteristic data, and the collaborator synchronization data to obtain the quota dataset to be processed corresponding to the target quota object.

[0112] As an optional implementation, the status verification and identification module 53 is specifically used to perform object consistency verification on the basic quota data, the business usage data, and the collaborator synchronization data according to the object identifier; determine the data validity verification result according to the quota validity status corresponding to the basic quota data, the business processing status corresponding to the business usage data, and the synchronization status corresponding to the collaborator synchronization data; when the object consistency verification passes and the data validity verification result meets preset conditions, determine the occupied quota data and available quota data according to the business usage data; and generate the current quota status number corresponding to the target quota object according to the occupied quota data, the available quota data, and the collaborator synchronization data.

[0113] As an optional implementation, the credit limit model prediction module 54 is specifically used to sort the historical processing records according to the processing time identifier, and extract credit limit change data, occupancy change data, processing type data, and processing result data from the sorted historical processing records to obtain a historical credit limit feature sequence; perform feature normalization processing and risk level encoding processing on the risk feature data to obtain a risk feature vector; perform feature fusion on the historical credit limit feature sequence and the risk feature vector to obtain credit limit prediction input data; input the credit limit prediction input data into the preset credit limit prediction model to obtain the model output credit limit status change probability, credit limit occupancy trend value, and risk matching coefficient; and determine the credit limit trend parameter and risk adjustment parameter corresponding to the target credit limit object based on the credit limit status change probability, the credit limit occupancy trend value, and the risk matching coefficient.

[0114] As an optional implementation, the parameter linkage adjustment module 55 is specifically used to extract basic credit limit parameters, credit limit threshold parameters, and maturity control parameters from the credit limit parameter data; determine the direction of credit limit change and the magnitude of credit limit adjustment for the target credit limit object within the target processing cycle based on the credit limit trend parameters; determine the risk correction coefficient corresponding to the credit limit threshold parameters based on the risk adjustment parameters; perform linkage adjustment on the basic credit limit parameters, the credit limit threshold parameters, and the maturity control parameters according to the direction of credit limit change, the magnitude of credit limit adjustment, and the risk correction coefficient to obtain candidate credit limit control parameters; perform parameter boundary verification and threshold relationship verification on the candidate credit limit control parameters, and determine the candidate credit limit control parameters that pass the verification as the target credit limit control parameters.

[0115] As an optional implementation, the quota operation execution module 56 is specifically used to extract occupied quota data, pending quota data, pending release quota data, and quota validity status from the current quota status data; calculate in real time the target occupied quota, target available quota, and quota occupancy ratio corresponding to the target quota object based on the occupied quota data, the pending quota data, and the pending release quota data; update the quota status corresponding to the target quota object based on the target occupied quota, the target available quota, the quota occupancy ratio, and the quota validity status; perform quota setting processing, quota change processing, quota deletion verification processing, or record query processing on the updated quota status according to the request type; and output the target occupied quota, the target available quota, the quota occupancy ratio, and the corresponding processing results as the quota status linkage processing results.

[0116] As an optional implementation, the credit limit status linkage processing device further includes a multi-level early warning mechanism module. Specifically, this module extracts a suggested control threshold, a first early warning threshold, and a second early warning threshold from the target credit limit control parameters; matches the target occupied credit limit, the target available credit limit, and the credit limit occupancy ratio with the suggested control threshold, the first early warning threshold, and the second early warning threshold to determine the credit limit early warning level corresponding to the target credit limit object; when the credit limit early warning level meets preset reminder conditions, generates credit limit reminder information based on the credit limit early warning level, the target occupied credit limit, the target available credit limit, and the credit limit occupancy ratio; and associates the credit limit early warning level and the credit limit reminder information with the credit limit status linkage processing result.

[0117] It should be noted that the above-mentioned credit limit status linkage processing device can execute the credit limit status linkage processing method provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in the embodiments of the credit limit status linkage processing device can be found in the credit limit status linkage processing method provided in the embodiments of this application.

[0118] Figure 6 This is a schematic diagram of the hardware structure of the electronic device for implementing the quota status linkage processing method provided in this application embodiment, as shown below. Figure 6 As shown, the electronic device 600 includes: One or more processors 610 and memory 620, Figure 6 Take the 610 processor as an example.

[0119] The processor 610 and the memory 620 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0120] The memory 620, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the quota status linkage processing method in the embodiments of this application. The processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 620, thereby implementing the quota status linkage processing method in the above-described method embodiments.

[0121] The memory 620 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the quota status linkage processing device. Furthermore, the memory 620 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 may optionally include memory remotely located relative to the processor 610, and these remote memories can be connected to the quota status linkage processing device 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.

[0122] The one or more modules are stored in the memory 620. When executed by the one or more processors 610, they perform the quota status linkage processing method in any of the above method embodiments. For example, they perform the above-described... Figure 2 Method steps S1 to S7, Figure 3 Method steps S41 to S45, Figure 4 Steps S61 to S64 in the method are implemented. Figure 5 The functions of modules 51-57 in the document.

[0123] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0124] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions that are executed by one or more processors, for example... Figure 6 One of the processors 610 can enable the one or more processors to execute the quota status linkage processing method in any of the above method embodiments, for example, to execute the above-described... Figure 2 Method steps S1 to S7, Figure 3 Method steps S41 to S45, Figure 4Steps S61 to S64 in the method are implemented. Figure 5 The functions of modules 51-57 in the document.

[0125] This application provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions, which, when executed by the electronic device, enable the electronic device to perform the credit limit status linkage processing method in any of the above method embodiments, for example, to perform the above-described... Figure 2 Method steps S1 to S7, Figure 3 Method steps S41 to S45, Figure 4 Steps S61 to S64 in the method are implemented. Figure 5 The functions of modules 51-57 in the document.

[0126] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0128] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for linking credit limit status processing, characterized in that, include: Parse the received quota processing request for the target quota object, and determine the request type and object identifier corresponding to the quota processing request; Based on the object identifier, obtain the basic data of the credit limit, business usage data, credit limit parameter data, historical processing records, risk characteristic data and collaborator synchronization data corresponding to the target credit limit object; The consistency verification and occupation status identification of the basic quota data, the business occupancy data and the collaborator synchronization data are performed to obtain the current quota status data. The historical processing records and the risk characteristic data are input into a preset credit limit prediction model to obtain the credit limit trend parameters and risk adjustment parameters corresponding to the target credit limit object; Based on the credit limit trend parameter and the risk adjustment parameter, the credit limit parameter data is adjusted in a linked manner to obtain the target credit limit control parameter; According to the request type, the current credit limit status data, and the target credit limit control parameters, execute the corresponding credit limit processing operation and output the credit limit status linkage processing result; The system associates and stores the data source, parameter change information, collaborator synchronization information, and operation basis corresponding to the linked processing results of the credit limit status, and generates a credit limit processing traceability record corresponding to the target credit limit object.

2. The credit limit status linkage processing method according to claim 1, characterized in that, The step of obtaining the basic credit limit data, business usage data, credit limit parameter data, historical processing records, risk characteristic data, and collaborator synchronization data corresponding to the target credit limit object based on the object identifier includes: The quota management database is queried based on the object identifier to obtain the basic quota data and quota parameter data corresponding to the target quota object; Call the data interface of at least one business processing system to obtain the business usage data corresponding to the target quota object; The historical operation database is queried based on the object identifier to obtain the historical processing records corresponding to the target quota object; Based on the object identifier, obtain the risk characteristic data and collaborator synchronization data associated with the target quota object; The basic credit limit data, the business usage data, the credit limit parameter data, the historical processing records, the risk characteristic data, and the collaborator synchronization data are subjected to field matching and time identifier alignment processing to obtain the unprocessed credit limit dataset corresponding to the target credit limit object.

3. The credit limit status linkage processing method according to claim 1, characterized in that, The process of performing consistency verification and occupancy status identification on the basic quota data, the business usage data, and the collaborator's synchronized data to obtain the current quota status data includes: Based on the object identifier, perform object consistency verification on the basic quota data, the business usage data, and the collaborator synchronization data; The data validity verification result is determined based on the validity status of the credit limit corresponding to the basic credit limit data, the business processing status corresponding to the business usage data, and the synchronization status corresponding to the synchronization data of the collaborating party. When the object consistency check passes and the data validity check result meets the preset conditions, the occupied quota data and available quota data are determined based on the service occupied data. Based on the occupied quota data, the available quota data, and the collaborator synchronization data, the current quota status data corresponding to the target quota object is generated.

4. The credit limit status linkage processing method according to claim 1, characterized in that, The step of inputting the historical processing records and the risk characteristic data into a preset credit limit prediction model to obtain the credit limit trend parameters and risk adjustment parameters corresponding to the target credit limit object includes: The historical processing records are sorted according to the processing time identifier, and the quota change data, usage change data, processing type data and processing result data are extracted from the sorted historical processing records to obtain the historical quota feature sequence. The risk feature data is subjected to feature normalization and risk level encoding to obtain a risk feature vector; The historical credit limit feature sequence and the risk feature vector are fused to obtain credit limit prediction input data; Input the credit limit prediction data into the preset credit limit prediction model to obtain the model output the probability of credit limit status change, credit limit occupancy trend value and risk matching coefficient; Based on the probability of credit limit status change, the credit limit occupancy trend value, and the risk matching coefficient, the credit limit trend parameter and risk adjustment parameter corresponding to the target credit limit object are determined.

5. The credit limit status linkage processing method according to claim 1, characterized in that, The step of adjusting the credit limit parameter data in conjunction with the credit limit trend parameter and the risk adjustment parameter to obtain the target credit limit control parameter includes: Extract the basic credit limit parameters, credit limit threshold parameters, and expiration control parameters from the credit limit parameter data; Based on the credit limit trend parameters, determine the direction of credit limit change and the magnitude of credit limit adjustment for the target credit limit object within the target processing cycle; Based on the risk adjustment parameters, determine the risk correction coefficient corresponding to the credit limit threshold parameter; Based on the direction of the credit limit change, the magnitude of the credit limit adjustment, and the risk correction coefficient, the basic credit limit parameter, the credit limit threshold parameter, and the maturity control parameter are adjusted in a coordinated manner to obtain candidate credit limit control parameters; The candidate quota control parameters are subjected to parameter boundary verification and threshold relationship verification, and the candidate quota control parameters that pass the verification are determined as the target quota control parameters.

6. The credit limit status linkage processing method according to claim 1, characterized in that, The step of performing corresponding credit limit processing operations according to the request type, the current credit limit status data, and the target credit limit control parameters, and outputting the credit limit status linkage processing result, includes: Extract the occupied quota data, pending business data, pending business data, and quota validity status from the current quota status data; Based on the occupied quota data, the pending business data, and the pending release business data, the target occupied quota, target available quota, and quota occupancy ratio corresponding to the target quota object are calculated in real time. Update the quota status corresponding to the target quota object based on the target quota occupied, the target available quota, the quota occupied ratio, and the quota validity status; According to the request type, perform quota setting processing, quota change processing, quota deletion verification processing, or record query processing on the updated quota status; The target occupied quota, the target available quota, the quota occupancy ratio, and the corresponding processing result are output as the quota status linkage processing result.

7. The credit limit status linkage processing method according to claim 6, characterized in that, Before outputting the target occupied quota, the target available quota, the quota occupancy ratio, and the corresponding processing result as the quota status linkage processing result, the method further includes: Extract suggested control thresholds, first warning thresholds, and second warning thresholds from the target quota control parameters; Based on the target quota occupied, the target available quota, and the quota occupied ratio, the quota warning level corresponding to the target quota object is determined by matching them with the suggested control threshold, the first warning threshold, and the second warning threshold, respectively. When the credit limit warning level meets the preset reminder conditions, a credit limit reminder message is generated based on the credit limit warning level, the target occupied credit limit, the target available credit limit, and the credit limit occupancy ratio. The credit limit warning level and the credit limit reminder information are linked to the credit limit status linkage processing result.

8. A credit limit status linkage processing device, characterized in that, include: The quota request parsing module is used to parse the received quota processing request for the target quota object and determine the request type and object identifier corresponding to the quota processing request; The quota data acquisition module is used to acquire, based on the object identifier, the quota basic data, business usage data, quota parameter data, historical processing records, risk characteristic data and collaborator synchronization data corresponding to the target quota object; The status verification and identification module is used to perform consistency verification and occupation status identification on the basic quota data, the business occupancy data and the collaborator synchronization data to obtain the current quota status data. The credit limit model prediction module is used to input the historical processing records and the risk characteristic data into a preset credit limit prediction model to obtain the credit limit trend parameters and risk adjustment parameters corresponding to the target credit limit object; The parameter linkage adjustment module is used to adjust the quota parameter data in linkage according to the quota trend parameter and the risk adjustment parameter to obtain the target quota control parameter; The quota operation execution module is used to perform corresponding quota processing operations according to the request type, the current quota status data and the target quota control parameters, and output the quota status linkage processing result; The traceability record generation module is used to associate and store the data source, parameter change information, collaborator synchronization information and operation basis corresponding to the credit limit status linkage processing result, and generate the credit limit processing traceability record corresponding to the target credit limit object.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the quota status linkage processing method according to any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by an electronic device, cause the electronic device to perform the limit status linkage processing method according to any one of claims 1-7.

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