Credit investigation data processing method and device

Abstract

This specification provides a credit data processing method and apparatus. One credit data processing method includes: handling interface calls from various data source systems to the data permission interface of a trusted data space; determining the data permission scope based on the data source category and credit authorization contract of each data source system; parsing the de-identified credit data uploaded by each data source system within a shared data sandbox of the trusted data space; inputting the parsing results into the credit assessment model corresponding to the credit subject for credit assessment to obtain credit indicators; returning credit indicators based on access requests from the credit access system; thereby achieving data parsing of the de-identified credit data of the credit subject through the trusted data space; and further utilizing the parsing results to achieve credit assessment and credit access.

Description

[0001] This patent application is a divisional application of Chinese patent application No. 2025120345370, filed on December 30, 2025, entitled "Credit Data Processing Method and Apparatus". Technical Field

[0002] This document relates to the field of data processing technology, and in particular to a credit data processing method and apparatus. Background Technology

[0003] With the continuous development and promotion of Internet technology, online services based on Internet technology have emerged, such as credit services. Credit services provide users with credit-related data, and the application scenarios of the credit-related data provided by credit services are becoming increasingly widespread. For example, transactions are conducted based on users' credit-related data in transaction scenarios. As the credit system continues to improve, users are paying more and more attention to credit, which puts credit service providers under greater service pressure and challenges. Summary of the Invention

[0004] This specification provides one or more embodiments of a credit reporting data processing method, comprising: for each data source system's data permission interface call to a trusted data space, determining the data permission range based on the data source category and credit authorization contract of each data source system; acquiring de-identified credit data uploaded by each data source system to a shared data sandbox in the trusted data space; obtaining the de-identified credit data by collecting and de-identifying data on the credit reporting object based on the data permission range; parsing the de-identified credit data within the shared data sandbox, and inputting the parsing results into the credit assessment model corresponding to the credit reporting object to perform credit assessment and obtain credit indicators; acquiring the credit indicators output by the shared data sandbox and synchronizing the credit indicators with the credit reporting access system according to the access request of the credit reporting access system.

[0005] This specification provides one or more embodiments of a credit data processing apparatus, comprising: a permission decision module configured to make interface calls to the data permission interfaces of each data source system to a trusted data space, and to obtain a data permission range based on the data source category and credit authorization contract of each data source system; a data acquisition module configured to acquire de-identified credit data uploaded by each data source system to a shared data sandbox in the trusted data space; the de-identified credit data being collected and de-identified based on the data permission range for the credit reporting object; a data parsing module configured to parse the de-identified credit data within the shared data sandbox, and input the parsing results into a credit assessment model corresponding to the credit reporting object to obtain credit indicators; and an indicator acquisition module configured to acquire the credit indicators output by the shared data sandbox and synchronize the credit indicators to the credit access system according to the access request of the credit access system.

[0006] This specification provides one or more embodiments of a credit data processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: make interface calls to the data permission interfaces of each data source system to a trusted data space; make permission decisions based on the data source category and credit authorization contract of each data source system to obtain a data permission range; acquire de-identified credit data uploaded by each data source system to a shared data sandbox in the trusted data space; collect and de-identify the de-identified credit data for credit subjects based on the data permission range; parse the de-identified credit data within the shared data sandbox and input the parsing results into a credit assessment model corresponding to the credit subject to obtain credit indicators; and acquire the credit indicators output by the shared data sandbox to synchronize the credit indicators with the credit access system according to access requests from the credit access system.

[0007] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions, which, when executed, perform the following steps: For interface calls to the data permission interfaces of each data source system to the trusted data space, determine the data permission scope based on the data source category and credit authorization contract of each data source system; acquire de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space; collect and de-identify the de-identified credit data for the credit reporting object based on the data permission scope; parse the de-identified credit data within the shared data sandbox and input the parsing results into the credit assessment model corresponding to the credit reporting object to perform credit assessment and obtain credit indicators; acquire the credit indicators output by the shared data sandbox and synchronize the credit indicators with the credit access system according to the access request of the credit access system. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 A schematic diagram illustrating the implementation environment of a credit data processing method provided in one or more embodiments of this specification; Figure 2 A flowchart illustrating a credit data processing method provided in one or more embodiments of this specification; Figure 3 A flowchart illustrating a credit data processing method for a credit repair scenario, provided for one or more embodiments of this specification; Figure 4 A schematic diagram of an embodiment of a credit data processing device provided in one or more embodiments of this specification; Figure 5 This is a schematic diagram of the structure of a credit data processing device provided for one or more embodiments of this specification. Detailed Implementation

[0009] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0010] The credit data processing method provided in one or more embodiments of this specification is applicable to the trusted data space implementation environment. (Refer to...) Figure 1 The implementation environment includes at least: The trusted data space operation system 101; in addition, the implementation environment may also include various data source systems and credit access systems 102; Among them, the operation system 101 is used to make permission decisions based on the data source category and credit authorization contract of each data source system to obtain the data permission scope of each data source system. In the shared data sandbox of the trusted data space, the de-identified credit data uploaded by each data source system is parsed. The parsing results are input into the credit assessment model corresponding to the credit subject to conduct credit assessment and obtain credit indicators. The credit indicators are synchronized to the credit access system according to the access request of the credit access system. The credit access system 102 is used to receive credit indicators synchronized by the operating system of the trusted data space; Each data source system is used to call the data permission interface of the trusted data space, and makes permission decisions to obtain the scope of data permissions based on the data source category and credit authorization contract of each data source system; there can be multiple data source systems: data source system 103-1... data source system 103-N; In this implementation environment, the Trusted Data Space Operation System 101, for the interface calls of each data source system to the Trusted Data Space data permission interface, makes permission decisions based on the data source category and credit authorization contract of each data source system to obtain the data permission scope. In the shared data sandbox of the Trusted Data Space, it performs data parsing on the de-identified credit data uploaded by each data source system, inputs the parsing results into the credit assessment model corresponding to the credit subject to perform credit assessment and obtain credit indicators. Based on the access request of the credit access system 102, it synchronizes the credit indicators to the credit access system 102. In this way, the Trusted Data Space is used to realize the data parsing of the de-identified credit data of the credit subject, and further utilizes the parsing results to realize credit assessment and credit access.

[0011] One or more embodiments of a credit data processing method provided in this specification are as follows: Reference Figure 2The credit data processing method provided in this embodiment can be applied to the operation system of trusted data space, specifically including steps S202 to S208.

[0012] Step S202: For the interface calls of each data source system to the data permission interface of the trusted data space, the data permission scope is obtained by making permission decisions based on the data source category and credit authorization contract of each data source system.

[0013] The data source systems mentioned in this embodiment refer to the institutional systems of data source institutions that provide credit data of credit subjects. Each data source system can be a data source system connected to a trusted data space. For example, data source systems include resource institution systems, payment institution systems, e-commerce systems, credit leasing systems, government agency systems, food delivery systems, ride-hailing systems, tourism agency systems, and / or talent recruitment systems. Resource institution systems can be resource borrowing institution systems that provide resource borrowing, lending, or borrowing to credit subjects. In addition, each data source system may also include resource management institution systems, which can be management institution systems that conduct resource management or fund management, such as financial institution systems. Optionally, each data source system includes resource institution systems, government agency systems, transaction institution systems, payment systems, and / or credit leasing systems connected to a trusted data space.

[0014] The trusted data space refers to a data circulation infrastructure that connects multiple entities based on consensus rules to achieve data resource sharing and utilization. The trusted data space may contain trusted management units and resource interaction units. The trusted management unit can provide functions related to identity authentication and management, digital contract and performance management, usage control, and / or evidence preservation and traceability to ensure the secure circulation of data within the trusted data space. The resource interaction unit can effectively manage and utilize data resources within the trusted data space. The data permission interface is an interface that makes decisions regarding the scope of data permissions for data source systems. The data permission interface can be the data permission interface of the trusted management unit of the trusted data space. The data permission scope can be the collection scope of credit data collected by each data source system from credit reporting objects.

[0015] The data source category of each data source system can be the data source role of each data source system. For example, if the data source system is a traffic management agency system, then the data source category of the data source system is the traffic management category. The credit authorization contract refers to the authorization contract signed between the credit reporting agency and the credit subject to conduct credit data collection and / or credit assessment.

[0016] In practical implementation, for each data source system's interface call to the trusted data space's data permission interface, permission decisions can be made based on the data source category and credit authorization contract of each data source system to obtain the data permission range. Specifically, each data source system can call the trusted data space's data permission interface, and further, the trusted data space's operation system can, for each data source system's interface call to the trusted data space's data permission interface, make permission decisions based on the data source category and credit authorization contract of each data source system to obtain the data permission range. In this way, permission decisions can be automated through data source category and credit authorization contract, improving the convenience and flexibility of permission decisions. Optionally, the data permission range of each data source system can be carried in the data collection request sent by the credit reporting system to each data source system.

[0017] Among them, the credit reporting system refers to the system that accesses credit indicators to the credit reporting access system, that is, it can be a system that provides access to credit indicators to the credit reporting access system; the data collection request refers to the request that instruct various data source systems to collect credit data.

[0018] Specifically, each data source system can call the data permission interface of the Trusted Data Space based on credit authorization credentials. Correspondingly, the Trusted Data Space operation system can authenticate each data source system based on credit authorization credentials for the data permission interface calls of each data source system. After successful authentication, it can make permission decisions based on the data source category and credit authorization contract of each data source system to obtain the data permission scope. On this basis, it can return the respective data permission scope to each data source system through the data permission interface, and can also synchronize the data permission scope of each data source system to the credit reporting system. Furthermore, the credit reporting system can periodically or according to the collection instructions of the credit reporting agency personnel send data collection requests to each data source system. The data collection request can carry the data permission scope of each data source system. Each data source system can collect credit data and perform data anonymization based on the data permission scope to obtain the anonymized credit data of the credit reporting object. Here, the credit reporting object can be an individual user or an institution. The institution can be any type of institution at the same level, or it can be an organization within the same level, such as an enterprise, educational institution, medical institution, or, for example, the technical department within an enterprise.

[0019] In practice, to protect the credit privacy of the credit subject and ensure that data not authorized by the credit subject cannot be collected, the credit subject can sign a credit authorization contract with the credit reporting system or credit reporting agency. In one optional implementation method provided in this embodiment, the credit authorization contract is obtained in the following way: Based on the credit subject's confirmation instruction to the authorization request notification, the authentication interface of the trusted data space is invoked to authenticate the credit subject's identity; If identity verification is successful, a credit authorization contract is generated that includes the identity hash value of the credit subject, the scope of data collection, the authorization period, and / or the data destruction policy.

[0020] Among them, the authorization request notification can be an authorization notice; the data destruction strategy refers to the destruction strategy for destroying the collected de-identified credit data; the data collection scope refers to the collection scope authorized by the credit subject for collecting credit data; optionally, the permission decision is made after the identity authentication of each data source system is passed based on the credit authorization certificate, and the credit authorization certificate is generated based on the credit authorization contract through the data catalog module of the trusted data space; the data catalog module can be the data catalog module of the resource interaction unit of the trusted data space.

[0021] Based on this, in order to meet the needs of credit subjects for destroying the collected de-identified credit data, in an optional implementation of this embodiment, after the credit authorization contract containing the identity hash value of the credit subject, the data collection scope, the authorization period and / or the data destruction strategy is executed, the following operations are also performed: The system invokes a smart contract to detect the authorization period, and upon detection that the authorization period has expired, it destroys or archives the collected de-identified credit data.

[0022] Optionally, smart contracts are obtained by constructing executable programs based on credit authorization contracts.

[0023] In practice, to protect the data privacy of credit subjects as much as possible, the data permission scope of each data source system can be dynamically determined. This ensures that each data source system collects data from credit subjects as needed. Specifically, in the process of determining the data permission scope based on the data source category and credit authorization contract of each data source system, the data source category and credit authorization contract can be input into the decision engine to determine the data permission scope. Alternatively, the credit purpose authorized by the credit subject can be read from the credit authorization contract, and the data permission scope can be determined based on the credit purpose and data source category. Here, the credit purpose can be the purpose of credit indicators, such as credit leasing.

[0024] It should be noted that the above-mentioned interface calls for the data permission interface of each data source system to the trusted data space, which make permission decisions based on the data source category and credit authorization contract of each data source system to obtain the data permission range, can be replaced by interface calls for the data permission interface of each data source system to the trusted data space, which make permission decisions based on the data source category and / or credit authorization contract of each data source system to obtain the data permission range; or it can be replaced by making permission decisions based on the data source category and / or credit authorization contract of each data source system to obtain the data permission range, and forming a new implementation method with other processing steps provided in this embodiment; step S202 may also be omitted, and steps S204 to S208 below can be executed directly.

[0025] Step S204: Obtain the de-identified credit data of the shared data sandbox uploaded by each data source system to the trusted data space.

[0026] The above-mentioned interface calls for data permission interfaces of each data source system to the Trusted Data Space determine the scope of data permissions based on the data source category and credit authorization contract of each data source system. In this step, the de-identified credit data uploaded by each data source system to the shared data sandbox of the Trusted Data Space is obtained. In this way, the de-identified credit data is isolated from the outside world through the shared data sandbox, so as to ensure the data security of the de-identified credit data.

[0027] The shared data sandbox in the trusted data space described in this embodiment refers to a data sandbox that can be used by various data source systems in the trusted data space. The data sandbox is a secure and controllable closed environment in the trusted data space, where data can only flow and be processed within the data sandbox and cannot be copied, exported, or tampered with to the outside.

[0028] De-identified credit data refers to credit data obtained by de-identifying the credit data collected from credit subjects. This de-identified credit data includes data from various institutional levels. For example, it includes tax or tax evasion records, utility bill payment records, and / or traffic violation records from first-level institutional systems; communication bill payment records, loan records, loan overdue records, parking fee payment records, and / or parking fee arrears records from second-level institutional systems; and lease performance records and / or lease default records from third-level institutional systems. Optionally, de-identified credit data is obtained by collecting and de-identifying credit data from credit subjects based on the data access permissions of each data source system. De-identification here includes converting credit data into credit data ranges, such as converting a user's default status into a default level.

[0029] Optionally, the shared data sandbox is allocated based on the application requests submitted by the credit reporting system; optionally, the de-identified credit data is uploaded based on the access tokens of the shared data sandbox submitted by each data source system, and the access tokens are synchronized from the credit reporting system to each data source system. Specifically, the shared data sandbox can belong to the credit reporting system. The credit reporting system can submit an application request to the operating system of the trusted data space. Based on the application request, the operating system can allocate a shared data sandbox and an access token for the shared data sandbox to the credit reporting system. The credit reporting system can synchronize the access token of the shared data sandbox to each data source system. Each data source system can upload de-identified credit data to the shared data sandbox of the trusted data space based on the access token of the shared data sandbox. Furthermore, it can obtain the de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space. Specifically, the operating system of the trusted data space can obtain the de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space.

[0030] It should be noted that the above-mentioned operation of obtaining the de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space can be replaced by obtaining the de-identified credit data or credit data uploaded by each data source system, and combined with other processing steps provided in this embodiment to form a new implementation method.

[0031] Step S206: Perform data parsing on the de-identified credit data within the shared data sandbox, and input the parsing results into the credit assessment model corresponding to the credit subject to obtain credit indicators.

[0032] The above-mentioned de-identified credit data uploaded by various data source systems to the shared data sandbox of the trusted data space is now being obtained. In this step, the de-identified credit data is parsed within the shared data sandbox of the trusted data space, and the parsing results are input into the credit assessment model corresponding to the credit subject to conduct credit assessment and obtain credit indicators.

[0033] The credit assessment model described in this embodiment can be a model for conducting credit assessments, specifically a large language model; the credit indicators refer to quantitative indicators that quantify the credit status of the credit subject, such as credit rating or credit score.

[0034] In practical applications, the de-identified credit data uploaded by various data source systems can be quite complex. To improve the accuracy and effectiveness of subsequent credit assessments, this embodiment provides an optional implementation where, during the data parsing process, the de-identified credit data uploaded by various data source systems undergoes data cleaning, data integration, and / or credit feature extraction to obtain credit features as the parsing result. Specifically, the following operations can be performed: Data cleaning and integration are performed on the de-identified credit data uploaded from various data source systems to obtain integrated credit data; Credit features are extracted from the integrated credit data to obtain the credit features as the analysis results.

[0035] Data integration refers to the aggregation of credit data of the same type; credit features refer to the integration of credit elements in credit data, such as the number of days of loan delinquency and the number of traffic violations committed by the credit subject.

[0036] Based on the above-mentioned data cleaning and integration of the de-identified credit data uploaded from various data source systems to obtain integrated credit data, in order to improve the accuracy of credit features obtained in subsequent credit feature extraction, and thus improve the accuracy and effectiveness of credit assessment, in an optional implementation of this embodiment, after performing data cleaning on the de-identified credit data uploaded from various data source systems, the following operations are also performed: Perform multi-source cross-verification on the cleaned and de-identified credit data from each data source system, and perform data integration operation after the cross-verification is passed; Optionally, multi-source cross-verification includes: verifying whether the cleaned and de-identified credit data of every two data source systems match. If they match, the cross-verification is considered successful; if they do not match, the cross-verification is considered unsuccessful and no action is required.

[0037] Specifically, during the credit assessment process using the credit assessment model, credit indicators can be calculated based on the credit characteristics and feature weights of each feature type in the analysis results. Furthermore, the feature weights and / or feature types used in the credit assessment process may need to be registered or reported to relevant departments in advance. Therefore, during the credit assessment process, the feature weights, feature types, and / or indicator calculation process can be synchronized to the trusted management unit of the trusted data space for credit indicator verification. After successful verification, the credit indicators can be output to ensure the effectiveness of the credit assessment. In one optional implementation method provided in this embodiment, the following operations are performed during the credit assessment process: Credit indicators are obtained by calculating credit indicators based on the credit features and feature weights of each feature type in the analysis results. The feature weights, feature types, and indicator calculation process are synchronized to the trusted management unit in the trusted data space, and the credit indicators are verified through the trusted management unit. After the verification is passed, the credit indicators are output.

[0038] Among them, credit features refer to the credit elements included in the analysis results, such as loan overdue date, loan overdue days, and traffic violation level; each feature type refers to the feature type of the credit feature, for example, if the credit feature is loan overdue date, then the feature type is loan overdue date type.

[0039] Specifically, during the process of verifying credit indicators through the trusted management unit, the trusted management unit can verify whether the feature weights and feature types match the feature weights and feature types registered with the relevant departments, and whether the indicator calculation process is valid. If yes, the indicator verification is confirmed to be passed; if no, the indicator verification is confirmed to be failed. If the indicator verification fails, the credit indicator can be cleared to conduct a new credit assessment.

[0040] It should be noted that the above-mentioned operation of parsing de-identified credit data in the shared data sandbox and inputting the parsing results into the credit assessment model corresponding to the credit subject to obtain credit indicators can be replaced by inputting de-identified credit data into the credit assessment model corresponding to the credit subject in the shared data sandbox to obtain credit indicators, or it can be replaced by conducting credit assessment on the credit subject based on de-identified credit data in the shared data sandbox to obtain credit indicators, and forming a new implementation method with other processing steps provided in this embodiment.

[0041] Step S208: Obtain the credit indicators output by the shared data sandbox to synchronize the credit indicators to the credit access system according to the access request of the credit access system.

[0042] The above-mentioned process involves parsing de-identified credit data within a shared data sandbox in a trusted data space, and inputting the parsing results into the credit assessment model corresponding to the credit subject to obtain credit indicators. In this step, the credit indicators output by the shared data sandbox are obtained to synchronize the credit indicators with the credit access system according to the access request of the credit access system, thereby achieving the effect of providing access to credit indicators to external credit access systems. The credit access system mentioned in this embodiment refers to an institutional system that needs to use the credit indicators of the credit subject, such as a credit leasing system, a resource system, and / or a talent recruitment system.

[0043] In practical implementation, based on the above-mentioned extraction of credit features from integrated credit data to obtain credit features as the analysis result, there may still be missing credit features. To address this, and to improve the comprehensiveness and completeness of credit assessment, in one optional implementation of this embodiment, a data collection request containing the missing credit features of the credit subject is generated and sent to the corresponding data source system to re-acquire the de-identified credit data corresponding to the missing credit features. Specifically, the following operations may also be performed: Identify the credit deficiencies of individuals based on their credit characteristics; Generate a data collection request containing missing credit information and send it to the corresponding data source system to re-acquire the de-identified credit data corresponding to the missing credit information.

[0044] Among them, missing credit items refer to missing credit items, such as communication payment items or parking fee payment items.

[0045] Specifically, after identifying the missing credit information of the credit subject based on credit characteristics, a matching data source system can be determined from various data source systems based on the missing credit information. The data permission range of the data source system can be adjusted based on the missing credit information to obtain an adjusted permission range. Furthermore, a data collection request containing the adjusted permission range can be generated and sent to the data source system to re-obtain de-identified credit data according to the adjusted permission range.

[0046] Furthermore, based on the re-acquisition of the anonymized credit data corresponding to the missing credit items, the credit subject has a need for credit repair. Therefore, in order to meet the diverse needs of the credit subject for credit repair, in one optional implementation of this embodiment, after the above-mentioned generation of a data collection request containing the missing credit items and sending it to the corresponding data source system to re-acquire the anonymized credit data corresponding to the missing credit items, the credit subject is further repaired based on the historical credit records of the credit subject queried from the blockchain and / or the anonymized credit data corresponding to the missing credit items to obtain the credit repair result. Specifically, the following operations can also be performed: Based on the credit repair request of the credit subject, the historical credit record of the credit subject is queried from the blockchain; Credit repair results are obtained by performing credit repair based on historical credit records and anonymized credit data corresponding to missing credit items.

[0047] Historical credit records may include the historical credit indicators of the credit subject.

[0048] Specifically, in the process of obtaining credit repair results by performing credit repair based on historical credit records and desensitized credit data corresponding to missing credit items, credit assessment can be performed based on historical credit records and / or desensitized credit data corresponding to missing credit items to obtain repaired credit indicators, and the credit indicators of the credit subject can be updated based on the repaired credit indicators.

[0049] In practical applications, the de-identified credit data provided by various data source systems may still be considered relatively private data. Therefore, each data source system may have a need to prevent external entities from knowing the original de-identified credit data. To address this, and to meet the diverse data privacy and security needs of each data source system, ensuring that the de-identified credit data of each data source system is not known to external systems, this embodiment provides an optional implementation method. Within the corresponding data sandbox in the trusted data space of each data source system, multi-party calculations are performed on the de-identified credit data uploaded by each data source system to obtain intermediate credit data. Then, the intermediate credit data is merged through a shared data sandbox to obtain merged credit data. Specifically, the following operations can also be performed: Obtain the de-identified credit data uploaded by each data source system to its respective data sandbox in the trusted data space; Within the data sandboxes corresponding to each data source system, credit features are extracted from the de-identified credit data uploaded by each data source system, and intermediate credit features are obtained through multi-party calculations based on the extracted credit features. Intermediate credit features are passed into a shared data sandbox for feature merging to obtain merged credit features.

[0050] Among them, multi-party computation can be multi-party secure computation, and multi-party computation can be replaced by privacy-preserving computation or privacy computation; intermediate credit features can be intermediate state features.

[0051] Based on this, in order to improve the flexibility of credit access during the process of synchronizing credit indicators with the credit access system according to the access request, this embodiment provides an optional implementation method in which the following operations are performed: Based on the system type of the credit access system carried in the access request, the credit indicators are converted according to the merged credit characteristics, and the conversion results are returned to the credit access system.

[0052] The system type can be the type or role of the credit access agency corresponding to the credit access system. For example, if the credit access system is a resource management agency system, then the system type is resource management type; if the credit access system is a credit leasing system, then the system type is credit leasing type.

[0053] Furthermore, in practical applications, different system types may focus on different credit characteristics. Therefore, in order to ensure that the conversion results returned from the credit reporting access system are more closely aligned with the service domain of the credit reporting access system, this embodiment provides an optional implementation method. During the process of converting credit indicators based on merged credit characteristics according to the conversion method corresponding to the system type of the credit reporting access system carried in the access request, key credit characteristics can be extracted from the merged credit characteristics based on the system type. Based on these key credit characteristics, the credit indicators can be enhanced to obtain enhanced credit indicators as the conversion result. Specifically, the following operations can be performed: Based on the system type, key credit features are extracted from the merged credit features, and the key credit features are input into the credit enhancement model corresponding to the system type to calculate the credit enhancement parameters. The enhanced credit score is calculated based on credit score indicators and credit score enhancement parameters as the conversion result.

[0054] Among them, credit enhancement parameters can be adjustment parameters or adjustment coefficients that adjust credit indicators.

[0055] Specifically, in the process of calculating enhanced credit indicators based on credit indicators and credit enhancement parameters, the product of the credit indicators and the credit enhancement parameters can be calculated as the enhanced credit indicators.

[0056] In practical applications, after receiving credit indicators, the credit access system may also possess internal credit data. There is a need to combine this internal credit data with the credit indicators to predict the credit risk of the credit subject. Therefore, to meet the credit risk prediction needs of the credit access system and enable it to easily import internal credit data for risk prediction, this embodiment provides an optional implementation method. After receiving credit indicators, the credit access system uses a data sandbox in the trusted data space to predict the credit risk of the credit subject based on the credit indicators and the credit data of the credit subject within the credit access system. Specifically, the following operations can be performed: Input the credit data of credit indicators and credit subjects in the credit access system into the data sandbox of the credit access system in the trusted data space; Credit risk data is obtained by using a data sandbox to predict the credit risk of credit subjects based on credit indicators and credit data from the credit access system.

[0057] In practical applications, credit subjects often need to submit credit access requests to the trusted data space to view their own credit indicators. To address this and meet the diverse needs of credit subjects for credit access, and to increase their willingness to authorize credit information, this embodiment provides an optional implementation that, based on the request type of the credit access request submitted by the credit subject to the trusted data space, matches the target data source system in each data source system and adjusts permissions to obtain the scope of adjusted permissions. Within the shared data sandbox, credit data is updated based on the credit data uploaded by the target data source system according to the adjusted permission scope, and the updated credit results are returned to the credit subject. Specifically, the following operations can also be performed: Based on the request type of the credit access request submitted by the credit subject to the trusted data space, the target data source system is matched in each data source system and the scope of the adjustment permission is obtained by adjusting the permissions. Import the credit data and credit processing model corresponding to the request type obtained by the target data source system through data collection according to the adjusted permission scope into the shared data sandbox; Credit data is input into the credit reporting processing model within the shared data sandbox for credit reporting update processing, and the updated credit reporting results are returned to the credit reporting object.

[0058] Credit data may include de-identified credit data and / or un-identified credit data; the type of de-identification of credit data may be determined based on the data source hierarchy of the target data source system.

[0059] Optionally, credit indicators include credit scores, credit dimension scores of multiple credit dimensions, and / or feature impact values ​​of multiple credit features under each credit dimension. The request types for credit access requests include credit repair requests for credit scores, credit objection requests for a target credit dimension among multiple credit dimensions, or credit objection requests for a target credit feature among multiple credit features.

[0060] Based on this, in an optional implementation of this embodiment, during the process of inputting credit data into the credit reporting processing model for credit reporting update processing and obtaining the credit reporting update result, the credit reporting update result is obtained by updating the credit reporting based on the credit data of the credit reporting object in the target credit reporting dimension and the credit reporting dimension score of the target credit reporting dimension. Specifically, this can be achieved in the following way: The credit data of the credit subject under the target credit dimension is input into the credit dimension model to calculate the credit dimension score, and the updated credit dimension score of the credit subject under the target credit dimension is obtained. If the updated credit score is higher than the target credit score, query the credit access service registered by the credit subject and apply for a rights upgrade to the corresponding credit access system in order to upgrade the credit subject's service rights in the credit access service.

[0061] Among them, the credit score of the target credit dimension can be the historical credit score of the credit subject in the target credit dimension, which can be obtained from the blockchain; the service rights can be the rights of the credit subject in the credit access service. For example, if the credit access service is a credit leasing service, the service rights are the rent reduction ratio; another example is that if the credit access service is a resource acquisition service, the service rights are the resource acquisition interest reduction ratio.

[0062] In addition, if the request type is a credit score repair request, it can be determined whether the credit score is lower than the score threshold. If not, the credit features used to calculate the credit score can be read, and a new credit score can be obtained by re-evaluating the credit score based on the credit features within the shared data sandbox, which can then be returned to the credit subject. If the credit score is lower than the score threshold, the credit data uploaded to the shared data sandbox by each data source system according to the data permission scope can be obtained. The credit data can be input into the credit repair model corresponding to the credit repair request within the shared data sandbox to perform credit repair and obtain an updated credit score, which can then be returned to the credit subject.

[0063] If the request type is a credit objection request for a target credit feature, the feature impact value of the target credit feature can be calculated based on the credit data of the credit subject under the target credit feature, and then returned to the credit subject.

[0064] It should be noted that the data obtained in this manual, such as anonymized credit data, has been authorized by the data subject, that is, authorized by the credit reporting subject, such as the user or enterprise, and does not involve the privacy of the user or enterprise.

[0065] It should be added that each optional implementation method and each feasible execution method in steps S202 to S208 provided in this embodiment can be executed independently as needed, or they can be combined and referenced with each other. At the same time, each specific execution step in each optional implementation method or each feasible execution method can also be executed independently or combined as needed. Any feature in each execution step can be deleted, or any feature in one execution step can be added to another execution step or replace any feature in another execution step. The execution conditions of "if" or "under what circumstances" involved in each step or operation can be directly deleted, and subsequent operations can be executed. This embodiment does not make specific limitations on this.

[0066] It should also be added that, depending on the actual application scenario, step S202 and any of the subsequent steps S204 to S208 can be deleted, or any feature in any step can be deleted. For example, the interface call for the data permission interface of each data source system to the trusted data space in step S202 can be deleted. The execution order of steps S202 to S208 can also be arbitrary.

[0067] The following description uses the application of a credit data processing method provided in this embodiment in a credit repair scenario as an example to further illustrate the credit data processing method provided in this embodiment. (See also...) Figure 3 The credit data processing method applied to credit repair scenarios can be used in the operation system of trusted data space, and specifically includes the following steps.

[0068] Step S302: For the interface calls of each data source system to the data permission interface of the trusted data space, the data permission scope is obtained by making permission decisions based on the data source category and credit authorization contract of each data source system.

[0069] Step S304: Obtain the de-identified credit data of the shared data sandbox uploaded by each data source system to the trusted data space.

[0070] Optionally, de-identified credit data can be obtained by collecting and de-identifying user data based on data access permissions.

[0071] Step S306: In the shared data sandbox, perform data cleaning and data integration on the de-identified credit data uploaded by each data source system to obtain integrated credit data, and extract credit features from the integrated credit data to obtain credit features.

[0072] Step S308: Input credit features into the credit assessment model within the shared data sandbox to conduct credit assessment and obtain credit indicators.

[0073] Step S310: Obtain the credit indicators output by the shared data sandbox to synchronize the credit indicators with the credit access system according to the access request of the credit access system.

[0074] Step S312: Obtain the de-identified credit data uploaded by each data source system to its respective data sandbox in the trusted data space.

[0075] Step S314: In the data sandbox corresponding to each data source system, credit features are extracted from the de-identified credit data uploaded by each data source system, and intermediate credit features are obtained by multi-party calculation based on the extracted credit features.

[0076] Step S316: The intermediate credit features are input into the shared data sandbox for feature merging to obtain merged credit features. The merged credit features are then input into the credit repair model for credit repair to obtain credit repair results. The credit repair results are then synchronized to the credit reporting system to perform credit repair for users.

[0077] It should be noted that any one or more of steps S302 to S316 can be replaced by the corresponding technical means provided in steps S202 to S208 as needed for implementation and deployment. Any one or more of steps S302 to S316 can also be combined into a new implementation method as needed for implementation and deployment. Furthermore, any one or more of steps S302 to S316 can also be combined with one or more of the steps provided in steps S202 to S208 to form a new implementation method, or combined with one or more of the optional implementation methods provided in steps S202 to S208 to form a new implementation method, as needed for actual deployment. These will not be elaborated on here.

[0078] This specification provides an embodiment of a credit data processing device as follows: In the above embodiments, a credit data processing method is provided, and correspondingly, a credit data processing device is also provided, which will be described below with reference to the accompanying drawings.

[0079] Reference Figure 4 The diagram illustrates an embodiment of a credit data processing device provided in this embodiment.

[0080] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0081] This embodiment provides a credit data processing device, including: The permission decision module 402 is configured to make interface calls to the data permission interface of each data source system for the trusted data space, and to make permission decisions based on the data source category and credit authorization contract of each data source system to obtain the data permission range; The data acquisition module 404 is configured to acquire de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space; the de-identified credit data is obtained by collecting and de-identifying data on the credit subject based on the data permission scope; The data parsing module 406 is configured to perform data parsing on the de-identified credit data within the shared data sandbox, and input the parsing results into the credit assessment model corresponding to the credit subject to perform credit assessment and obtain credit indicators. The indicator acquisition module 408 is configured to acquire the credit indicators output by the shared data sandbox and synchronize the credit indicators to the credit access system according to the access request of the credit access system.

[0082] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0083] The following is an example of a credit data processing device provided in this manual: Corresponding to the credit data processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a credit data processing device, which is used to execute the credit data processing method provided above. Figure 5 This is a schematic diagram of the structure of a credit data processing device provided for one or more embodiments of this specification.

[0084] This embodiment provides a credit data processing device, including: like Figure 5As shown, device 500 mainly consists of a communication interface 502, a user interface 504, a processor 506, and a data storage 508. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 510. The communication interface 502 enables device 500 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 502 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 502 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 502 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 502 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 504 includes receiving user input and providing output to the user. Therefore, user interface 504 may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 504 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 504 may include software, circuitry, or other forms of logic capable of transmitting and receiving data to and from external user input / output devices. Additionally or alternatively, device 500 may support remote access from other devices via communication interface 502 or another physical interface (not shown). User interface 504 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 504 may also be configured as a display device for rendering or displaying text fragments.

[0085] Processor 506 may include one or more general-purpose processors and / or special-purpose processors. Data storage 508 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 506. Data storage 508 may include removable and non-removable components.

[0086] Processor 506 is capable of executing program instructions 518 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 508 to perform the various functions described herein. Data storage 508 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 500, enable device 500 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 518 by processor 506 may result in processor 506 using data 512. For example, program instructions 518 may include an operating system 522 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 500 and one or more application programs 520 (e.g., a browser, social application, or game application). Similarly, data 512 may include operating system data 516 and application data 514. Operating system data 516 is primarily accessible to operating system 522, while application data 514 is primarily accessible to one or more application programs 520. Application data 514 may reside in a file system visible or hidden from the user of device 500. Application 520 can communicate with operating system 522 through one or more application programming interfaces (APIs). These APIs facilitate application 520 in reading and / or writing application data 514, transmitting or receiving information via communication interface 502, and receiving or displaying information on user interface 504. In some terms, application 520 may be simply referred to as "app". Furthermore, application 520 can be downloaded to device 500 through one or more online app stores or app markets. However, applications can also be installed on device 500 in other ways, such as through a web browser or a physical interface on device 500 (e.g., a USB port).

[0087] In one specific embodiment, the credit data processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the credit data processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: For the interface calls of each data source system to the data permission interface of the trusted data space, the data permission range is obtained by making permission decisions based on the data source category and credit authorization contract of each data source system; Obtain de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space; the de-identified credit data is obtained by collecting and de-identifying data from the credit investigation object based on the data permission scope; Within the shared data sandbox, the de-identified credit data is parsed, and the parsing results are input into the credit assessment model corresponding to the credit subject to obtain credit indicators. Obtain the credit indicators output by the shared data sandbox and synchronize the credit indicators with the credit access system according to the access request of the credit access system.

[0088] This specification provides an embodiment of a computer-readable storage medium as follows: Corresponding to the credit data processing method described above, and based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0089] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, perform the following steps: For the interface calls of each data source system to the data permission interface of the trusted data space, the data permission range is obtained by making permission decisions based on the data source category and credit authorization contract of each data source system; Obtain de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space; the de-identified credit data is obtained by collecting and de-identifying data from the credit investigation object based on the data permission scope; Within the shared data sandbox, the de-identified credit data is parsed, and the parsing results are input into the credit assessment model corresponding to the credit subject to obtain credit indicators. Obtain the credit indicators output by the shared data sandbox and synchronize the credit indicators with the credit access system according to the access request of the credit access system.

[0090] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a credit data processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0091] This specification provides an example of a computer program product as follows: Corresponding to the credit data processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0092] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps: For the interface calls of each data source system to the data permission interface of the trusted data space, the data permission range is obtained by making permission decisions based on the data source category and credit authorization contract of each data source system; Obtain de-identified credit data uploaded by each data source system to the shared data sandbox of the trusted data space; the de-identified credit data is obtained by collecting and de-identifying data from the credit investigation object based on the data permission scope; Within the shared data sandbox, the de-identified credit data is parsed, and the parsing results are input into the credit assessment model corresponding to the credit subject to obtain credit indicators. Obtain the credit indicators output by the shared data sandbox and synchronize the credit indicators with the credit access system according to the access request of the credit access system.

[0093] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a credit data processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0094] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiment, equipment embodiment and computer-readable storage medium embodiment are all similar to the method embodiment, so the description is relatively simple. When reading the relevant content of the device embodiment, equipment embodiment and computer-readable storage medium embodiment, please refer to the description of the method embodiment.

[0095] Although one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is only one of many possible execution orders and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims.

[0096] This specification uses specific terms to describe embodiments thereof. Terms such as "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of those different embodiments or examples, without contradiction.

[0097] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0098] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0099] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0100] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0101] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0102] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable test processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable test processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable test processing equipment to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable test processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0107] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0108] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0109] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of features includes not only those features but also other features not expressly listed, or features inherent to such process, method, article, or apparatus. Without further limitations, a feature defined by the phrase "comprising one..." does not exclude the presence of other identical features in the process, method, article, or apparatus that includes said feature.

[0110] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0111] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0112] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A method for processing credit information data, characterized in that, The method includes: For the interface calls of each data source system to the data permission interface of the trusted data space, the credit authorization contract signed between the institution and the credit reporting system and the data source category of each data source system are input into the decision engine to make permission decisions, obtain the data permission range of each data source system, and return it to each data source system through the data permission interface; Obtain de-identified credit data uploaded by each data source system to the shared data sandbox belonging to the credit reporting system within the trusted data space; obtain the de-identified credit data by collecting and de-identifying data from the institution based on the data permission scope; Credit indicators are obtained by conducting credit assessments on the institution based on the de-identified credit data within the shared data sandbox. Obtain the credit indicators output by the shared data sandbox and synchronize the credit indicators with the credit access system according to the access request of the credit access system.

2. The credit data processing method according to claim 1, characterized in that, The shared data sandbox is allocated based on the application requests submitted by the credit reporting system.

3. The credit data processing method according to claim 2, characterized in that, The de-identified credit data is uploaded based on the access tokens submitted by each data source system to the shared data sandbox; The access token is synchronized from the credit scoring system to each data source system.

4. The credit data processing method according to claim 1, characterized in that, The credit assessment of the institution based on the anonymized credit data includes: The de-identified credit data is parsed, and the parsing results are input into the credit assessment model to conduct a credit assessment of the institution. The data parsing of the de-identified credit data includes: The de-identified credit data uploaded from each of the aforementioned data source systems is cleaned and integrated to obtain integrated credit data. Credit features are extracted from the integrated credit data to obtain credit features as the parsing result.

5. The credit data processing method according to claim 4, characterized in that, The method further includes: The credit deficiencies of the institution are determined based on the aforementioned credit characteristics; A data collection request containing the missing credit items is generated and sent to the corresponding data source system to re-acquire the de-identified credit data corresponding to the missing credit items.

6. The credit data processing method according to claim 5, characterized in that, After the step of generating a data collection request containing the missing credit item and sending it to the corresponding data source system to re-acquire the de-identified credit data corresponding to the missing credit item is executed, the method further includes: Based on the credit repair request from the institution, the historical credit records of the institution are queried from the blockchain; Credit repair is performed based on the historical credit records and the anonymized credit data corresponding to the missing credit items to obtain the credit repair result.

7. The credit data processing method according to claim 1, characterized in that, The method further includes: Obtain the de-identified credit data uploaded by each data source system to its respective data sandbox in the trusted data space; Within the data sandboxes corresponding to each data source system, credit features are extracted from the de-identified credit data uploaded by each data source system, and intermediate credit features are obtained through multi-party calculations based on the extracted credit features. The intermediate credit features are passed into the shared data sandbox for feature merging to obtain merged credit features.

8. The credit data processing method according to claim 7, characterized in that, The data source systems include resource agency systems, government agency systems, transaction agency systems, payment systems, and / or credit leasing systems that are connected to the trusted data space.

9. The credit data processing method according to claim 7, characterized in that, The step of synchronizing the credit indicators with the credit access system according to the access request of the credit access system includes: According to the conversion method corresponding to the system type of the credit access system carried in the access request, the credit indicators are converted based on the merged credit characteristics, and the conversion result is returned to the credit access system.

10. The credit data processing method according to claim 9, characterized in that, The conversion method corresponding to the system type of the credit access system carried in the access request, and the indicator conversion based on the merged credit features, includes: Based on the system type, key credit features are extracted from the merged credit features, and the key credit features are input into the credit enhancement model corresponding to the system type to calculate the enhancement parameters and obtain the credit enhancement parameters. The enhanced credit score is calculated based on the credit score indicators and the credit score enhancement parameters, and this is the conversion result.

11. The credit data processing method according to claim 1, characterized in that, The credit authorization agreement is obtained in the following manner: Based on the organization's confirmation instruction to the authorization request notification, the authentication interface of the trusted data space is invoked to authenticate the organization's identity. If identity authentication is successful, a credit authorization contract is generated that includes the organization's identity hash value, data collection scope, authorization period, and / or data destruction policy.

12. The credit information data processing method of claim 11, wherein, The permission decision is made after the identity authentication of each data source system is passed based on the credit authorization credential; The credit authorization certificate is generated based on the credit authorization contract through the data directory module of the trusted data space.

13. The credit scoring data processing method of claim 11, wherein, After the execution of the credit authorization contract operation, which generates the identity hash value of the institution, the data collection scope, the authorization period, and / or the data destruction policy, the following steps are also included: The smart contract is invoked to detect the authorization period, and upon detection that the authorization period has expired, the collected de-identified credit data is destroyed or archived. The smart contract is constructed from the credit authorization contract using an executable program.

14. The credit information data processing method of claim 4, wherein, After performing data cleaning operations on the de-identified credit data uploaded by the various data source systems, the process further includes: The cleaned and de-identified credit data from each data source system is cross-verified from multiple sources, and the data integration operation is performed after the cross-verification is passed. The multi-source cross-verification includes: verifying whether the cleaned and de-identified credit data of each pair of data source systems match; if they match, the cross-verification is deemed successful.

15. The credit information data processing method of claim 4, wherein, The credit assessment of the institution includes: Credit indicators are calculated based on the credit features under each feature type and the feature weights of each feature type in the analysis results. The feature weights, feature types, and indicator calculation processes are synchronized to the trusted management unit of the trusted data space, and the credit indicators are verified through the trusted management unit. After the verification is passed, the credit indicators are output.

16. The credit information data processing method of claim 1, wherein, After receiving the credit indicators, the credit access system performs the following operations: The credit indicators and the credit data of the institution in the credit access system are input into the data sandbox of the credit access system in the trusted data space; Credit risk data is obtained by using the data sandbox to predict the credit risk of the institution based on the credit indicators and the credit data of the credit access system.

17. The credit data processing method according to claim 1, characterized in that, The method further includes: Based on the request type of the credit access request submitted by the institution to the trusted data space, the target data source system is matched in each data source system and the scope of the adjusted permissions is obtained by adjusting the permissions. The credit data obtained by the target data source system through data collection according to the adjusted permission range and the credit processing model corresponding to the request type are imported into the shared data sandbox; The credit data is input into the credit reporting processing model within the shared data sandbox for credit reporting update processing, and the credit reporting update result is obtained and returned to the institution.

18. The credit data processing method of claim 17, wherein, The credit scoring indicators include credit scores, credit dimension scores of multiple credit dimensions, and / or feature impact values ​​of multiple credit features under each credit dimension; The types of credit access requests include credit repair requests for the credit score, credit dispute requests for a target credit dimension among the multiple credit dimensions, or credit dispute requests for a target credit feature among the multiple credit features.

19. The credit scoring data processing method of claim 18, wherein, The step of inputting the credit data into the credit reporting processing model for credit reporting update processing to obtain the credit reporting update result includes: The credit data of the institution under the target credit dimension is input into the credit dimension model to calculate the credit dimension score, thereby obtaining the updated credit dimension score of the institution under the target credit dimension. If the updated credit score is higher than the target credit score, the organization queries the credit access service it has registered with and applies for a rights upgrade to the corresponding credit access system to upgrade the organization's service rights in the credit access service.

20. A credit data processing apparatus, characterized by comprising: The device includes: The permission decision module is configured to call the data permission interface of each data source system to the trusted data space. It inputs the credit authorization contract signed between the institution and the credit reporting system and the data source category of each data source system into the decision engine to make permission decisions, obtain the data permission range of each data source system, and return it to each data source system through the data permission interface. The data acquisition module is configured to acquire de-identified credit data uploaded by each data source system to the shared data sandbox belonging to the credit reporting system within the trusted data space; the de-identified credit data is obtained by collecting and de-identifying data from the institution based on the data permission scope; The data parsing module is configured to perform credit assessment on the institution based on the de-identified credit data within the shared data sandbox to obtain credit indicators; The indicator acquisition module is configured to acquire the credit indicators output by the shared data sandbox and synchronize the credit indicators to the credit access system according to the access request of the credit access system.

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