Fusion information data management method, data management platform, medium and program product

Abstract

The invention discloses a fusion information data management method, a data management platform, a medium and a program product, and the method comprises the steps: extracting qualified fusion information data in fusion information data; acquiring a data quality management demand, and determining a data quality range according to the data quality management demand; under the condition of determining that the qualified fusion information data does not meet the data quality management requirement, carrying out a rectification process on the qualified fusion information data; performing data sampling detection at a preset progress point of the rectification process to obtain a plurality of sampling detection results; if the sampling detection result corresponding to any preset progress point is unqualified, re-executing the step of carrying out the rectification process on the qualified fusion information data; if the sampling detection results corresponding to the preset progress points of the preset first number are all qualified, outputting rectification qualified fusion information data after the rectification process; and storing the rectification qualified fusion information data into a data management library of the corresponding user. According to the method and the device, the difficulty of fusion information data management is reduced, and the accuracy and the timeliness of interaction information are further improved.

Description

Technical Field

[0001] The present application belongs to the field of data management, and in particular, relates to a Rongxin data management method, a data management platform, a medium and a program product. Background Art

[0002] As the value of data as an important asset in the banking industry increases, companies are paying more and more attention to data. In order to effectively improve data management capabilities, tap into internal and external information resources, and provide support for banking business decisions, major banks have launched data governance work.

[0003] At present, banks lack unified data governance and control products. Each data platform manages its own metadata information internally. The metadata demands of each data platform and IT users are generally exchanged through Excel and other documents through the company's information technology center. In terms of data quality and data standardization, it is mainly completed by adding custom-developed ETL verification jobs and stored procedures in the ODS system, such as verification and verification of the data dictionary. In this way, multiple applications and multiple exchanges are required, which increases the difficulty of Rongxin's data management and reduces the accuracy and timeliness of the interactive information. Summary of the invention

[0004] The present application provides a Rongxin data management method, a data management platform, a medium and a program product, which are used to reduce the difficulty of Rongxin data management and thereby improve the accuracy and timeliness of interactive information.

[0005] In a first aspect, the present application provides a Rongxin data management method, which extracts qualified Rongxin data from Rongxin data based on the latest data standard, wherein the latest data standard is obtained by adding field naming verification and field type verification to the original data standard through the standard root; Obtain data quality management requirements and determine the data quality scope based on the data quality management requirements; In the event that the qualified Rongxin data does not meet the data quality management requirements based on the data quality scope, a rectification process will be carried out on the qualified Rongxin data; Performing data sampling inspection at preset progress points of the rectification process to obtain a number of sampling inspection results, wherein the inspection standard of the data sampling inspection is the data quality range, the sampling inspection result is qualified or unqualified, and the number of preset progress points has a preset first number; If the sampling test result corresponding to any preset progress point is unqualified, the steps of the rectification process for qualified Rongxin data will be re-executed; If the sampling test results corresponding to the preset first number of preset progress points are all qualified, then the rectification qualified Rongxin data after the rectification process is output; The rectified qualified Rongxin data will be stored in the data management library of the corresponding user.

[0006] By adopting the above technical solutions and using the latest data standards to screen Rongxin data, the standardization of data in field naming and types is improved. After determining the data quality management needs and scope, the data that does not meet the requirements is rectified, and multiple preset progress points are set for sampling inspection during the rectification process, which improves the controllability and effectiveness of the rectification process. By performing multiple sampling verifications on the rectification results, the rectified data is output only when the test results of all preset progress points are qualified, which improves the overall quality level of Rongxin data. When any test point is found to be unqualified, the rectification process will be triggered to be re-executed, forming a closed-loop quality management system and reducing the risk of missing data quality defects. Finally, the qualified data will be stored in the user data management library, making the data more available and reliable.

[0007] In conjunction with some embodiments of the first aspect, in some embodiments, extracting qualified Rongxin data from Rongxin data based on the latest data standard specifically includes: Build a standard root mapping library, which contains the standard roots of commonly used fields in the business field and the corresponding field type set; Based on the standard root mapping library, the fields in the original data standard are segmented to obtain the field root sequence; Match the field root sequence with the standard root; If the match succeeds, then the validation field type of the field root sequence is validated to see if it belongs to the field type set; Mark the fields that failed to match or failed field type verification, and generate field correction suggestions; Update the original data standards according to the field correction suggestions to obtain the latest data standards.

[0008] By adopting the above technical solution and establishing a standard root word mapping library, the standard roots of commonly used fields in the business field are associated with the field type set, providing basic support for field standardization. The fields in the original data standard are segmented and matched with the standard roots to achieve standardized verification of field naming. By verifying whether the field type belongs to the predetermined type set, the standardization of the field type is improved. For fields that fail to match or fail type verification, correction suggestions are generated and the data standard is updated accordingly, so that the data standard is continuously optimized and improved. This standardization method based on root word mapping reduces the subjectivity of human judgment, improves the accuracy and efficiency of field standardization, and makes the data standard more scientific and standardized.

[0009] In conjunction with some embodiments of the first aspect, in some embodiments, data sampling detection is performed at preset progress points in the rectification process to obtain several sampling detection results, specifically including: Construct a multi-dimensional scoring matrix based on the data quality range, which includes indicators of completeness, accuracy, consistency and timeliness; The stratified random sampling method was used to select samples from the qualified Rongxin data; Perform multi-dimensional scoring on the samples based on the multi-dimensional scoring matrix to obtain the scoring results of each dimension; Based on the preset dimension weights, the scoring results of each dimension are weighted and calculated to obtain a comprehensive score; Compare the comprehensive score with the preset qualified threshold to obtain the sampling test results.

[0010] By adopting the above technical solutions, the stratified random sampling method is used to improve the representativeness of the samples and ensure that the evaluation results can accurately reflect the overall data quality status. By scoring the samples in multiple dimensions and combining the preset dimension weights for weighted calculation, the comprehensive score obtained more objectively reflects the overall level of data quality. The method of comparing the comprehensive score with the preset qualified threshold to obtain the test results establishes a clear quality judgment standard, making the quality evaluation results measurable and comparable, and improving the scientificity and operability of data quality management.

[0011] In conjunction with some embodiments of the first aspect, in some embodiments, after storing the rectified qualified Rongxin data in the data management library of the corresponding user, the method further includes: Receive a data call request sent by a user, the data call request including target data feature description information and usage scenario description information; Label the rectified qualified Rongxin data to obtain data label information, which includes feature identifiers that match the feature description information of the target data; Generate a data usage permission matrix based on the usage scenario description information, and the data usage permission matrix includes data access level information; Generate a data call credential based on the data access level information. The data call credential includes access time limit parameters, usage scope parameters, and desensitization requirement parameters. Configure the data access interface parameters according to the data call credentials. The data access interface parameters are used to control the access method of the rectification qualified Rongxin data.

[0012] By adopting the above technical solution, the qualified Rongxin data is labeled, which facilitates the rapid location of appropriate data according to the user's data feature requirements. A data usage permission matrix is ​​generated based on the usage scenario description information, and a data call certificate containing parameters such as access time limit, usage scope and desensitization requirements is generated, and a refined data access control mechanism is established. By configuring the data access interface parameters to control the data access method, the manageability and security of data use are achieved. This scenario-based refined permission management method improves the flexibility of data services while ensuring data security, so that data resources can be fully utilized within a controlled range.

[0013] In conjunction with some embodiments of the first aspect, in some embodiments, labeling is performed on the rectification qualified Rongxin data to obtain data label information, specifically including: Parse the target data feature description information to obtain a feature keyword sequence; Construct a preset data label system, which includes a business attribute label subset, a time attribute label subset, and a scene attribute label subset; Perform feature analysis on the qualified Rongxin data based on the feature keyword sequence to generate an initial data label set; The initial data label set is screened using the preset label optimization rules to obtain the target data label set; Calculate the correlation strength coefficient between each label in the target data label set, generate the label relationship network structure, and obtain the data label information.

[0014] By adopting the above technical solution, the characteristic keyword sequence is obtained by parsing the characteristic description information of the target data, and the characteristic analysis of the rectification qualified Rongxin data is performed based on the preset data label system to generate the initial data label set, and then the preset label optimization rules are used to screen to obtain the target data label set, and finally the correlation strength coefficient between labels is calculated to generate the label relationship network structure. This labeling processing method can accurately label and associate data from multiple dimensions. The label system covers three levels: business attributes, time attributes, and scene attributes, making the data feature description more comprehensive and systematic. Redundant and invalid labels can be removed by screening through label optimization rules, and the accuracy and applicability of labels can be improved. The establishment of the label relationship network structure reveals the association relationship and action strength between different labels, which helps to better understand and utilize the intrinsic connection between data.

[0015] In combination with some embodiments of the first aspect, in some embodiments, after configuring the data access interface parameters according to the data call credentials, the method further includes: Collect data call behavior characteristic information, including call timestamp, call frequency statistics and data usage identification; Set the call warning threshold parameters according to the preset call warning rules; Calculate the data reuse rate index based on the data call behavior feature information; Construct a data call scoring rule set, and perform quantitative evaluation on data usage compliance based on the data call scoring rule set to obtain compliance evaluation results; Update the data usage rights matrix based on compliance assessment results.

[0016] By adopting the above technical solution, by collecting data call behavior feature information and setting call warning threshold parameters, combined with data reuse rate indicator calculation and quantitative evaluation of data usage compliance, dynamic monitoring of data usage and permission adjustment are achieved. By recording behavioral characteristics such as call timestamps and call frequencies, the system can promptly detect abnormal data access patterns. The setting of warning threshold parameters provides a quantitative basis for early identification of potential risks. The calculation of data reuse rate indicators reflects the efficiency of data resource utilization, while the data call scoring rule set evaluates the compliance of data usage from multiple dimensions. Dynamically updating the data usage permission matrix based on the compliance assessment results not only ensures the security of data access, but also provides a flexible permission adjustment mechanism, enhances data security management and control capabilities, reduces the risk of data abuse, and also optimizes the allocation efficiency of data resources.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, collecting data calling behavior feature information specifically includes: Obtain data call log records, and extract caller identity information and operation behavior sequence from the data call log records; Perform time series analysis on the operation behavior sequence to obtain data call time parameters and call frequency parameters, and build a data call link diagram based on the operation behavior sequence; Extract data usage characteristics from the data call link diagram to obtain a data usage identifier; The data call time parameter, the call frequency parameter and the data usage method identifier are combined to obtain the data call behavior characteristic information.

[0018] By adopting the above technical solutions, the construction of the data call link diagram makes the data flow process visual and easy to track the data usage path. By analyzing the time characteristics and frequency characteristics of the operation behavior sequence, the regularity and abnormal pattern of data usage can be identified. This comprehensive data call behavior analysis method improves the traceability and controllability of data usage behavior and provides strong technical support for data security management.

[0019] In a second aspect, an embodiment of the present application provides a Rongxin data management platform, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0020] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a data management platform, enables the data management platform to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a data management platform, the data management platform executes the method described in any possible implementation manner in the first aspect.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application provides a Rongxin data management method. By adopting the above technical solution and using the latest data standards to screen Rongxin data, the standardization of data in field naming and types is improved. After determining the data quality management needs and scope, the data that does not meet the requirements is rectified, and multiple preset progress points are set during the rectification process for sampling inspection, which improves the controllability and effectiveness of the rectification process. By performing multiple sampling verifications on the rectification results, the rectified data is output only when the test results of all preset progress points are qualified, thereby improving the overall quality level of Rongxin data. When any test point is found to be unqualified, the rectification process will be triggered to be re-executed, forming a closed-loop quality management system and reducing the risk of missing data quality defects. Finally, the qualified data is stored in the user data management library, making the data more available and reliable.

[0023] 2. This application provides a Rongxin data management method, which facilitates the rapid location of appropriate data according to the user's data feature requirements by labeling the Rongxin data that has passed the rectification. A data usage permission matrix is ​​generated based on the usage scenario description information, and a data call certificate containing parameters such as access time limit, scope of use and desensitization requirements is generated to establish a refined data access control mechanism. By configuring the data access interface parameters to control the data access method, the manageability and security of data use are achieved. This scenario-based refined permission management method improves the flexibility of data services while ensuring data security, allowing data resources to be fully utilized within a controlled range.

[0024] 3. This application provides a Rongxin data management method, which realizes dynamic monitoring of data usage and permission adjustment by collecting data call behavior feature information and setting call warning threshold parameters, combining data reuse rate indicator calculation and quantitative evaluation of data usage compliance. The system can promptly detect abnormal data access patterns by recording behavioral characteristics such as call timestamps and call frequencies. The setting of warning threshold parameters provides a quantitative basis for early identification of potential risks. The calculation of data reuse rate indicators reflects the efficiency of data resource utilization, while the data call scoring rule set evaluates the compliance of data usage from multiple dimensions. Dynamically updating the data usage permission matrix based on the compliance assessment results not only ensures the security of data access, but also provides a flexible permission adjustment mechanism, enhances data security management and control capabilities, reduces the risk of data abuse, and also optimizes the allocation efficiency of data resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of the homepage of a data management platform provided by this application.

[0026] Figure 2 It is a schematic diagram of the functional module structure of a data management platform provided by this application.

[0027] Figure 3 It is a flow chart of a Rongxin data management method in an embodiment of the present application.

[0028] Figure 4 It is another flow chart of a Rongxin data management method in an embodiment of the present application.

[0029] Figure 5 It is a schematic diagram of the physical device structure of a Rongxin data management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0031] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0032] It should be noted that the Rongxin data management method provided in this application is applied to the data management platform. The data management platform is an effective technical tool for data standard management, metadata management, and data quality management proposed by financial institutions to promote enterprise information collection and management. It is also a unified work platform for financial institutions’ business personnel and IT staff to communicate, feedback, track, and solve information collection and management problems. The homepage interface of the data management platform is as follows Figure 1 shown.

[0033] like Figure 2 As shown, the data management platform includes an application layer, a data service layer, a data platform layer, a data layer and an infrastructure layer.

[0034] Among them, the application layer includes modules such as data asset view, customer view, risk compliance and business analysis.

[0035] The data service layer includes several data functional components, including reports, autonomous analysis, online query, batch data, quality reports, analysis and mining, data caching and service logs.

[0036] The data platform layer includes data visualization module, data collection module, data integration module, data calculation module, intelligent scheduling module and workflow engine module. The data visualization module includes data reports, data analysis, visualization screen, data dashboard and configurable graphics; the data collection module includes streaming collection, real-time collection, batch collection and crawler; the data integration module includes data cleaning, data verification and data conversion; the data calculation module includes offline calculation, stream calculation and real-time calculation; the intelligent scheduling module includes resource scheduling, task scheduling and data scheduling.

[0037] The data layer includes metadata area, standard market, archive area, existing warehouse / market and source data area, etc. The metadata area includes meta-model, metadata and meta-process; the standard market includes account information, product information, asset information and customer information, etc.; the existing warehouse / market includes risk market, marketing market and assessment market, etc.

[0038] The infrastructure layer includes Hadoop, graph database, MPP database, relational storage, full-text search engine database, computing resources, network resources and storage resources.

[0039] In addition, the data management platform also includes data management module and data security module. The data management module includes metadata management, data catalog management, data standard management, data source management, lineage analysis, indicator system management, data distribution and data quality. The data security module includes data permissions, data security, data download, data encryption and data desensitization.

[0040] The following uses an embodiment and combines Figure 3 , a Rongxin data management method in an embodiment of the present application is described: See also Figure 3 , which is a flow chart of a Rongxin data management method in an embodiment of the present application.

[0041] S101. Extract qualified Rongxin data from Rongxin data based on the latest data standards; The data management platform extracts qualified Rongxin data from Rongxin data based on the latest data standards. The latest data standards are obtained by adding field naming verification and field type verification to the original data standards through standard root words. Specifically: build a standard root word mapping library, which contains the standard root words of commonly used fields in the business field and the corresponding field type set; Based on the standard root mapping library, the fields in the original data standard are segmented to obtain the field root sequence; Match the field root sequence with the standard root; If the match succeeds, then the validation field type of the field root sequence is validated to see if it belongs to the field type set; Mark the fields that failed to match or failed field type verification, and generate field correction suggestions; Update the original data standards according to the field correction suggestions to obtain the latest data standards.

[0042] In this step, the data management platform first needs to obtain the latest data standards. Data standards refer to the specifications and requirements for the format, content, quality, etc. of data, which ensure the consistency, accuracy and integrity of the data. The latest data standards are obtained by adding field naming verification and field type verification through standard roots on the basis of the original data standards. This process can optimize the data standards and improve the standardization and accuracy of the data standards. After obtaining the latest data standards, the data management platform will extract Rongxin data based on the standards and screen out qualified Rongxin data that meets the standard requirements.

[0043] In order to efficiently obtain the latest data standards, the data management platform can build a standard root mapping library. The mapping library contains the standard roots of commonly used fields in the business field and the corresponding field type set. By performing word segmentation on the fields in the original data standard, the field root sequence is obtained, and then the field root sequence is matched with the standard root. If the match is successful, it is further verified whether the verification field type of the field root sequence belongs to the field type set. For fields that fail to match or fail field type verification, the data management platform can mark them and generate corresponding field correction suggestions. Finally, the original data standard is updated according to the field correction suggestions to obtain the latest data standard.

[0044] S102. Obtain data quality management requirements, and determine the data quality scope according to the data quality management requirements; In this step, the data management platform needs to obtain the data quality management requirements proposed by users or business parties. Data quality management requirements reflect the expectations and requirements of users or business parties for data quality, such as requirements for data accuracy, completeness, consistency, timeliness, etc. The data management platform can understand and collect their data quality management requirements through communication and research with users or business parties. After clarifying the data quality management requirements, the data management platform will determine the scope of data quality based on these requirements, that is, the data quality indicators and dimensions that need to be focused on and managed.

[0045] In order to better obtain and understand data quality management requirements, the data management platform can provide a set of standardized requirements templates or questionnaires. By guiding users or business parties to fill out these templates or questionnaires, the data management platform can systematically and comprehensively collect their requirements for various aspects of data quality. At the same time, the data management platform can also establish a data quality requirements library to record and manage various historical data quality requirements, and classify, organize and analyze them. By mining and reusing these requirements, the data management platform can more efficiently and accurately identify and determine new data quality management requirements.

[0046] S103. In the event that the qualified Rongxin data is determined to not meet the data quality management requirements based on the data quality scope, a rectification process is performed on the qualified Rongxin data; In this step, the data management platform needs to determine whether the qualified Rongxin data determined based on the data quality range meets the data quality management requirements. If the quality level of the qualified Rongxin data does not meet the standards specified by the data quality management requirements, it means that the current Rongxin data still has certain defects and deficiencies in quality, and necessary rectification and improvement are required. At this time, the data management platform will start the rectification process for the qualified Rongxin data, and through a series of data cleaning, conversion, correction and other operations, improve the quality of the qualified Rongxin data so that it meets the data quality management requirements.

[0047] In the process of rectifying qualified Rongxin data, the data management platform can adopt a variety of data quality improvement technologies and means. For example, for incomplete or missing data, data completion, data extrapolation and other technologies can be used to fill and correct; for inaccurate or erroneous data, data verification, data correction and other technologies can be used to identify and correct; for inconsistent data, data standardization, data coordination and other technologies can be used to unify and standardize. At the same time, the data management platform can also use the data quality rule engine to automatically detect and identify various quality issues in qualified Rongxin data, and automatically propose rectification suggestions and solutions based on predefined quality rules and strategies.

[0048] In the process of rectifying qualified Rongxin data, some data rectification may introduce new quality problems. For example, when completing missing data, new inaccurate data may be introduced; when unifying inconsistent data, data semantics may change or be lost. In order to avoid and reduce these problems, the data management platform can establish a monitoring and verification mechanism for data rectification, monitor the quality status of new or changed data in real time during the rectification process, and verify and correct it in time once new quality problems are found. At the same time, the data management platform can also record and track various operations and changes in the data rectification process through technologies such as metadata management and data lineage analysis, ensure the traceability and explainability of data rectification, and provide support for the location and backtracking of data quality issues.

[0049] S104, performing data sampling inspection at preset progress points in the rectification process to obtain several sampling inspection results; The data management platform performs data sampling inspection at the preset progress points of the rectification process and obtains a number of sampling inspection results. The inspection standard of the data sampling inspection is the data quality range. The sampling inspection results are qualified or unqualified. The number of preset progress points has a preset first number. Specifically: a multi-dimensional scoring matrix is ​​constructed based on the data quality range. The multi-dimensional scoring matrix includes completeness, accuracy, consistency and timeliness indicators; The stratified random sampling method was used to select samples from the qualified Rongxin data; Perform multi-dimensional scoring on the samples based on the multi-dimensional scoring matrix to obtain the scoring results of each dimension; Based on the preset dimension weights, the scoring results of each dimension are weighted and calculated to obtain a comprehensive score; Compare the comprehensive score with the preset qualified threshold to obtain the sampling test results.

[0050] In this step, the data management platform needs to sample and test the qualified Rongxin data after rectification at the preset progress points of the rectification process. The preset progress points refer to some key nodes or milestones pre-set in the rectification process, at which the data quality is tested and evaluated to understand the intermediate effect and progress of the rectification. The data management platform will randomly select a certain number of data samples at each preset progress point, perform quality inspections on these sample data according to the inspection standards and rules defined in the data quality range, and obtain the corresponding sampling inspection results to evaluate and judge the rectification effect at the current stage.

[0051] In order to make the sampling test results more comprehensive and reliable, the data management platform can use stratified random sampling to select sample data. Stratified random sampling is a method of dividing the data into different levels according to certain characteristics or attributes, and then performing random sampling within each level. This method can ensure that the sample data at different levels has a certain degree of representativeness and balance, thereby improving the accuracy and credibility of the sampling test results. At the same time, the data management platform can also dynamically adjust the proportion and frequency of sampling, and flexibly set sampling test plans at different progress points according to the actual progress and effect of rectification, so as to optimize the efficiency and cost of detection.

[0052] S105. If the sampling test result corresponding to any preset progress point is unqualified, the steps of rectifying the qualified Rongxin data are re-executed; In this step, the data management platform needs to make a comprehensive judgment and evaluation of the sampling test results of each preset progress point. If the test result of any progress point among all the preset progress points is unqualified, it means that the qualified Rongxin data after the current rectification still does not meet the requirements of data quality management needs, and the rectification process cannot be completed. It is necessary to restart a new round of data rectification process for the problems and defects found in the inspection. The data management platform will trace back and analyze the rectification process based on the unqualified test results, find the causes of the quality problems, and formulate targeted rectification plans and measures to process and improve the qualified Rongxin data again until the data quality management needs are met.

[0053] In order to improve the efficiency and quality of the data rectification process, the data management platform can establish a complete set of rectification problem analysis and decision-making mechanisms. Through in-depth analysis and mining of unqualified test results, the data management platform can identify common problems and key influencing factors in the rectification process, and design standardized rectification treatment plans and specifications for these problems and factors, forming a reusable rectification knowledge base and best practices. At the same time, the data management platform can also use data quality monitoring and early warning mechanisms to monitor the changing trends of key quality indicators in real time during the execution of the rectification process. Once abnormal fluctuations or exceeding the threshold are found, timely warnings and countermeasures will be taken to avoid the expansion and spread of problems.

[0054] In the process of repeatedly executing the data rectification process, the rectification effect may stagnate or the same type of problems may recur. This shows that the current rectification plan and means may have limitations or blind spots and cannot effectively solve some fundamental or hidden quality problems. In this regard, the data management platform needs to optimize and improve the rectification process itself. On the one hand, by introducing new data processing technologies and tools, such as artificial intelligence and machine learning, the intelligence and adaptability of the rectification process can be enhanced to improve the rectification effect; on the other hand, by strengthening communication and collaboration with experts in the business field, fully absorbing and integrating their understanding and insights of business data, optimizing the key parameters and rules in the rectification process, and making it more in line with the actual needs of the business. At the same time, the data management platform can also regularly review and summarize the rectification process, learn lessons from success and failure, and continuously improve and innovate the rectification mechanism.

[0055] S106. If the sampling test results corresponding to the preset first number of preset progress points are all qualified, outputting the rectification qualified credit data after the rectification process; In this step, the data management platform needs to make a final judgment on the sampling test results of all preset progress points. If the sampling test results corresponding to the preset first number of preset progress points are all qualified, it means that the qualified Rongxin data after the rectification process has met the standards of data quality management requirements, and the entire data rectification process can be successfully completed and ended. At this time, the data management platform will officially output and publish the rectified qualified Rongxin data as the basic data for subsequent business applications and data analysis. Through these high-quality rectification data, the normal development and stable operation of the business can be effectively supported and promoted.

[0056] In order to facilitate the management and use of rectified qualified Rongxin data, the data management platform can package, catalog and publish the rectified data according to certain organizational methods and standards. For example, the rectified qualified data can be divided into different data sets or data modules according to the data's business type, source system, update time and other attributes, and the corresponding data directory and index can be made to facilitate users to quickly retrieve and locate the required data. For another example, according to the data's security level and usage permissions, the rectified qualified data can be identified as different access categories, and corresponding permission control and encryption measures can be implemented to ensure the security and confidentiality of the data. At the same time, the data management platform can also provide flexible and diverse data publishing and subscription mechanisms, allowing users to obtain rectified qualified data on demand, improving the convenience and real-time nature of data use.

[0057] In the process of outputting the qualified rectification data of Rongxin, problems such as data format or interface mismatch and poor data transmission performance may occur, which will affect the effective use of data in the business system. In order to solve these problems, the data management platform needs to strengthen coordination and docking with data users. On the one hand, by formulating unified data formats and interface standards, clarifying the output specifications and requirements of qualified rectification data, and ensuring data interoperability and consistency; on the other hand, by optimizing the channels and protocols for data transmission, increasing the bandwidth and concurrency of data transmission, reducing data arrival delays, and improving the real-time and stability of data supply. At the same time, the data management platform can also establish a complete data usage feedback and optimization mechanism, regularly collect opinions and suggestions from data users, and continuously improve the content and form of data output to make it more in line with the actual needs of the business.

[0058] S107. Store the rectified qualified Rongxin data into the data management library of the corresponding user.

[0059] In this step, the data management platform needs to persistently store and manage the qualified Rongxin data. By storing these high-quality data in the corresponding user's data management library, a long-term, stable and reliable data resource library can be provided for users, so that they can call and use the required business data at any time. The data management library is a data warehouse or data platform specially used to store, organize and manage enterprise data assets. It can systematically classify, catalog, retrieve and access the qualified data, making the data a valuable resource that can be used, controlled and activated by the enterprise.

[0060] In the process of building a user data management library, the data management platform can adopt a variety of advanced data organization and storage technologies. For example, the data lake architecture can be used to store the rectification qualified data in the original format in the distributed file system, and through metadata management and data retrieval tools, rapid retrieval and access to massive heterogeneous data can be achieved; for another example, the data warehouse architecture can be used to model and organize the rectification qualified data according to the subject domain or business process, forming a set of analysis-oriented, highly integrated structured data system, which is convenient for users to conduct multi-dimensional and multi-granular data analysis and mining. At the same time, the data management platform can also use the big data platform and cloud computing platform to provide distributed storage and elastic computing capabilities for massive data, ensuring the high scalability and high availability of the data management library.

[0061] In the above embodiment, by adopting the above technical solution, Rongxin data is screened by using the latest data standards, thereby improving the standardization of data in field naming and type. After determining the data quality management requirements and scope, the data that does not meet the requirements is rectified, and multiple preset progress points are set during the rectification process for sampling inspection, thereby improving the controllability and effectiveness of the rectification process. By performing multiple sampling verifications on the rectification results, the rectified data is output only when the test results of all preset progress points are qualified, thereby improving the overall quality level of Rongxin data. When any test point is found to be unqualified, the rectification process will be triggered to be re-executed, forming a closed-loop quality management system and reducing the risk of missing data quality defects. Finally, the qualified data is stored in the user data management library, making the data more available and reliable.

[0062] The first embodiment described above mainly describes the quality management and rectification process of Rongxin Data, focusing on the quality dimension of data governance, and ensuring that Rongxin Data meets the expected quality standards through standardized extraction, quality testing, rectification verification and other steps. Figure 4 , another Rongxin data management method in the embodiment of the present application is described: See also Figure 4 , which is another flow chart of a Rongxin data management method in an embodiment of the present application.

[0063] S201, receiving a data call request sent by a user, labeling the rectified qualified Rongxin data, and obtaining data label information; The data management platform receives the data call request sent by the user, labels the qualified Rongxin data, and obtains the data label information. The data call request contains the target data feature description information and the usage scenario description information. The data label information includes the feature identifier that matches the target data feature description information. Specifically: parse the target data feature description information to obtain the feature keyword sequence; Construct a preset data label system, which includes a business attribute label subset, a time attribute label subset, and a scene attribute label subset; Perform feature analysis on the qualified Rongxin data based on the feature keyword sequence to generate an initial data label set; The initial data label set is screened using the preset label optimization rules to obtain the target data label set; Calculate the correlation strength coefficient between each label in the target data label set, generate the label relationship network structure, and obtain the data label information.

[0064] In this step, the data management platform first receives the data call request sent by the user. The data call request contains the user's characteristic description information of the target data and the description information of the usage scenario, reflecting the user's demand for data. After clarifying the user's needs, the data management platform will label the qualified Rongxin data. Labeling is to label the data with various attributes and characteristics to form structured and standardized data label information for subsequent data retrieval, matching and application. The data label information includes various feature identifiers that match the characteristic description of the target data, reflecting the attribute characteristics of the data in multiple dimensions such as content, type, quality, and timeliness.

[0065] In order to achieve data labeling efficiently and accurately, the data management platform can adopt a variety of automated semantic analysis and feature extraction technologies. First, the target data feature description information provided by the user is parsed, and the keywords are extracted to form a feature keyword sequence. Then, the feature keyword sequence is matched and mapped using the pre-built data label system. The data label system is a multi-dimensional, multi-level label classification framework, including multiple subsets such as business attribute labels, time attribute labels, and scene attribute labels, covering the key features of data in different application scenarios. By comparing the feature keywords with the label system, the data management platform can automatically generate an initial data label set. Next, the initial label set is screened and optimized using some preset label optimization rules, such as label importance, coverage, and mutual exclusivity, and finally a high-quality target data label set is obtained. In addition, the data management platform can also calculate the correlation strength between each label in the target label set, generate a label relationship network structure, and fully present the internal connection and combination logic between data labels.

[0066] In the process of data labeling, problems such as incomplete label coverage, redundant labels, and inconsistent label semantics may be encountered, which will affect the label quality and subsequent data retrieval performance. To address these problems, the data management platform can take some optimization and improvement measures. For example, by introducing external knowledge sources such as industry dictionaries and knowledge graphs, the label system can be expanded and improved to improve the coverage and professionalism of labels; for example, by setting statistical thresholds such as the frequency of label use and the probability of co-occurrence, redundant and low-quality labels can be regularly eliminated or merged to maintain the simplicity and effectiveness of the label system; and through manual review and correction mechanisms, the automatically generated labels can be quality-checked and calibrated to continuously optimize the consistency and accuracy of label semantics.

[0067] S202, generating a data usage permission matrix according to the usage scenario description information; The data management platform generates a data usage permission matrix based on the usage scenario description information, and the data usage permission matrix contains data access level information.

[0068] In this step, the data management platform will generate a data usage permission matrix based on the usage scenario description information provided by the user. The data usage permission matrix is ​​a permission configuration scheme used to regulate and control user access to and use of data. It comprehensively considers multiple factors such as the user's role, responsibilities, business scenarios, etc., and subdivides and divides the user's data usage permissions to form a hierarchical authorization management mechanism. Among them, the core content of the permission matrix is ​​the data access level information, which clearly stipulates the specific permission settings such as visibility, operability, and shareability of data for users of different levels. Through the data usage permission matrix, the data management platform can more finely control data resources and ensure the security and compliance of data during circulation and use.

[0069] In order to dynamically generate a permission matrix based on scenario description information, the data management platform can design a set of regularized scenario analysis and permission mapping mechanisms. First, the scenario description information provided by the user is semantically parsed and refined to identify key scenario elements, such as business processes, usage purposes, and roles involved. Then, these scenario elements are matched with predefined scenario models and automatically classified into a standard scenario type. Different scenario types correspond to different permission definition templates, which pre-set common user roles and corresponding data permission configuration items in the scenario. The data management platform can automatically fill in and modify the role type and permission details in the template according to the scenario matching results until a complete and customized data permission matrix is ​​generated. In this process, some policy rules and constraints can also be used to automatically check and correct possible errors and conflicts in the permission matrix, such as permission leakage and excessive permissions, to further enhance the rationality of the permission matrix.

[0070] S203, generating a data call credential based on the data access level information; The data management and control platform generates data call credentials based on data access level information. The data call credentials include access time limit parameters, usage scope parameters and desensitization requirement parameters.

[0071] In this step, the data control platform will further generate data call credentials based on the data access level information generated in the previous step. The data call credential is an authorization authentication token automatically generated by the system when the user applies for data access rights. It solidifies the user's identity information and access rights details in the credential in an encrypted form. The user needs to present the credential when calling data in the future to verify the identity and control access rights. The data call credential usually contains multiple control parameters such as access time limit, scope of use, desensitization requirements, etc., which comprehensively constrain and manage the user's data access behavior, further refining and strengthening the security of data access.

[0072] In order to dynamically generate call credentials according to the data access level, the data control platform can adopt a series of encryption algorithms and security protocols. First, the data access level information is encoded and serialized to form a standardized authorization information text. Then, the authorization text is encrypted using symmetric or asymmetric encryption algorithms, such as AES, RSA, etc., to generate a digital signature or encrypted token. The digital signature can verify the integrity and credibility of the authorization text, while the encrypted token can protect the confidentiality of the authorization information. Next, the encrypted authorization information is bound to the user identity ID, timestamp, etc. to generate the final data call credential. Multiple access control parameter values ​​in the credential can also be automatically converted and extracted from the data access level according to the preset mapping relationship. In this process, the data control platform can flexibly select and customize various security measures, such as key management, access log auditing, multi-factor authentication, etc., to enhance the anti-counterfeiting and security of the credential.

[0073] S204, configuring data access interface parameters according to the data call credential; The data management and control platform configures data access interface parameters based on data call credentials. The data access interface parameters are used to control the access method of the rectification qualified Rongxin data.

[0074] In this step, the data control platform will configure the relevant parameters of the data access interface according to the data call credentials generated in the previous step. The data access interface is a set of standardized data call APIs provided by the data control platform for users. Users submit data call requests and obtain data return results through the access interface. Interface parameters are a series of configuration options that control data access behavior, such as data table name, field list, query conditions, result format, etc. By dynamically setting and controlling interface parameters, security restrictions on data access can be further strengthened, and data can be exposed strictly in accordance with the authority boundaries specified by the credentials to avoid unauthorized access and data leakage.

[0075] In order to achieve automatic adaptation of interface parameters according to the call credentials, the data control platform can maintain a set of parameter mapping and conversion rules. First, the data call credentials are parsed to extract key parameter information, including data range, access time, desensitization requirements, etc. Then, these parameter information are matched with the background data model and interface definition specifications to identify the corresponding interface control parameters. During the matching process, the pre-configured adaptation rules will play a role to achieve automatic mapping and conversion from credential parameters to interface parameters, ensuring the consistency between credential requirements and interface performance. Finally, the converted parameter values ​​are backfilled into the interface definition. When the data request is submitted, these parameters will automatically take effect to control the actual data access behavior. In this process, the data control platform can also use some parameter legitimacy verification and security inspection mechanisms to timely discover and block illegal parameter modification behaviors, and further strengthen the protection capabilities of the interface.

[0076] S205, collecting data calling behavior feature information; The data control platform collects data call behavior feature information, which includes call timestamp, call frequency statistics and data usage identification. Specifically: obtain data call log records, and extract the caller identity information and operation behavior sequence from the data call log records; Perform time series analysis on the operation behavior sequence to obtain data call time parameters and call frequency parameters, and build a data call link diagram based on the operation behavior sequence; Extract data usage characteristics from the data call link diagram to obtain a data usage identifier; The data call time parameter, the call frequency parameter and the data usage method identifier are combined to obtain the data call behavior characteristic information.

[0077] In this step, the data control platform will collect and record the user's data call behavior throughout the process and extract various data call behavior feature information. The data call behavior feature information comprehensively reflects the use of various dimensions of the user's call data, including the time, frequency, and method of the call. By collecting these behavioral characteristics, the data control platform can grasp the dynamics and rules of data usage in real time, evaluate the rationality and legitimacy of data calls, and provide basic data support for subsequent behavior audits, risk analysis, call optimization and other control measures.

[0078] In the process of collecting data call behavior characteristics, the data control platform can obtain the original information of data calls through multiple channels through various means such as log analysis and traffic monitoring. First, the data control platform needs to monitor all links of data calls, deploy collection probes at different locations such as gateways, interfaces, and databases, and capture data requests and response messages in real time and record them as call logs. Then, the original call logs are parsed and extracted to sort out structured behavior records, including the caller's identity ID, timestamp, operation type, data object and other elements. Next, the behavior records are statistically analyzed in multiple dimensions. On the one hand, based on the time series aggregation call records, frequency statistics reflecting the call intensity are generated, including real-time concurrent numbers, total calls per hour / day / week / month, etc. On the other hand, based on the call link tracking call process, the final data usage method is determined, such as caching, downloading, and re-sharing. Finally, the statistical results are associated with the original behavior records and comprehensively sorted into a complete set of data call behavior feature portraits.

[0079] S206, setting a call warning threshold parameter according to a preset call warning rule; In this step, the data management platform needs to set the corresponding call warning threshold parameters according to the call warning rules preset by the system. The call warning rules are a set of judgment criteria for abnormal data access behaviors built into the system. Through these rules, various illegal call behaviors such as misoperation, malicious crawling, and unauthorized access can be discovered and identified in a timely manner. The warning threshold parameters are a series of critical values ​​quantified based on the warning rules, which are used to define whether the call behavior triggers a warning to achieve the automation of the warning. Through the dynamic configuration of the warning threshold parameters, the data management platform can flexibly adjust the sensitivity and granularity of the warning to find the best balance between prevention and control and convenience.

[0080] In order to realize the conversion of early warning rules into early warning threshold parameters, the data management platform first needs to comprehensively sort out and structure the early warning rules to form a triple form, such as <calling subject, data object, early warning condition>. Among them, the early warning condition part needs to be refined and quantified as much as possible to clarify the judgment basis and numerical boundary for triggering the early warning. For example, the original rule "prohibit high-frequency calls to sensitive data in a short period of time" is quantified as "the frequency of calls to the same data table by the same user cannot exceed 100 times per minute". In the process of rule quantification, the data call behavior feature information collected in the previous steps can be fully referred to, and the distribution of various indicators of normal call behavior, such as mean, variance, median, etc., can be counted. On this basis, an appropriate safety margin is set to finally determine the early warning threshold parameters. The setting of threshold parameters must also fully consider factors such as the security level of the data and the importance of the business, and set differentiated early warning trigger thresholds for different data and different scenarios. After the threshold is set, it can be used to judge whether the user's call behavior is compliant in real time.

[0081] S207, calculating a data reuse rate index based on the data call behavior feature information; In this step, the data management platform will analyze and calculate the data reuse rate index based on the collected data call behavior feature information. Data reuse rate is an important data asset management indicator, which is used to evaluate the reuse of the same data in different scenarios and among different users, reflecting the efficiency of data asset utilization and value creation capabilities. By calculating the data reuse rate, the data management platform can gain insight into the current status of data sharing applications, explore data usage hotspots, identify access bottlenecks, and thus optimize data governance strategies and maximize data value.

[0082] The data management platform can use a variety of quantitative analysis models and statistical algorithms when calculating the data reuse rate. The basic idea is to count the calls of a certain data within a certain period, and measure the reuse level by calculating indicators such as the number of access users, the number of visits, and the number of usage scenarios. Specifically: First, the concentration index of data access users can be calculated, such as the proportion of users who contribute to the top 20% of the access volume, etc., to intuitively present the "28" distribution effect; second, the data usage scenarios can be clustered and analyzed to measure the frequency and proportion of cross-scenario calls, and to characterize the degree of data sharing among vertical businesses; third, the time dimension distribution characteristics of data access can be statistically analyzed, such as monthly active rate, average daily visits, etc., to examine the supporting role of data for business continuity; fourth, based on data kinship, the secondary processing and utilization of statistical management data can be tracked and evaluated. The effect of data value-added application. Finally, the weighted sum of each sub-indicator can be formed to form a comprehensive data reuse rate scoring system.

[0083] S208, constructing a data call scoring rule set, and performing a quantitative assessment on data usage compliance according to the data call scoring rule set to obtain a compliance assessment result; In this step, the data management platform needs to build a complete set of data call scoring rules, and based on this set of rules, conduct a systematic compliance assessment of the data call behavior on the platform, and finally generate a quantitative compliance assessment result. The data call scoring rules are a set of clear and standardized data usage behavior evaluation criteria, covering all key links of data calls, and comprehensively characterizing the compliance of data calls from multiple dimensions such as permission management, access control, and data usage. Through this set of objective and fair scoring rules, the data management platform can efficiently identify and quantify various risks and problems in data calls, and promote the transformation of data management from passive prevention and control to active auditing, so as to better regulate user behavior and ensure data security.

[0084] In order to establish a scientific and complete scoring rule set, the data management platform needs to systematically sort out the existing data use management system, industry standards and specifications, compliance audit requirements, etc., extract and summarize the evaluation points, and select representative and operational scoring dimensions and indicators, such as data access frequency, access period, data usage scope, purpose of use, etc., to build the overall framework of the rule set. On this basis, for each scoring indicator, the scoring criteria are quantitatively defined, and a unified scoring scale and weight are formulated. For example, for data access frequency, multiple thresholds can be set, and access exceeding the threshold will deduct the corresponding points, and differentiated deductions can be set according to the importance and sensitivity of different data. In actual evaluation, the system will automatically obtain the associated information of each data call, such as data content, user role, usage link, etc., and substitute it into the scoring rules to calculate the score. Finally, the comprehensive compliance score of the data call operation is summarized to measure its compliance risk level.

[0085] S209. Update the data usage permission matrix based on the compliance assessment results.

[0086] After the compliance assessment in the previous step is completed, the data management platform needs to dynamically update and adjust the data usage permission matrix based on the assessment results. The data usage permission matrix records the division of access rights for different types of users to different data, and is the basic basis for controlling data access behavior. Dynamic optimization of the permission matrix based on compliance scores can be targeted to reduce or relax data access rights for specific users, avoid data abuse or omissions at the source, and effectively improve data security. This closed-loop matrix tuning mechanism makes the platform's permission management more intelligent, sophisticated, and flexible, and truly realizes dynamic authorization and timely control based on user behavior performance.

[0087] Specifically, after obtaining the compliance score of each data call, the data management platform will associate the score result with the user's previous score history and continuously track the compliance trend of the user's data usage behavior. On the one hand, if the user's compliance score continues to rise and is better than the average level of similar users, the platform may consider appropriately relaxing its permissions, such as opening access to more data and increasing the visible range of data, to encourage compliance use; on the other hand, if the user's compliance score continues to be low or fluctuates abnormally, the platform may reduce its permissions accordingly based on the preset disciplinary measures, such as limiting its access frequency, narrowing the scope of accessible data, etc., until it completely prohibits its access, so as to effectively curb violations. At the same time, when the user's data access behavior undergoes major adjustments, such as changes in access objects and sudden increases in usage frequency, even if the score has not yet reached the red line, the system can pre-adjust its permission matrix to avoid potential risks caused by excessive authorization. In addition, the data management platform can also intuitively present the current score status and permission distribution of different users through technical means such as visualization, so that managers can gain real-time insight into security dynamics and provide reference for further optimizing permission strategies.

[0088] In the above embodiment, by labeling the Rongxin data that has passed the rectification, it is convenient to quickly locate the appropriate data according to the user's data feature requirements. A data usage permission matrix is ​​generated based on the usage scenario description information, and a data call certificate containing parameters such as access time limit, usage scope and desensitization requirements is generated, and a refined data access control mechanism is established. By configuring the data access interface parameters to control the data access method, the manageability and security of data use are achieved. This scenario-based refined permission management method improves the flexibility of data services while ensuring data security, so that data resources can be fully utilized within a controlled range.

[0089] The following describes the data management platform in the embodiment of the present invention from the perspective of hardware processing. Figure 5 , which is a schematic diagram of the physical device structure of a Rongxin data management platform provided in an embodiment of the present application.

[0090] It should be noted that Figure 5 The structure of the data management platform shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0091] like Figure 5As shown, the data management platform includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0092] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0093] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0094] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0096] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0097] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0098] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0099] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0100] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A Rongxin data management method, applied to a data management platform, characterized in that: include: Extract qualified Rongxin data from Rongxin data based on the latest data standard, where the latest data standard is obtained by adding field naming verification and field type verification to the original data standard through standard root; Obtaining data quality management requirements, and determining the data quality scope according to the data quality management requirements; In the case where it is determined based on the data quality range that the qualified credit data does not meet the data quality management requirements, a rectification process is performed on the qualified credit data; Performing data sampling inspection at preset progress points of the rectification process to obtain a number of sampling inspection results, wherein the inspection standard of the data sampling inspection is the data quality range, the sampling inspection result is qualified or unqualified, and the number of the preset progress points is a preset first number; If the sampling test result corresponding to any of the preset progress points is unqualified, the step of performing the rectification process on the qualified Rongxin data is re-executed; If the sampling test results corresponding to the preset first number of preset progress points are all qualified, then outputting the rectification qualified credit data after the rectification process is performed; The rectified qualified Rongxin data is stored in the data management library of the corresponding user.

2. The method according to claim 1, characterized in that The step of extracting qualified Rongxin data from Rongxin data based on the latest data standard specifically includes: Construct a standard root mapping library, which contains standard roots of commonly used fields in the business field and corresponding field type sets; Based on the standard root mapping library, the fields in the original data standard are segmented to obtain a field root sequence; Matching the field root sequence with the standard root; If the match is successful, verify whether the verification field type of the field root sequence belongs to the field type set; Mark the fields that failed to match or failed field type verification, and generate field correction suggestions; The original data standard is updated according to the field correction suggestion to obtain the latest data standard.

3. The method according to claim 1, characterized in that The data sampling test is performed at the preset progress points of the rectification process to obtain several sampling test results, including: Constructing a multi-dimensional scoring matrix based on the data quality range, wherein the multi-dimensional scoring matrix includes completeness, accuracy, consistency and timeliness indicators; A stratified random sampling method is used to select samples from the qualified Rongxin data; Perform multi-dimensional scoring on the sample based on the multi-dimensional scoring matrix to obtain scoring results for each dimension; Perform weighted calculation on the scoring results of each dimension based on the preset dimension weights to obtain a comprehensive score; The comprehensive score is compared with a preset qualified threshold to obtain a sampling test result.

4. The method according to claim 1, characterized in that After storing the rectification qualified credit data in the data management library of the corresponding user, the method further includes: Receive a data call request sent by a user, wherein the data call request includes target data feature description information and usage scenario description information; Labeling the rectified qualified Rongxin data to obtain data label information, wherein the data label information includes a feature identifier that matches the feature description information of the target data; Generate a data usage permission matrix according to the usage scenario description information, wherein the data usage permission matrix includes data access level information; Generate a data call credential based on the data access level information, wherein the data call credential includes an access time limit parameter, a usage scope parameter, and a desensitization requirement parameter; The data access interface parameters are configured according to the data call credentials, and the data access interface parameters are used to control the access method of the rectification qualified Rongxin data.

5. The method according to claim 4, characterized in that The labeling process of the rectification qualified Rongxin data to obtain data label information specifically includes: Parsing the target data feature description information to obtain a feature keyword sequence; Constructing a preset data tag system, wherein the preset data tag system includes a business attribute tag subset, a time attribute tag subset, and a scene attribute tag subset; Perform feature analysis on the rectification qualified Rongxin data based on the feature keyword sequence to generate an initial data label set; Using a preset label optimization rule to screen the initial data label set to obtain a target data label set; The correlation strength coefficients between the labels in the target data label set are calculated to generate a label relationship network structure and obtain data label information.

6. The method according to claim 4 or 5, characterized in that: After configuring the data access interface parameters according to the data calling credentials, the method further includes: Collecting data call behavior characteristic information, wherein the data call behavior characteristic information includes a call timestamp, a call frequency statistic value, and a data usage mode identifier; Set the call warning threshold parameters according to the preset call warning rules; Calculate a data reuse rate index based on the data calling behavior characteristic information; Constructing a data call scoring rule set, and performing a quantitative assessment on data usage compliance according to the data call scoring rule set to obtain a compliance assessment result; The data usage permission matrix is ​​updated according to the compliance assessment result.

7. The method according to claim 6, characterized in that The collected data calling behavior characteristic information specifically includes: Obtaining data call log records, and extracting caller identity information and operation behavior sequence from the data call log records; Performing a time series analysis on the operation behavior sequence to obtain a data call time parameter and a call frequency parameter, and constructing a data call link diagram based on the operation behavior sequence; Extracting data usage characteristics from the data call link diagram to obtain a data usage identifier; The data calling time parameter, the calling frequency parameter and the data usage mode identifier are combined to obtain data calling behavior characteristic information.

8. A Rongxin data management system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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