Supervision submission data management method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and particularly discloses a supervision submitted data management method and device, computer equipment and a storage medium. According to the method, the quality of the data source can be monitored, the quality standard is dynamically generated according to the data characteristics of the supervision submitted data and the supervision requirements, and the quality of the supervision submitted data is evaluated according to the generated quality standard, so that the quality problems of the data source and the supervision submitted data can be accurately identified, and the data quality is ensured to reach the supervision standard; and the data quality of supervision submitted data is improved. When the method is applied to the supervision submitted data quality management service of a financial system, the quality problems of the data source and the supervision submitted data can be accurately identified, and the data quality is further improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, computer equipment and storage medium for managing regulatory reporting data. Background Art

[0002] With the rapid development of the financial industry and the increasing complexity of its operations, financial regulators are imposing increasingly stringent data reporting requirements on the financial system. Regulatory reporting data refers to data that the financial system submits to regulatory authorities on a regular basis in accordance with regulatory requirements. The purpose of regulatory reporting data is to provide financial regulators with a timely understanding of the operating conditions and risk profile of financial institutions, enabling them to take timely regulatory measures to protect financial market stability and investor interests. Therefore, the quality of regulatory reporting data is directly related to financial regulators' accurate assessment of financial institutions' operating conditions and risk levels, as well as the stable operation of financial markets.

[0003] However, the financial system currently lacks comprehensive, real-time monitoring and assessment of data quality. This prevents data quality issues from being promptly identified and addressed, leading to persistent quality issues in the regulatory reporting data sent by the financial system to financial regulators. Therefore, improving the quality of regulatory reporting data has become an urgent issue. Summary of the Invention

[0004] This application provides a regulatory reporting data management method, device, computer equipment and storage medium, aiming to improve the quality of regulatory reporting data.

[0005] In a first aspect, the present application provides a method for managing regulatory reporting data, the method comprising:

[0006] Obtaining data sources, performing data preprocessing and data quality assessment on the data sources, and generating data drop tables;

[0007] Extract regulatory reporting data from the data table according to preset data regulatory requirements;

[0008] Analyze the regulatory reporting data to obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements;

[0009] Conducting a quality assessment on the regulatory submission data according to the data quality standards and obtaining a quality assessment report;

[0010] When the quality assessment report shows that the quality meets the standards, the regulatory reporting data is sent to the target recipient.

[0011] In a second aspect, the present application further provides a regulatory reporting data management device, the device comprising:

[0012] The data processing module is used to obtain the data source, perform data preprocessing and data quality assessment on the data source, and generate a data drop table;

[0013] A data extraction module is used to extract regulatory reporting data from the data table according to preset data supervision requirements;

[0014] A standards generation module, configured to analyze the regulatory reporting data, obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements;

[0015] A quality assessment module, configured to perform a quality assessment on the regulatory submission data according to the data quality standards and obtain a quality assessment report;

[0016] The data sending module is used to send the regulatory reporting data to the target recipient when the quality assessment report shows that the quality meets the standards.

[0017] In a third aspect, the present application also provides a computer device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the regulatory reporting data management method as described above when executing the computer program.

[0018] In a fourth aspect, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the regulatory reporting data management method as described above.

[0019] The present application discloses a method, apparatus, computer equipment and storage medium for managing regulatory reporting data, which obtains a data source, performs data preprocessing and data quality assessment on the data source, and generates a data drop table; extracts regulatory reporting data from the data drop table according to preset data supervision requirements; analyzes the regulatory reporting data to obtain data characteristics, and generates data quality standards for the regulatory reporting data based on the data characteristics and the data supervision requirements; performs quality assessment on the regulatory reporting data based on the data quality standards to obtain a quality assessment report; and sends the regulatory reporting data to the target recipient when the quality assessment report shows that the quality meets the standards. The present application can monitor the quality of the data source, dynamically generate quality standards based on the data characteristics and regulatory requirements of the regulatory reporting data, and perform quality assessment on the regulatory reporting data based on the generated quality standards. It can accurately identify quality problems of the data source and regulatory reporting data, ensure that the data quality meets the regulatory standards, and improve the data quality of the regulatory reporting data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 This is a schematic flow chart of a method for managing regulatory reporting data provided in the first embodiment of the present application;

[0022] Figure 2 This is a schematic flow chart of a method for managing regulatory reporting data provided in the second embodiment of the present application;

[0023] Figure 3 This is a schematic flow chart of a method for managing regulatory reporting data provided in the third embodiment of the present application;

[0024] Figure 4 A schematic block diagram of a regulatory reporting data management device provided in an embodiment of the present application;

[0025] Figure 5 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0028] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It should be further understood that the term “and / or” used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0030] The embodiments of the present application provide a method, apparatus, computer equipment, and storage medium for managing regulatory reporting data. Specifically, the regulatory reporting data management method can be applied to a server, and by performing quality monitoring on the data source, dynamically generating quality standards based on the data characteristics of the regulatory reporting data and regulatory requirements, and performing quality assessment on the regulatory reporting data based on the generated quality standards, the data quality management system is improved, and quality issues of the data source and regulatory reporting data can be accurately identified, ensuring that the data quality meets regulatory standards, thereby improving the data quality of the regulatory reporting data. Specifically, the server can be an independent server or a server cluster.

[0031] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0032] See also Figure 1 , Figure 1 This is a schematic flow chart of a regulatory reporting data management method provided in an embodiment of the present application. This regulatory reporting data management method can be applied to a server to monitor the quality of data sources, dynamically generate quality standards based on the data characteristics of regulatory reporting data and regulatory requirements, and perform quality assessments on regulatory reporting data based on the generated quality standards. This improves the data quality management system, accurately identifies quality issues with data sources and regulatory reporting data, ensures that data quality meets regulatory standards, and improves the quality of regulatory reporting data.

[0033] like Figure 1 As shown, the regulatory reporting data management method specifically includes steps S101 to S105.

[0034] S101: Acquire a data source, perform data preprocessing and data quality assessment on the data source, and generate a data table;

[0035] In one embodiment, various data sources containing business data are identified and determined, which may include relational databases, non-relational databases, file systems, data warehouses, etc. Stable connections are established with each data source to ensure smooth access and reading of data.

[0036] In one embodiment, a data processing tool is used to perform pre-processing such as synchronization, cleaning, and processing on the source data in each data source. The Linkdo scheduling tool is used for regular scheduling, and the pre-processed data is compared with the source data to evaluate the data quality. If the quality of the pre-processed data meets the standards, the data is stored in a preset database according to a preset data table structure to form a data table, which provides a data basis for the subsequent extraction of regulatory reporting data.

[0037] Specifically, data preprocessing includes removing noise from the data, correcting erroneous data, and filling in missing data (such as using the mean, median, or estimated value to fill missing numeric fields); converting the data into a unified format and encoding rules, such as unifying the date format, and converting values ​​in different units to the same unit to facilitate subsequent processing and analysis.

[0038] Furthermore, the data preprocessing and data quality assessment of the data source to generate a data drop table further includes steps S1011 to S1013:

[0039] S1011. Obtain idle edge processing nodes and computing power strength of each edge processing node.

[0040] In one embodiment, a network scanning tool or edge computing management platform is used to regularly scan edge processing nodes in the network to identify nodes that are in an idle state. Specifically, whether a node is in an idle state can be determined by indicators such as CPU usage, memory usage, and network bandwidth.

[0041] Use performance testing tools (such as benchmark software) to evaluate the computing power of idle edge processing nodes, test their CPU (Central Processing Unit) processing capabilities, memory read and write speeds, etc., and evaluate their computing power intensity.

[0042] S1012: Determine a target edge processing node corresponding to the data source based on the computing power intensity and the data size and data complexity of the data source;

[0043] Furthermore, the step S1012 includes: determining the preprocessing level and quality assessment level of the data source based on the data size and data complexity of the data source; and determining the target edge processing node corresponding to the data source based on the computing power intensity, the preprocessing level and the quality assessment level.

[0044] In one embodiment, the complexity of the analyzed data includes the diversity of data types, the complexity of data structures, the difficulty of data processing (such as the need for complex cleaning and conversion operations), etc.

[0045] In a specific embodiment, the preprocessing level and quality assessment level of a data source can be obtained using a level judgment model. The level judgment model is trained using historical data sources and their corresponding preprocessing levels and quality assessment levels. The level judgment model can analyze the data source, automatically analyzing its data size and complexity, and then assess the preprocessing level and quality assessment level of the data source.

[0046] In one embodiment, higher preprocessing levels and / or higher quality assessment levels require higher computing power. Edge processing nodes with computing power slightly matching the current preprocessing level and / or quality assessment level are matched based on the preprocessing level and / or quality assessment level. For example, edge processing nodes with the highest computing power intensity are assigned to data sources with high preprocessing levels and high quality assessment levels, thereby accelerating preprocessing and quality assessment efficiency.

[0047] S1013: Perform data preprocessing and data quality assessment on the data source based on the target processing node to generate the data drop table.

[0048] Transmit the data source to the target edge processing node, ensuring the security and integrity of the data transmission. Perform data preprocessing and quality assessment tasks on the target edge processing node. When the data source quality meets the requirements, generate a data drop table.

[0049] In the above embodiment, tasks can be reasonably allocated according to the characteristics of the data source and the computing power of the edge processing node to ensure the efficient execution of data preprocessing and quality assessment, and provide reliable data support for subsequent regulatory reporting.

[0050] S102. Extracting regulatory reporting data from the data table according to preset data regulatory requirements;

[0051] In one embodiment, regulatory reporting data refers to data that the financial system submits to regulatory authorities on a regular basis in accordance with regulatory requirements. The purpose of regulatory reporting data is to enable regulatory authorities to promptly understand the operating conditions and risk profiles of financial institutions so that they can take timely regulatory measures to protect financial market stability and investor interests.

[0052] In one embodiment, data supervision requirements can be obtained by parsing relevant documents issued by regulatory agencies, and may include regulations on the data content, data format, reporting frequency, data scope, etc. of regulatory reporting data.

[0053] Based on regulatory requirements, data extraction rules are determined to clearly define which data fields and records are to be extracted from the data table. Data extraction tools are then used to filter out data that meets regulatory requirements from the data table according to the established data extraction rules. This data is then extracted to form the data set to be submitted, i.e., the regulatory submission data.

[0054] S103. Analyze the regulatory reporting data to obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements;

[0055] In one embodiment, the regulatory reporting data is analyzed to obtain data characteristics, including data type, data format, data source, and data importance, for example, the data type of each field in the regulatory reporting data; the distribution of the data, such as the value range, central tendency, and degree of dispersion of numerical data, and the keyword frequency and text length distribution of text data; and the relationship between different fields, such as whether there is a functional dependency and whether certain business rules need to be met.

[0056] Based on data regulatory requirements and data characteristics, clearly define data quality standards for regulatory reporting data, such as accuracy requirements for numerical data (e.g., retaining several decimal places) and correctness requirements for text data. Data quality standards may also include standards for data accuracy, completeness, consistency, and timeliness.

[0057] In another embodiment, the data management requirements of the financial enterprise itself may be combined with the data supervision requirements and data characteristics of the regulatory agency to determine data quality standards so that data that meets the quality standards meets both the requirements of the regulatory agency and the data management needs of the financial enterprise itself.

[0058] S104. Perform a quality assessment on the regulatory submission data according to the data quality standards to obtain a quality assessment report;

[0059] In one embodiment, the regulatory submission data is comprehensively quality checked and verified according to the generated data quality standards, including a one-by-one assessment of various aspects of the data, such as accuracy, completeness, and consistency.

[0060] During the quality assessment process, quality assessment indicators are calculated, such as the accuracy ratio, completeness percentage, and consistency pass rate of the data, to quantify the quality of the assessment data. Based on the calculated quality assessment indicators and the preset quality acceptance threshold, it is determined whether the regulatory submission data meets the quality requirements.

[0061] In one embodiment, a quality assessment report is prepared based on the quality assessment results, including the overall situation of the data quality assessment, the assessment results of various quality indicators, the data quality issues found, and a detailed description of the issues.

[0062] Follow the specified report format and template to ensure that the report content is clear and easy to understand. You can also use charts, tables, etc. to intuitively display the data quality assessment results.

[0063] Furthermore, the step S104 includes: executing the quality assessment task at a preset time based on a preset timing scheduling tool, so as to perform a quality assessment on the regulatory reporting data according to the data quality standard and obtain the quality assessment report.

[0064] In one embodiment, the preset time can be set according to the regulatory agency's data supervision requirements. For example, if the regulatory agency requires that regulatory reporting data be generated and submitted daily, the scheduling time of the quality assessment task can be set to be executed during the early morning business off-peak period (such as 2 a.m.) to ensure that the quality assessment of the newly generated regulatory reporting data can be carried out in a timely manner every day, while avoiding excessive impact on system resource usage during business peak periods.

[0065] When the preset schedule arrives, the scheduled scheduling tool automatically triggers the execution of the quality assessment task, automatically loading the regulatory submission data to be assessed and the data quality standards, and performing various quality checks and assessments on the regulatory submission data in accordance with the data quality standards. After completing each quality assessment, the assessment results are summarized and a detailed assessment report is generated. This reduces manual intervention and improves the efficiency of data quality assessment.

[0066] S105: When the quality assessment report shows that the quality meets the standards, the regulatory reporting data is sent to the target recipient.

[0067] In one embodiment, the target recipient is typically a system, platform, or department designated by the regulatory agency. Regulatory reporting data that meets quality standards is packaged in the format required by the target recipient and encrypted to ensure data security and confidentiality during transmission. The packaged regulatory reporting data is sent to the target recipient, and information such as the time and volume of data sent is recorded.

[0068] After the target recipient receives the data, obtain the delivery confirmation information to ensure that the data is successfully delivered. At the same time, pay attention to the target recipient's feedback on the data and deal with any problems in a timely manner.

[0069] The above embodiment provides a method, device, computer equipment and storage medium for managing regulatory reporting data, obtains a data source, and performs data preprocessing and data quality assessment on the data source to generate a data drop table; extracts regulatory reporting data from the data drop table according to preset data supervision requirements; analyzes the regulatory reporting data to obtain data characteristics, and generates data quality standards for the regulatory reporting data based on the data characteristics and the data supervision requirements; performs quality assessment on the regulatory reporting data according to the data quality standards to obtain a quality assessment report; and sends the regulatory reporting data to the target recipient when the quality assessment report shows that the quality meets the standards. This application can monitor the quality of the data source, dynamically generate quality standards based on the data characteristics and regulatory requirements of the regulatory reporting data, and perform quality assessment on the regulatory reporting data based on the generated quality standards, thereby improving the data quality management system, accurately identifying quality problems of the data source and regulatory reporting data, ensuring that the data quality meets the regulatory standards, and improving the data quality of the regulatory reporting data.

[0070] See also Figure 2 , Figure 2 This is a schematic flow chart of a regulatory reporting data management method provided in an embodiment of the present application. The regulatory reporting data management method can be applied to a server to determine the frequency of data source quality assessment based on data tags and key field information, making data quality monitoring more flexible. For important data sources, more frequent and stricter monitoring can be carried out to promptly discover and solve problems; and for data sources with better quality, the evaluation frequency can be appropriately reduced to reduce resource consumption. This targeted monitoring method improves the efficiency and effectiveness of data quality management and ensures the quality of regulatory reporting data.

[0071] like Figure 2 As shown, the regulatory reporting data management method specifically includes steps S201 to S205.

[0072] S201: Preprocess the source data in the data source to obtain the data source to be evaluated and key field information of the data source to be evaluated;

[0073] In one embodiment, data preprocessing includes, but is not limited to, removing noise from the data, correcting erroneous data, and filling in missing data. Data can be converted to a unified format and encoding rules, such as a unified date format and converting values ​​in different units to the same unit to facilitate subsequent processing and analysis.

[0074] Analyze and determine which fields are key fields based on business rules and regulatory reporting requirements. Key fields are typically used to uniquely identify data records or have a significant impact on the business, such as customer number, account number, transaction amount, etc.

[0075] Perform keyword recognition on the data source to be evaluated to obtain key field information included in the data source to be evaluated.

[0076] S202: Compare the source data with the data in the data source to be evaluated, and determine the data label corresponding to the data source to be evaluated;

[0077] In one embodiment, the data in the data source to be evaluated is compared with the source data to check whether the data in the data source to be evaluated is consistent with the source data. For example, the account balance corresponding to a customer number in the source data is checked to see whether it is the same as the account balance corresponding to the same customer number in the data source to be evaluated.

[0078] In one embodiment, based on the comparison results, the data source is labeled to generate data tags. Data tags include, but are not limited to, data consistency tags, data integrity tags, data accuracy tags, and specific problem tags, such as "business data issues, synchronization issues, processing issues," etc.

[0079] S203: Determine a data source quality assessment frequency of the data source to be assessed based on the data tag and the key field information;

[0080] Furthermore, the step S203 includes: determining the data supervision level of the data source to be evaluated based on the data tag and the key field information; and determining the data source quality evaluation frequency based on the data supervision level.

[0081] In one embodiment, data tags are classified and graded according to their nature and impact, for example, tags that affect core business data are graded as high-level and tags that have less impact are graded as low-level.

[0082] In one embodiment, the importance level of the data source to be evaluated is determined based on the key field information. For example, the importance level of the data source to be evaluated is determined based on the number of key fields contained in the data source to be evaluated. If the data source to be evaluated does not contain any key fields, the importance level of the data source to be evaluated is average. The more key fields the data source to be evaluated contains, the higher the importance level of the data source to be evaluated. It is understandable that the corresponding relationship between the number of key fields and the importance level can be freely set by the user according to actual needs.

[0083] Key fields can be determined based on the impact of the field data on the financial enterprise's business. For example, if the recorded data corresponding to fields such as customer number and account balance has a greater impact on the business, while the customer address field has a relatively smaller impact, then customer number and account balance are key fields. Key fields can also be determined based on regulatory requirements. For example, if regulatory requirements are strict for fields such as customer identity information and transaction records, then customer identity information and transaction records are key fields.

[0084] For example, if the data label indicates that there are serious consistency, completeness, accuracy or timeliness issues, and these issues involve key fields, then it is determined to be a high-level regulatory level. Data sources at the high-level regulatory level require strict quality assessment and monitoring. If the data label indicates that there are some consistency, completeness, accuracy or timeliness issues, but these issues do not involve key fields, or the impact of the issues is relatively low, then it is determined to be an intermediate regulatory level. Data sources at the intermediate regulatory level require regular quality assessment and monitoring. If the data label indicates that the data quality is good and there are fewer consistency, completeness, accuracy or timeliness issues, then it is determined to be a low-level regulatory level. Data sources at the low-level regulatory level can undergo appropriately simplified quality assessment and monitoring.

[0085] In one embodiment, different data source quality assessment frequencies are applied to different regulatory levels. This allows for prioritized management and monitoring of important data sources, while ensuring appropriate management and monitoring of less important data sources. Specifically, for data sources with higher regulatory levels, the assessment frequency is increased to strengthen data source quality monitoring, while for data sources with lower regulatory levels, the assessment frequency can be reduced to reduce resource consumption. It is understood that the specific assessment frequency corresponding to different regulatory levels can be freely set by the user based on actual needs.

[0086] S204: Perform data quality assessment on the data source to be assessed according to the data source quality assessment frequency and the preset data source quality assessment standard to obtain a quality assessment result;

[0087] In one embodiment, the preset data source quality assessment standards include, but are not limited to, accuracy standards, completeness standards, consistency standards, timeliness standards and other assessment dimensions.

[0088] In one embodiment, a detailed data quality assessment is performed on the data source to be assessed according to the data source quality assessment frequency and the preset data source quality assessment criteria. Specifically, the quality indicators of each field in the data source to be assessed, such as accuracy rate, completeness percentage, consistency pass rate, etc., are calculated.

[0089] Based on the calculated quality indicators and the quality thresholds in the quality assessment standards, the data source to be evaluated is determined to meet the quality requirements. If the quality indicators of all fields meet the standards, the data source is considered to meet the standards; otherwise, the data source is considered to fail the standards.

[0090] In one embodiment, the quality assessment results for each key field and data tag are summarized to generate an overall quality assessment result. A quality assessment report is prepared, which includes but is not limited to the overall assessment, the assessment results of each quality indicator, the data quality issues found, and a detailed description of the issues.

[0091] S205: When the quality assessment result shows that the quality meets the standards, generate the data table.

[0092] In one embodiment, the structure of the data table can be determined according to business requirements and data characteristics, including constraints such as field name, data type, field length, primary key and foreign key.

[0093] The data from the data sources to be evaluated that meet the quality standards are stored in the target database according to the designed data table structure to generate a data table. The data table will serve as the basis for the subsequent extraction and processing of regulatory reporting data.

[0094] In one embodiment, for a data source to be evaluated that does not meet quality standards, an alarm is issued to relevant personnel according to a preset alarm method to remind the relevant personnel to repair the data source to be evaluated to ensure the data quality of the data source.

[0095] In one embodiment, after the data table is generated, the data table can also be verified, including but not limited to verifying data integrity: verifying whether the data in the data table is complete and ensuring that all key fields and data records are correctly stored. Verifying data accuracy: verifying whether the data in the data table is accurate and ensuring that the data content is consistent with the source data.

[0096] In the above embodiment, the frequency of data source quality assessments is determined based on data tags and key field information, making data quality monitoring more flexible. For important data sources, more frequent and rigorous monitoring can be performed to promptly identify and resolve issues. For higher-quality data sources, the assessment frequency can be appropriately reduced to reduce resource consumption. This targeted monitoring approach improves the efficiency and effectiveness of data quality management and ensures the quality of data submitted for regulatory approval.

[0097] See also Figure 3 , Figure 3This is a schematic flow chart of a regulatory reporting data management method provided in an embodiment of the present application. This regulatory reporting data management method can be applied to a server to generate data quality standards based on data quality requirements and data characteristics. This ensures that the generated regulatory reporting data quality assessment standards meet regulatory requirements and adapt to data characteristics, thereby providing a scientific and reasonable basis for the quality assessment of regulatory reporting data.

[0098] like Figure 3 As shown, the regulatory reporting data management method specifically includes steps S301 to S303.

[0099] S301. Obtaining data quality requirements for regulatory reporting data according to the data regulatory requirements;

[0100] S302: Based on the data characteristics and the data quality requirements, filter the quality standards corresponding to each evaluation dimension in a preset quality standard database;

[0101] S303: Integrate the quality standards corresponding to the evaluation dimensions according to a preset template to generate the data quality standard.

[0102] In one embodiment, data supervision requirements, laws, and policy documents issued by regulatory agencies are parsed to clarify regulatory agencies' data quality requirements for accuracy, completeness, timeliness, and other aspects of regulatory reporting data.

[0103] Based on regulatory requirements and data characteristics, determine the data quality dimensions that regulatory reporting data needs to meet, such as accuracy, completeness, timeliness, consistency, uniqueness, validity, etc., and set specific quality goals and indicators for each dimension.

[0104] In another embodiment, the quality assessment criteria may also be determined in combination with the financial system's own expectations and standards for the quality of regulatory reporting data.

[0105] In one embodiment, the quality requirements for regulatory submissions are mapped to standards in a quality standards database to identify possible standards for each quality dimension. Then, based on the data characteristics, specific quality standards that meet the characteristics of the regulatory submissions are identified from the selected possible standards. For example, for date fields, validity standards that apply to date formats and time ranges are selected.

[0106] In one embodiment, a preset template includes various components of a data quality standard. The quality standards for each evaluation dimension, selected from a quality standard database, are integrated according to the template structure, ensuring that each quality dimension has a clear standard description and evaluation method, thereby obtaining a data quality standard. The integrated quality evaluation standards are then checked for consistency across different dimensions to avoid conflicting or duplicated standards.

[0107] The above embodiment generates data quality standards based on data quality requirements and data characteristics, which can ensure that the generated regulatory reporting data quality assessment standards not only meet regulatory requirements but also adapt to the characteristics of the data, thereby providing a scientific and reasonable basis for the quality assessment of regulatory reporting data.

[0108] See also Figure 4 , Figure 4 The embodiment of the present application provides a schematic block diagram of a supervisory reporting data management device, which is used to execute the aforementioned supervisory reporting data management method. The supervisory reporting data management device can be configured on a server.

[0109] like Figure 4 As shown, the regulatory reporting data management device 400 includes:

[0110] The data processing module 401 is used to obtain a data source, perform data preprocessing and data quality assessment on the data source, and generate a data drop table;

[0111] The data extraction module 402 is used to extract regulatory reporting data from the data table according to preset data regulatory requirements;

[0112] A standards generation module 403 is configured to analyze the regulatory reporting data to obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements;

[0113] A quality assessment module 404 is configured to perform a quality assessment on the regulatory submission data according to the data quality standards to obtain a quality assessment report;

[0114] The data sending module 405 is used to send the regulatory reporting data to the target recipient when the quality assessment report shows that the quality meets the standards.

[0115] Furthermore, the data processing module 401 includes:

[0116] A data preprocessing unit, configured to perform data preprocessing on the source data in the data source to obtain the data source to be evaluated and key field information of the data source to be evaluated;

[0117] a data label determination unit, configured to compare the source data with the data in the data source to be evaluated, and determine a data label corresponding to the data source to be evaluated;

[0118] An evaluation frequency determining unit, configured to determine a data source quality evaluation frequency of the data source to be evaluated based on the data tag and the key field information;

[0119] A data quality assessment unit, configured to perform data quality assessment on the data source to be assessed according to the data source quality assessment frequency and a preset data source quality assessment standard, and obtain a quality assessment result;

[0120] The data drop list generating unit is used to generate the data drop list when the quality assessment result is that the quality meets the standard.

[0121] Furthermore, the evaluation frequency determination unit includes:

[0122] A supervision level determination subunit, configured to determine the data supervision level of the data source to be evaluated based on the data tag and the key field information;

[0123] The evaluation frequency determination subunit is used to determine the data source quality evaluation frequency according to the data supervision level.

[0124] Furthermore, the standard generation module 403 includes:

[0125] A quality requirement acquisition unit, configured to acquire data quality requirements of the regulatory reporting data according to the data regulatory requirements;

[0126] A quality standard screening unit, configured to screen the quality standards corresponding to each evaluation dimension in a preset quality standard database based on the data characteristics and the data quality requirements;

[0127] The evaluation standard generating unit is used to integrate the quality standards corresponding to each of the evaluation dimensions according to a preset template to generate the data quality standard.

[0128] Furthermore, the data processing module 401 further includes:

[0129] A processing node acquisition unit, configured to acquire an idle edge processing node and the computing power strength of each edge processing node;

[0130] a processing node determining unit, configured to determine a target edge processing node corresponding to the data source based on the computing power intensity and the data size and data complexity of the data source;

[0131] A data drop table generating unit is used to perform data preprocessing and data quality assessment on the data source based on the target processing node to generate the data drop table.

[0132] Furthermore, the processing node determination unit includes:

[0133] A level evaluation subunit, configured to determine a preprocessing level and a quality evaluation level of the data source according to the data size and data complexity of the data source;

[0134] The node determination subunit is used to determine the target edge processing node corresponding to the data source according to the computing power intensity, the preprocessing level and the quality assessment level.

[0135] Furthermore, the quality assessment module 404 is specifically configured to execute the quality assessment task at a preset time based on a preset timing scheduling tool, so as to perform quality assessment on the regulatory reporting data according to the data quality standard and obtain the quality assessment report.

[0136] It should be noted that those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] The above-mentioned device can be realized in the form of a computer program. The computer program can be used in Figure 5 Runs on the computer device shown.

[0138] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0139] See Figure 5 The computer device includes a processor, a memory, and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0140] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to execute any one of the regulatory reporting data management methods.

[0141] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0142] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the regulatory reporting data management methods.

[0143] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0145] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0146] Obtaining data sources, performing data preprocessing and data quality assessment on the data sources, and generating data drop tables;

[0147] Extract regulatory reporting data from the data table according to preset data regulatory requirements;

[0148] Analyze the regulatory reporting data to obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements;

[0149] Conducting a quality assessment on the regulatory submission data according to the data quality standards and obtaining a quality assessment report;

[0150] When the quality assessment report shows that the quality meets the standards, the regulatory reporting data is sent to the target recipient.

[0151] In one embodiment, when performing data preprocessing and data quality assessment on the data source and generating a data drop list, the processor is configured to implement:

[0152] Performing data preprocessing on the source data in the data source to obtain the data source to be evaluated and key field information of the data source to be evaluated;

[0153] Comparing the source data with the data in the data source to be evaluated, and determining the data label corresponding to the data source to be evaluated;

[0154] Determining a data source quality assessment frequency of the data source to be assessed based on the data tag and the key field information;

[0155] Performing data quality assessment on the data source to be assessed according to the data source quality assessment frequency and the preset data source quality assessment standard to obtain a quality assessment result;

[0156] When the quality assessment result is that the quality meets the standards, the data drop table is generated.

[0157] In one embodiment, when determining the data source quality assessment frequency of the data source to be assessed based on the data tag and the key field information, the processor is configured to implement:

[0158] Determining the data supervision level of the data source to be evaluated based on the data tag and the key field information;

[0159] The frequency of the data source quality assessment is determined according to the data supervision level.

[0160] In one embodiment, when generating the data quality standard for the regulatory reporting data based on the data characteristics and the data regulatory requirements, the processor is configured to implement:

[0161] Obtain the data quality requirements for regulatory reporting data according to the data regulatory requirements;

[0162] Based on the data characteristics and the data quality requirements, screening the quality standards corresponding to each evaluation dimension in a preset quality standard database;

[0163] According to the preset template, the quality standards corresponding to each of the evaluation dimensions are integrated to generate the data quality standard.

[0164] In one embodiment, when performing data preprocessing and data quality assessment on the data source and generating a data drop list, the processor is further configured to implement:

[0165] Obtaining idle edge processing nodes and computing power strength of each edge processing node;

[0166] Determining a target edge processing node corresponding to the data source based on the computing power intensity and the data size and data complexity of the data source;

[0167] The data source is pre-processed and the data quality is evaluated based on the target processing node to generate the data drop table.

[0168] In one embodiment, when determining the target edge processing node corresponding to the data source based on the computing power intensity and the data size and data complexity of the data source, the processor is configured to implement:

[0169] Determining a preprocessing level and a quality assessment level of the data source based on the data size and data complexity of the data source;

[0170] Determine a target edge processing node corresponding to the data source according to the computing power intensity, the preprocessing level, and the quality assessment level.

[0171] In one embodiment, when performing a quality assessment on the regulatory submission data according to the data quality standard and obtaining a quality assessment report, the processor is configured to:

[0172] Based on a preset timing scheduling tool, the quality assessment task is executed at the preset time to perform quality assessment on the regulatory reporting data according to the data quality standards and obtain the quality assessment report.

[0173] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any one of the regulatory reporting data management methods provided in the embodiments of the present application.

[0174] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped on the computer device.

[0175] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for managing regulatory reporting data, characterized in that: include: Obtaining data sources, performing data preprocessing and data quality assessment on the data sources, and generating data drop tables; Extract regulatory reporting data from the data table according to preset data regulatory requirements; Analyze the regulatory reporting data to obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements; Conducting a quality assessment on the regulatory submission data according to the data quality standards and obtaining a quality assessment report; When the quality assessment report shows that the quality meets the standards, the regulatory reporting data is sent to the target recipient.

2. The method for managing regulatory reporting data according to claim 1, characterized in that: The data preprocessing and data quality assessment of the data source to generate a data drop table includes: Performing data preprocessing on the source data in the data source to obtain the data source to be evaluated and key field information of the data source to be evaluated; Comparing the source data with the data in the data source to be evaluated, and determining the data label corresponding to the data source to be evaluated; Determining a data source quality assessment frequency of the data source to be assessed based on the data tag and the key field information; Performing data quality assessment on the data source to be assessed according to the data source quality assessment frequency and the preset data source quality assessment standard to obtain a quality assessment result; When the quality assessment result is that the quality meets the standards, the data drop table is generated.

3. The method for managing regulatory reporting data according to claim 2, characterized in that: The determining, based on the data tag and the key field information, a data source quality assessment frequency of the data source to be assessed includes: Determining the data supervision level of the data source to be evaluated based on the data tag and the key field information; The frequency of the data source quality assessment is determined according to the data supervision level.

4. The method for managing regulatory reporting data according to claim 1, characterized in that: Generating the data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements includes: Obtain the data quality requirements for regulatory reporting data according to the data regulatory requirements; Based on the data characteristics and the data quality requirements, screening the quality standards corresponding to each evaluation dimension in a preset quality standard database; According to the preset template, the quality standards corresponding to each of the evaluation dimensions are integrated to generate the data quality standard.

5. The method for managing regulatory reporting data according to claim 1, characterized in that: The data preprocessing and data quality assessment of the data source to generate a data drop table further includes: Obtaining idle edge processing nodes and computing power strength of each edge processing node; Determining a target edge processing node corresponding to the data source based on the computing power intensity and the data size and data complexity of the data source; The data source is pre-processed and the data quality is evaluated based on the target processing node to generate the data drop table.

6. The method for managing regulatory reporting data according to claim 5, characterized in that: The determining, based on the computing power intensity and the data size and data complexity of the data source, a target edge processing node corresponding to the data source includes: Determining a preprocessing level and a quality assessment level of the data source based on the data size and data complexity of the data source; Determine a target edge processing node corresponding to the data source according to the computing power intensity, the preprocessing level, and the quality assessment level.

7. The method for managing regulatory reporting data according to any one of claims 1 to 6, characterized in that: The quality assessment of the regulatory submission data is performed according to the data quality standards to obtain a quality assessment report, including: Based on a preset timing scheduling tool, the quality assessment task is executed at the preset time to perform quality assessment on the regulatory reporting data according to the data quality standards and obtain the quality assessment report.

8. A supervisory reporting data management device, characterized in that: include: The data processing module is used to obtain the data source, perform data preprocessing and data quality assessment on the data source, and generate a data drop table; A data extraction module is used to extract regulatory reporting data from the data table according to preset data supervision requirements; A standards generation module, configured to analyze the regulatory reporting data, obtain data characteristics, and generate data quality standards for the regulatory reporting data based on the data characteristics and the data regulatory requirements; A quality assessment module, configured to perform a quality assessment on the regulatory submission data according to the data quality standards and obtain a quality assessment report; The data sending module is used to send the regulatory reporting data to the target recipient when the quality assessment report shows that the quality meets the standards.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the regulatory reporting data management method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to implement the regulatory reporting data management method according to any one of claims 1 to 7.

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