A data checking method and device

Abstract

This application provides a data inspection method and apparatus. The method includes: acquiring regulatory clause data; analyzing the regulatory clause data to obtain key attributes of the clauses; scanning the system database to be inspected to obtain scanned data; inspecting the scanned data based on the key attributes of the clauses to obtain inspection results; identifying abnormal data and abnormal scenarios that do not comply with the regulatory clause data based on the inspection results; and outputting the abnormal data and abnormal scenarios. As can be seen, this method and apparatus can inspect data, promptly identify data that does not meet regulatory requirements, and help improve data standardization to meet the data requirements of regulatory authorities.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a data inspection method and apparatus. Background Technology

[0002] Banking back-office systems are frequently subject to on-site inspections by regulators, particularly at the data level. In practice, it has been found that data stored in system databases may not meet regulatory requirements, or certain required data may be missing, thus failing to comply with regulations. This demonstrates that existing methods are inadequate for supervising system databases and for proactively identifying non-compliant data, thereby reducing data compliance. Summary of the Invention

[0003] The purpose of this application is to provide a data inspection method and apparatus that can inspect data, promptly identify data that does not meet regulatory requirements, and improve data standardization in order to meet the regulatory requirements for data.

[0004] The first aspect of this application provides a data inspection method, including: +

[0005] Obtain regulatory data;

[0006] The key attributes of the regulatory clauses were obtained by analyzing the data.

[0007] The system database to be inspected is scanned to obtain scan data;

[0008] The scanned data is checked based on the key attributes of the aforementioned terms to obtain the check results;

[0009] Based on the inspection results, identify abnormal data and abnormal scenarios that do not comply with the regulatory provisions.

[0010] Output the abnormal data and the abnormal scenario.

[0011] In the above implementation process, this method can first acquire regulatory clause data; then analyze the regulatory clause data to obtain key attributes of the clauses; next, scan the system database to be inspected to obtain scanned data; then, perform data checks on the scanned data based on the key attributes of the clauses to obtain inspection results; then, identify abnormal data and abnormal scenarios that do not comply with the regulatory clause data based on the inspection results; finally, output the abnormal data and abnormal scenarios. It is evident that this method can inspect data, promptly identify data that does not meet regulatory requirements, and help improve data standardization to meet the data requirements of regulators.

[0012] Furthermore, the analysis of the regulatory clause data yields key attributes of the clauses, including:

[0013] The regulatory clause data is parsed to obtain parsed text data;

[0014] The text parsing data is identified to obtain the key attributes of the terms at the data level.

[0015] Furthermore, the key attributes of the terms include at least the regulatory scenario, terms keywords, and regulatory parameters.

[0016] Furthermore, the step of performing data checks on the scanned data based on the key attributes of the terms to obtain the check results includes:

[0017] The scanned data is identified based on the key attributes of the aforementioned terms, resulting in a data table to be inspected.

[0018] Based on the data table to be inspected, an impact scenario analysis is performed to obtain the scenario analysis results;

[0019] Based on the scenario analysis results and the key attributes of the terms, a similarity matching check is performed on the data table to be checked to obtain the check results.

[0020] Further, the similarity matching check process performed on the data table to be checked based on the scenario analysis results and the key attributes of the terms to obtain the check results includes:

[0021] Based on the key attributes of the terms, the database-level tables of the data to be inspected are matched to obtain the attribute similarity identification results.

[0022] Based on the attribute similarity recognition results and the scene analysis results, empty data in the data table to be checked are identified, and empty data check results are obtained.

[0023] Based on the key attributes of the terms and the results of the scenario analysis, a data consistency correlation check is performed on the data table to be checked to obtain the correlation check results.

[0024] Based on the key attributes of the terms and the results of the scenario analysis, the data table to be inspected is probed by enumerating attribute values ​​to obtain the probe results.

[0025] Based on the key attributes of the terms and the results of the scenario analysis, the time range entry anomaly identification is performed on the data table to be inspected to obtain the time anomaly identification result.

[0026] The inspection results are obtained by summarizing the empty data inspection results, the correlation inspection results, the detection results, and the time anomaly identification results.

[0027] A second aspect of this application provides a data inspection apparatus, the data inspection apparatus comprising:

[0028] The acquisition unit is used to acquire regulatory clause data;

[0029] The analysis unit is used to analyze the regulatory clause data to obtain the key attributes of the clauses;

[0030] The scanning unit is used to scan the system database to be inspected and obtain scan data.

[0031] An inspection unit is used to perform data inspection on the scanned data according to the key attributes of the clause, and obtain inspection results;

[0032] A determining unit is used to determine, based on the inspection results, abnormal data and abnormal scenarios that do not comply with the regulatory provisions.

[0033] The output unit is used to output the abnormal data and the abnormal scenario.

[0034] In the above implementation process, the device can acquire regulatory clause data through the acquisition unit; analyze the regulatory clause data through the analysis unit to obtain the key attributes of the clauses; scan the system database to be inspected through the scanning unit to obtain scanned data; inspect the scanned data according to the key attributes of the clauses through the inspection unit to obtain inspection results; determine abnormal data and abnormal scenarios that do not comply with the regulatory clause data based on the inspection results through the determination unit; and finally output the abnormal data and abnormal scenarios through the output unit. It is evident that this device can inspect data and promptly identify data that does not meet regulatory requirements, which helps improve data standardization and thus meet the data requirements of regulatory authorities.

[0035] Furthermore, the analysis unit includes:

[0036] The parsing subunit is used to parse the regulatory clause data to obtain parsed text data.

[0037] The identification subunit is used to identify the text parsing data and obtain the key attributes of the terms at the data level.

[0038] Furthermore, the key attributes of the terms include at least the regulatory scenario, terms keywords, and regulatory parameters.

[0039] Furthermore, the inspection unit includes:

[0040] An identification subunit is used to identify the scanned data based on the key attributes of the terms, thereby obtaining a data table to be inspected.

[0041] The analysis subunit is used to perform impact scenario analysis based on the data table to be inspected, and obtain the scenario analysis results.

[0042] The inspection subunit is used to perform a similarity matching check on the data table to be inspected based on the scenario analysis results and the key attributes of the terms, and to obtain the inspection results.

[0043] Furthermore, the inspection subunit includes:

[0044] The matching module is used to perform DB-level table matching on the data table to be inspected based on the key attributes of the terms, and obtain the attribute similarity identification result;

[0045] The identification module is used to identify empty data in the data table to be checked based on the attribute similarity identification result and the scene analysis result, and obtain the empty data inspection result;

[0046] The detection module is used to perform a data consistency correlation check on the data table to be checked based on the key attributes of the terms and the scenario analysis results, and to obtain the correlation check results.

[0047] The detection module is used to detect attribute enumeration values ​​of the data table to be inspected based on the key attributes of the terms and the scenario analysis results, and to obtain the detection results.

[0048] The identification module is also used to identify time range entry anomalies in the data table to be inspected based on the key attributes of the terms and the scenario analysis results, and to obtain time anomaly identification results.

[0049] The aggregation module is used to aggregate the empty data inspection results, the correlation inspection results, the detection results, and the time anomaly identification results to obtain the inspection results.

[0050] A third aspect of this application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the data inspection method described in any one of the first aspects of this application.

[0051] A fourth aspect of this application provides a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the data inspection method described in any one of the first aspects of this application. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a data inspection method provided in an embodiment of this application;

[0054] Figure 2 A flowchart illustrating another data inspection method provided in an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the structure of a data inspection device provided in an embodiment of this application;

[0056] Figure 4 This is a schematic diagram of another data inspection device provided in an embodiment of this application. Detailed Implementation

[0057] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0058] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] Example 1

[0060] Please refer to Figure 1 , Figure 1 This embodiment provides a flowchart illustrating a data inspection method. The data inspection method includes:

[0061] S101. Obtain regulatory data.

[0062] S102. Analyze the regulatory clause data to obtain the key attributes of the clauses.

[0063] In this embodiment, the key attributes of the clauses include at least the regulatory scenario, clause keywords, and regulatory parameters.

[0064] S103. Perform a data scan on the system database to be inspected to obtain scan data.

[0065] S104. Perform data checks on the scanned data based on the key attributes of the clauses to obtain the check results.

[0066] S105. Based on the inspection results, identify abnormal data and abnormal scenarios that do not comply with regulatory provisions.

[0067] S106. Output abnormal data and abnormal scenarios.

[0068] The financial sector is governed by numerous financial regulatory provisions and guidelines, requiring that the data layer of banking back-office systems strictly adhere to these provisions and guidelines. However, in practice, it has been found that some data stored in system databases inevitably contains aspects that do not meet regulatory requirements (such as missing data that is required to be stored).

[0069] Based on this, this application proposes an intelligent inspection method for historical data, which enables imperfect data to be detected and processed in advance, thereby meeting the regulatory requirements for data.

[0070] In this embodiment, the subject executing the method can be a computing device such as a computer or server, and no limitation is made in this embodiment.

[0071] In this embodiment, the subject executing the method can also be a smart device such as a smartphone or tablet, and no limitation is made in this embodiment.

[0072] As can be seen, implementing the data inspection method described in this embodiment can inspect data, promptly identify data that does not meet regulatory requirements, and help improve data standardization in order to meet the regulatory requirements for data.

[0073] Example 2

[0074] Please refer to Figure 2 , Figure 2 This embodiment provides a flowchart illustrating a data inspection method. The data inspection method includes:

[0075] S201, Obtain regulatory data.

[0076] S202. Perform text parsing on the regulatory clause data to obtain text parsing data.

[0077] In this embodiment, the method can identify the details of the terms in regulatory documents and identify and parse the keywords of the terms at the data level.

[0078] In this embodiment, the method can parse the imported clause text based on AI technologies such as OCR, identify regulatory clauses, and then extract clause analysis scenarios, keywords, and parameter checks.

[0079] In this embodiment, the imported clauses may also be supplemented by manual interpretation of the details of the regulatory clauses or by specifying the input of certain data information, with the aim of identifying the key attributes of the data layer required for regulation.

[0080] For example, regulatory requirements stipulate the identification of four fields: “currency-level account control status”, “account control status determination date”, “customer status”, and “customer status determination date”.

[0081] S203. Identify the text parsing data to obtain the key attributes of the terms at the data level.

[0082] In this embodiment, the key attributes of the clauses include at least the regulatory scenario, clause keywords, and regulatory parameters.

[0083] S204. Perform a data scan on the system database to be inspected to obtain scan data.

[0084] S205. Identify the scanned data based on the key attributes of the clauses to obtain the data table to be inspected.

[0085] S206. Perform impact scenario analysis based on the data table to be inspected, and obtain the scenario analysis results.

[0086] S207. Based on the key attributes of the clauses, perform DB-level table matching on the data table to be inspected to obtain attribute similarity identification results.

[0087] In this embodiment, the method can perform target scanning and detection operations on specified system database data.

[0088] In this embodiment, the method can identify the corresponding table based on the terms, analyze the impact scenarios, and then perform a table similarity matching check.

[0089] In this embodiment, the method can identify attribute similarity by matching DB-level tables based on the extracted keyword attributes.

[0090] In this embodiment, the method can also determine whether the attribute exists, whether the historical data is empty causing big data anomalies, and whether there is a situation that affects the reporting of regulatory information.

[0091] In this embodiment, the method can also determine whether there is a regulatory risk when the validity period of the ID card is empty.

[0092] In this embodiment, the method can also determine whether there is a risk of abnormal data entry when the date format is incorrect.

[0093] In this embodiment, the method can also determine a scenario where a notification cannot be sent when the email address is formatted incorrectly.

[0094] S208. Based on the attribute similarity recognition results and scene analysis results, identify the empty data in the data table to be checked, and obtain the empty data check results.

[0095] In this embodiment, the method can detect whether there are related changes associated with different attributes of the same database table.

[0096] For example, for some card-related control tags, if some data tag anomalies cannot be detected in the corresponding historical table or there are no related modification traces, the control time for all existing customers can be corrected.

[0097] S209. Based on the key attributes of the clauses and the results of the scenario analysis, perform a data consistency correlation check on the data table to be inspected to obtain the correlation check results.

[0098] In this embodiment, the method can perform data consistency checks on the same table across different systems. This identifies situations where multiple databases across multiple systems have the same field key, but the data values ​​stored for the same field differ between the different databases.

[0099] S210. Based on the key attributes of the clauses and the results of the scenario analysis, perform attribute enumeration value probing on the data table to be inspected to obtain the probing results.

[0100] S211. Based on the key attributes of the clauses and the results of scenario analysis, perform time range entry anomaly identification on the data table to be inspected, and obtain the time anomaly identification results.

[0101] S212. Summarize the results of empty data inspection, correlation inspection, detection, and time anomaly identification to obtain the inspection results.

[0102] S213. Based on the inspection results, identify abnormal data and abnormal scenarios that do not comply with regulatory provisions.

[0103] S214. Output abnormal data and abnormal scenarios.

[0104] In this embodiment, the method can identify and calculate the rules, and determine whether the filling rules meet the requirements by probing the attribute enumeration values, and whether the rules are in compliance.

[0105] For example, if the account status requirements are 10: Normal; 11: Closed; 12: Only pays, no more collects; 13: Neither collects nor pays; 14: Other control measures are missing, this method will provide a corresponding prompt.

[0106] In this embodiment, the method can also record and identify the existing time range.

[0107] In this embodiment, the method can specify certain data ranges based on the functional points to be verified. For example, it can target data within a certain number of days, or a batch of data, such as an account or a batch of orders, and the data range size can be adjusted.

[0108] In this embodiment, the method can intelligently identify and mark rules, and can extract non-compliant data or scenarios separately, providing assembly prompts to facilitate detailed identification by R&D.

[0109] For example, the monitoring content could include issues such as the label of the XX table not flipping correctly, missing change logs, the certificate validity period label flipping, and the flipping date not being recorded.

[0110] In this embodiment, the subject executing the method can be a computing device such as a computer or server, and no limitation is made in this embodiment.

[0111] In this embodiment, the subject executing the method can also be a smart device such as a smartphone or tablet, and no limitation is made in this embodiment.

[0112] As can be seen, implementing the data inspection method described in this embodiment can inspect data, promptly identify data that does not meet regulatory requirements, and help improve data standardization in order to meet the regulatory requirements for data.

[0113] Example 3

[0114] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a data inspection device provided in this embodiment. Figure 3 As shown, the data inspection device includes:

[0115] Acquisition unit 310 is used to acquire regulatory clause data;

[0116] Analysis unit 320 is used to analyze regulatory clause data to obtain key attributes of the clauses;

[0117] Scanning unit 330 is used to scan the system database to be inspected to obtain scan data;

[0118] Inspection unit 340 is used to perform data inspection on scanned data based on key attributes of the clauses and obtain inspection results;

[0119] Unit 350 is used to identify abnormal data and abnormal scenarios that do not comply with regulatory requirements based on the inspection results.

[0120] Output unit 360 is used to output abnormal data and abnormal scenarios.

[0121] In this embodiment, the explanation of the data inspection device can be referred to the description in Embodiment 1 or Embodiment 2, and will not be repeated here.

[0122] As can be seen, implementing the data inspection device described in this embodiment can inspect data, promptly identify data that does not meet regulatory requirements, and help improve data standardization in order to meet the regulatory requirements for data.

[0123] Example 4

[0124] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of a data inspection device provided in this embodiment. Figure 4 As shown, the data inspection device includes:

[0125] Acquisition unit 310 is used to acquire regulatory clause data;

[0126] Analysis unit 320 is used to analyze regulatory clause data to obtain key attributes of the clauses;

[0127] Scanning unit 330 is used to scan the system database to be inspected to obtain scan data;

[0128] Inspection unit 340 is used to perform data inspection on scanned data based on key attributes of the clauses and obtain inspection results;

[0129] Unit 350 is used to identify abnormal data and abnormal scenarios that do not comply with regulatory requirements based on the inspection results.

[0130] Output unit 360 is used to output abnormal data and abnormal scenarios.

[0131] As an optional implementation, the analysis unit 320 includes:

[0132] The parsing subunit 321 is used to perform text parsing on the regulatory clause data to obtain parsed text data.

[0133] The identification subunit 322 is used to identify the text parsing data and obtain the key attributes of the terms at the data level.

[0134] In this embodiment, the key attributes of the clauses include at least the regulatory scenario, clause keywords, and regulatory parameters.

[0135] As an optional implementation, the inspection unit 340 includes:

[0136] The identification subunit 341 is used to identify scanned data based on the key attributes of the clauses to obtain a data table to be inspected.

[0137] Analysis subunit 342 is used to perform impact scenario analysis based on the data table to be inspected and obtain the scenario analysis results;

[0138] Inspection subunit 343 is used to perform similarity matching checks on the data table to be inspected based on the scenario analysis results and key attributes of the clauses, and obtain the inspection results.

[0139] As an optional implementation, the inspection subunit 343 includes:

[0140] The matching module is used to match the database tables under inspection based on the key attributes of the clauses, and obtain the attribute similarity identification results.

[0141] The identification module is used to identify empty data in the data table to be checked based on the attribute similarity identification results and the scene analysis results, and to obtain the empty data check results.

[0142] The detection module is used to perform data consistency correlation checks on the data table to be inspected based on the key attributes of the clauses and the results of scenario analysis, and to obtain the correlation check results.

[0143] The detection module is used to detect attribute enumeration values ​​in the data table to be inspected based on the key attributes of the clauses and the results of scenario analysis, and to obtain the detection results.

[0144] The identification module is also used to identify time range entry anomalies in the data table to be inspected based on the key attributes of the clauses and the results of scenario analysis, and to obtain the time anomaly identification results.

[0145] The summary module is used to summarize the results of empty data inspection, correlation inspection, detection, and time anomaly identification to obtain the inspection results.

[0146] In this embodiment, the explanation of the data inspection device can be referred to the description in Embodiment 1 or Embodiment 2, and will not be repeated here.

[0147] As can be seen, implementing the data inspection device described in this embodiment can inspect data, promptly identify data that does not meet regulatory requirements, and help improve data standardization in order to meet the regulatory requirements for data.

[0148] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the data inspection method in embodiment 1 or embodiment 2 of this application.

[0149] This application provides a computer-readable storage medium storing computer program instructions. When these computer program instructions are read and executed by a processor, the data checking method described in embodiment 1 or embodiment 2 of this application is performed.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0151] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0152] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0155] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A data inspection method, characterized in that, include: Obtain regulatory data; The key attributes of the regulatory clauses were obtained by analyzing the data. The system database to be inspected is scanned to obtain scan data; The scanned data is identified based on the key attributes of the aforementioned terms, resulting in a data table to be inspected. Based on the data table to be inspected, an impact scenario analysis is performed to obtain the scenario analysis results; Based on the key attributes of the terms, the database-level tables of the data to be inspected are matched to obtain the attribute similarity identification results. Based on the attribute similarity recognition results and the scene analysis results, empty data in the data table to be checked are identified, and empty data check results are obtained. Based on the key attributes of the terms and the results of the scenario analysis, a data consistency correlation check is performed on the data table to be checked to obtain the correlation check results. Based on the key attributes of the terms and the results of the scenario analysis, the data table to be inspected is probed by enumerating attribute values ​​to obtain the probe results. Based on the key attributes of the terms and the results of the scenario analysis, the time range entry anomaly identification is performed on the data table to be inspected to obtain the time anomaly identification result. The results of the empty data inspection, the correlation inspection, the detection, and the time anomaly identification are summarized to obtain the inspection result; Based on the inspection results, identify abnormal data and abnormal scenarios that do not comply with the regulatory provisions. Output the abnormal data and the abnormal scenario.

2. The data inspection method according to claim 1, characterized in that, The analysis of the regulatory clause data yields key attributes of the clauses, including: The regulatory clause data is parsed to obtain parsed text data; The text parsing data is identified to obtain the key attributes of the terms at the data level.

3. The data inspection method according to claim 2, characterized in that, The key attributes of the terms include at least the regulatory scenario, terms keywords, and regulatory parameters.

4. A data inspection device, characterized in that, The data inspection device includes: The acquisition unit is used to acquire regulatory clause data; The analysis unit is used to analyze the regulatory clause data to obtain the key attributes of the clauses; The scanning unit is used to scan the system database to be inspected and obtain scan data. An inspection unit is used to perform data inspection on the scanned data according to the key attributes of the clause, and obtain inspection results; A determining unit is used to determine, based on the inspection results, abnormal data and abnormal scenarios that do not comply with the regulatory provisions. The output unit is used to output the abnormal data and the abnormal scenario; The inspection unit includes: An identification subunit is used to identify the scanned data based on the key attributes of the terms, thereby obtaining a data table to be inspected. The analysis subunit is used to perform impact scenario analysis based on the data table to be inspected, and obtain the scenario analysis results. The inspection subunit is used to perform a similarity matching check on the data table to be inspected based on the scenario analysis results and the key attributes of the terms, and to obtain the inspection results. The inspection subunit includes: The matching module is used to match the database tables under inspection based on the key attributes of the clauses, and obtain the attribute similarity identification results. The identification module is used to identify empty data in the data table to be checked based on the attribute similarity identification results and the scene analysis results, and to obtain the empty data check results. The detection module is used to perform data consistency correlation checks on the data table to be inspected based on the key attributes of the clauses and the results of scenario analysis, and to obtain the correlation check results. The detection module is used to detect attribute enumeration values ​​in the data table to be inspected based on the key attributes of the clauses and the results of scenario analysis, and to obtain the detection results. The identification module is also used to identify time range entry anomalies in the data table to be inspected based on the key attributes of the clauses and the results of scenario analysis, and to obtain the time anomaly identification results. The summary module is used to summarize the results of empty data inspection, correlation inspection, detection, and time anomaly identification to obtain the inspection results.

5. The data inspection device according to claim 4, characterized in that, The analysis unit includes: The parsing subunit is used to parse the regulatory clause data to obtain parsed text data. The identification subunit is used to identify the text parsing data and obtain the key attributes of the terms at the data level.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the data inspection method according to any one of claims 1 to 3.

7. A readable storage medium, characterized in that, The readable storage medium stores computer program instructions, which, when read and executed by a processor, perform the data inspection method according to any one of claims 1 to 3.

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