Data audit system based on any time span

Abstract

The present invention discloses a data audit system based on an arbitrary time span, which relates to the field of data processing technology, including a data resource management module, a data monitoring module, a data audit information collection module, an internal audit analysis module, an external audit analysis module, a comprehensive data audit evaluation module, an early warning terminal and a database. In the process of data audit information collection, the division of internal data audit and external data audit is conducive to building a more comprehensive and systematic data quality evaluation framework. Internal audit focuses on the accuracy, completeness and operational efficiency of data, while external audit focuses on compliance and audit coverage. The dual audit mechanism ensures quality monitoring of data in all dimensions, avoids data quality problems caused by the lack of audit in a certain aspect, and helps to improve the overall data quality management level.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data audit system based on an arbitrary time span. Background Art

[0002] As the value of data becomes increasingly prominent, data security and compliance issues have also received much attention. Enterprises need to ensure the legal source of data, its compliance with regulations, and prevent data leakage and abuse. Data audit systems based on any time span can help enterprises monitor the flow and use of data, promptly identify and deal with potential data security risks, and ensure the security and compliance of enterprise data.

[0003] Prior art, such as the invention patent application with publication number CN109408489A, discloses a data audit method and system, which obtains an audit initiation instruction, wherein the audit initiation instruction includes a data loading rule and a data audit rule; then obtains data from at least one data source according to the data loading rule, and stores the data; then inputs the data into a Spark cluster, and the Spark cluster audits the data according to the data audit rule, thereby obtaining an audit result. The method of the present invention is suitable for auditing data across databases, has a low system cost, does not rely on the performance of the database itself during the audit process, reduces the cost of data migration, solves the problem of large differences in different audit operations of the original audit system, and achieves controllable process and results. At the same time, it adopts rule-driven and Spark processing technology to solve the problems of insufficient resources and high data processing costs in the past. Users can adjust rules and change logic at any time, which is more flexible and convenient.

[0004] There are at least the following technical problems with the above scheme: 1. The above scheme lacks a detailed data classification and storage mechanism, resulting in inefficient data management and query. In the process of data processing and analysis, data classification and storage is a crucial link, which can help designated organizations locate required data more quickly and improve the accuracy of data processing. Although the above scheme mentions data storage, it does not specifically explain how to classify and store it, which may lead to data confusion in actual applications and increase the difficulty and time cost of data retrieval.

[0005] 2. In the data audit process, the above scheme lacks monitoring and analysis of data volume information, resulting in the inability to timely evaluate the performance and efficiency of data processing. Real-time monitoring and analysis of data volume information is conducive to the designated organization to adjust the data processing strategy in a timely manner, including key indicators such as data growth rate, total data volume and storage utilization rate. However, this link is not involved in the above scheme, so that the designated organization cannot make timely and effective responses when facing data processing performance bottlenecks.

[0006] 3. The above scheme lacks the distinction and analysis of internal and external data audit information, resulting in an audit assessment that is not comprehensive and in-depth enough. During the data audit process, internal data audits mainly focus on data accuracy, completeness and operational efficiency, while external data audits focus more on compliance and audit coverage. There is no clear distinction between internal and external data audit information, nor is there any mention of how to analyze this information, which affects the comprehensiveness and accuracy of the data audit assessment. Summary of the invention

[0007] The present invention aims to provide a data audit system applicable to any time span, aiming at the problems existing in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a data audit system based on an arbitrary time span, including: a data resource management module, which is used to collect the total data resources generated by a specified organization in the business process, and then classify and store the total data resources.

[0009] The data monitoring module is used to obtain the data volume information corresponding to the specified organization during the data audit process, and then analyze the data volume evaluation index corresponding to the specified organization during the data audit process.

[0010] The data audit information collection module is used to divide the data audit process into internal data audit and external data audit, and obtain the internal data audit information and external data audit information of the specified organization during the data audit process.

[0011] The internal audit analysis module is used to analyze the internal data audit evaluation coefficient corresponding to the designated organization in the internal data audit process based on the internal data audit information of the designated organization in the internal data audit process.

[0012] The external audit analysis module is used to analyze the external data audit evaluation coefficient corresponding to the designated organization in the external data audit process based on the external data audit information of the designated organization in the external data audit process.

[0013] The comprehensive data audit assessment module is used to assess whether the data quality of the designated organization in the data audit process complies with regulations based on the data volume assessment index, internal data audit assessment coefficient and external data audit assessment coefficient of the designated organization in the data audit process.

[0014] The early warning terminal is used to issue early warning prompts when the corresponding data quality of the designated organization does not meet the requirements during the data audit process.

[0015] The beneficial effects of the present invention are: 1. The data audit system based on an arbitrary time span provided by the present invention obtains the corresponding data volume information of the designated organization in the data audit process through the data monitoring module during the data resource management process, which is conducive to timely insight into the dynamic changes and trends of data processing. First, by analyzing the data growth rate, total data and storage utilization rate, the designated organization can better manage data resources to ensure that there will be no storage bottlenecks or performance degradation during data use. Real-time monitoring and analysis greatly improve the flexibility of data management, enabling the designated organization to formulate response strategies in advance for potential data problems, thereby ensuring efficient application of data.

[0016] 2. In the process of data audit information collection, the embodiment of the present invention is conducive to building a more comprehensive and systematic data quality assessment framework through the division of internal data audit and external data audit. Internal audit focuses on the accuracy, completeness and operational efficiency of data, while external audit focuses on compliance and audit coverage. The dual audit mechanism ensures the quality monitoring of data in all dimensions, avoids data quality problems caused by the lack of audit in a certain aspect, and helps to improve the overall data quality management level.

[0017] 3. The embodiment of the present invention integrates the data volume assessment index, internal and external audit assessment coefficients in the comprehensive data audit assessment module, which is conducive to a comprehensive assessment of the corresponding data quality of the designated organization during the data audit process. It is not only a quantification of the data status, but also provides a standardized method for evaluating the overall data quality for the designated organization. Through this comprehensive assessment, the designated organization can quickly identify areas where data quality is non-compliant, make timely adjustments and optimizations, thereby reducing potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention provides a data audit system based on an arbitrary time span, the system comprising: a data resource management module, a data monitoring module, a data audit information collection module, an internal audit analysis module, an external audit analysis module, a comprehensive data audit evaluation module, an early warning terminal and a database.

[0022] The data resource management module is connected to the data monitoring module and the database respectively, the data monitoring module is connected to the data audit information collection module and the database respectively, the data audit information collection module is connected to the internal audit analysis module, the external audit analysis module and the database respectively, the internal audit analysis module and the external audit analysis module are both connected to the comprehensive data audit evaluation module, and the comprehensive data audit evaluation module is connected to the early warning terminal and the database respectively.

[0023] The data resource management module is used to collect the total data resources generated by a specified organization in the business process, and then classify and store the total data resources.

[0024] In a specific embodiment, the data volume information includes data growth rate, total data volume and storage utilization rate, internal data audit information includes data accuracy, data completeness and operational efficiency, and external data audit information includes compliance and audit coverage.

[0025] It should be noted that data accuracy refers to whether there are errors in the data stored in the real-time database. For example, the format of customer data is name, age and gender, but a certain customer data subset in the customer data queried during the data audit process only includes name and age, which indicates that the customer data subset is erroneous data. Data completeness refers to whether there are missing data stored in the real-time database. For example, the operational data subset stored at a certain time point is 20 items, but only 18 items are stored in the real-time database, which means that 2 items of data stored in the real-time database are missing, indicating that the data stored in the real-time database is incomplete.

[0026] In a specific embodiment, the total data resources are classified and stored, and the specific analysis process is as follows: each backup database and real-time database are set in the designated organization, and all data generated by the designated organization in the business process are stored in each backup database and real-time database respectively, so as to obtain each backup data and real-time queryable data, and classify the data according to the data type, time node and data importance, the data type includes text data, picture data and audio and video data, the time node includes year, month, day and hour, and the data importance includes high, medium and low;

[0027] When the staff conducts data audit, they enter a specified command line in the real-time database. The specified command line includes a data type and a time node, and then obtains the data corresponding to the data type at the time node. The time node directory contains each data subset corresponding to the data type generated by the specified company at the time node. Each data subset corresponds to each data importance. Each data subset is sorted in order from large to small in terms of data importance. The staff obtains the corresponding data subset based on the data importance query corresponding to each data subset.

[0028] It should be noted that, for example, when the amount of consumption of a customer in a specified organization is greater than the set advanced threshold, it indicates that the importance of the customer data corresponding to the customer is high, thereby obtaining the data importance corresponding to each data type. When the staff performs data audit by entering a command line containing only a certain time node in the real-time database, the real-time database displays all data types and all data subsets of the time node.

[0029] The data monitoring module is used to obtain the data volume information corresponding to the specified organization during the data audit process, and then analyze the data volume evaluation index corresponding to the specified organization during the data audit process.

[0030] In a specific embodiment, the analysis specifies the data volume evaluation index corresponding to the organization in the data audit process. The specific analysis process is as follows: when the staff conducts data audit, the time point of the data audit is recorded as the audit time point, the total amount of data stored in the real-time database at the audit time point is obtained from the real-time database, and the audit time period is set. The audit time point is subtracted from the audit time period to obtain the corresponding amount of data generated in the audit time period before the audit time point, and the amount of data generated is divided by the audit time period. The result is the data growth rate. The real-time database contains various storage layers, and various data types are stored in each storage layer. The storage space capacity and the amount of data stored corresponding to each storage layer are obtained from the real-time database. The amount of data stored corresponding to each storage layer is divided by the storage space capacity corresponding to each storage layer to obtain the storage utilization rate of each storage layer. The storage utilization rate of each storage layer is calculated by the mean, and the result is the storage utilization rate corresponding to the real-time database;

[0031] By calculating the formula , get the data volume evaluation index corresponding to the specified organization in the data audit process ,in , , They represent the data growth rate, total data volume, and storage utilization rate of a specified organization during the data audit process. , , They are respectively expressed as the growth rate of standard control data, the total amount of standard control data, and the standard control storage utilization rate. , , They are respectively the weight factor corresponding to the set data growth rate, the weight factor corresponding to the total amount of data, and the weight factor corresponding to the storage usage rate.

[0032] It should be noted that the standard control data volume information corresponding to the designated organization in the data audit process is obtained from the database. The standard control data volume information includes the standard control data growth rate, the total amount of standard control data and the standard control storage utilization rate. , , The values ​​of are all greater than and less than .

[0033] It should also be noted that according to expert opinions and the actual company's operating conditions, for example, the data growth rate reflects the dynamic changes in the data volume, which has a direct impact on system resource allocation and performance estimation. The weight factor of the data growth rate is set to 0.4. The total amount of data is directly related to the database capacity and potential risks. The weight factor is set to 0.3. The storage utilization rate is related to the current resource utilization and future expansion needs. The weight factor is set to 0.3. Finally, the weight factors corresponding to the set data growth rate, the total amount of data, and the storage utilization rate are obtained.

[0034] The data audit information collection module is used to divide the data audit process into internal data audit and external data audit, and obtain the internal data audit information and external data audit information of the specified organization during the data audit process.

[0035] In a specific embodiment, the internal data audit information and external data audit information of the designated organization during the data audit process are obtained, and the specific acquisition process is as follows: when the internal personnel of the designated organization conduct internal data audits, the total amount of data audited, the amount of erroneous data, and the amount of missing data audited during the internal data audit are recorded, the total amount of data minus the amount of erroneous data and divided by the total amount of data, the result obtained is the data accuracy, the total amount of data minus the amount of missing data and divided by the total amount of data, the result obtained is the data completeness, the total time for internal personnel to conduct internal data audits is recorded, the total amount of data audited during the internal data audit is divided by the total time, and the result obtained is the operating efficiency.

[0036] When external personnel enter the real-time database of a designated organization to conduct data audits, they shall record the amount of data that complies with the external data audit regulations and the total amount of data in the external audit process according to the external data audit regulations. The amount of data in the external data audit regulations shall be divided by the total amount of data in the external audit process. The result obtained is the compliance degree. The total amount of data in the external audit process shall be divided by the total amount of data stored in the real-time database to obtain the audit coverage.

[0037] In the process of data audit information collection, the embodiment of the present invention is conducive to building a more comprehensive and systematic data quality assessment framework through the division of internal data audit and external data audit. Internal audit focuses on the accuracy, completeness and operational efficiency of data, while external audit focuses on compliance and audit coverage. The dual audit mechanism ensures the quality monitoring of data in all dimensions, avoids data quality problems caused by the lack of audit in a certain aspect, and helps to improve the overall data quality management level.

[0038] The internal audit analysis module is used to analyze the internal data audit evaluation coefficient corresponding to the designated organization in the internal data audit process based on the internal data audit information of the designated organization in the internal data audit process.

[0039] In a specific embodiment, the analysis specifies the internal data audit assessment coefficient corresponding to the organization during the internal data audit process. The specific analysis process is as follows: by calculating the formula: , get the internal data audit assessment coefficient corresponding to the internal data audit process of the specified organization ,in , , They represent the data accuracy, data completeness and operational efficiency of a specified organization during its internal data audit process. , , They are respectively expressed as standard control data accuracy, standard control data completeness, and standard control operation efficiency. , , They are the set data accuracy weight factor, data integrity weight factor, and operation efficiency weight factor respectively.

[0040] It should be noted that the standard control internal data audit information corresponding to the designated organization in the internal data audit process is obtained from the database. The standard control internal data audit information includes the standard control data accuracy, standard control data completeness and standard control operation efficiency. , , The values ​​of are all greater than and less than , based on expert opinions and the actual company's internal data audit situation, the set data accuracy weight factor, data completeness weight factor and operation efficiency weight factor are finally obtained.

[0041] It should also be noted that , , The setting process and , , The setting process is the same as , so I will not go into details here.

[0042] The external audit analysis module is used to analyze the external data audit evaluation coefficient corresponding to the designated organization in the external data audit process based on the external data audit information of the designated organization in the external data audit process.

[0043] In a specific embodiment, the analysis specifies the external data audit assessment coefficient corresponding to the organization during the external data audit process. The specific analysis process is as follows: by calculating the formula: , get the external data audit assessment coefficient corresponding to the specified organization in the external data audit process ,in , They represent the compliance and audit coverage of the specified organization in the external data audit process. , They are respectively expressed as standard control compliance and standard control audit coverage. , They are the set compliance weight factor and audit coverage weight factor respectively.

[0044] It should be noted that the standard control external data audit information corresponding to the designated organization in the external data audit process is obtained from the database. The standard control external data audit information includes standard control compliance and standard control audit coverage. , The values ​​of are all greater than and less than .

[0045] It should also be noted that , The setting process and , , The setting process is the same as , so I will not go into details here.

[0046] The comprehensive data audit assessment module is used to assess whether the data quality of the designated organization in the data audit process complies with regulations based on the data volume assessment index, internal data audit assessment coefficient and external data audit assessment coefficient of the designated organization in the data audit process.

[0047] In a specific embodiment, the evaluation specifies whether the data quality corresponding to the designated organization during the data audit process meets the requirements. The specific evaluation process is as follows: based on the data volume evaluation index, internal data audit evaluation coefficient and external data audit evaluation coefficient corresponding to the designated organization during the data audit process, the comprehensive data quality evaluation coefficient corresponding to the designated organization during the data audit process is calculated. The values ​​of the comprehensive data quality evaluation coefficient are -1 and 1. When the value of the comprehensive data quality evaluation coefficient is 1, it indicates that the data quality corresponding to the designated organization during the data audit process meets the requirements. When the value of the comprehensive data quality evaluation coefficient is -1, it indicates that the data quality corresponding to the designated organization during the data audit process does not meet the requirements, and then the real-time database within the designated organization is processed for data quality non-compliance.

[0048] In a specific embodiment, the calculation obtains the comprehensive data quality assessment coefficient corresponding to the specified organization in the data audit process, and the specific calculation process is as follows: According to the data quality assessment expression: , get the comprehensive data quality assessment coefficient corresponding to the specified organization in the data audit process ,in , , They are respectively represented by the set data volume assessment index weight factor, internal data audit assessment coefficient weight factor, and external data audit assessment coefficient weight factor. It is expressed as the set comprehensive data quality assessment coefficient threshold. For example, when the data growth rate, total data volume and storage utilization rate of the specified organization in the data audit process are greater than the standard control data volume information, the data accuracy, data completeness and operation efficiency are greater than the standard control internal data audit information, and the compliance and audit coverage are also greater than the standard control external data audit information, it means that the corresponding data quality of the specified organization in the data audit process meets the requirements.

[0049] It should be noted that , , The values ​​of are all greater than and less than , It is used as a basis for evaluating whether the data quality of the designated organization in the data audit process meets the requirements. For example, when When the value is 100, When the value is 120, it indicates that the data quality of the designated organization during the data audit process meets the requirements.

[0050] It should also be noted that , , The setting process and , , The setting process is the same as , so I will not go into details here.

[0051] In a specific embodiment, the data quality non-compliance processing is performed on the real-time database within the specified organization, and the specific analysis process is as follows: the corresponding intervals of the control comprehensive data quality assessment coefficients when the data quality is non-compliant after the data audit are obtained from the database, and each control comprehensive data quality assessment coefficient interval corresponds to each data audit non-compliance level, and the comprehensive data quality assessment coefficient corresponding to the specified organization in the data audit process is compared with each control comprehensive data quality assessment coefficient interval. When the comprehensive data quality assessment coefficient corresponding to the specified organization in the data audit process belongs to a certain control comprehensive data quality assessment coefficient interval, the data audit non-compliance level of the specified organization in the data audit process is the data audit non-compliance level corresponding to the control comprehensive data quality assessment coefficient interval, and each data audit failure level corresponds to the number of comparisons with the backup database and the amount of data compared with the backup database, respectively, and then the data in the real-time database is compared with the data stored in each backup database, and if there are errors and missing data stored in the real-time database, the erroneous data is modified and the missing data is added in the real-time database through the specified command line statement.

[0052] The embodiment of the present invention integrates the data volume assessment index, internal and external audit assessment coefficients in the comprehensive data audit assessment module, which is conducive to a comprehensive assessment of the corresponding data quality of the designated organization during the data audit process. It is not only a quantification of the data status, but also provides a standardized method for evaluating the overall data quality for the designated organization. Through this comprehensive assessment, the designated organization can quickly identify areas where data quality is non-compliant, make timely adjustments and optimizations, thereby reducing potential risks.

[0053] The early warning terminal is used to issue early warning prompts when the corresponding data quality of the designated organization does not meet the requirements during the data audit process.

[0054] The database is used to store the total amount of data stored in the real-time database at the audit time point, the storage space capacity and the amount of data stored corresponding to each storage layer, the growth rate of standard control data, the total amount of standard control data and the standard control storage utilization rate corresponding to the organization during the data audit process, the accuracy of standard control data, the completeness of standard control data and the efficiency of standard control operation corresponding to the organization during the internal data audit process, the range of comprehensive data quality assessment coefficients for each control when the data quality is non-compliant after the audit, the accuracy of standard control data, the completeness of standard control data and the efficiency of standard control operation corresponding to the organization during the internal data audit process, and the compliance of standard control and the audit coverage of standard control corresponding to the organization during the external data audit process.

[0055] The data audit system based on an arbitrary time span provided by the present invention obtains the corresponding data volume information of the designated organization in the data audit process through the data monitoring module during the data resource management process, which is conducive to timely insight into the dynamic changes and trends of data processing. First, by analyzing the data growth rate, total data volume and storage utilization rate, the designated organization can better manage data resources to ensure that there will be no storage bottlenecks or performance degradation problems during data use. Real-time monitoring and analysis greatly improve the flexibility of data management, enabling the designated organization to formulate response strategies in advance for potential data problems, thereby ensuring the efficient application of data.

[0056] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A data audit system based on any time span, characterized by: include: The data resource management module is used to collect the total data resources generated by the specified organization in the business process, and then classify and store the total data resources; The data monitoring module is used to obtain the data volume information corresponding to the specified organization during the data audit process, and then analyze the data volume evaluation index corresponding to the specified organization during the data audit process; The data audit information collection module is used to divide the data audit process into internal data audit and external data audit, and obtain the internal data audit information and external data audit information of the specified organization during the data audit process; An internal audit analysis module is used to analyze the internal data audit evaluation coefficient corresponding to the designated organization in the internal data audit process based on the internal data audit information of the designated organization in the internal data audit process; The external audit analysis module is used to analyze the external data audit evaluation coefficient corresponding to the designated organization in the external data audit process based on the external data audit information of the designated organization in the external data audit process; The comprehensive data audit assessment module is used to assess the performance of a designated organization based on the data volume assessment index, internal data audit assessment coefficient, and external data audit assessment coefficient corresponding to the designated organization during the data audit process. Whether the corresponding data quality meets the requirements during the data audit process; Early warning terminal, used to issue early warning prompts when the data quality of the designated organization does not meet the requirements during the data audit process; The data volume information includes data growth rate, total data volume and storage utilization rate; the internal data audit information includes data accuracy, data integrity and operational efficiency; and the external data audit information includes compliance and audit coverage; The total data resources are classified and stored, and the specific analysis process is as follows: Set up backup databases and real-time databases in designated organizations, store all data generated by designated organizations in business processes in the backup databases and real-time databases, and then obtain backup data and real-time queryable data. Classify data according to data type, time node and data importance. Data types include text data, image data and audio and video data. Time nodes include year, month, day and hour. Data importance includes high, medium and low. When the staff conducts data audit, they input a specified command line in the real-time database. The specified command line includes a data type and a time node, and then obtains the data corresponding to the data type at the time node. The time node directory contains the data subsets corresponding to the data type generated by the specified company at the time node. Each data subset corresponds to the importance of each data. Each data subset is sorted in descending order of data importance. The staff obtains the corresponding data subset according to the data importance corresponding to each data subset. The analysis specifies the data volume assessment index corresponding to the organization during the data audit process. The specific analysis process is as follows: When the staff conducts data audit, the time point of the data audit is recorded as the audit time point, the total amount of data stored in the real-time database at the audit time point is obtained from the real-time database, the audit time period is set, the audit time point is subtracted from the audit time period, and the corresponding amount of data generated in the audit time period before the audit time point is obtained. The amount of data generated is divided by the audit time period, and the result obtained is the data growth rate. The real-time database contains various storage layers, and various data types are stored in each storage layer. The storage space capacity and the amount of data stored corresponding to each storage layer are obtained from the real-time database, and the amount of data stored corresponding to each storage layer is divided by the storage space capacity corresponding to each storage layer to obtain the storage utilization rate of each storage layer. The storage utilization rate of each storage layer is calculated by the mean, and the result obtained is the storage utilization rate corresponding to the real-time database; By calculating the formula , get the data volume evaluation index corresponding to the specified organization in the data audit process ,in , , They represent the data growth rate, total data volume, and storage utilization rate of a specified organization during the data audit process. , , They are respectively expressed as the growth rate of standard control data, the total amount of standard control data, and the standard control storage utilization rate. , , They are the weight factor corresponding to the set data growth rate, the weight factor corresponding to the total amount of data, and the weight factor corresponding to the storage usage rate; The specific acquisition process of obtaining the internal data audit information and external data audit information of the designated organization during the data audit process is as follows: When designating internal personnel of the organization to conduct internal data audits, record the total amount of data audited, the amount of erroneous data, and the amount of missing data audited during the internal data audit process. Subtract the amount of erroneous data from the total amount of data and divide it by the total amount of data. The result is the data accuracy. Subtract the amount of missing data from the total amount of data and divide it by the total amount of data. The result is the data completeness. Record the total time that internal personnel conduct internal data audits. Divide the total amount of data audited during the internal data audit process by the total time. The result is the operation efficiency. When external personnel enter the real-time database of a designated organization to conduct data audits, they shall record the amount of data that complies with the external data audit regulations and the total amount of data in the external audit process according to the external data audit regulations. The amount of data in the external data audit regulations shall be divided by the total amount of data in the external audit process. The result obtained is the compliance degree. The total amount of data in the external audit process shall be divided by the total amount of data stored in the real-time database to obtain the audit coverage.

2. The data audit system based on any time span according to claim 1 is characterized in that: The analysis specifies the internal data audit assessment coefficient of the organization during the internal data audit process. The specific analysis process is as follows: By calculating the formula , get the internal data audit assessment coefficient corresponding to the internal data audit process of the specified organization ,in , , They represent the data accuracy, data completeness and operational efficiency of a specified organization during its internal data audit process. , , They are respectively expressed as standard control data accuracy, standard control data completeness, and standard control operation efficiency. , , They are the set data accuracy weight factor, data integrity weight factor, and operation efficiency weight factor respectively.

3. The data audit system based on any time span according to claim 2 is characterized in that: The analysis specifies the external data audit assessment coefficient corresponding to the organization during the external data audit process. The specific analysis process is as follows: By calculating the formula , get the external data audit assessment coefficient corresponding to the specified organization in the external data audit process ,in , They represent the compliance and audit coverage of the specified organization in the external data audit process. , They are respectively expressed as standard control compliance and standard control audit coverage. , They are the set compliance weight factor and audit coverage weight factor respectively.

4. The data audit system based on any time span according to claim 3 is characterized in that: The assessment specifies whether the data quality of the organization in the data audit process meets the requirements. The specific assessment process is as follows: According to the data volume assessment index, internal data audit assessment coefficient and external data audit assessment coefficient corresponding to the designated organization in the data audit process, the comprehensive data quality assessment coefficient corresponding to the designated organization in the data audit process is calculated. The values ​​of the comprehensive data quality assessment coefficient are -1 and 1. When the value of the comprehensive data quality assessment coefficient is 1, it indicates that the data quality corresponding to the designated organization in the data audit process meets the requirements. When the value of the comprehensive data quality assessment coefficient is -1, it indicates that the data quality corresponding to the designated organization in the data audit process does not meet the requirements, and then the real-time database in the designated organization is processed for data quality non-compliance.

5. The data audit system based on any time span according to claim 4 is characterized in that: The calculation obtains the comprehensive data quality assessment coefficient corresponding to the specified organization in the data audit process. The specific calculation process is as follows: Evaluate expressions based on data quality: , get the comprehensive data quality assessment coefficient corresponding to the specified organization in the data audit process ,in , , They are respectively represented by the set data volume assessment index weight factor, internal data audit assessment coefficient weight factor, and external data audit assessment coefficient weight factor. Represents the set comprehensive data quality assessment coefficient threshold.

6. The data audit system based on any time span according to claim 5 is characterized in that: The specific analysis process of handling data quality non-compliance of the real-time database within the specified organization is as follows: The corresponding intervals of the comprehensive data quality assessment coefficients of the controls when the data quality is non-compliant after the data audit are obtained from the database. The intervals of the comprehensive data quality assessment coefficients of the controls correspond to the non-compliance levels of the data audit. The comprehensive data quality assessment coefficients of the designated organization corresponding to the data audit process are compared with the intervals of the comprehensive data quality assessment coefficients of the controls. When the comprehensive data quality assessment coefficients of the designated organization corresponding to the data audit process belong to a certain interval of the comprehensive data quality assessment coefficients of the controls, the non-compliance level of the data audit of the designated organization in the data audit process is the non-compliance level of the data audit corresponding to the interval of the comprehensive data quality assessment coefficients of the controls. The non-compliant levels of the data audit correspond to the number of times compared with the backup database and the amount of data compared with the backup database. Then the data in the real-time database are compared with the data stored in the backup databases. If there are errors and missing data stored in the real-time database, the error data are modified and the missing data are added in the real-time database by specifying command line statements.

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