Method and device for evaluating data quality in database, electronic equipment and storage medium
By using custom technical metadata attribute standards and mapping relationships to assess data quality, the problem of data quality assessment after aggregating different data sources was solved, thereby improving the credibility and effectiveness of the data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LIHE TECH (HUNAN) CO LTD
- Filing Date
- 2026-01-23
- Publication Date
- 2026-06-05
Smart Images
Figure CN122152798A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data analysis technology, and in particular to a method, apparatus, electronic device, and storage medium for assessing data quality in a database. Background Technology
[0002] Environmental monitoring involves surface water, pollution sources, air quality, and marine monitoring, generating a large amount of data. Due to the involvement of multiple departments, this data is often fragmented. However, with the development of environmental protection services, this fragmentation hinders data analysis and application. Therefore, it is necessary to aggregate large amounts of monitoring data into a single data source to improve the effectiveness of data analysis and application. However, the aggregated data may have poor quality due to inconsistent sources, affecting the accuracy and reliability of the analysis results. Therefore, a quality assessment is required to ensure the data's quality.
[0003] Because the data is collected from different data sources, the industry's existing mainstream data quality assessment methods, which judge data quality from six dimensions including accuracy, completeness, uniqueness, consistency, validity, and timeliness, are not very applicable here. Therefore, it is necessary to explore new data quality assessment methods to judge data quality, improve the credibility and validity of data, thereby ensuring the quality of data application and enhancing environmental protection business capabilities. Summary of the Invention
[0004] This application provides a data quality assessment method for databases to address the technical problem that existing data quality assessment methods are not well-suited for monitoring data collected from different data sources.
[0005] This application is achieved through the following solution: A method for assessing data quality in a database, comprising the following steps: S1. Determine the attributes of the technical metadata that needs to be evaluated, including setting the field attributes in the data table and their evaluation scores; S2. Establish technical metadata standards, and treat multiple attribute standards of a field as a single technical metadata. If a certain attribute does not have a standard set, that attribute will not be evaluated. S3. Specify the mapping relationship between metadata, including the mapping relationship between technical metadata and fields in the data table. Here, a technical metadata item sets one or more field mappings and specifies the data to be evaluated. S4. Based on the technical metadata standards, the mapping relationship between metadata, and the data to be evaluated, evaluate and compare the data, calculate the data quality evaluation score and the data quality evaluation pass rate, and judge the data quality based on the data quality evaluation pass rate.
[0006] Furthermore, in step S1, the attributes of the technical metadata include: data type, length, precision, number of decimal places, whether nullables are allowed, and reference data.
[0007] Furthermore, in step S1, setting the evaluation score for the field attributes in the data table specifically includes the following steps: S11. Set evaluation scores based on the importance of technical metadata attributes, and divide technical metadata attributes into three levels: general, medium, and important. S12. The evaluation score of technical metadata attributes at the medium level shall not exceed twice the evaluation score of general technical metadata attributes, the evaluation score of technical metadata attributes at the important level shall not exceed twice the evaluation score of medium technical metadata attributes, and the evaluation score of technical metadata attributes at the general level shall not exceed 2 points.
[0008] Furthermore, the evaluation score is incremented by 0.5.
[0009] Furthermore, step S4 specifically includes the following steps: S41. Calculate the total score for a single attribute based on its individual attribute score and the number of attributes to be evaluated for each attribute. S42. Obtain the total data quality assessment score by summing the total scores of all attributes; S43. During the data quality assessment process, if a non-compliant data is found, an attribute score is deducted. Therefore, the score of a single attribute is multiplied by the number of non-compliant items to obtain the deduction for a single attribute. The sum of the deductions for all attributes is the total deduction for the data quality assessment. The total data quality assessment score is obtained by subtracting the total deduction from the total data quality assessment score. S44. Divide the data quality assessment score by the total data quality assessment score to obtain the data quality assessment pass rate; S45. Determine whether the data quality is qualified based on the pass rate of the obtained data quality assessment.
[0010] Further, step S41 specifically includes the following steps: S411. Determine the quantity of a single technical metadata to be evaluated, including the sum of the data quantities to be evaluated for all mapping table fields of a single technical metadata. S412. Based on the sum of the number of individual technical metadata to be evaluated, obtain the number of individual attribute standards to be evaluated. Determine whether a standard is set for a single attribute in a single technical metadata. If a standard is set, sum the results; otherwise, do not include it in the summation sequence. S413. Multiply the individual attribute assessment score by the number of criteria to be assessed for that individual attribute to obtain the total assessment score for that individual attribute: ; In the formula: Ma (i) The amount of data to be evaluated for a single technical metadata, single mapping table field; k The number of mappings for a single technology metadata; W i Whether to set a standard for a single attribute of a single technical metadata, and when to set a standard. W i =1, when no standard is set W i =0; m The amount of technical metadata; V a (i) Evaluate the score for a single attribute; S a (i) The total score for evaluating a single attribute.
[0011] Further, step S45 specifically includes the following steps: S451. Data quality is divided into four levels: excellent, good, qualified, and unqualified. S452, When the data quality assessment pass rate R a When ≥90%, the data quality is considered excellent; when 90% > R a When ≥80%, the data quality is judged as good; when 80% > R a When ≥60%, the data quality is considered acceptable; when R a If the percentage is less than 60%, the data quality is considered unacceptable.
[0012] This application also provides a data quality assessment device for a database, comprising: The technology metadata attribute determination module is used to determine the attributes of the technology metadata that needs to be evaluated, including setting the field attributes in the data table and their evaluation scores; The technical metadata standardization module is used to define technical metadata standards. It treats multiple attribute standards for a field as a single technical metadata set, and does not evaluate an attribute if no standard is set for it. The mapping relationship specification module is used to specify the mapping relationship between metadata, including the mapping relationship between technical metadata and fields in the data table. Here, a technical metadata item sets one or more field mappings and specifies the data to be evaluated. The data quality assessment module is used to evaluate and compare data based on technical metadata standards, mapping relationships between metadata, and the data to be evaluated, calculate data quality assessment scores and data quality assessment pass rates, and judge data quality based on the data quality assessment pass rates.
[0013] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the data quality assessment method in the database.
[0014] This application also provides a storage medium including a stored program that, when the program is executed, controls the device where the storage medium is located to perform the steps of the data quality assessment method in the database.
[0015] Compared with the prior art, this application has the following advantages: This application provides a data quality assessment method, apparatus, electronic device, and storage medium for databases. The data quality assessment method assesses data quality by customizing technical metadata attribute standards and customizing assessment data. It maps the metadata in the database to the technical metadata standards, periodically compares the technical metadata standards with the database metadata, and evaluates the data quality in the database using quality assessment methods and formulas, thereby improving the reliability and validity of the data. The assessment method is flexible and applicable to various data sources. In addition, this application considers each attribute and each piece of data during the assessment, striving for a comprehensive assessment to ensure the accuracy of the assessment quality.
[0016] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description
[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.
[0018] Figure 1 This is a schematic diagram of the data quality assessment method in the database according to a preferred embodiment of this application.
[0019] Figure 2 This is a flowchart illustrating a sub-step of step S1 in a preferred embodiment of this application.
[0020] Figure 3 This is a flowchart illustrating a sub-step of step S4 in a preferred embodiment of this application.
[0021] Figure 4This is a flowchart illustrating a sub-step of step S41 in a preferred embodiment of this application.
[0022] Figure 5 This is a flowchart illustrating a sub-step of step S45 in a preferred embodiment of this application.
[0023] Figure 6 This is a schematic diagram of a database data quality assessment device module according to a preferred embodiment of this application.
[0024] Figure 7 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application.
[0025] Figure 8 This is an internal structural diagram of a computer device according to a preferred embodiment of this application. Detailed Implementation
[0026] The embodiments of this application are described in detail below with reference to the accompanying drawings; however, this application may be implemented in a variety of different ways as defined and covered below.
[0027] like Figure 1 As shown, a preferred embodiment of this application provides a data quality assessment method for a database, including the following steps: S1. Determine the attributes of the technical metadata that needs to be evaluated, including setting the field attributes in the data table and their evaluation scores; S2. Establish technical metadata standards, and treat multiple attribute standards of a field as a single technical metadata. If a certain attribute does not have a standard set, that attribute will not be evaluated. S3. Specify the mapping relationship between metadata, including the mapping relationship between technical metadata and fields in the data table. Here, a technical metadata item sets one or more field mappings and specifies the data to be evaluated. S4. Based on the technical metadata standards, the mapping relationship between metadata, and the data to be evaluated, evaluate and compare the data, calculate the data quality evaluation score and the data quality evaluation pass rate, and judge the data quality based on the data quality evaluation pass rate.
[0028] This embodiment provides a data quality assessment method for databases. This method evaluates data quality by defining custom technical metadata attribute standards and custom assessment data. It maps metadata in the database to technical metadata standards, periodically compares the technical metadata standards with the database metadata, and assesses data quality in the database using quality assessment methods and formulas, thereby improving data credibility and validity. The assessment method is flexible and applicable to various data sources. Furthermore, this application considers each attribute and each piece of data during the assessment, striving for a comprehensive evaluation and ensuring the accuracy of the assessment quality. This application solves the problem of monitoring the quality of data collected from different data sources using existing data quality assessment methods, ensuring the accuracy and reliability of data analysis results from different sources.
[0029] Specifically, such as Figure 2 As shown, in step S1, the attributes of the technical metadata include: data type, length, precision, decimal places, whether nullables are allowed, and reference data. Setting the evaluation score for field attributes in the data table specifically includes the following steps: S11. Set evaluation scores based on the importance of technical metadata attributes, and divide technical metadata attributes into three levels: general, medium, and important. S12. The evaluation score of technical metadata attributes at the medium level shall not exceed twice the evaluation score of general technical metadata attributes, the evaluation score of technical metadata attributes at the important level shall not exceed twice the evaluation score of medium technical metadata attributes, and the evaluation score of general technical metadata attributes shall not exceed 2 points. For ease of subsequent calculation, the evaluation scores shall be in increments of 0.5.
[0030] When calculating the total score, the technical metadata attributes of each data point are taken into account; therefore, the score should not be set too high, as detailed in Table 1. Table 1: Technical Metadata Attributes and Evaluation Scores In step S2 above, technical metadata standards are established, and multiple attribute standards for a field are treated as a single technical metadata. If a standard is not set for a certain attribute, that attribute will not be evaluated, as shown in Table 2. Set technical metadata standards such as gender and site MN, including data type, length, precision, number of decimal places, whether to allow null values, and reference data standard attributes. Set these if there are standards, otherwise leave them empty.
[0031] Table 2: Technical Metadata Standard Table In step S3 above, the mapping relationship between metadata is specified, including the mapping relationship between technical metadata and fields in the data table. A technical metadata may set one or more field mappings and specify the data to be evaluated, as shown in Table 3: a technical metadata may set multiple field mappings at the same time, such as setting multiple field mappings for gender and site MN.
[0032] Table 3: Technical Metadata Mapping Table As shown in Table 4, the data that needs to be evaluated for the mapping fields is obtained from the mapping data table.
[0033] Table 4: Data to be evaluated Preferably, such as Figure 3 As shown, step S4 specifically includes the following steps: S41. Calculate the total score for a single attribute based on its individual attribute score and the number of attributes to be evaluated for each attribute. S42. Obtain the total data quality assessment score by summing the total scores of all attributes; S43. During the data quality assessment process, if a non-compliant data is found, an attribute score is deducted. Therefore, the score of a single attribute is multiplied by the number of non-compliant items to obtain the deduction for a single attribute. The sum of the deductions for all attributes is the total deduction for the data quality assessment. The total data quality assessment score is obtained by subtracting the total deduction from the total data quality assessment score. S44. Divide the data quality assessment score by the total data quality assessment score to obtain the data quality assessment pass rate; S45. Determine whether the data quality is qualified based on the pass rate of the obtained data quality assessment.
[0034] This embodiment evaluates and compares data based on technical metadata standards, metadata mapping, and the data to be evaluated, and calculates the average data quality score. To ensure that the evaluation is unbiased, the total score is calculated by combining the selected amount of data, the number of mappings, the amount of technical metadata, and the attribute scores. If each attribute of each data does not meet the requirements, a point is deducted. This is used to calculate the data quality evaluation score and pass rate, which can accurately reflect the quality of the data.
[0035] Specifically, such as Figure 4 As shown, step S41 specifically includes the following steps: S411. Determine the quantity of a single technical metadata to be evaluated, including the sum of the data quantities to be evaluated for all mapping table fields of a single technical metadata. S412. Based on the sum of the number of individual technical metadata to be evaluated, obtain the number of individual attribute standards to be evaluated. Determine whether a standard is set for a single attribute in a single technical metadata. If a standard is set, sum the results; otherwise, do not include it in the summation sequence. S413. Multiply the individual attribute assessment score by the number of criteria to be assessed for that individual attribute to obtain the total assessment score for that individual attribute: ; In the formula: M a (i) The amount of data to be evaluated for a single technical metadata, single mapping table field; k The number of mappings for a single technology metadata; W i Whether to set a standard for a single attribute of a single technical metadata, and when to set a standard. W i =1, when no standard is set W i =0; m The amount of technical metadata; V a (i) Evaluate the score for a single attribute; S a (i) The total score for evaluating a single attribute.
[0036] Specifically, as shown in Tables 3 and 4, gender has one table field mapping, and the amount of data mapped by one table field is 10. Therefore, the number of gender metadata to be evaluated is 10. Site MN has three table field mappings, and the amount of data mapped by each field is 8, 10, and 6 respectively. Therefore, the number of site MN metadata to be evaluated is 8 + 10 + 6 = 24.
[0037] Regarding the length attribute, Table 1 shows that its score is 0.5 points. Table 2 shows that this attribute is not set in the gender metadata, but it is set in the site MN metadata. Therefore, the total score for the length attribute is... S a (i) =0.5×(0×10+1×24)=12 points.
[0038] Based on the calculation method above, the total evaluation score for each attribute is shown in Table 5: Table 5: Overall Score Table for Technical Metadata Attribute Evaluation In step S42, the total data quality assessment score is obtained by summing the total scores of all attributes, specifically as follows: ; The total scores for all attributes are shown in Table 5. Therefore, the total data quality assessment score is... S a =17+12+0+0+17+15=61 points.
[0039] Specifically, in step S43, during the data quality assessment process, if a non-compliant data item is found, an attribute score is deducted. Therefore, the score of a single attribute is multiplied by the number of non-compliant items to obtain the deduction for that single attribute. The sum of the deductions for all attributes is the total deduction for the data quality assessment. The data quality assessment score is then obtained by subtracting the total deduction from the total data quality assessment score. D a : ; In the formula, FC a (i) This represents the number of non-compliant items for a single attribute. n The number of attributes in the technical metadata; D a This is the score for data quality assessment.
[0040] As shown in Tables 1 to 4, the non-conformities for each attribute during the data quality assessment process are illustrated in Table 6. Table 6: Number of Non-compliant Technical Metadata Attributes Therefore, the total deduction for quality assessment = 0.5×0 + 0.5×3 + 0.5×0 + 0.5×0 + 0.5×0 + 1.5×3 = 6 points. Thus, the data quality assessment score can be calculated. D a =61-6=55 points.
[0041] Specifically, in step S44, the data quality assessment pass rate is obtained by dividing the data quality assessment score by the total data quality assessment score as follows: ; The total data quality assessment score calculated using the above formula S a Data quality assessment score D a It can be seen that the data quality assessment pass rate R a =55 / 61×100%≈90.16%.
[0042] Specifically, such as Figure 5 As shown, step S45 specifically includes the following steps: S451. Data quality is divided into four levels: excellent, good, qualified, and unqualified. S452, When the data quality assessment pass rate R a When ≥90%, the data quality is considered excellent; when 90% > R a When ≥80%, the data quality is judged as good; when 80% > R a When ≥60%, the data quality is considered acceptable; when R a If the percentage is less than 60%, the data quality is considered unacceptable.
[0043] Based on the above calculation, the data quality assessment pass rate Ra≈90.16%>90%, therefore the data quality assessment is excellent.
[0044] Alternatively, data quality can be judged as follows: when the data quality assessment pass rate is greater than or equal to 80%, i.e. Ra≥80%, the assessment is considered passed; otherwise, it is considered failed. Specific settings can be configured as needed.
[0045] like Figure 6 As shown, this application also provides a data quality assessment device for a database, comprising: The technology metadata attribute determination module is used to determine the attributes of the technology metadata that needs to be evaluated, including setting the field attributes in the data table and their evaluation scores; The technical metadata standardization module is used to define technical metadata standards. It treats multiple attribute standards for a field as a single technical metadata set, and does not evaluate an attribute if no standard is set for it. The mapping relationship specification module is used to specify the mapping relationship between metadata, including the mapping relationship between technical metadata and fields in the data table. Here, a technical metadata item sets one or more field mappings and specifies the data to be evaluated. The data quality assessment module is used to evaluate and compare data based on technical metadata standards, mapping relationships between metadata, and the data to be evaluated, calculate data quality assessment scores and data quality assessment pass rates, and judge data quality based on the data quality assessment pass rates.
[0046] like Figure 7 As shown, a preferred embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the data quality assessment method in the database described in the above embodiments.
[0047] like Figure 8 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 8 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the data quality assessment method described above in the database.
[0048] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0049] A preferred embodiment of this application also provides a storage medium including a stored program that, when the program is executed, controls the device where the storage medium is located to perform the steps of the data quality assessment method in the database described in the above embodiments.
[0050] In summary, the data quality assessment method of this application can accurately determine data quality, improve the credibility and validity of data, thereby ensuring the quality of data application, enhancing environmental protection business capabilities, and the assessment method is relatively flexible and applicable to various data sources.
[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0052] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this application's embodiments that contribute to the prior art or the technical solutions can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, 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 media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.
[0053] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.
[0054] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.
[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0057] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0058] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A method for assessing data quality in a database, characterized in that, Including the following steps: S1. Determine the attributes of the technical metadata that needs to be evaluated, including setting the field attributes in the data table and their evaluation scores; S2. Establish technical metadata standards, and treat multiple attribute standards of a field as a single technical metadata. If a certain attribute does not have a standard set, that attribute will not be evaluated. S3. Specify the mapping relationship between metadata, including the mapping relationship between technical metadata and fields in the data table. Here, a technical metadata item sets one or more field mappings and specifies the data to be evaluated. S4. Based on the technical metadata standards, the mapping relationship between metadata, and the data to be evaluated, evaluate and compare the data, calculate the data quality evaluation score and the data quality evaluation pass rate, and judge the data quality based on the data quality evaluation pass rate.
2. The data quality assessment method in the database according to claim 1, characterized in that, In step S1, the attributes of the technical metadata include: data type, length, precision, number of decimal places, whether nullables are allowed, and reference data.
3. The data quality assessment method in the database according to claim 2, characterized in that, Step S1, setting the evaluation score for the field attributes in the data table specifically includes the following steps: S11. Set evaluation scores based on the importance of technical metadata attributes, and divide technical metadata attributes into three levels: general, medium, and important. S12. The evaluation score of technical metadata attributes at the medium level shall not exceed twice the evaluation score of general technical metadata attributes, the evaluation score of technical metadata attributes at the important level shall not exceed twice the evaluation score of medium technical metadata attributes, and the evaluation score of technical metadata attributes at the general level shall not exceed 2 points.
4. The data quality assessment method in the database according to claim 3, characterized in that, The evaluation scores are in increments of 0.
5.
5. The data quality assessment method in the database according to claim 1, characterized in that, Step S4 specifically includes the following steps: S41. Calculate the total score for a single attribute based on its individual attribute score and the number of attributes to be evaluated for each attribute. S42. Obtain the total data quality assessment score by summing the total scores of all attributes; S43. During the data quality assessment process, if a non-compliant data is found, an attribute score is deducted. Therefore, the score of a single attribute is multiplied by the number of non-compliant items to obtain the deduction for a single attribute. The sum of the deductions for all attributes is the total deduction for the data quality assessment. The total data quality assessment score is obtained by subtracting the total deduction from the total data quality assessment score. S44. Divide the data quality assessment score by the total data quality assessment score to obtain the data quality assessment pass rate; S45. Determine whether the data quality is qualified based on the pass rate of the obtained data quality assessment.
6. The data quality assessment method in the database according to claim 5, characterized in that, Step S41 specifically includes the following steps: S411. Determine the quantity of a single technical metadata to be evaluated, including the sum of the data quantities to be evaluated for all mapping table fields of a single technical metadata. S412. Based on the sum of the number of individual technical metadata to be evaluated, obtain the number of individual attribute standards to be evaluated. Determine whether a standard is set for a single attribute in a single technical metadata. If a standard is set, sum the results; otherwise, do not include it in the summation sequence. S413. Multiply the individual attribute assessment score by the number of criteria to be assessed for that individual attribute to obtain the total assessment score for that individual attribute: ； In the formula: M a (i) The amount of data to be evaluated for a single technical metadata, single mapping table field; k The number of mappings for a single technology metadata; W i Whether to set a standard for a single attribute of a single technical metadata, and when to set a standard. W i =1, when no standard is set W i =0; m The amount of technical metadata; V a (i) Evaluate the score for a single attribute; S a (i) The total score for evaluating a single attribute.
7. The data quality assessment method in the database according to claim 5, characterized in that, Step S45 specifically includes the following steps: S451. Data quality is divided into four levels: excellent, good, qualified, and unqualified. S452, When the data quality assessment pass rate R a When ≥90%, the data quality is considered excellent; when 90% > R a When ≥80%, the data quality is judged as good; when 80% > R a When ≥60%, the data quality is considered acceptable; when R a If the percentage is less than 60%, the data quality is considered unacceptable.
8. A data quality assessment device for a database, characterized in that, include: The technology metadata attribute determination module is used to determine the attributes of the technology metadata that needs to be evaluated, including setting the field attributes in the data table and their evaluation scores; The technical metadata standardization module is used to define technical metadata standards. It treats multiple attribute standards for a field as a single technical metadata set, and does not evaluate an attribute if no standard is set for it. The mapping relationship specification module is used to specify the mapping relationship between metadata, including the mapping relationship between technical metadata and fields in the data table. Here, a technical metadata item sets one or more field mappings and specifies the data to be evaluated. The data quality assessment module is used to evaluate and compare data based on technical metadata standards, mapping relationships between metadata, and the data to be evaluated, calculate data quality assessment scores and data quality assessment pass rates, and judge data quality based on the data quality assessment pass rates.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the data quality assessment method in the database as described in any one of claims 1 to 7.
10. A storage medium comprising a stored program that, when the program is executed, controls a device in which the storage medium resides to perform the steps of the data quality assessment method in a database as described in any one of claims 1 to 7.