Engineering entity quality defect data standardization method based on line three-dimensional design

Abstract

The invention provides an engineering entity quality defect data standardization method based on line three-dimensional design. The method comprises the following steps: collecting related data of line three-dimensional design; processing the collected related data, and establishing an association relationship between the data; according to the three-dimensional design requirement of the line, making a corresponding data standardization standard, and according to the specification and the standard, carrying out standardization processing on the data; verifying the standardized data; storing the standardized data in a database; and based on the standardized data, analyzing and evaluating the quality defects of the engineering entity, identifying potential quality problems and defects, and providing corresponding solutions and improvement measures. Through the steps of data collection and association establishment, data standardization, data storage and analysis and the like, integration, standardization and management of data are realized, and accurate data support is provided for analysis and evaluation of engineering entity quality defects; and the quality and the reliability of the engineering entity can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of engineering data processing, and in particular to a method for standardizing engineering entity quality defect data based on line three-dimensional design. Background Art

[0002] Engineering entity quality defects refer to problems or defects in the design, construction and material selection of engineering entities (such as buildings, bridges, roads, etc.) during the implementation of engineering projects, which lead to substandard or non-conformance of the quality of engineering entities. The significance of data standardization includes the following points: 1. Improving data consistency and comparability: Data standardization can convert data from different sources, formats and units into a unified standard format and unit, making the data consistent and comparable, and conducting data analysis, data mining and decision support. 2. Reducing the complexity of data processing and management: Data standardization can simplify the data processing and management process, reduce the workload of data conversion, data cleaning and data integration; through unified data standards, data integration, query and sharing can be more convenient. 3. Improving data quality and accuracy: Data standardization can help identify and correct errors, missing, duplication and other problems in the data, improve the quality and accuracy of the data, and standardize data to reduce the impact of data quality problems on subsequent analysis and decision-making. 4. Supporting the sustainable use and sharing of data: Through data standardization, data can be made more universal and sustainable, promote data sharing and reuse, and standardized data can better adapt to future needs and application scenarios. Although data standardization has great advantages, the existing technology does not carry out data standardization. The reasons may be as follows: the diversity of data sources, the differences in data formats, data structures and data standards of different data sources, make data standardization more complicated and difficult. Data quality issues: Low data quality, missing data, incomplete data and other issues will affect the accuracy and reliability of data standardization, and may require additional data cleaning and processing. Organizational and management challenges: Data standardization requires the organization and management of various data standards and metadata, involving data governance, data architecture and data management, which requires a lot of manpower and resources. Technical limitations: Current technical tools and methods may have certain limitations in data standardization and cannot fully meet the complex and diverse data standardization needs. In general, the significance of data standardization is to improve the consistency and comparability of data, reduce the complexity of data processing and management, improve data quality and accuracy, and support the sustainable use and sharing of data. However, the implementation of data standardization may face difficulties such as the diversity of data sources, data quality issues, organizational and management challenges, and technical limitations. Therefore, it is necessary to comprehensively consider factors such as technology, resources and organization to formulate reasonable data standardization strategies and plans.

[0003] Prior art 1, application number: CN 202310345751.6 discloses a method for constructing a data structure of material attribute information of substation engineering equipment based on BIM, including the following steps: collecting equipment material design data and abstracting it into an attribute table; standardizing attribute names; grouping attributes to form a standardized attribute information data structure table; grouping the attribute information table to form a standard attribute information data structure table; establishing a database to store the attribute table; establishing an equipment material database to store the attribute information data structure table corresponding to various library types; assigning and applying the attribute table in BIM practice; in BIM project practice, relying on the standard attribute information data structure table, through various assignment operations, efficient design collaboration and data interoperability are achieved. Although it has the advantages of achieving data interoperability and efficient collaboration, and greatly improving the efficiency and quality of BIM design of substation engineering. However, there is a lack of processing solutions for multi-source data, resulting in an incomplete constructed data structure.

[0004] Prior art 2, application number: CN 202311054745.1 discloses a method and system for structured storage of engineering quality acceptance form data, the method comprising the following steps: obtaining various engineering quality acceptance form data of the engineering quality system, and performing data preprocessing on various engineering quality acceptance form data to obtain the engineering quality acceptance form data to be processed; using a quality noise reduction algorithm to perform noise reduction on the engineering quality acceptance form data to be processed, to obtain the engineering quality acceptance form noise reduction data; performing data standardization on the engineering quality acceptance form noise reduction data to obtain the engineering quality acceptance form standard data; using an association rule analysis algorithm to perform in-depth mining and analysis on the engineering quality acceptance form standard data to obtain the key features of the quality acceptance form data. Although it can support the real-time reporting of a large number of engineering quality acceptance forms, the reported form data is structured and stored, thereby realizing quality acceptance in the engineering industry. However, low data quality, missing data, and incomplete data lead to inaccurate data standardization processing results.

[0005] Prior art three, application number: CN 202311738232.2 discloses a digital construction management system for cement engineering projects, including a data layer, a platform layer, a business layer, and a service layer; the data layer includes: a basic database and a management standard library; a basic database for establishing management elements such as project construction operations and steps, personnel, equipment, plans, quality, and safety; establishing the internal management relationship between standard data of project management elements; the platform layer is used for user basic information maintenance; the business layer is used to divide the on-site construction business of cement engineering projects, and engineering management personnel carry out on-site construction management based on the business layer; the service layer is used for analysis and display of labor, shifts, and material consumption; although the unified standardization and intelligence of cement engineering project management are realized, the standardization and refinement level of cement engineering management are improved, and the high requirements and high standards of management in cement engineering construction are met, and the hidden costs of engineering management are reduced. However, it is only for cement engineering, which makes it impossible to fully meet the complex and diverse data standardization needs.

[0006] At present, there are data sources diversity in the prior art 1, prior art 2 and prior art 3, and there are differences in the data format, data structure and data standard of different data sources, which leads to complex and difficult data standardization. Therefore, the present invention provides a method for standardizing engineering entity quality defect data based on line three-dimensional design, which can integrate, clean, convert and standardize line three-dimensional design data from different data sources, thereby improving the accuracy, consistency and comparability of the data, and providing reliable data support for subsequent quality analysis and decision-making. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a method for standardizing engineering entity quality defect data based on line three-dimensional design, comprising the following steps:

[0008] Collect relevant data of line 3D design, process the collected relevant data, and establish the relationship between the data;

[0009] According to the requirements of the three-dimensional design of the line, formulate corresponding data standardization standards, standardize the data according to the specifications and standards, and verify the standardized data;

[0010] The standardized data is stored in the database; based on the standardized data, the quality defects of the engineering entity are analyzed and evaluated, potential quality problems and defects are identified, and corresponding solutions and improvement measures are provided.

[0011] Optionally, the process of normalizing the data includes the following steps:

[0012] Obtain the three-dimensional design requirements of the line, obtain the data elements and data expressions, and determine the data fields and attributes that need to be standardized;

[0013] According to the analyzed data elements and requirements, formulate corresponding standardized specifications and standards, and determine the data conversion and processing methods according to the formulated standardized specifications and standards;

[0014] Verify the standardized data to check whether it meets the standardization specifications and standard requirements; if any abnormalities are found, make corrections.

[0015] Optionally, the process of determining the data fields and attributes that need to be standardized includes the following steps:

[0016] Collect line 3D design requirements;

[0017] Determine the data elements that need to be standardized, and for each data element, determine its attributes;

[0018] Based on the analysis results of data elements and attributes, determine the specific data fields and attributes that need to be standardized; determine the need for unit conversion for line length fields, unified representation for material specification fields, and calculation and judgment for quality indicator fields.

[0019] Optionally, the analysis requirements include whether the calculation and representation of line length is involved; whether the standards and representation of material specifications are involved; and whether the calculation and judgment criteria of quality indicators are involved.

[0020] Optionally, the process of developing corresponding standardized specifications and standards includes the following steps:

[0021] Formulate corresponding standardized specifications and standards based on the analyzed data elements and requirements;

[0022] Determine the data conversion and processing methods according to the established standardized specifications and standards;

[0023] The collected line three-dimensional design data is standardized according to the determined standardized specifications and standards.

[0024] Optionally, the standardization process includes unit conversion, specification code mapping, and quality indicator calculation and determination processing.

[0025] Optionally, the process of validating the standardized data includes the following steps:

[0026] Collect standardized data and verify the collected data according to the established standardized specifications and standards;

[0027] Check the verification results. If any data abnormalities or non-compliance with standardized specifications and standards are found, make corrections until the data meets the requirements;

[0028] Record the verification results and correction process. When performing data verification, use a table to record the verification results of each data field.

[0029] Optionally, the process of using a table to record the validation results for each data field includes the following steps:

[0030] Create a table and add field names in the first row of the table, that is, the name of each data field. Each field name corresponds to a column in the table.

[0031] In the verification result column of each field, fill in the corresponding verification result in the corresponding row;

[0032] In the original value column of the table, the corresponding rows are filled with the original data values, and in the converted value column, the corresponding rows are filled with the standardized data values.

[0033] Optionally, the process of analyzing and evaluating the quality defects of the engineering entity includes the following steps:

[0034] Create a database table structure to store standardized data; use database management tools to import standardized data into the corresponding database table, and store the standardized data in the database according to the specified fields and format;

[0035] Based on the standardized data, use database query tools to perform data query and analysis;

[0036] Based on the queried data, analyze and evaluate the quality defects of the engineering entity; based on the results of the analysis and evaluation, formulate improvement plans and measures according to the type and severity of the quality defects.

[0037] Optionally, query based on line information, material specifications and quality inspection index conditions to obtain relevant data records.

[0038] The present invention collects relevant data of line three-dimensional design, and establishes an association relationship between the data to ensure the integrity and consistency of the data; through data collection and association establishment, the relevant data of line three-dimensional design can be obtained, providing a data basis for subsequent data standardization and analysis. At the same time, establishing an association relationship between the data can help identify the source and relevance of the data, and provide support for subsequent data analysis and evaluation. According to the requirements of line three-dimensional design, corresponding data standardization standards are formulated, and the data are standardized to ensure the consistency and comparability of the data; through data standardization, data in different formats and units can be converted into a unified standard format and unit, improving the consistency and comparability of the data; it is helpful for subsequent data analysis and comparison, and provides an accurate data basis for the analysis and evaluation of engineering entity quality defects. The standardized data is stored in a database, and the quality defects of the engineering entity are analyzed and evaluated based on the standardized data; through data storage and analysis, the management and maintenance of the standardized data can be realized, ensuring the security and availability of the data. At the same time, based on the standardized data, analysis and evaluation can identify the problems and causes of the quality defects of the engineering entity, and provide corresponding solutions and improvement measures; it is helpful to improve the quality and reliability of the engineering entity and reduce the risks and losses caused by quality defects.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 This is a flow chart of a method for standardizing engineering entity quality defect data based on line three-dimensional design in Example 1 of the present invention;

[0043] Figure 2 A process diagram of processing the collected relevant data in Embodiment 2 of the present invention;

[0044] Figure 3 A flowchart of establishing an association relationship between data in Example 3 of the present invention;

[0045] Figure 4 This is a process diagram of standardizing data in Example 4 of the present invention;

[0046] Figure 5 A process diagram for determining data fields and attributes that need to be standardized in Embodiment 5 of the present invention;

[0047] Figure 6 A process diagram for formulating corresponding standardized specifications and standards in Example 6 of the present invention;

[0048] Figure 7 This is a process diagram for verifying the standardized data in Example 7 of the present invention;

[0049] Figure 8 A process diagram of using a table to record the verification result of each data field in Example 8 of the present invention;

[0050] Fig. 9 A process diagram for analyzing and evaluating quality defects of an engineering entity in Embodiment 9 of the present invention;

[0051] Fig.10 This is an engineering drawing for analyzing and evaluating quality defects of an engineering entity in Example 10 of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0054] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as described in detail. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0055] Example 1: Figure 1As shown, an embodiment of the present invention provides a method for standardizing engineering entity quality defect data based on line three-dimensional design, comprising the following steps:

[0056] S100: Collect relevant data of the three-dimensional design of the line, including design drawings, engineering specifications, construction records, material selection, etc.; process the collected relevant data and establish a correlation relationship between the data;

[0057] S200: According to the requirements of the three-dimensional design of the line, formulate corresponding data standardization standards, standardize the data according to the specifications and standards, and verify the standardized data;

[0058] Among them, the standards include the measurement units for determining line length, the method of expressing material specifications, and quality inspection indicators;

[0059] S300: Store the standardized data in the database; analyze and evaluate the quality defects of the engineering entity based on the standardized data, identify potential quality problems and defects, and provide corresponding solutions and improvement measures.

[0060] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the relevant data of the three-dimensional design of the line are first collected, and the relevant data include design drawings, engineering specifications, construction records and material selection, etc.; the collected relevant data are processed to establish the association relationship between the data; secondly, according to the requirements of the three-dimensional design of the line, the corresponding data standardization standards are formulated, and the data are standardized according to the specifications and standards; the standardized data are verified; the standards include the measurement unit for determining the length of the line, the method for expressing the material specifications and the quality inspection indicators, etc.; finally, the standardized data is stored in the database; based on the standardized data, the quality defects of the engineering entity are analyzed and evaluated, potential quality problems and defects are identified, and corresponding solutions and improvement measures are provided. Step S100 of the above scheme collects the relevant data of the three-dimensional design of the line, and establishes the association relationship between the data to ensure the integrity and consistency of the data; through data collection and association establishment, the relevant data of the three-dimensional design of the line can be obtained, providing a data basis for subsequent data standardization and analysis. At the same time, establishing the association relationship between the data can help identify the source and relevance of the data, and provide support for subsequent data analysis and evaluation. Step S200 formulates corresponding data standardization standards according to the requirements of line three-dimensional design, standardizes the data, and ensures the consistency and comparability of the data; through data standardization, data in different formats and units can be converted into a unified standard format and unit to improve the consistency and comparability of the data; it is helpful for subsequent data analysis and comparison, and provides an accurate data basis for the analysis and evaluation of engineering entity quality defects. Step S300 stores the standardized data in the database, and analyzes and evaluates the quality defects of the engineering entity based on the standardized data; through data storage and analysis, the management and maintenance of the standardized data can be achieved to ensure the security and availability of the data. At the same time, based on the analysis and evaluation of the standardized data, the problems and causes of the quality defects of the engineering entity can be identified, and corresponding solutions and improvement measures can be provided; it is helpful to improve the quality and reliability of the engineering entity and reduce the risks and losses caused by quality defects.

[0061] In summary, the method for standardizing engineering entity quality defect data based on line three-dimensional design in this embodiment realizes data integration, standardization and management through steps such as data collection and association establishment, data standardization, and data storage and analysis, and provides accurate data support for the analysis and evaluation of engineering entity quality defects; it helps to improve the quality and reliability of engineering entities and reduce the risks and losses caused by quality defects.

[0062] Example 2: Figure 2 As shown, based on Example 1, the process of processing the collected relevant data provided by the embodiment of the present invention includes the following steps:

[0063] S101: Compare data from different data sources, check whether the data range and value meet the preset specifications, identify errors in the data, including wrong values, formats and logic, etc. through data outlier detection, data range verification and data association relationship analysis; repair the identified erroneous data, and update and synchronize the repaired data;

[0064] S102: The collected repaired data are converted uniformly, data in different formats are standardized and converted into a uniform format; data in different units are converted into the same unit;

[0065] For length data, feet are converted to meters or inches are converted to centimeters; for weight data, pounds are converted to kilograms; if there are data of different data types, data type conversion is performed as needed, such as converting string type data to numeric type, or converting numeric type data to string type, etc.;

[0066] S103: Integrate the converted data from different data sources and establish an association relationship: Integrate the data from different data sources through a unique identifier and establish an association relationship between the data; the unique identifier includes a line number and a device ID, etc.

[0067] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first compares data from different data sources, checks whether the range and value of the data meet the preset specifications, and identifies errors in the data, including erroneous values, formats and logic, etc., by detecting data outliers, verifying data ranges and analyzing data association relationships; repairs the identified erroneous data, and updates and synchronizes the repaired data; secondly, the collected repaired data is uniformly converted, and data in different formats are standardized and converted into a unified format; data in different units are converted into the same unit; for length data, feet are converted into meters or inches are converted into centimeters; for weight data, pounds are converted into kilograms; if there are data of different data types, data type conversion is performed as needed, and string type data is converted into numeric type, or numeric type data is converted into string type, etc.; finally, the converted data from different data sources are integrated and association relationships are established: data from different data sources are integrated through unique identifiers, and association relationships between data are established; unique identifiers include line numbers and device IDs, etc. Step S101 of the above scheme identifies errors in the data, including wrong values, formats and logic, by comparing data from different data sources, checking the range and value of the data, and data analysis methods; the identified erroneous data can be repaired to improve the accuracy and consistency of the data; the consistency and credibility of the data can be improved by detecting data outliers, verifying the data range and analyzing the data association relationship, and reducing the impact of erroneous data on subsequent analysis and decision-making. Step S102 can convert data in different formats into a unified format by standardizing them, improving the consistency and comparability of the data; by converting the units of data in different units, they can be unified into the same unit, which is convenient for data comparison and analysis. Step S103 can integrate scattered data by integrating data from different data sources and establishing associations between the data, which is convenient for subsequent data analysis and use; by establishing associations between the data, the consistency and comparability of the data can be improved, so that data from different data sources can be compared and analyzed in association; through unique identifiers, data traceability and tracing can be achieved, which is convenient for tracking the source and processing of data and improving the credibility and traceability of data.

[0068] In summary, this embodiment can improve the quality and availability of data, provide accurate data support for the analysis and evaluation of engineering entity quality defects, and further support decision-making and improvement work.

[0069] Example 3: Figure 3 As shown, based on Example 2, the process of establishing the association relationship between data provided by the embodiment of the present invention includes the following steps:

[0070] S1031: Determine a unique identifier for establishing an association relationship between data, integrate data from different data sources into a unified data set by merging data; match data from different data sources according to the unique identifier;

[0071] S1032: Connect the data according to the association rule, associate the matched data, and establish an association relationship between the data;

[0072] The association rule is defined as: according to the association rule, determine the field used to associate the data, the field is a unique identifier field or a field with similar meaning, used to match in two data sets; use a specific association method (such as inner join, left join, right join, outer join) to connect the data, the connection operation matches the data according to the association field, and merges the records that meet the association conditions in the two data sets;

[0073] Inner join: only the records with matching related fields in the two data sets are retained, and non-matching records will be discarded;

[0074] Left join: retain all records in the left dataset and merge the records with matching associated fields in the right dataset; if there are no matching records in the right dataset, they will be filled with null values;

[0075] Right join: retain all records in the right dataset and merge the records whose associated fields match in the left dataset; if there are no matching records in the left dataset, they will be filled with null values;

[0076] Outer join: Returns all records in the left and right datasets, regardless of whether the associated fields match; if there is no matching record in a dataset, it will be filled with null values;

[0077] After the connection operation is completed, the association relationship between the data is established, and the records matched by the association fields will be merged into a unified result set, thus establishing the association relationship between the data;

[0078] In the specific implementation process, the association operation functions provided in the SQL query language are used to perform data connection and association operations; data connection and association operations are performed according to specific data sets and association rules to establish association relationships between data;

[0079] S1033: Confirm whether the association relationship is correctly established by verifying and comparing the associated data; if the data is updated, update and synchronize the association relationship through regular data update and synchronization operations.

[0080] The working principle and beneficial effects of the above technical solution are as follows: the embodiment first determines a unique identifier for establishing an association relationship between data, integrates data from different data sources through data merging, and aggregates them into a unified data set; matches data in different data sources according to the unique identifier; secondly, the data is connected according to the association rule, and the matched data is associated to establish an association relationship between the data; finally, by verifying and comparing the associated data, it is confirmed whether the association relationship is correctly established; if the data is updated, the association relationship is updated and synchronized through regular data update and synchronization operations. Step S1031 of the above solution determines a unique identifier for establishing an association relationship between data to ensure the uniqueness of each data in the entire data set; the unique identifier is the basis for establishing an association relationship between data, and by integrating data sources, data from different data sources are aggregated into a unified data set to provide a data basis for subsequent data association operations. Step S1032 performs data connection and association operations according to the association rules, merges the data matching the associated fields in two or more data sets, and establishes an association relationship between the data; through data connection and association, data with an association relationship can be merged to form a more comprehensive and accurate data view, providing accurate data support for subsequent data analysis and decision-making. Step S1033 verifies the established association relationship to ensure the accuracy and consistency of the association relationship; maintains the timeliness and accuracy of the association relationship through regular data updates and synchronization operations; verification of the association relationship can help discover inconsistent or erroneous data and improve the accuracy and reliability of data association; data updates and synchronization operations can maintain the consistency of the association relationship with the source data and ensure that the association relationship is always based on the latest data.

[0081] In summary, this embodiment establishes association relationships between data so that data can be better associated and integrated with each other, providing accurate and comprehensive data support for subsequent data analysis, decision-making and business applications; by establishing association relationships, the correlation, dependency and influence between data can be discovered, helping users to better understand the data and explore the potential association patterns and value in the data, thereby supporting deeper data analysis, decision-making and business innovation.

[0082] Example 4: Figure 4 As shown, based on Example 1, the process of standardizing data provided by the embodiment of the present invention includes the following steps:

[0083] S201: Obtain the three-dimensional design requirements of the line, obtain the data elements and data expressions, and determine the data fields and attributes that need to be standardized, including relevant data elements such as line length, material specifications and quality indicators;

[0084] S202: Based on the analyzed data elements and requirements, formulate corresponding standardized specifications and standards, including the measurement unit for determining line length, the method for expressing material specifications, and the calculation formula and judgment criteria for quality inspection indicators, etc.; based on the formulated standardized specifications and standards, determine the data conversion and processing methods, including unit conversion, mapping of specification codes, calculation and judgment of quality indicators, etc.;

[0085] S203: Verify the standardized data to check whether the data complies with the standardization specifications and standard requirements; if any abnormality is found, make corrections.

[0086] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first obtains the three-dimensional design requirements of the line, obtains the data elements and data expressions, determines the data fields and attributes that need to be standardized, including the line length, material specifications and quality indicators and other related data elements; secondly, according to the analyzed data elements and requirements, formulates the corresponding standardized specifications and standards, including the measurement unit for determining the line length, the method for expressing the material specifications, and the calculation formula and judgment criteria for the quality inspection indicators; according to the formulated standardized specifications and standards, determines the data conversion and processing methods, including unit conversion, mapping of specification codes, calculation and judgment of quality indicators, etc.; finally, verifies the standardized data to check whether the data meets the standardized specifications and standard requirements; if an abnormality is found, makes corrections. Step S201 of the above scheme obtains the three-dimensional design requirements of the line, determines the data elements and related attributes that need to be standardized, such as line length, material specifications and quality indicators, etc.; by clarifying the data elements and attributes, provides the basis and direction for data processing for subsequent standardized processing, and ensures that the standardized data can meet the design requirements and standards. Step S202: According to the data elements and requirements, formulate corresponding standardized specifications and standards, including measurement units, representation methods and calculation formulas, etc.; determine data conversion and processing methods, such as unit conversion, specification code mapping and quality indicator calculation methods, etc.; by formulating standardized specifications and methods, ensure the consistency and comparability of data, so that the data has a unified expression form and standard, and improve the data quality and credibility. Step S203: Verify the standardized data to check whether the data meets the standardized specifications and requirements; through standardized processing, the data has a consistent expression form and standard, improves the comparability and reliability of the data; through data verification, ensure that the standardized data meets the specifications and requirements, and reduce data errors and inconsistencies.

[0087] In summary, this embodiment improves the consistency, comparability and credibility of data, and provides an accurate data basis for subsequent analysis and evaluation of engineering entity quality defects; standardization can make data have a unified expression form and standard, facilitate data comparison and analysis, and identify potential quality problems and defects. At the same time, data standardization also helps to improve the efficiency and level of engineering quality management, and provide a scientific basis and reference for the improvement and enhancement of engineering entity quality.

[0088] Example 5: Figure 5 As shown, based on Example 4, the process of determining the data fields and attributes that need to be standardized provided by the embodiment of the present invention includes the following steps:

[0089] S2011: Collect the three-dimensional design requirements of the line, including the functional requirements of the line, the design parameter requirements, the relevant specifications and requirements of the line construction, and the industry standards related to the line design; analyze whether the requirements involve the calculation and representation of the line length, whether they involve the standards and representation of material specifications, whether they involve the calculation and judgment standards of quality indicators, etc.;

[0090] S2012: Determine the data elements that need to be standardized, including determining that line length, material specifications and quality indicators are data elements that need to be standardized; for each data element, determine its attributes;

[0091] Among them, the attributes of line length include units (such as meters, kilometers), precision (number of decimal places), etc.; the attributes of material specifications may include specification codes and dimensions, etc.; the attributes of quality indicators may include calculation formulas and judgment criteria, etc.;

[0092] S2013: Based on the analysis results of data elements and attributes, determine the specific data fields and attributes that need to be standardized; determine the need for unit conversion for line length fields, unified representation for material specification fields, calculation and judgment for quality indicator fields, etc.

[0093] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first collects the three-dimensional design requirements of the line, including the functional requirements of the line, the design parameter requirements, the relevant specifications and requirements of the line construction and the industry standards related to the line design; analyzes whether the requirements involve the calculation and representation of the line length, etc.; whether the standards and representation of the material specifications, etc.; whether the calculation and judgment criteria of the quality indicators, etc. are involved; secondly, determines the data elements that need to be standardized, including determining that the line length, material specifications and quality indicators are the data elements that need to be standardized; for each data element, determines its attributes; wherein the attributes of the line length include units (such as meters, kilometers), precision (number of decimal places), etc.; the attributes of the material specifications may include specification codes and dimensions, etc.; the attributes of the quality indicators may include calculation formulas and judgment criteria, etc.; finally, according to the analysis results of the data elements and attributes, determines the specific data fields and attributes that need to be standardized; determines whether the line length field needs to be converted into units, the material specification field needs to be uniformly represented, and the quality indicator field needs to be calculated and judged, etc. Step S2011 of the above scheme ensures a comprehensive understanding of the line design requirements and provides an accurate basis for subsequent data standardization. Step S2012 specifies the data fields and attributes that need to be standardized, providing specific guidance and basis for subsequent standardization. Step S2013 standardizes the data to improve the consistency and comparability of the data; the standardized data can meet the requirements of line three-dimensional design and standardization specifications, providing an accurate and reliable data basis for subsequent analysis and application.

[0094] In summary, this embodiment clarifies the scope and objectives of data standardization processing, ensures that the data can meet the requirements of line three-dimensional design and standardization specifications, and improves the consistency and comparability of data; improves the quality and reliability of line design data, reduces the problems caused by inconsistent data, and provides accurate data support for subsequent line design, construction, operation and maintenance, thereby improving work efficiency and quality. At the same time, the standardized data also provides a reliable basis for subsequent data analysis, mining and decision-making, supporting more accurate judgments and decisions.

[0095] Example 6: Figure 6 As shown, based on Example 4, the process of formulating corresponding standardized specifications and standards provided in the embodiment of the present invention includes the following steps:

[0096] S2021: Formulate corresponding standardized specifications and standards based on the analyzed data elements and requirements; Determine the measurement unit of the line length, and determine the measurement unit to be used according to the requirements, such as meters, kilometers, etc.; Determine the representation of material specifications: Determine the representation of material specifications according to the requirements, such as specification codes, dimensions, etc.; Determine the calculation formula and judgment criteria of quality indicators: Determine the calculation formula and judgment criteria of quality indicators according to the requirements to ensure the accuracy and consistency of the data;

[0097] S2022: Determine the data conversion and processing methods according to the established standardized specifications and standards; Unit conversion: Perform unit conversion on the line length field according to the determined measurement unit; Specification code mapping: Map the specification code on the material specification field according to the determined representation method and unify the representation method; Quality index calculation and determination: Calculate and determine the quality index field according to the determined calculation formula and determination standard;

[0098] S2023: Standardize the collected line 3D design data according to the determined standardized specifications and standards; perform unit conversion, specification code mapping, quality index calculation and determination, and convert the data into a unified representation and measurement unit.

[0099] The working principle and beneficial effects of the above technical solution are as follows: First, according to the analyzed data elements and requirements, the embodiment formulates corresponding standardized specifications and standards; determines the measurement unit of the line length, and determines the measurement unit used according to the requirements, such as meters, kilometers, etc.; determines the representation of material specifications: determines the representation method of material specifications according to the requirements, such as specification codes, dimensions, etc.; determines the calculation formula and judgment standard of quality indicators: determines the calculation formula and judgment standard of quality indicators according to the requirements to ensure the accuracy and consistency of data; secondly, according to the formulated standardized specifications and standards, determines the data conversion and processing method; unit conversion: according to the determined measurement unit, the line length field is converted into a unit; specification code mapping: according to the determined representation method, the material specification field is mapped to the specification code, and the representation method is unified; quality indicator calculation and judgment: according to the determined calculation formula and judgment standard, the quality indicator field is calculated and judged; finally, according to the determined standardized specifications and standards, the collected line three-dimensional design data is standardized; unit conversion, specification code mapping, quality indicator calculation and judgment and other processing are performed to convert the data into a unified representation method and measurement unit. Step S2021 of the above scheme clarifies the norms and standards for standardized processing, provides specific basis and criteria for data conversion and processing, ensures that data is processed according to unified norms, and improves the consistency and comparability of data. Step S2022 ensures that data conforms to standardized norms and standard requirements, unifies the representation and measurement units of data, reduces errors and inconsistencies in the data processing process, and improves the accuracy and reliability of data. Step S2023 converts data into a unified representation and measurement unit to make the data consistent and comparable, improves the consistency and reliability of data, and provides an accurate and reliable data basis for subsequent analysis and application.

[0100] In summary, through the above specific steps, this embodiment can formulate corresponding standardized specifications and standards, determine the data conversion and processing methods, and standardize the line three-dimensional design data. This helps to improve the consistency and comparability of data, ensure that the data meets the standardization requirements, and improve work efficiency and quality; the standardized data also provides a reliable basis for subsequent data analysis, mining and decision-making, and supports more accurate judgment and decision-making. At the same time, standardized processing can also reduce errors and inconsistencies in the data processing process and improve the accuracy and reliability of data.

[0101] Example 7: Figure 7 As shown, based on Example 4, the process of verifying the standardized data provided by the embodiment of the present invention includes the following steps:

[0102] S2031: Collect standardized data and verify the collected data according to the established standardized specifications and standards, including:

[0103] Unit verification: Checks whether the line length field has been successfully converted to ensure that all line lengths are measured in the same unit.

[0104] Specification code verification: Check whether the material specification field has been successfully mapped to the specification code to ensure that all material specifications are represented in a unified manner;

[0105] Quality indicator verification: Calculate and judge the quality indicator fields according to the determined calculation formula and judgment criteria to ensure that the calculation results of the data meet the standard requirements;

[0106] S2032: Check the verification results. If the data is found to be abnormal or does not meet the standardization specifications and standard requirements, make corrections; corrections include data cleaning, data error correction or re-standardization until the data meets the requirements;

[0107] S2033: Record the verification results and correction process. When performing data verification, use a table to record the verification results of each data field, including the field name, original value, converted value, verification result (pass / fail), and related exception information.

[0108] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first collects standardized data, and verifies the collected data according to the formulated standardized specifications and standards, including: unit verification: checking whether the line length field has been successfully converted to ensure that all line lengths use a unified unit of measurement; specification code verification: checking whether the material specification field has been successfully mapped to the specification code to ensure that all material specifications use a unified representation method; quality indicator verification: calculating and judging the quality indicator field according to the determined calculation formula and judgment criteria to ensure that the calculation result of the data meets the standard requirements; secondly, checking the verification results, if the data is found to be abnormal or does not meet the standardized specifications and standard requirements, make corrections; corrections include data cleaning, data error correction or re-standardization processing and other operations until the data meets the requirements; finally, record the verification results and correction process. When performing data verification, use a table to record the verification results of each data field, including the field name, original value, converted value, verification result (pass / fail) and related abnormal information. The unit verification of step S2031 of the above scheme ensures that all line lengths use a unified unit of measurement by checking whether the line length field has been successfully converted to a unified unit; converts line lengths of different units into a unified unit to make the data comparable and consistent; ensures that the data has a consistent metric standard on the same feature, providing an accurate basis for subsequent analysis and comparison; specification code verification maps different specification codes to a unified representation method to provide consistency and comparability of specification information; ensures that the specification information in the data has a unified expression form, facilitating subsequent data analysis and application; quality indicator verification accurately calculates and determines quality indicators according to the prescribed calculation methods and standards; ensures that the quality indicators of the data meet the expected requirements, providing a reliable basis for subsequent data analysis and decision-making. Step S2032 corrects data that does not meet standardization requirements to make it meet standardization specifications and requirements; ensures the accuracy, consistency and reliability of data, providing a reliable basis for subsequent data analysis and application. Step S2033 records the verification status and correction process of each field to ensure the traceability and transparency of the data processing process; it can trace the source, processing method and correction process of the data when necessary to ensure the credibility and reliability of the data processing process; the writing of records and documents also facilitates communication and sharing within the team to ensure that every member understands the process and results of data processing.

[0109] In summary, through the above steps, this embodiment can ensure that the standardized data is verified and corrected to meet the standardization specifications and requirements; improve the quality, consistency and reliability of the data, and provide a reliable basis for subsequent data analysis, decision-making and application. At the same time, recording and tracing the data processing process also helps to ensure the traceability and transparency of data processing, and improve the credibility and reliability of the data processing process.

[0110] Example 8: Figure 8 As shown, based on Example 7, the process of using a table to record the verification result of each data field provided in the embodiment of the present invention includes the following steps:

[0111] S20331: Create a table with columns including field name, original value, converted value, verification result and exception information, etc.; add field names in the first row of the table, i.e. the name of each data field, and each field name corresponds to a column of the table;

[0112] S20332: In the verification result column of each field, fill in the corresponding row with the corresponding verification result; if a field passes the verification, fill in "pass" in the verification result column of the field; if a field fails the verification, fill in "fail";

[0113] S20333: In the original value column of the table, fill in the original data value in the corresponding row, and in the converted value column, fill in the standardized data value in the corresponding row; if a field fails to pass the verification or there is an abnormality, fill in the relevant exception description in the exception information column to explain the problem in detail.

[0114] The working principle and beneficial effects of the above technical solution are as follows: First, this embodiment creates a table, and the columns include field name, original value, converted value, verification result and abnormal information, etc.; add the field name in the first row of the table, that is, the name of each data field, and each field name corresponds to a column of the table; secondly, in the verification result column of each field, the corresponding row fills in the corresponding verification result. ; If a field passes the verification, fill in "pass" in the verification result column of the field; if a field fails the verification, fill in "fail"; finally, in the original value column of the table, the corresponding row fills in the original data value, and in the converted value column, the corresponding row fills in the data value after standardization; if a field fails the verification or there is an abnormality, fill in the relevant abnormal description in the abnormal information column, and explain the problem in detail. Step S20331 of the above scheme creates a clearly structured table for recording and displaying the processing status and verification results of each field; provides a visual tool to facilitate viewing and analyzing the verification results of data fields, and ensure the traceability and transparency of the data processing process. Step S20332 records whether each field has passed the verification for subsequent analysis and processing; accurately records the verification status of each field so that abnormalities can be discovered in time during data processing and subsequent correction operations can be performed. Step S20333 records the original value and the standardized value of each field for subsequent analysis and comparison; retains the original data and the processed data to ensure the consistency and traceability of data processing.

[0115] In summary, this embodiment uses a table to record the verification results and related information of each data field through the above steps; it provides a tool for visualization and traceability of the data processing process, which has the following significance: Visual verification results. By filling in the verification result column, you can intuitively check whether each field has passed the verification, which helps to find data anomalies and problems in time, and speed up the location and correction of problems. Provide data traceability. By recording the original value and the converted value, as well as the abnormal information, the data processing process and the correction process can be traced, ensuring the traceability and transparency of the data processing process, and providing a reliable basis for data analysis and decision-making. Facilitate data analysis and comparison. By recording the original value and the converted value, data analysis and comparison can be easily performed, which helps to discover the characteristics, trends and associations of the data, and facilitates subsequent data analysis.

[0116] Example 9: Fig. 9 As shown, based on Example 1, the process of analyzing and evaluating the quality defects of an engineering entity provided in the embodiment of the present invention includes the following steps:

[0117] S301: Create a database table structure, including a line information table, a material specification table, and a quality inspection index table, etc., for storing standardized data; use a database management tool to import the standardized data into a corresponding database table, and store the standardized data in the database according to specified fields and formats;

[0118] S302: Based on the standardized data, use a database query tool to query and analyze the data, query according to line information, material specifications, quality inspection indicators and other conditions, and obtain relevant data records;

[0119] S303: Analyze and evaluate the quality defects of the engineering entity according to the queried data; formulate improvement plans and measures based on the results of the analysis and evaluation and according to the type and severity of the quality defects;

[0120] Among them, the improvement plans and measures include:

[0121] Quality defect type: unqualified materials;

[0122] Improvement plan: Strengthen supplier management to ensure that materials meet specifications and standards;

[0123] Improvement measures: Establish a supplier evaluation system, regularly inspect and review the supplier's quality management system, and strengthen quality inspection and random inspection of materials;

[0124] Types of quality defects: unreasonable construction technology;

[0125] Improvement plan: Optimize construction process and improve construction efficiency and quality;

[0126] Improvement measures: Re-evaluate the construction process, find improved methods and techniques, provide training and guidance, and ensure that construction personnel correctly implement the process;

[0127] Types of quality defects: design errors or imperfections;

[0128] Improvement plan: Improve the design process and improve design quality and accuracy;

[0129] Improvement measures: Establish a design review process, increase the design review process, strengthen the training and communication of designers, and ensure the rationality and feasibility of the design plan;

[0130] Types of quality defects: construction not up to standard;

[0131] Improvement plan: Strengthen construction management to ensure that construction quality meets specifications;

[0132] Improvement measures: Strengthen on-site supervision and inspection, provide training and guidance, establish a construction quality management system, and strengthen communication and collaboration among construction personnel.

[0133] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, a database table structure is first created, including a line information table, a material specification table, and a quality inspection index table, etc., for storing standardized data; the standardized data is imported into the corresponding database table using a database management tool, and the standardized data is stored in the database according to the specified fields and formats; secondly, based on the standardized data, a database query tool is used to query and analyze the data, and the query is performed according to conditions such as line information, material specifications, and quality inspection indicators to obtain relevant data records; finally, according to the queried data, the quality defects of the engineering entity are analyzed and evaluated; based on the results of the analysis and evaluation, improvement plans and measures are formulated according to the type and severity of the quality defects. Step S301 of the above solution establishes a structured data storage system to facilitate data management and query; provides a reliable data storage platform to ensure the integrity and consistency of the data; stores the processed data in the database to facilitate subsequent query and analysis, and establishes a data set available for query and analysis, providing a data basis for subsequent quality defect analysis. Step S302 obtains relevant data records from the database according to specific query conditions for further analysis; uses the query function of the database to quickly and accurately obtain the required data to provide support for subsequent quality defect analysis. Step S303 uses data analysis methods and models to identify potential quality problems and defects, help discover and solve engineering entity quality problems, and improve engineering quality and safety; formulate specific improvement plans based on the type and severity of quality defects, provide specific methods and measures to solve problems, and promote the improvement and continuous improvement of engineering quality.

[0134] In summary, through the above steps, this embodiment can analyze and evaluate the quality defects of engineering entities based on standardized data; provide scientific data support and decision-making basis, help identify and solve potential quality problems, and improve the quality and safety of engineering entities. At the same time, through the storage and query functions of the database, a large amount of standardized data can be easily managed and retrieved, improving the accessibility and utilization value of data; it helps to improve the efficiency and accuracy of engineering quality management, reduce quality risks, and improve the success rate and sustainable development capabilities of engineering projects.

[0135] Example 10: Fig.10 As shown, based on Example 9, the process of analyzing and evaluating the quality defects of an engineering entity provided in the embodiment of the present invention includes the following steps:

[0136] S3031: Perform descriptive statistics on the data, including calculating the mean, median, standard deviation, maximum and minimum values, etc., to obtain the basic characteristics and distribution of the data; analyze the correlation between various factors by calculating the correlation coefficient or drawing relevant charts to obtain the degree of influence of different factors on quality defects; draw frequency distribution histograms or probability distribution diagrams according to different quality indicators to obtain the distribution of quality problems and potential defect areas;

[0137] S3032: Cluster the data to identify data groups with similar characteristics and find potential quality problem clustering areas or features; build a classification model based on known quality problem samples and corresponding features to predict and identify whether new samples have quality defects; discover the correlation between different factors by mining association rules in the data set and identify the combination of factors that may cause quality problems;

[0138] S3033: Based on the analysis results, identify potential quality issues and defects, including issues with specific engineering entities, material specifications, and construction processes; evaluate quality issues to determine their severity and scope of impact.

[0139] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first performs descriptive statistics on the data, including calculating the mean, median, standard deviation, maximum and minimum values, etc., to obtain the basic characteristics and distribution of the data; by calculating the correlation coefficient or drawing the relevant chart, the correlation between the various factors is analyzed to obtain the degree of influence of different factors on quality defects; according to different quality indicators, a frequency distribution histogram or probability distribution diagram is drawn to obtain the distribution of quality problems and potential defect areas; secondly, the data is clustered to identify data groups with similar characteristics, and potential quality problem clustering areas or features are found; based on the known quality problem samples and the corresponding features, a classification model is constructed (the classification model can be used to predict and identify whether a new sample has quality defects in the analysis and evaluation of engineering entity quality defects. By constructing a suitable classification model, it can be used Use known quality problem samples and corresponding features to classify new samples and determine whether they have potential quality problems, provide reference and decision-making basis for formulating improvement plans and measures, and use existing training data sets to build a prediction model by learning the relationship between sample features and corresponding category labels; when new sample data is input into the classification model, the model will predict and classify which category the sample belongs to based on the learned rules and patterns, which is used to predict and identify whether new samples have quality defects; by mining the association rules in the data set, the association between different factors is discovered, and the combination of factors that may cause quality problems is identified; finally, based on the analysis results, potential quality problems and defects are identified, including problems with specific engineering entities, material specifications, and construction processes; quality problems are evaluated to determine their severity and scope of impact. Step S3031 of the above scheme performs descriptive statistics, correlation analysis and distribution analysis on the data; obtains the basic characteristics and distribution of the data, understands the central trend, degree of dispersion and distribution of the data, and the correlation between different factors; through descriptive statistics, the average level and degree of variation of quality indicators can be evaluated to help understand the current status and characteristics of the problem; through correlation analysis, the degree of influence of different factors on quality defects can be determined to help determine the key factors; through distribution analysis, the distribution of quality problems and potential defect areas can be understood.Step S3032: Data clustering, classification model construction and association rule mining, identify data groups with similar characteristics, and discover potential quality problem clustering areas or features; build a classification model to predict and identify whether new samples have quality defects; mine association rules in the data set to identify factor combinations that may cause quality problems; through data clustering, data groups with similar characteristics can be found to help identify potential quality problem clustering areas or features; through classification model construction, known quality problem samples and features can be used to predict and identify new samples to determine whether they have quality defects; through association rule mining, the correlation between different factors can be discovered to help identify factor combinations that may cause quality problems. Step S3033: Identification and evaluation of quality issues. Based on the analysis results, potential quality issues and defects are identified, including issues with specific engineering entities, material specifications, and construction processes. Quality issues are evaluated to determine their severity and scope of impact. By identifying quality issues and defects, specific engineering entities, material specifications, and construction processes can be identified to provide a basis for developing improvement plans and measures. Through the evaluation of quality issues, the severity and scope of impact of the issues can be determined to help prioritize and resolve important quality issues and improve engineering quality and safety.

[0140] In summary, this embodiment conducts a comprehensive analysis and evaluation of the quality defects of the engineering entity through the above steps, identifies potential problems and defects, provides a scientific basis for formulating improvement plans and measures, improves engineering quality and safety, reduces the occurrence of quality defects, and enhances the success rate and sustainable development capabilities of the project.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for standardizing engineering entity quality defect data based on line three-dimensional design, characterized in that: The following steps are involved: Collect relevant data of line 3D design, process the collected relevant data, and establish the relationship between the data; According to the requirements of line 3D design, formulate corresponding data standardization standards, and standardize the data according to the specifications and standards; Verify the standardized data; The standardized data is stored in the database; based on the standardized data, the quality defects of the engineering entity are analyzed and evaluated, potential quality problems and defects are identified, and corresponding solutions and improvement measures are provided.

2. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 1, characterized in that: The process of standardizing data includes the following steps: Obtain the three-dimensional design requirements of the line, obtain the data elements and data expressions, and determine the data fields and attributes that need to be standardized; According to the analyzed data elements and requirements, formulate corresponding standardized specifications and standards, and determine the data conversion and processing methods according to the formulated standardized specifications and standards; Verify the standardized data to check whether the data meets the standardization specifications and standard requirements; If any abnormality is found, make corrections.

3. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 2, characterized in that: The process of determining the data fields and attributes that need to be standardized includes the following steps: Collect line 3D design requirements; Determine the data elements that need to be standardized, and for each data element, determine its attributes; Based on the analysis results of data elements and attributes, determine the specific data fields and attributes that need to be standardized; determine the need for unit conversion for line length fields, unified representation for material specification fields, and calculation and judgment for quality indicator fields.

4. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 3, characterized in that: Whether the analysis requirements involve the calculation and representation of line length; whether they involve the standards and representation of material specifications; whether they involve the calculation and judgment criteria of quality indicators.

5. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 2, characterized in that: The process of formulating corresponding standardized specifications and standards includes the following steps: Formulate corresponding standardized specifications and standards based on the analyzed data elements and requirements; Determine the data conversion and processing methods according to the established standardized specifications and standards; The collected line three-dimensional design data is standardized according to the determined standardized specifications and standards.

6. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 5, characterized in that: Standardization processing includes unit conversion, specification code mapping, and quality indicator calculation and judgment processing.

7. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 2, characterized in that: The process of validating the standardized data includes the following steps: Collect standardized data and verify the collected data according to the established standardized specifications and standards; Check the verification results. If any data abnormalities or non-compliance with standardized specifications and standards are found, make corrections until the data meets the requirements; Record the verification results and correction process. When performing data verification, use a table to record the verification results of each data field.

8. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 7, characterized in that: The process of using a table to record the validation results for each data field includes the following steps: Create a table and add field names in the first row of the table, that is, the name of each data field. Each field name corresponds to a column in the table. In the verification result column of each field, fill in the corresponding verification result in the corresponding row; In the original value column of the table, the corresponding rows are filled with the original data values, and in the converted value column, the corresponding rows are filled with the standardized data values.

9. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 1, characterized in that: The process of analyzing and evaluating engineering entity quality defects includes the following steps: Create a database table structure to store standardized data; use database management tools to import standardized data into the corresponding database table, and store the standardized data in the database according to the specified fields and format; Based on the standardized data, use database query tools to perform data query and analysis; Analyze and evaluate the quality defects of engineering entities based on the queried data; Based on the results of analysis and evaluation, improvement plans and measures are developed according to the type and severity of quality defects.

10. The method for standardizing engineering entity quality defect data based on line three-dimensional design according to claim 9, characterized in that: Query according to line information, material specifications and quality inspection index conditions to obtain relevant data records.

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