Historical data analysis driving-based motor vehicle inspection data detection method and system

Abstract

The invention relates to the technical field of information system testing, and particularly discloses a motor vehicle inspection data detection method and system based on historical data analysis driving, and the method comprises the steps: building a data analysis model library; importing the vehicle historical detection data of the detected motor vehicle safety detection system into a historical data analysis table, comparing and analyzing the vehicle historical detection data in the historical data analysis table and the abnormal data rules in the data analysis model library, and outputting a comparison and analysis result of the vehicle historical detection data; and importing the vehicle station detection data of the detected motor vehicle safety detection system into the station detection data table, inputting a license plate number, carrying out comparative analysis on the vehicle station detection data in the station detection data table and the vehicle historical detection data in the historical data analysis table, and outputting a vehicle station detection data comparative analysis result. According to the method, illegal data rules can be automatically found, the detection accuracy and comprehensiveness are improved, and abundant user interaction and visualization means are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of information system testing, and more specifically, to a motor vehicle inspection data detection method based on historical data analysis and a motor vehicle inspection data detection system based on historical data analysis and driving. Background Art

[0002] In the automobile inspection industry, motor vehicle safety technical inspection agencies are inspection agencies that ensure that vehicles meet operational safety performance and environmental protection requirements, which is crucial to road safety. However, with the growth of inspection demand and the tightening of standards, some inspection stations may take unfair measures such as modifying system parameters to circumvent the actual inspection results in pursuit of profits, which seriously threatens public safety. The parameter management of traditional automobile inspection systems relies on manual monitoring and fixed processes, which has limitations: manual monitoring is difficult to fully cover and monitor in real time, and fixed processes lack flexibility and intelligence, making it difficult to cope with changes in illegal means. Illegal behaviors include modifying system parameters, falsifying inspection results, and using illegal equipment, which not only undermine fair competition in the market, but also increase the risk of traffic accidents. Summary of the invention

[0003] In response to the above-mentioned problems existing in the prior art, the present invention provides a motor vehicle inspection data detection method and system driven by historical data analysis, which can automatically discover the patterns of violation data, improve detection accuracy and comprehensiveness, and provide rich user interaction and visualization means; as well as ensure software security, realize real-time, accurate and comprehensive violation detection, and maintain software fairness, security and credibility.

[0004] As a first aspect of the present invention, a motor vehicle inspection data detection method based on historical data analysis is provided, and the motor vehicle inspection data detection method based on historical data analysis includes the following steps: Step S1: Establish multiple abnormal data model libraries according to abnormal data rule numbers, data types, key attributes, relational operators, attribute values ​​and states; wherein each abnormal data model library corresponds to an abnormal data rule; Step S2: establishing a data analysis model library according to the abnormal data model library number, data model name, rule sequence and state; wherein the data analysis model library includes multiple abnormal data model libraries; Step S3: importing the vehicle historical detection data of the motor vehicle safety detection system under test into the historical data analysis table, and starting the analysis task of the vehicle historical detection data to compare and analyze the vehicle historical detection data in the historical data analysis table with the abnormal data rules in the data analysis model library, and then outputting the comparison and analysis results of the vehicle historical detection data; Step S4: Import the vehicle workstation detection data of the motor vehicle safety detection system under test into the workstation detection data table, and input the license plate number to start the analysis task of the vehicle workstation detection data, so as to compare and analyze the vehicle workstation detection data in the workstation detection data table with the vehicle historical detection data in the historical data analysis table, and then output the comparison and analysis results of the vehicle workstation detection data.

[0005] Furthermore, the management functions of the abnormal data model library include: Initialization: clearing the data elements in the abnormal data model library and establishing an initial data element set in the abnormal data model library; Add: add data elements to the abnormal data model library according to agreed rules; Deletion: deleting abnormal data elements or discarded data elements in the abnormal data model library; Modification: modifying the parameters or attributes of the data elements in the abnormal data model library; Search: Search for a set of data elements that meet the conditions in the abnormal data model library according to the name, parameter or other attributes.

[0006] Furthermore, the management functions of the data analysis model library include: Initialization: clearing the data elements in the data analysis model library and establishing an initial data element set in the data analysis model library; Add: add data elements to the data analysis model library according to agreed rules; Deletion: deleting abnormal data elements or discarded data elements in the data analysis model library; Modify: modify the parameters or attributes of the data elements in the data analysis model library; Search: Search for a set of data elements that meet the conditions in the data analysis model library according to the name, parameter or other attributes.

[0007] Furthermore, the vehicle history detection data of the motor vehicle safety detection system under test includes detection basic information, detection loading brake data, detection parking brake data, detection curb mass data and vehicle uniqueness check data.

[0008] Furthermore, the vehicle workstation detection data of the motor vehicle safety detection system under test includes detection basic information, detection loading brake data, detection parking brake data and detection curb mass data.

[0009] Furthermore, the step S3 further includes: After the analysis task of the vehicle historical detection data is started, if the vehicle historical detection data in the historical data analysis table is successfully compared with the abnormal data rules in the data analysis model library, a vehicle historical detection data comparison analysis result is generated, and the vehicle historical detection data comparison analysis result is exported; wherein the vehicle historical detection data comparison analysis result includes a record number, a record name, a data analysis model library number, a data analysis model library name, an abnormal data model library number sequence, a status, an execution start time, and an execution end time; After the analysis task of the vehicle historical detection data is started, if the comparison between the vehicle historical detection data in the historical data analysis table and the abnormal data rules in the data analysis model library fails, the comparison information is output to confirm the reason for the comparison failure.

[0010] As a second aspect of the present invention, a motor vehicle inspection data detection system driven by historical data analysis is provided, wherein the motor vehicle inspection data detection system driven by historical data analysis includes an abnormal data model library establishment module, a data analysis model library establishment module, a vehicle historical inspection data analysis module, a historical data analysis record generation module, a historical data analysis record export module, a vehicle workstation inspection data comparison module, a workstation data comparison record generation module and a vehicle comparison record export module; wherein, The abnormal data model library establishment module is used to establish multiple abnormal data model libraries according to the abnormal data rule number, data type, key attribute, relational operator, attribute value and state; wherein each abnormal data model library corresponds to an abnormal data rule; The data analysis model library establishment module is used to establish a data analysis model library according to the abnormal data model library number, data model name, rule sequence and status; wherein the data analysis model library includes multiple abnormal data model libraries; The vehicle historical detection data analysis module is used to import the vehicle historical detection data of the motor vehicle safety detection system under test into the historical data analysis table, and start the analysis task of the vehicle historical detection data to compare and analyze the vehicle historical detection data in the historical data analysis table with the abnormal data rules in the data analysis model library; The historical data analysis record generation module is used to generate the comparison and analysis results of the vehicle historical detection data; The historical data analysis record export module is used to export the comparison and analysis results of the vehicle historical detection data; The vehicle workstation detection data comparison module is used to import the vehicle workstation detection data of the motor vehicle safety detection system under test into the workstation detection data table, and input the license plate number to start the analysis task of the vehicle workstation detection data, so as to compare and analyze the vehicle workstation detection data in the workstation detection data table with the vehicle historical detection data in the historical data analysis table; The workstation data comparison record generation module is used to generate the comparison and analysis results of the vehicle workstation detection data; The vehicle comparison record export module is used to export the comparison analysis results of the vehicle workstation detection data.

[0011] The motor vehicle inspection data detection method driven by historical data analysis provided by the present invention has the following beneficial effects: by combining the collaborative work of the client and the server, the system can monitor data in real time, detect anomalies, and perform detailed comparative analysis, thereby improving the accuracy and efficiency of data analysis and providing users with powerful analytical capabilities and comprehensive decision-making support. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0013] Figure 1 A flow chart of a motor vehicle inspection data detection method driven by historical data analysis provided by the present invention.

[0014] Figure 2 This is a structural diagram of the motor vehicle inspection data detection system driven by historical data analysis provided by the present invention. DETAILED DESCRIPTION

[0015] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of a motor vehicle inspection data detection method based on historical data analysis driven according to the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] In this embodiment, a motor vehicle inspection data detection method based on historical data analysis is provided. Figure 1 As shown, the motor vehicle inspection data detection method based on historical data analysis and driving includes the following steps: Step S1: Establish multiple abnormal data model libraries according to abnormal data rule numbers, data types, key attributes, relational operators, attribute values ​​and states, for quickly identifying abnormal data in subsequent data analysis processes; wherein each abnormal data model library corresponds to an abnormal data rule; Preferably, the data elements in the abnormal data model library are abnormal data rules, and the abnormal data model library is formed by the standardized design of the abnormal data rule expression. The management functions of the abnormal data model library include: Initialization: clearing the data elements in the abnormal data model library and establishing an initial data element set in the abnormal data model library; Add: add data elements to the abnormal data model library according to agreed rules; Deletion: deleting abnormal data elements or discarded data elements in the abnormal data model library; Modification: modifying the parameters or attributes of the data elements in the abnormal data model library; Search: Search for a set of data elements that meet the conditions in the abnormal data model library according to the name, parameter or other attributes.

[0018] Step S2: establishing a data analysis model library according to the abnormal data model library number, data model name, rule sequence and status, for guiding subsequent data analysis tasks; wherein the data analysis model library includes multiple abnormal data model libraries; Preferably, the data analysis model library is a set of data rules for matching and analyzing input data strings by applying data rules. Therefore, different data analysis models can be designed by combining the application of data rules. On this basis, the data analysis model library is designed. The data elements in the data analysis model library are the data analysis models. The data analysis model library is formed by the standardized design of the data analysis model expression. The management functions of the data analysis model library include: Initialization: clearing the data elements in the data analysis model library and establishing an initial data element set in the data analysis model library; Add: add data elements to the data analysis model library according to agreed rules; Deletion: deleting abnormal data elements or discarded data elements in the data analysis model library; Modify: modify the parameters or attributes of the data elements in the data analysis model library; Search: Search for a set of data elements that meet the conditions in the data analysis model library according to the name, parameter or other attributes.

[0019] Step S3: importing the vehicle historical detection data of the motor vehicle safety detection system under test into the historical data analysis table of the local detection database, and starting the analysis task of the vehicle historical detection data to compare and analyze the vehicle historical detection data in the historical data analysis table with the abnormal data rules in the data analysis model library, and then outputting the comparison and analysis results of the vehicle historical detection data; Preferably, the vehicle history detection data of the motor vehicle safety detection system under test includes detection basic information, detection loading brake data, detection parking brake data, detection curb mass data and vehicle uniqueness check data.

[0020] Preferably, the step S3 further includes: After the analysis task of the vehicle historical detection data is started, if the vehicle historical detection data in the historical data analysis table is successfully compared with the abnormal data rules in the data analysis model library, a vehicle historical detection data comparison analysis result is generated, and the vehicle historical detection data comparison analysis result is exported; wherein the vehicle historical detection data comparison analysis result includes a record number, a record name, a data analysis model library number, a data analysis model library name, an abnormal data model library number sequence, a status, an execution start time, and an execution end time; After the analysis task of the vehicle historical detection data is started, if the comparison between the vehicle historical detection data in the historical data analysis table and the abnormal data rules in the data analysis model library fails, the comparison information is output to confirm the reason for the comparison failure.

[0021] In specific implementation, the client sends the first request to the server by creating a websocket connection, and the server responds and sends a signal of successful connection; select a detection item for analysis, the client sends a comparison request to the server, and the server sends a detection item comparison start signal after receiving the request. After the task is successfully started, the detection items in the abnormal data model library are analyzed according to the detection items in the data analysis model library, and the analysis results are sent back to the client one by one every 600 milliseconds. The analysis results include comparison information and task status, which are updated in real time after receiving by the client and displayed in a table, and the progress of the comparison process is calculated and displayed at the same time; Step S4: Import the vehicle workstation detection data of the motor vehicle safety detection system under test into the workstation detection data table of the local detection database, and input the license plate number to start the analysis task of the vehicle workstation detection data, so as to compare and analyze the vehicle workstation detection data in the workstation detection data table with the vehicle historical detection data in the historical data analysis table, and then output the comparison and analysis results of the vehicle workstation detection data.

[0022] Preferably, the vehicle workstation detection data of the motor vehicle safety detection system under test includes detection basic information, detection loading brake data, detection parking brake data and detection curb mass data.

[0023] As another embodiment of the present invention, a motor vehicle inspection data detection system based on historical data analysis is provided. Figure 2 As shown, the motor vehicle inspection data detection system driven by historical data analysis includes an abnormal data model library establishment module, a data analysis model library establishment module, a vehicle historical inspection data analysis module, a historical data analysis record generation module, a historical data analysis record export module, a vehicle workstation inspection data comparison module, a workstation data comparison record generation module and a vehicle comparison record export module; wherein, The abnormal data model library establishment module is used to establish multiple abnormal data model libraries according to the abnormal data rule number, data type, key attribute, relational operator, attribute value and state; wherein each abnormal data model library corresponds to an abnormal data rule; The data analysis model library establishment module is used to establish a data analysis model library according to the abnormal data model library number, data model name, rule sequence and status; wherein the data analysis model library includes multiple abnormal data model libraries; The vehicle historical detection data analysis module is used to import the vehicle historical detection data of the motor vehicle safety detection system under test into the historical data analysis table, and start the analysis task of the vehicle historical detection data to compare and analyze the vehicle historical detection data in the historical data analysis table with the abnormal data rules in the data analysis model library; The historical data analysis record generation module is used to generate the comparison analysis results of the vehicle historical detection data; and through the websocket protocol, it is displayed in real time on the front-end interface, supporting a variety of visualization methods, such as line graphs, bar graphs, pie charts, etc., to help users understand the data and analysis results more intuitively. At the same time, the system also provides a detailed analysis report, including the analysis process, analysis results, recommended measures, etc., to provide users with comprehensive decision-making support; It should be noted that a data analysis record will be generated after the task is completed, and the record will be displayed in the form of a list. Users can search by entering conditions such as record name and analysis time to quickly find data records that meet the conditions. Each record in the list contains key information in the analysis process, such as analysis model, analysis parameters, analysis results, etc. By clicking on a record in the list, users can view more detailed record information. This information may include a detailed description of the analysis process, detailed data of the analysis results, recommended measures, etc. This information provides users with a comprehensive analysis perspective, helping them to gain a deeper understanding of the analysis process and results; The historical data analysis record export module is used to export the comparison and analysis results of the vehicle historical detection data; wherein, the historical data analysis record export module also supports the user to export the qualified data records as files. The user can choose to export to files in Excel, CSV and other formats for subsequent analysis; The vehicle workstation detection data comparison module is used to import the vehicle workstation detection data of the motor vehicle safety detection system under test into the workstation detection data table, and input the license plate number to start the analysis task of the vehicle workstation detection data, so as to compare and analyze the vehicle workstation detection data in the workstation detection data table with the vehicle historical detection data in the historical data analysis table; It should be noted that the system will pre-process and clean the collected vehicle station detection data to ensure the accuracy and consistency of the data. Then, the system will compare and analyze the vehicle station detection data. The comparison analysis may include data comparison at different time points, data comparison between different vehicles, etc. Through comparison analysis, the system can find anomalies and inconsistencies in the vehicle station detection data and provide support for subsequent decision-making; The workstation data comparison record generation module is used to generate the comparison and analysis results of the vehicle workstation detection data; wherein, the comparison and analysis results of the vehicle workstation detection data are displayed in the form of a list, and the user can search by entering conditions such as record name and comparison time to quickly find the comparison results that meet the conditions. Each result in the list contains key information and comparison results in the comparison process. By clicking on a result in the list, the user can view more detailed comparison information. This information may include a detailed description of the comparison process, detailed data of the comparison results, recommended measures, etc. This information provides users with a comprehensive analysis perspective, which helps them to have a deeper understanding of the comparison process and results; The vehicle comparison record export module is used to export the comparison analysis results of the vehicle workstation detection data. Among them, the vehicle comparison record export module also supports users to export qualified comparison results as files. Users can choose to export files in formats such as Excel and CSV to facilitate subsequent analysis and processing. The exported file contains all the information in the list, including key information and comparison results in the comparison process.

[0024] In specific implementation, the present invention analyzes the original data in the local detection database, discovers the violation rules through the multidimensional data analysis model, and constructs a set of motor vehicle safety technical inspection data detection methods and systems driven by historical data analysis. The system uses the websocket service module to establish front-end and back-end communication in real time to ensure the instant push of comparison results and data analysis abnormality warning. The data analysis model library is successfully established through the abnormal data number provided by the abnormal data model library, which provides a solid foundation for subsequent data analysis. In the data analysis module, users can select the data analysis model library and rely on the background intelligent analysis algorithm to efficiently perform data comparison tasks. The comparison results are output in time through the data analysis recording module, and all abnormal information will be accurately captured. Vehicle data comparison starts the comparison task by selecting the license plate number. The system automatically executes the set data comparison task, and records the comparison process and comparison results in detail, generates vehicle comparison records, and exports vehicle comparison records under the premise of need. Finally, the data analysis record report printing module will automatically generate a detailed data analysis report, providing strong support for motor vehicle safety technical inspection.

[0025] In an embodiment of the present invention, by collecting and analyzing a large amount of data models and historical data, and using advanced data mining technology, intelligent detection and early warning of the running behavior of motor vehicle safety technical inspection software can be achieved. The data analysis module can automatically analyze the original data in the local detection database, and through data analysis records, find out the data operation rules and whether there are any abnormalities; through the vehicle data comparison module, the vehicle automatically performs the set data comparison tasks and records the comparison process and comparison results in detail. The present invention can be set as multiple analysis models, each model completes a specific single or several analysis tasks. In actual operation, multiple analyses are completed by referencing multiple models, and the model can be continuously added and improved, which is more intelligent and efficient.

[0026] In an embodiment of the present invention, the vehicle history detection data analysis module is configured to receive and process the raw data in the local detection database, and identify the patterns of violation data and deep-level software violations through a multidimensional data analysis model; wherein, the vehicle history detection data analysis module includes multiple analysis model units, each unit is set to perform a specific single or multiple analysis tasks, and the scalable design of comprehensive data analysis is completed by integrating the output results of multiple models, allowing the continuous addition and improvement of the analysis model units to adapt to the ever-changing means of violation. Wherein, the vehicle history detection data analysis module includes but is not limited to time series analysis, association rule mining, cluster analysis and anomaly detection models to identify and analyze violation behavior patterns in multiple dimensions. The vehicle history detection data analysis module is constructed using machine learning or deep learning algorithms to improve the accuracy and efficiency of violation identification.

[0027] Specifically, user interaction is achieved through a graphical user interface, providing an intuitive and easy-to-use operation interface, which facilitates users to perform operations such as search, reset, import and export; the functions of the vehicle history detection data analysis module include: Search function allows users to enter record name and / or import time as search conditions, and the system automatically refreshes the data list according to the conditions to display data records that meet the conditions; The reset function is used to clear the contents of the search input box and automatically refresh the data list to display a complete list of all data records; Data import function, by clicking the import button to activate the import interface, the user enters the record name, selects the file to be uploaded, and clicks OK to complete the upload of the data file; the data import function supports multiple file formats, including but not limited to CSV, Excel and database backup files, to meet the data import requirements from different sources; Data export function: after the user clicks the export button, the system pops up the export path selection interface, the user selects the save path and clicks save, the system generates and saves the data analysis report according to the preset conditions. The data export function allows the user to customize the content format of the export report, including but not limited to data tables, charts and comprehensive analysis summaries; The user authority management function is used to set different access and operation permissions for different users to ensure data security and reasonable use of system functions.

[0028] The motor vehicle inspection data detection system driven by historical data analysis provided by the present invention not only fully meets the historical data detection needs to be detected, but also realizes the reading of historical data into the inspection software and the operation and analysis according to the model in the model library through its unique method and system design, thereby completing the continuous addition and model improvement. This innovation not only significantly improves the accuracy and reliability of motor vehicle inspection results, but also brings a new revolution to the field of motor vehicle safety technology inspection with its wide range of applications and excellent safety.

[0029] The motor vehicle inspection data detection method based on historical data analysis provided by the embodiment of the present invention is specifically a method for intelligently detecting violations in the use of motor vehicle safety technical inspection software, (1) efficient processing: by building an abnormal data model library, abnormal data can be quickly identified to improve data analysis efficiency; (2) accurate comparison: using the data analysis model library and historical data analysis table to achieve accurate data comparison and ensure the accuracy of the analysis results; (3) intuitive display: supporting a variety of visualization methods to help users intuitively understand the data and analysis results, and providing detailed analysis reports; (4) comprehensive records: after the task is executed, a data analysis record is generated, and the user can quickly search and view detailed record information. It also supports data export function; (5) vehicle workstation inspection data comparison: collecting and cleaning vehicle workstation inspection data, performing comparative analysis, finding abnormalities and inconsistencies, providing support for decision-making, and displaying detailed comparison results.

[0030] In summary, the present invention is particularly suitable for scenarios where large amounts of data need to be monitored in real time, detected as anomalies, and subjected to detailed comparative analysis. By combining the collaborative work of the client and the server, the system can monitor data in real time, detect anomalies, and conduct detailed comparative analysis, thereby improving the accuracy and efficiency of data analysis and providing users with powerful analytical capabilities and comprehensive decision-making support.

[0031] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A motor vehicle inspection data detection method based on historical data analysis, characterized in that: The motor vehicle inspection data detection method based on historical data analysis and driving comprises the following steps: Step S1: Establish multiple abnormal data model libraries according to abnormal data rule numbers, data types, key attributes, relational operators, attribute values ​​and states; wherein each abnormal data model library corresponds to an abnormal data rule; Step S2: establishing a data analysis model library according to the abnormal data model library number, data model name, rule sequence and state; wherein the data analysis model library includes multiple abnormal data model libraries; Step S3: importing the vehicle historical detection data of the motor vehicle safety detection system under test into the historical data analysis table, and starting the analysis task of the vehicle historical detection data to compare and analyze the vehicle historical detection data in the historical data analysis table with the abnormal data rules in the data analysis model library, and then outputting the comparison and analysis results of the vehicle historical detection data; Step S4: Import the vehicle workstation detection data of the motor vehicle safety detection system under test into the workstation detection data table, and input the license plate number to start the analysis task of the vehicle workstation detection data, so as to compare and analyze the vehicle workstation detection data in the workstation detection data table with the vehicle historical detection data in the historical data analysis table, and then output the comparison and analysis results of the vehicle workstation detection data.

2. The motor vehicle inspection data detection method based on historical data analysis and drive according to claim 1 is characterized in that: The management functions of the abnormal data model library include: Initialization: clearing the data elements in the abnormal data model library and establishing an initial data element set in the abnormal data model library; Add: add data elements to the abnormal data model library according to agreed rules; Deletion: deleting abnormal data elements or discarded data elements in the abnormal data model library; Modification: modifying the parameters or attributes of the data elements in the abnormal data model library; Search: Search for a set of data elements that meet the conditions in the abnormal data model library according to the name, parameter or other attributes.

3. The motor vehicle inspection data detection method based on historical data analysis and drive according to claim 1 is characterized in that: The management functions of the data analysis model library include: Initialization: clearing the data elements in the data analysis model library and establishing an initial data element set in the data analysis model library; Add: add data elements to the data analysis model library according to agreed rules; Deletion: deleting abnormal data elements or discarded data elements in the data analysis model library; Modify: modify the parameters or attributes of the data elements in the data analysis model library; Search: Search for a set of data elements that meet the conditions in the data analysis model library according to the name, parameter or other attributes.

4. The motor vehicle inspection data detection method based on historical data analysis and drive according to claim 1, characterized in that: The vehicle history detection data of the motor vehicle safety detection system under test includes detection basic information, detection loading brake data, detection parking brake data, detection curb mass data and vehicle uniqueness inspection data.

5. The motor vehicle inspection data detection method based on historical data analysis and drive according to claim 1, characterized in that: The vehicle station detection data of the motor vehicle safety detection system under test includes detection basic information, detection loading brake data, detection parking brake data and detection curb mass data.

6. The motor vehicle inspection data detection method based on historical data analysis and drive according to claim 1, characterized in that: The step S3 further includes: After the analysis task of the vehicle historical detection data is started, if the vehicle historical detection data in the historical data analysis table is successfully compared with the abnormal data rules in the data analysis model library, a vehicle historical detection data comparison analysis result is generated, and the vehicle historical detection data comparison analysis result is exported; wherein the vehicle historical detection data comparison analysis result includes a record number, a record name, a data analysis model library number, a data analysis model library name, an abnormal data model library number sequence, a status, an execution start time, and an execution end time; After the analysis task of the vehicle historical detection data is started, if the comparison between the vehicle historical detection data in the historical data analysis table and the abnormal data rules in the data analysis model library fails, the comparison information is output to confirm the reason for the comparison failure.

7. A motor vehicle inspection data detection system driven by historical data analysis, used to implement the motor vehicle inspection data detection method driven by historical data analysis as claimed in any one of claims 1 to 6, characterized in that: The motor vehicle inspection data detection system driven by historical data analysis includes an abnormal data model library establishment module, a data analysis model library establishment module, a vehicle historical inspection data analysis module, a historical data analysis record generation module, a historical data analysis record export module, a vehicle workstation inspection data comparison module, a workstation data comparison record generation module and a vehicle comparison record export module; wherein, The abnormal data model library establishment module is used to establish multiple abnormal data model libraries according to the abnormal data rule number, data type, key attribute, relational operator, attribute value and state; wherein each abnormal data model library corresponds to an abnormal data rule; The data analysis model library establishment module is used to establish a data analysis model library according to the abnormal data model library number, data model name, rule sequence and status; wherein the data analysis model library includes multiple abnormal data model libraries; The vehicle historical detection data analysis module is used to import the vehicle historical detection data of the motor vehicle safety detection system under test into the historical data analysis table, and start the analysis task of the vehicle historical detection data to compare and analyze the vehicle historical detection data in the historical data analysis table with the abnormal data rules in the data analysis model library; The historical data analysis record generation module is used to generate the comparison and analysis results of the vehicle historical detection data; The historical data analysis record export module is used to export the comparison and analysis results of the vehicle historical detection data; The vehicle workstation detection data comparison module is used to import the vehicle workstation detection data of the motor vehicle safety detection system under test into the workstation detection data table, and input the license plate number to start the analysis task of the vehicle workstation detection data, so as to compare and analyze the vehicle workstation detection data in the workstation detection data table with the vehicle historical detection data in the historical data analysis table; The workstation data comparison record generation module is used to generate the comparison and analysis results of the vehicle workstation detection data; The vehicle comparison record export module is used to export the comparison analysis results of the vehicle workstation detection data.

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