Engine test data generalization mining platform and method based on metadata mapping

Abstract

The invention discloses an engine test data generalization mining platform and method based on metadata mapping, and the platform comprises a data mapping configuration module which is used for automatically and manually matching a one-to-one mapping relation between an engine test parameter and metadata in the platform in a combined manner and visually displaying the one-to-one mapping relation, and obtaining engine test data; the data analysis module is used for analyzing and processing the obtained engine test data; and the analysis result output module is used for outputting an analysis processing result of the engine test data. The method comprises the following steps: automatically and manually combining to match a one-to-one mapping relationship between engine test parameters and platform internal metadata, and visually displaying the one-to-one mapping relationship to obtain engine test data; analyzing and processing the obtained engine test data; and outputting an analysis processing result of the engine test data. The technical problems that engine test data cannot be efficiently mined, each project lacks a unified platform for analysis, and data processing is not standard are solved.

Description

Technical Field

[0001] The present application relates to the technical field of engine data processing, and in particular, to a generalized mining platform and method for engine test data based on metadata mapping. Background Art

[0002] Engine test data mining is to analyze and process test data to obtain information such as current engine performance and operating status, so that designers can promptly discover potential engine problems and reduce the possibility of engine failure. Due to the increasing size of data, complex data source types and formats, high degree of freedom, the lack of systematic summary and solidification of data processing steps, personalized processing requirements for each project and each test project, low data quality, and frequent need for manual cleaning in the later stage, it is difficult to achieve the "unification" of engine data analysis. Designers need to spend a lot of energy in the early data processing stage, and a lot of manpower is spent on repetitive work, resulting in a lack of in-depth mining and analysis of test data. Although there are some general engine test data analysis systems in China, most of them are targeted at specific data types and formats, lack the ability to flexibly process data structure, and cannot meet the design needs of designers of different projects. Each project also needs to customize dedicated data mining tools. Summary of the invention

[0003] On the one hand, the present application provides a universal mining platform for engine test data based on metadata mapping to solve the technical problems that engine test data cannot be mined efficiently, various projects lack a unified platform for analysis, and data processing is not standardized.

[0004] This application is implemented through the following scheme:

[0005] A generalized mining platform for engine test data based on metadata mapping, including:

[0006] The data mapping configuration module is used to match the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual methods and visualize the mapping relationship to obtain engine test data;

[0007] A data analysis module, used for analyzing and processing the acquired engine test data;

[0008] The analysis result output module is used to output the analysis and processing results of the engine test data.

[0009] Furthermore, the data mapping configuration module is specifically used to:

[0010] The metadata inside the platform is predefined according to the test needs. The engine test parameter variables are first automatically mapped with the metadata parameter names set inside the platform using the string matching method. For the remaining parameters that cannot be automatically mapped, the analyst manually selects them for mapping and matching in the metadata configuration interface. Finally, the mapping relationship between the metadata and the engine test parameters is saved, thereby achieving the correspondence between the engine parameter variables of different projects and the metadata inside the platform and obtaining the engine test data.

[0011] Furthermore, the data mapping configuration module is also used to:

[0012] Analysts can add, modify, delete, import and export metadata and the mapping relationships formed, so as to realize the information transmission between different analysts under offline stand-alone conditions; complete data label management, and add label information such as test items, test phenomena, technical status, and whether there are test anomalies to the entered data.

[0013] Furthermore, the data analysis module specifically includes:

[0014] A data conversion module, used to convert the units of the data including time, temperature and pressure in the engine test parameters, and to perform similar conversion operations on the engine test data;

[0015] The data cleaning module is used to detect anomalies in the measured engine test data, complete the redundancy fault tolerance check of the engine test data based on the comparison between two parameters, and clean the abnormal data points;

[0016] Data dimension enhancement module, used to perform personalized calculations on engine test data according to requirements, including calculation of temperature rise of lubricating oil system parameters;

[0017] A data steady-state extraction module is used to extract steady-state data that can characterize the overall operating state of the engine according to the steady-state criterion, so as to characterize the current health status of the engine;

[0018] The data feature extraction module is used to analyze the engine test data according to the overall logical criteria provided, and extract the engine parameter content including the extreme values ​​of engine parameters, starting time, ignition time, and specific state points.

[0019] Furthermore, the data analysis module is also specifically used for:

[0020] It supports the free combination of the included processing sub-modules by dragging and dropping. According to the data analysis needs in actual work, the operation sub-modules that need to be executed can be selected in an individualized manner, so as to achieve the purpose of process-based problem solving.

[0021] Furthermore, the analysis result output module includes:

[0022] Report display module, used to display data processing results through reports, including engine statistics, starting characteristic information, and characteristic data analysis results;

[0023] The curve chart display module is used to display the parameter change diagram of a single test run data and the comparative analysis diagram of multiple test run data through curve charts;

[0024] The running report generation module is used to embed the automatic generation function of the performance analysis running report using the Python program, and automatically generate the test data processing report in PDF format, which can replace the existing test situation running account management.

[0025] Furthermore, the analysis result output module also includes:

[0026] The import and export module is used for analysts to import and export analysis results, thereby achieving data synchronization between different analysts.

[0027] On the other hand, the present application also provides a generalized mining method for engine test data based on metadata mapping, comprising the steps of:

[0028] The engine test data can be obtained by matching the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual methods and displaying them visually;

[0029] Analyze and process the acquired engine test data;

[0030] Output the analysis and processing results of engine test data.

[0031] Furthermore, the one-to-one mapping relationship between the engine test parameters and the platform internal metadata is matched by combining automatic and manual operations and displayed visually to obtain the engine test data, specifically including the steps of:

[0032] The metadata inside the platform is pre-set according to the test needs. The engine test parameter variables are first automatically mapped with the metadata parameter names set inside the platform according to the string matching method. For the remaining parameters that cannot be automatically mapped, the analyst manually selects them for mapping and matching in the metadata configuration interface. Finally, the mapping relationship between the metadata and the engine test parameters is saved, thereby realizing the correspondence between the engine parameter variables of different projects and the metadata inside the platform and obtaining the engine test data.

[0033] Furthermore, the one-to-one mapping relationship between the engine test parameters and the platform internal metadata is matched by combining automatic and manual operations and displayed visually to obtain the engine test data, which specifically includes the steps of:

[0034] Analysts can add, modify, delete, import and export metadata and the mapping relationships formed, so as to realize the information transmission between different analysts under offline stand-alone conditions; complete data label management, and add label information such as test items, test phenomena, technical status, and whether there are test anomalies to the entered data.

[0035] This application has the following beneficial effects:

[0036] The present application provides a universal mining platform and method for engine test data based on metadata mapping. The universal mining platform includes a data mapping configuration module for matching the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual methods and visually displaying the acquired engine test data, a data analysis module for analyzing and processing the acquired engine test data, and an analysis result output module for outputting the analysis and processing results of the engine test data.

[0037] This application matches the different forms of engine parameter variables in practice with the internal metadata specified in the program. Compared with the traditional processing mode, this mapping method separates the data mapping configuration from the data analysis to the greatest extent. The analyst does not need to make a lot of tedious manual modifications within the program to adapt to different parameter variable names. The correspondence between the parameter variables and the internal metadata of the program can be automatically mapped to the greatest extent. The operation is simple and the mapping relationship can be displayed visually, which is convenient for use by different personnel. Analysts can quickly realize data analysis without programming background, which greatly reduces the burden on analysts and can be applied to a wider range of applications, thereby solving the problem that data analysis cannot be "unified" among projects due to complex data types and formats and high degree of freedom.

[0038] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application.

[0040] Figure 1 It is a schematic diagram of a module of a generalized mining platform for engine test data based on metadata mapping according to a preferred embodiment of the present application;

[0041] Figure 2 It is a schematic diagram of submodules of the data analysis module of the preferred embodiment of the present application;

[0042] Figure 3It is a schematic diagram of submodules of the analysis result output module of the preferred embodiment of the present application;

[0043] Figure 4 It is a schematic diagram of submodules of an analysis result output module of another preferred embodiment of the present application;

[0044] Figure 5 It is a flowchart of a generalized mining method for engine test data based on metadata mapping according to a preferred embodiment of the present application;

[0045] Figure 6 It is a schematic diagram of the sub-step flow of step S1 of the preferred embodiment of the present application;

[0046] Figure 7 is a schematic flow chart of sub-steps of step S1 of another preferred embodiment of the present application;

[0047] Figure 8 is a schematic block diagram of an electronic device entity in a preferred embodiment of the present application;

[0048] Fig. 9 It is a diagram of the internal structure of a computer device of a preferred embodiment of the present application. DETAILED DESCRIPTION

[0049] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings, but the present application can be implemented in a variety of different ways defined and covered below.

[0050] like Figure 1 As shown, the preferred embodiment of the present application provides a generalized mining platform for engine test data based on metadata mapping, including:

[0051] The data mapping configuration module is used to match the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual methods and visualize the mapping relationship to obtain engine test data;

[0052] A data analysis module, used for analyzing and processing the acquired engine test data;

[0053] The analysis result output module is used to output the analysis and processing results of the engine test data.

[0054] The present application provides a universal mining platform for engine test data based on metadata mapping, the universal mining platform includes a data mapping configuration module for matching the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual operations and visually displaying, obtaining engine test data, a data analysis module for analyzing and processing the obtained engine test data, and an analysis result output module for outputting the analysis and processing results of the engine test data. This embodiment corresponds the engine parameter variables in different forms in practice to the internal metadata specified in the program. Compared with the traditional processing mode, this mapping method separates the data mapping configuration from the data analysis to the greatest extent, and the analysts do not need to make a lot of tedious manual modifications in the program to adapt to different parameter variable names. The corresponding relationship between the parameter variables and the internal metadata of the program can be automatically mapped to the greatest extent. The operation is simple and the mapping relationship is visually displayed, which is convenient for use by different personnel. The analysts can quickly realize data analysis without programming background, which greatly reduces the burden of the analysts and can be applied to a wider range of applications, thereby solving the problem that the data analysis cannot be "unified" due to the complex data types and formats and high degree of freedom between various projects.

[0055] Specifically, the data mapping configuration module is specifically used to:

[0056] The metadata inside the platform is predefined according to the test needs. The engine test parameter variables are first automatically mapped with the metadata parameter names set inside the platform using the string matching method. For the remaining parameters that cannot be automatically mapped, the analyst manually selects them for mapping and matching in the metadata configuration interface. Finally, the mapping relationship between the metadata and the engine test parameters is saved, thereby achieving the correspondence between the engine parameter variables of different projects and the metadata inside the platform and obtaining the engine test data.

[0057] This embodiment adopts a method of first automatically mapping the engine test parameter variables with the metadata parameter names set inside the platform, and then the analyst manually selects the mapping and matching method in the metadata configuration interface to achieve the matching of engine parameter variables of different projects with the internal variables of the program, and realizes the rapid and comprehensive correspondence between the engine test parameter variables and the metadata set inside the platform, taking into account the efficiency and coverage of the matching, and ensuring that the analyst can perform rapid analysis such as engine steady-state analysis and feature extraction without modifying the program of the subsequent data analysis module. The analyst does not need to perform a large number of tedious manual modifications inside the program to adapt to different parameter variable names. The corresponding relationship between the parameter variables and the internal variables of the program can be automatically mapped to the greatest extent. The operation is simple and the mapping relationship can be visually displayed, which is convenient for use by different personnel. The analyst can quickly realize data analysis without a programming background, which greatly reduces the burden on the analyst and can be applied to a wider range of applications.

[0058] Preferably, the data mapping configuration module is also used for:

[0059] Analysts can add, modify, delete, import and export metadata and mapping relationships, and realize information transmission between different analysts under offline stand-alone conditions; complete data label management, and add label information such as test items, test phenomena, technical status, and whether there are test anomalies to the entered data. Ensure that the data mapping configuration process is flexible and reliable, with a wide range of applications, meeting different data types and formats, and improving the ability to flexibly process data structure.

[0060] Preferably, if Figure 2 As shown, the data analysis module specifically includes:

[0061] A data conversion module, used to convert the units of the data including time, temperature and pressure in the engine test parameters, and to perform similar conversion operations on the engine test data;

[0062] The data cleaning module is used to detect anomalies in the measured engine test data, complete the redundancy fault tolerance check of the engine test data based on the comparison between two parameters, and clean the abnormal data points;

[0063] Data dimension enhancement module, used to perform personalized calculations on engine test data according to requirements, including calculation of temperature rise of lubricating oil system parameters, etc.

[0064] A data steady-state extraction module is used to extract steady-state data that can characterize the overall operating state of the engine according to the steady-state criterion, so as to characterize the current health status of the engine;

[0065] The data feature extraction module is used to analyze the engine test data according to the overall logical criteria provided, and extract the engine parameter content including the extreme values ​​of engine parameters, starting time, ignition time, and specific state points.

[0066] The data analysis module of this embodiment is based on the experience of engine test data analysis and according to the platform data analysis execution process, and proposes five sub-modules: data conversion module, data cleaning module, data dimension enhancement module, data steady-state extraction module, and data feature extraction module. Each sub-module performs calculations in a triggered manner in sequence and is designed as a semi-open architecture. Each project can use the Python scripting language to personalize the configuration of the algorithm processing module according to analysis requirements.

[0067] Preferably, the data analysis module is further used for:

[0068] It supports the free combination of the included processing sub-modules by dragging and dropping. According to the data analysis needs in actual work, the operation sub-modules that need to be executed can be selected in an individualized manner, so as to achieve the purpose of process-based problem solving.

[0069] Preferably, if Figure 3 As shown, the analysis result output module includes:

[0070] Report display module, used to display data processing results through reports, including engine statistics, starting characteristic information, and characteristic data analysis results;

[0071] The curve chart display module is used to display the parameter change diagram of a single test run data and the comparative analysis diagram of multiple test run data through curve charts;

[0072] The running report generation module is used to embed the automatic generation function of the performance analysis running report using the Python program, and automatically generate the test data processing report in PDF format, which can replace the existing test situation running account management.

[0073] This embodiment can display the data processing results in various forms and from various angles and generate various processing reports, thereby improving the visualization effect and adaptability of the data processing results.

[0074] Preferably, if Figure 4 As shown, the analysis result output module also includes:

[0075] The import and export module is used for analysts to import and export analysis results, thereby achieving data synchronization between different analysts, facilitating the consistency, relevance and rigor of analysis results, avoiding duplication of work, improving the scientific nature and convenience of analysis, improving analysis efficiency and reducing costs.

[0076] like Figure 5 As shown, the present application also provides a generalized mining method for engine test data based on metadata mapping, comprising the steps of:

[0077] S1. Acquire the engine test data by matching the one-to-one mapping relationship between the engine test parameters and the platform internal metadata through a combination of automatic and manual methods and displaying them visually;

[0078] S2. Analyze and process the acquired engine test data;

[0079] S3. Output the analysis and processing results of the engine test data.

[0080] Preferably, if Figure 6As shown, the one-to-one mapping relationship between the engine test parameters and the platform internal metadata is matched by combining automatic and manual methods and displayed visually to obtain the engine test data, which specifically includes the following steps:

[0081] S11. Pre-set the metadata inside the platform according to the test needs. First, automatically map the engine test parameter variables with the metadata parameter names set inside the platform according to the string matching method. For the remaining parameters that cannot be automatically mapped, the analyst manually selects them for mapping and matching in the metadata configuration interface. Finally, the mapping relationship between the metadata and the engine test parameters is saved, thereby realizing the correspondence between the engine parameter variables of different projects and the metadata inside the platform, and obtaining the engine test data.

[0082] Preferably, if Figure 7 As shown, the one-to-one mapping relationship between the engine test parameters and the platform internal metadata is matched by combining automatic and manual methods and visualized to obtain the engine test data, which specifically includes the following steps:

[0083] S12. Allow analysts to add, modify, delete, import, and export metadata and the mapping relationships formed, so as to realize information transmission between different analysts under offline stand-alone conditions; complete data label management, and add label information such as test items, test phenomena, technical status, and whether there are test anomalies to the entered data.

[0084] This embodiment not only matches the one-to-one mapping relationship between the engine test parameters and the internal metadata of the platform through a combination of automatic and manual methods and displays them visually, but also provides analysts with the ability to add, modify, delete, import, export, and complete data label management on the metadata and the formed mapping relationship, and add label information such as test items, test phenomena, technical status, and whether there are test anomalies to the entered data, thereby achieving information transmission between different analysts under offline stand-alone conditions and ensuring that the data mapping configuration process is flexible and reliable, with a wide range of applicability, meeting different data types and formats, improving the ability to flexibly process data structured data, and meeting the data mining and analysis needs of different test projects.

[0085] like Figure 8 As shown, a preferred embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the generalized engine test data mining method based on metadata mapping in the above-mentioned embodiment when executing the computer program.

[0086] like Fig. 9 As shown, the preferred embodiment of the present application also provides a computer device, which can be a terminal or a liveness detection server, and its internal structure diagram can be as shown in Fig. 9As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned generalized mining method for engine test data based on metadata mapping are implemented.

[0087] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] A preferred embodiment of the present application also provides a storage medium, which includes a stored program, and when the program is run, controls the device where the storage medium is located to execute the steps of the generalized engine test data mining method based on metadata mapping in the above embodiment.

[0089] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0090] If the functions described in the method of this embodiment are implemented in the form of software functional units and sold or used as independent products, they can be stored in one or more computing devices readable storage media. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0091] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0092] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0096] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A generalized mining platform for engine test data based on metadata mapping, characterized in that: include: The data mapping configuration module is used to match the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual methods and visualize the mapping relationship to obtain engine test data; A data analysis module, used for analyzing and processing the acquired engine test data; The analysis result output module is used to output the analysis and processing results of the engine test data.

2. The engine test data universal mining platform based on metadata mapping according to claim 1 is characterized in that: The data mapping configuration module is specifically used for: The metadata inside the platform is predefined according to the test needs. The engine test parameter variables are first automatically mapped with the metadata parameter names set inside the platform using the string matching method. For the remaining parameters that cannot be automatically mapped, the analyst manually selects them for mapping and matching in the metadata configuration interface. Finally, the mapping relationship between the metadata and the engine test parameters is saved, thereby achieving the correspondence between the engine parameter variables of different projects and the metadata inside the platform and obtaining the engine test data.

3. The engine test data universal mining platform based on metadata mapping according to claim 2 is characterized in that: The data mapping configuration module is also used for: It allows analysts to add, modify, delete, import, and export metadata and mapping relationships, and enables information transmission between different analysts under offline stand-alone conditions; Complete data label management and add label information such as test items, test phenomena, technical status, and whether there are any test abnormalities to the entered data.

4. The engine test data universal mining platform based on metadata mapping according to claim 1 is characterized in that: The data analysis module specifically includes: A data conversion module, used to convert the units of the data including time, temperature and pressure in the engine test parameters, and to perform similar conversion operations on the engine test data; The data cleaning module is used to detect anomalies in the measured engine test data, complete the redundancy fault tolerance check of the engine test data based on the comparison between two parameters, and clean the abnormal data points; Data dimension enhancement module, used to perform personalized calculations on engine test data according to requirements, including calculation of temperature rise of lubricating oil system parameters; A data steady-state extraction module is used to extract steady-state data that can characterize the overall operating state of the engine according to the steady-state criterion, so as to characterize the current health status of the engine; The data feature extraction module is used to analyze the engine test data according to the overall logical criteria provided, and extract the engine parameter content including the extreme values ​​of engine parameters, starting time, ignition time, and specific state points.

5. The generalized engine test data mining platform based on metadata mapping according to claim 4 is characterized in that: The data analysis module is also specifically used for: It supports the free combination of the included processing sub-modules by dragging and dropping. According to the data analysis needs in actual work, the operation sub-modules that need to be executed can be selected in an individualized manner, so as to achieve the purpose of process-based problem solving.

6. The engine test data universal mining platform based on metadata mapping according to claim 1 is characterized in that: The analysis result output module includes: Report display module, used to display data processing results through reports, including engine statistics, starting characteristic information, and characteristic data analysis results; The curve chart display module is used to display the parameter change diagram of a single test run data and the comparative analysis diagram of multiple test run data through curve charts; The running report generation module is used to embed the automatic generation function of the performance analysis running report using the Python program, and automatically generate the test data processing report in PDF format, which can replace the existing test situation running account management.

7. The generalized engine test data mining platform based on metadata mapping according to claim 6 is characterized in that: The analysis result output module also includes: The import and export module is used for analysts to import and export analysis results, thereby achieving data synchronization between different analysts.

8. A generalized mining method for engine test data based on metadata mapping, characterized in that: Includes steps: The engine test data can be obtained by matching the one-to-one mapping relationship between engine test parameters and platform internal metadata through a combination of automatic and manual methods and displaying them visually; Analyze and process the acquired engine test data; Output the analysis and processing results of engine test data.

9. The generalized mining method for engine test data based on metadata mapping according to claim 8, characterized in that: The method of matching the one-to-one mapping relationship between the engine test parameters and the internal metadata of the platform by combining automatic and manual operations and visually displaying the mapping relationship to obtain the engine test data specifically includes the following steps: The metadata inside the platform is pre-set according to the test needs. The engine test parameter variables are first automatically mapped with the metadata parameter names set inside the platform according to the string matching method. For the remaining parameters that cannot be automatically mapped, the analyst manually selects them for mapping and matching in the metadata configuration interface. Finally, the mapping relationship between the metadata and the engine test parameters is saved, thereby realizing the correspondence between the engine parameter variables of different projects and the metadata inside the platform and obtaining the engine test data.

10. The generalized mining method for engine test data based on metadata mapping according to claim 9, characterized in that: The one-to-one mapping relationship between the engine test parameters and the platform internal metadata is matched by combining automatic and manual operations and displayed visually to obtain the engine test data, which specifically includes the following steps: It allows analysts to add, modify, delete, import, and export metadata and mapping relationships, and enables information transmission between different analysts under offline stand-alone conditions; Complete data label management and add label information such as test items, test phenomena, technical status, and whether there are any test abnormalities to the entered data.

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