Engineering test-oriented time series data segmentation system and method

Abstract

The invention relates to an engineering test-oriented time series data segmentation system and method, and the system is provided with a segmentation condition management module which manages parameter information corresponding to a target segmentation item and segmentation logic information corresponding to the target segmentation item. Therefore, when a user needs to segment the time series data, the time series data can be automatically segmented by using the target segmentation condition information in the segmentation condition management module, the complexity of performing time series data segmentation operation by manual selection each time is reduced, the data processing efficiency is improved, and the user experience is improved. And the time sequence data segmentation precision is improved to a certain extent. According to the engineering test-oriented time series data segmentation system and method, automatic time series data segmentation can be realized, the operation is convenient, the time series data segmentation efficiency is effectively improved, and the data management and operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a time series data segmentation system and method for engineering testing. Background Art

[0002] For some complex mechanisms, it is necessary to test their overall operating status, obtain their time-related test data, and then analyze the test data of related projects. However, for some test projects, it is only necessary to use the data of a test segment in the entire test timing data, and then analyze the related data. The data of the specific test segment is not necessarily divided according to the standard time point conditions. Therefore, in the prior art, technicians are usually required to manually select the data of the test segment, which is very inconvenient. For example:

[0003] In the process of civil aircraft development, a large number of different flight test time series data are needed for data processing and analysis. The collection time of flight test data is generally counted from takeoff to landing. Generally speaking, the sampling frequency and format of the acquired flight test data are different, and different test disciplines have different requirements for time series data parameters. Take the difference in time series data required for performance and handling stability professional data analysis as an example:

[0004] The start and end times required for performance data processing need to be determined through parameter calculation, and generally the entire timing data from takeoff to landing is selected; while the start and end times required for control stability data processing are generally selected by manually drawn curves, and the required timing data is not limited to the entire test flight data.

[0005] And because the parameters required for each professional data analysis and processing are different, each professional designer also needs to export the time series data required for subsequent data analysis and processing based on actual conditions.

[0006] At present, in view of the different requirements of different professions for flight test data, the conventional practice is for designers of each profession to obtain the flight test data package and download it locally, and then use the client software to read the data in different formats and manually process and export it. In this process, each flight test data package requires designers to manually select the time range or parameters according to the flight test data curve, and the manual repetitive cost is high. Secondly, the consistency of the data format exported by each profession independently may be affected, which increases the complexity of later management.

[0007] In summary, the processing method for segmenting time series data in the prior art mainly relies on manual work, and the processing efficiency is low. Summary of the invention

[0008] In view of the above-mentioned defects of the prior art, the present invention proposes a technical solution that can automatically segment the entire segment of time series data according to user needs and extract the time series data of the required test segment, effectively assist in improving the efficiency of time series data segmentation, and provide a time series data segmentation system and method for engineering testing that is more convenient for users to operate.

[0009] In order to achieve the above-mentioned purpose, the time series data segmentation system and method for engineering testing of the present invention are as follows:

[0010] In a first aspect, an embodiment of the present invention provides a time series data segmentation system for engineering testing, the main feature of which is that the system includes:

[0011] A time series data storage module, used for storing the time series data to be segmented;

[0012] A segmentation condition management module, used to manage target segmentation condition information, wherein the target segmentation condition information includes parameter information corresponding to each target segmentation item and segmentation logic information corresponding to each target segmentation item;

[0013] The time series data segmentation module is used to respond to the received target segmentation project information, segment the time series data to be segmented according to the parameter information corresponding to the target segmentation project and the segmentation logic information corresponding to the target segmentation project, so as to obtain the segmented target time series data corresponding to the target segmentation project.

[0014] In some embodiments, the system further comprises:

[0015] The data processing module is used to perform a data preprocessing operation on the original time series data to generate the time series data to be segmented, wherein the data preprocessing operation includes:

[0016] Removing abnormal values ​​from the original time series data based on a numerical range determination condition preset in the system;

[0017] Removing noise data from the original time series data based on a noise parameter determination condition preset in the system;

[0018] The missing values ​​in the original time series data are supplemented based on the missing value supplementation conditions preset in the system.

[0019] In some embodiments, the data preprocessing operation further includes:

[0020] In response to the data format adjustment instruction, the format of the original time series data is adjusted;

[0021] In response to the frequency adjustment instruction, the frequency of the original time series data is adjusted.

[0022] In some embodiments, the segmentation condition management module includes:

[0023] A parameter information management unit, used to manage the parameter information corresponding to each of the target segmentation items, wherein the parameter information corresponding to each of the target segmentation items includes custom parameter information and pre-stored parameter information, wherein each of the custom parameter information is generated according to a received parameter setting instruction corresponding to the target segmentation item, and each of the pre-stored parameter information is composed of custom parameter information corresponding to the target segmentation item generated in the past;

[0024] A segmentation logic information management unit is used to manage the segmentation logic information corresponding to each of the target segmentation items, each of the segmentation logic information includes custom segmentation logic information and pre-stored segmentation logic information, wherein each of the custom segmentation logic information is generated according to the received segmentation logic setting instructions corresponding to the target segmentation item, and each of the pre-stored segmentation logic information is composed of the custom segmentation logic information corresponding to the target segmentation item generated in the past.

[0025] In some embodiments, the segmentation condition management module further includes:

[0026] A custom parameter verification unit, used to verify whether the custom parameter information meets the parameter design conditions preset by the system, if the custom parameter information meets the parameter design conditions preset by the system, the custom parameter information is stored or applied, if the custom parameter information does not meet the parameter design conditions preset by the system, a parameter setting abnormality prompt is issued;

[0027] The custom segmentation logic verification unit is used to verify whether the custom segmentation logic information meets the segmentation logic design conditions preset by the system. If the custom segmentation logic information meets the segmentation logic design conditions preset by the system, the custom segmentation logic information is stored or applied; if the custom segmentation logic information does not meet the segmentation logic design conditions preset by the system, a segmentation logic abnormality prompt is issued.

[0028] In some embodiments, the system further comprises:

[0029] The data merging module is used to merge the target time series data after multiple segments are divided in response to the merging instruction to generate a merged target time series data group.

[0030] In some embodiments, the system further comprises:

[0031] The visualization operation module is used to receive or modify the original time series data and target segmentation condition information, and to feedback and display system operation information, wherein the system operation information includes time series data processing progress information and operation instruction feedback information.

[0032] In some embodiments, the system further comprises:

[0033] The authority management module is used to manage the authority of the target user according to the authority management conditions preset by the system;

[0034] The data recording module is used to record the timing segmentation operation process.

[0035] In some embodiments, the system is constructed using a browser and server architecture model, wherein the time series data storage module, segmentation condition management module, time series data segmentation module, data processing module, data merging module, permission management module and data recording module in the system are located on the server side, and the visualization operation module in the system is located on the browser side, and the server interacts with the browser for information.

[0036] In a second aspect, an embodiment of the present invention further provides a time series data segmentation method for engineering testing, wherein the method comprises:

[0037] In response to the received target segmentation project information, the time series data to be segmented is segmented through the parameter information corresponding to the target segmentation project in the system and the segmentation logic information corresponding to the target segmentation project to obtain the segmented target time series data corresponding to the target segmentation project, wherein the target segmentation condition information includes the parameter information corresponding to each target segmentation project and the segmentation logic information corresponding to each target segmentation project.

[0038] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the second aspect above is implemented.

[0039] The beneficial effects of the time series data segmentation system and method for engineering testing of the present invention are as follows:

[0040] The time series data segmentation system for engineering testing of the present invention is provided with a segmentation condition management module, in which parameter information corresponding to the target segmentation project and segmentation logic information corresponding to the target segmentation project are managed, so that when the user needs to segment the time series data, the target segmentation condition information in the segmentation condition management module can be used to automatically segment the time series data, reducing the tediousness of manual selection every time to perform time series data segmentation operations, improving the efficiency of data processing, and improving the accuracy of time series data segmentation to a certain extent. The time series data segmentation system and method for engineering testing of the present invention can realize automatic time series data segmentation, and are easy to operate, effectively improve the efficiency of time series data segmentation, and reduce data management and operation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention.

[0042] Figure 1 It is an operation flow chart of the time series data segmentation system for engineering testing of the present invention in one embodiment.

[0043] Figure 2 It is a data processing relationship diagram of a time series data segmentation system for engineering testing of the present invention in one embodiment.

[0044] Figure 3 It is a flowchart of time series data processing of a time series data segmentation method for engineering testing of the present invention in one embodiment.

[0045] Figure 4 This is a data processing signaling diagram of a timing data segmentation method for engineering testing of the present invention in one embodiment.

[0046] Figure 5 It is a technical architecture diagram of a time series data segmentation method for engineering testing of the present invention in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the technical means, creative features, objectives and effects of the invention easier to understand, the invention is further described below with reference to specific diagrams. However, the invention is not limited to the following implementation cases.

[0048] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0049] Time series data, also known as time series data, refers to the data columns of the same indicator recorded in chronological order. The data sources of time series data mainly come from machines, equipment and sensors in industries that are strongly related to the Industrial Internet of Things, such as energy, engineering, and transportation, such as the speed and engine speed of a car, and the power, voltage, and current of a wind turbine. Its typical characteristics include:

[0050] Many measurement points: The number of industrial equipment can reach millions, and the number of data measurement points can reach hundreds of millions, and will increase dynamically with business growth.

[0051] High sampling frequency: In some vibration status monitoring scenarios, the sampling frequency can reach 1kHz.

[0052] High storage cost: The monthly data increment can reach more than 10TB, and massive historical data needs to be stored for a long time.

[0053] As the name implies, a time series database is a type of database that manages time series data. The main reason why time series databases have only emerged in recent years is the explosive growth of IoT devices and data volumes. By analyzing and managing historical and newly generated time series data, it can help industrial enterprises with digital transformation and Industry 4.0 upgrades, thereby reducing costs, increasing efficiency, and improving quality.

[0054] In actual application, different time period data are required for testing different projects, so the original time series data needs to be segmented before use.

[0055] In the prior art, the division of time series data is usually carried out by technicians manually selecting the division, which is not only inconvenient to operate, but sometimes even prone to operational errors due to the large workload, with single functional characteristics and low efficiency. At the same time, the division of time series data for different testing disciplines is performed manually, and each discipline independently exports data without a standard data export workflow method. For example, when synthesizing time series data in different formats, the consistency of the exported data may be affected, increasing the complexity of later data management.

[0056] Based on the above problems, the present invention provides a time series data segmentation system and method for engineering testing. The following is a specific embodiment to specifically illustrate the technical solution:

[0057] In a first aspect, the present invention provides a time series data segmentation system for engineering testing, the system comprising:

[0058] A data processing module is used to perform data preprocessing operations on the original time series data to generate the time series data to be segmented; using this module can not only adjust the original time series data to a format that can be edited by the system, but also make the subsequent data segmentation operations more accurate and reduce errors caused by factors such as abnormal values ​​and noise values ​​in the original time series data;

[0059] A time series data storage module is used to store the time series data to be segmented; related time series data can be stored in this module, and the time series database processes and stores the time series data, providing efficient data writing and query;

[0060] The segmentation condition management module is used to manage target segmentation condition information, which includes parameter information corresponding to each target segmentation item and segmentation logic information corresponding to each target segmentation item; the target segmentation condition information includes the segmentation conditions corresponding to the target segmentation items originally stored in the system, and the segmentation conditions corresponding to the target segmentation items input by the user in real time. Among them, when the system contains the segmentation conditions corresponding to the items that meet the user's test, the user only needs to call the relevant segmentation conditions in a selection manner to use them, and when the system does not contain the segmentation conditions corresponding to the items that meet the user's test, the segmentation conditions corresponding to the target segmentation items input by the user in real time can be applied, and after verification, they can be stored in the system for subsequent use.

[0061] The time series data segmentation module is used to respond to the received target segmentation project information, and segment the time series data to be segmented through the parameter information corresponding to the target segmentation project and the segmentation logic information corresponding to the target segmentation project to obtain the segmented target time series data corresponding to the target segmentation project; using this module can avoid the trouble of users manually selecting segmentation points and improve segmentation efficiency.

[0062] The data merging module is used to respond to the merging instruction to merge the target time series data after multiple segments are divided to generate a merged target time series data group; the module can be used to integrate the time series data required by the user.

[0063] A visual operation module is used to receive or change the original time series data and target segmentation condition information, and to feedback and display system operation information, wherein the system operation information includes time series data processing progress information and operation instruction feedback information; users can operate and use the system through the visual operation module.

[0064] The authority management module is used to perform authority management on the target user according to the authority management conditions preset by the system; the use of this module can improve the security and stability of the system when in use, and prevent unauthorized people from arbitrarily operating the system.

[0065] The data recording module is used to record the time series segmentation operation process, and the relevant records can be stored in the system in the form of logs.

[0066] The data preprocessing operation includes:

[0067] Based on the numerical range judgment condition preset in the system, outliers in the original time series data are removed; wherein the numerical range can be designed by the designer according to actual requirements and then stored in the system, and the outliers in the original time series data can be effectively screened out by using this operation step;

[0068] The noise data in the original time series data is removed based on the noise parameter determination conditions preset in the system; wherein the noise parameter determination conditions can be designed by the designer according to actual requirements and then stored in the system, and the noise parameters in the original time series data can be screened out by using this operation step;

[0069] The missing values ​​in the original time series data are supplemented based on the missing value supplementation conditions preset in the system. Because, in most cases, complete and continuous data need to be analyzed when analyzing time series data, but the actual original data may have null values ​​due to some detection factors. In this case, the data can be processed by null value supplementation to facilitate better analysis of the data later. The specific method of null value supplementation can be constructed by the corresponding null value supplementation method in the prior art as needed, which will not be elaborated here.

[0070] In response to the data format adjustment instruction, the format of the original time series data is adjusted. By using this processing step, the original time series data can be adjusted to a required format for subsequent operations;

[0071] The frequency of the original time series data is adjusted in response to the frequency adjustment instruction. By using this processing step, the data can be adjusted to a required frequency for subsequent operations.

[0072] In this embodiment, frequency adjustment can be achieved in the following manner:

[0073] (1) Obtain the object frequencyFromData that encapsulates various export-related configuration parameters from the front end, including the time range, the original frequency of the data source, and the required target frequency; encapsulate the object frequencyNeed of the output configuration parameters, including the export format, path, file name and other details; specify the maximum number of data queries each time to avoid memory overflow caused by loading too much data at one time.

[0074] (2) The backend initializes the export object and sets the string for subsequent database table path construction.

[0075] (3) Check whether frequencyFromData and frequencyNeed are valid. If frequencyFromData>frequencyNeed, create a temporary table and use average aggregation to achieve data down-conversion. Otherwise, create a temporary table and use linear interpolation to increase the frequency.

[0076] (4) For both frequency increase and frequency decrease, calculate the total number of records in the temporary table, calculate the offset based on the maximum number of queries, use a loop to traverse each offset to implement batch query, and then append the query results to the export object, returning the data in the summary for export.

[0077] This step implements the following function: Depending on the major, subsequent calculation subjects need to use data of different frequencies for analysis and calculation, so a frequency conversion function is needed to support the export of such data.

[0078] In other embodiments, other pre-processing operations may also be performed to process the data.

[0079] In this embodiment, the segmentation condition management module includes:

[0080] A parameter information management unit is used to manage the parameter information corresponding to each of the target segmentation items, the parameter information corresponding to each of the target segmentation items includes custom parameter information and pre-stored parameter information, wherein each of the custom parameter information is generated according to the received parameter setting instruction corresponding to the target segmentation item, and each of the pre-stored parameter information is composed of custom parameter information corresponding to the target segmentation item generated in the past; wherein the parameter information refers to parameter items associated with the item to be tested, for example, when data analysis of a certain project is to be performed, the original time series data may include N parameter items, but not all of these N parameter items need to be used, and only some of the parameter items may be needed to analyze and calculate the item to be tested, and by using the parameter information management unit, when the user enters the custom parameter information for the first time or when it includes pre-stored parameter information, the parameter items associated with the item to be tested can be automatically selected, avoiding the need for the user to manually select the parameter items related to the item to be tested every time.

[0081] The segmentation logic information management unit is used to manage the segmentation logic information corresponding to each of the target segmentation items, each of the segmentation logic information includes custom segmentation logic information and pre-stored segmentation logic information, wherein each of the custom segmentation logic information is generated according to the received segmentation logic setting instruction corresponding to the target segmentation item, and each of the pre-stored segmentation logic information is composed of custom segmentation logic information corresponding to the target segmentation item generated in the past; wherein the segmentation logic information refers to the segmentation logic associated with the item to be tested, for example, the segmentation logic of some items to be tested is to segment with certain values ​​as nodes, and the segmentation logic of some items to be tested is to segment with the difference degree of a certain parameter as nodes, and the specific segmentation logic is set by the user according to the specific project needs. When the system already has segmentation logic information associated with the item to be tested, the relevant segmentation logic information can be directly used, and when the system does not store the segmentation logic information associated with the item to be tested, the user can manually input the segmentation logic information associated with the item to be tested to form the custom segmentation logic information for use when the system segments the time series data, and the relevant custom segmentation logic information can be stored in the system after verification to form the pre-stored segmentation logic information.

[0082] A custom parameter verification unit is used to verify whether the custom parameter information meets the parameter design conditions preset by the system. If the custom parameter information meets the parameter design conditions preset by the system, the custom parameter information is stored or applied. If the custom parameter information does not meet the parameter design conditions preset by the system, a parameter setting abnormality prompt is given; it is used to verify the custom parameter information input by the user, assist the user to input parameters that meet the rules, and avoid the user from accidentally entering incorrect custom parameter information. The parameter design conditions are preset in the system by the user according to the needs, and the relevant parameter design conditions are associated with the items to be tested.

[0083] The custom segmentation logic verification unit is used to verify whether the custom segmentation logic information meets the segmentation logic design conditions preset by the system. If the custom segmentation logic information meets the segmentation logic design conditions preset by the system, the custom segmentation logic information is stored or applied. If the custom segmentation logic information does not meet the segmentation logic design conditions preset by the system, a segmentation logic abnormality prompt is issued, wherein the preset segmentation logic design conditions are preset by the user according to the needs and in the system, the relevant segmentation logic design conditions are associated with the items to be tested.

[0084] That is, the conditional processing time series segmentation scheme is implemented through the conditional processing module (i.e., the segmentation condition management module and the time series data segmentation module), which provides two methods: preset criteria and custom criteria. Designers can define specific logical conditions, such as the wheel speed is zero, the radio altitude reaches a specific value, etc. The system will automatically determine the start and end time points of the data based on these conditions, thereby realizing automatic data segmentation.

[0085] Logical conditions are more than just simple data slicing. Conditional processing segmentation allows designers to define algorithms to accurately segment data based on preset or custom conditions. This means that the solution can handle complex logical judgments and data dependencies to ensure that the segmented data segments meet specific business rules or analysis requirements. If the selected parameters cannot meet the defined conditions, the system will return an exception error to prompt the designer to make corrections.

[0086] When processing data, the starting and ending times of the original time series data are regarded as the initial start and end times. Users can convert the initial start and end times into the required conditional start and end times through the conditional processing module. Among them, the conditional processing module provides two conditional processing methods: preset criteria (i.e. pre-stored parameter information and pre-stored segmentation logic information) and custom criteria (i.e. custom parameter information and custom segmentation logic information). Before use, it is necessary to confirm that the parameters required for conditional judgment are in the selected parameter list. The preset criteria define general start and end time criteria, such as the judgment logic of wheel speed being 0 and the judgment logic of radio altitude reaching 50ft. Custom criteria allow users to modify the judgment parameters and values. If the selected parameters cannot meet the criteria, the conditional parameter check will return an abnormal error.

[0087] In the specific implementation, the B / S (Browser / Server) architecture can be adopted, that is, the browser and server architecture mode constitutes the time series data segmentation system for engineering testing, in which the time series data storage module, segmentation condition management module, time series data segmentation module, data processing module and data merging module are located on the server side, and the visual operation module is located on the browser side. This architectural design mode not only makes the time series data segmentation operation more convenient, effectively improves the data processing efficiency, reduces errors, and adjusts the time series data format through the data processing module for later data management, but also reduces the user's management and operation costs, and is more conducive to the collaboration of designers and data information sharing.

[0088] That is, the front end of the system includes user pages and front-end frameworks, which provide an interactive graphical interface directly to users through web pages and browsers, are responsible for processing operations and requests from the user interface, and send the requests to the back-end server for processing. Specifically, the front-end construction uses the Vue framework and the UI component Element UI to render pages and interactive elements, and calls the back-end through RESTful services to achieve communication and data interaction. Users can interact through interface controls, including but not limited to buttons, radio buttons, check boxes, input boxes, icons and other elements, select parameters to be exported through the file tree, and obtain the export content list display. The conditional export method is displayed and integrated in the interface in the form of a UI. Users can click and select to define the merge method, time type and export conditions to improve ease of use and data export efficiency.

[0089] By utilizing the engineering test-oriented timing data segmentation system in this embodiment, the timing segments required for data processing of each research and development specialty can be effectively processed and segmented, and the calculation timing data in the corresponding format can be efficiently exported.

[0090] The following will be combined Figure 1 The operation flow of the time series data segmentation system for engineering testing in this embodiment is further described:

[0091] S1: User login and authority verification. The user enters the user name and password through the website interface to log in. The website verifies the user's identity and checks the authority. The system authenticates the user through the authority management module and then feeds back the verification result to the user's website.

[0092] S2: Select the data source. The user selects the source of the time series data to be exported on the website. That is, the user can export the source of the time series data to be exported through the WEB terminal. Then the time series data storage module responds to the user's needs and retrieves the corresponding time series data to be segmented.

[0093] S3: Select the parameters to be exported. The present invention provides two selection methods: manual selection and parameter template selection. Manual selection means that the user inputs the corresponding custom parameter information, and parameter template selection means that the user can select the corresponding pre-stored parameter information in the system for use.

[0094] S4: Data preprocessing. Before data segmentation, users can use the data processing module to perform data preprocessing operations on the time series data. The data preprocessing operations include preprocessing and cleaning steps. The cleaning steps may include operations such as outlier detection, data smoothing, and missing value filling. The preprocessing may include operations such as format conversion and frequency conversion.

[0095] S5: Determine the time series data range according to the export requirements. If the required data is a time series data fragment, you can select the time range through custom time range or conditional logic processing to obtain the exported time period. If the entire time series data is exported, there is no need for segmentation processing (that is, when some detection projects need to steal the entire data, data segmentation is not required).

[0096] S6: Configure export options. The user sets the format of the exported data (such as csv, txt, etc.), data merging method, and time type, and obtains the exported time period or specific conditions based on S3. Using this step, the data can be processed into the format required by the user.

[0097] S7: Trigger data export. After the user confirms the export settings, the system starts exporting data.

[0098] S8: Backend data processing: the backend accesses the selected data source according to the json data transmitted by the frontend, extracts the time series data that meets the conditions, and performs logical processing according to the format set by the user. It should be noted that (the json data format mentioned here can be adjusted according to actual needs, and the actual application is not limited to this);

[0099] S9: Byte stream feedback: After the backend processing is completed, the generated data file byte stream is transmitted to the user device or a download link is provided. That is, the data processed by the server is fed back to the user end.

[0100] S10: Export history, maintain export history, enable users to view previous export data, re-download or share.

[0101] S11: Notification and feedback: Send a notification to the user when the export is completed to ensure that the user knows the export status. Indicate the success or failure of the operation and provide relevant feedback information.

[0102] The above-mentioned time series data segmentation system for engineering testing can be applied to the development process of aircraft (such as civil aircraft), forming a web-based civil aircraft time series data segmentation export architecture. The architecture composition during specific application can be referred to Figure 2 As shown, Figure 2 This is a data processing relationship diagram of the time series data segmentation system for engineering testing of the present invention in one embodiment. The "Data Segmentation" box in the figure shows the contents of the operations to be performed on the operation end, where batch processing refers to batch parameter selection (i.e. parameter information selection) and range selection (i.e. segmentation logic information selection) for a batch of time series data of the same type and different batches, and the "Data Export" box shows the data processing operations performed by the server end. Figure 2 It can be seen that the time series data can be input through the external system, and the time series data processed by the server can be obtained by the IoTDB module (i.e., IoTDB time series database). In this embodiment, the data in json format is transmitted to the server (other embodiments can also use other formats of data for transmission), and the processed data will be fed back to the user end.

[0103] Specifically, the architecture consists of the following modules:

[0104] Time series data acquisition module: queries the imported time series data in the database according to user instructions, and displays the selected time series data file tree and parameter list on the front-end web page (i.e., the user end). In other embodiments, other methods can also be used to display data and related segmentation conditions.

[0105] Before data segmentation, users can perform preprocessing and cleaning steps on the time series data, such as outlier detection, data smoothing, missing value filling, etc. Through these preprocessing, the quality of the segmented data can be ensured to avoid the influence of noise or outliers in the original data on subsequent analysis and calculation.

[0106] Data segmentation module: provides time series data parameter selection components and time range segmentation components, allowing users to more conveniently select the required time series data segments or parameter sets. Among them, manual selection supports users to manually select parameters according to the displayed parameter list, and parameter templates can store selected parameters as custom templates for subsequent use; time range selection (i.e. selection of segmentation logic information) also supports users to manually set the time range.

[0107] Furthermore, in order to facilitate designers in selecting a timing range, the present invention proposes a method for dividing a conditional processing timing range.

[0108] To facilitate understanding, let's take a specific segmentation project as an example to illustrate the timing segmentation requirements:

[0109] Taking the time series data range required for performance and handling stability professional data analysis as an example, it was observed in the actual research and development process that designers often determine the specific data analysis range by calculating a certain value or taking a certain parameter value, and completing the start and end time judgment of the calculation in the data preprocessing stage, which is conducive to reducing the amount of subsequent data analysis and processing calculations and improving the running speed. The conditional processing time series segmentation method is implemented through the conditional processing module, which provides two methods: preset criteria and custom criteria. Designers can define specific logical conditions, such as wheel speed is zero, radio altitude reaches a specific value, etc. The system will automatically determine the start and end time points of the data based on these conditions, thereby realizing automatic data segmentation.

[0110] Logical conditions are more than just simple data slicing. Conditional processing segmentation allows designers to define algorithms to accurately segment data based on preset or custom conditions. This means that the solution can handle complex logical judgments and data dependencies to ensure that the segmented data segments meet specific business rules or analysis requirements. If the selected parameters cannot meet the defined conditions, the system will return an exception error to prompt the designer to make corrections.

[0111] Combine the following Figure 3 The time series data processing flow is further explained (the return in the figure means feedback of relevant information):

[0112] First, the starting and ending time of the original time series data are regarded as the initial start and end time. The user can convert the initial start and end time into the required conditional start and end time through the condition processing module. Among them, the condition processing module (i.e., the segmentation condition management module) provides two condition processing methods: preset criteria (i.e., pre-stored parameter information and pre-stored segmentation logic information) and custom criteria (i.e., custom parameter information and custom segmentation logic information). Before use, it is necessary to confirm that the parameters required for conditional judgment are in the selected parameter list. The preset criteria define general start and end time criteria, such as the judgment logic of wheel speed being 0 and the judgment logic of radio altitude reaching 50ft. The custom criteria allow users to modify the judgment parameters and values. If the selected parameters cannot meet the criteria, the condition parameter check will return an abnormal error.

[0113] Data export module: responsible for retrieving data from the database and generating corresponding data export files according to user needs. Among them, type transformation supports the conversion of time series data into common formats such as .txt, .csv, .mat, etc.; data merging supports the selection of multiple segments of data or multiple files for data export; frequency transformation supports the operation of increasing or decreasing the frequency of exported data; if it does not meet the export data specifications, the abnormal judgment will not allow the data to be exported. At the same time, data segmentation and export also support batch processing of multiple time series files. When executing the export of multiple time series data files, a compressed package containing multiple files will be automatically exported. The data processing module not only supports data frequency changes and format adjustments in the data preprocessing stage, but also supports data frequency changes and format adjustments during data export or processing.

[0114] It should be noted that the modules and units in this embodiment only reflect the division of related functions, and are not used to limit the hardware structure. The related modules and units can be composed of the same hardware module or a combination of different hardware modules.

[0115] When the system in this embodiment is applied to the time segmentation of civil aircraft, the following beneficial effects can be achieved:

[0116] 1. Solve the problem that the data export method in the prior art only supports manual selection of range to export data, has single functional characteristics, and is inefficient;

[0117] 2. It solves the problem that each profession in the existing technology independently exports data but lacks a standard data export workflow method. For example, when synthesizing time series data in different formats, the consistency of the exported data may be affected, which increases the complexity of later data management, making data processing for different professions more convenient;

[0118] 3. It solves the problem that the processing of original flight test data by various professionals currently requires reliance on local equipment or client software installation, which increases the management and operation costs of users and is not conducive to the collaboration and data information sharing of designers. The background server is used for unified management to facilitate collaboration among different designers.

[0119] In a second aspect, the present invention provides a time series data segmentation method for engineering testing, wherein the method comprises:

[0120] In response to the received target segmentation project information, the time series data to be segmented is segmented through the parameter information corresponding to the target segmentation project in the system and the segmentation logic information corresponding to the target segmentation project to obtain the segmented target time series data corresponding to the target segmentation project, wherein the target segmentation condition information includes the parameter information corresponding to each target segmentation project and the segmentation logic information corresponding to each target segmentation project.

[0121] The time series data to be segmented can be obtained by performing a data preprocessing operation, wherein the data preprocessing operation includes:

[0122] Identify the target user's operation permissions. If the corresponding operation permissions are met, the system will select and execute subsequent steps according to the user's operation. If not, a prompt will be given that the permissions do not meet the requirements.

[0123] Removing abnormal values ​​from the original time series data based on a numerical range determination condition preset in the system;

[0124] Removing noise data from the original time series data based on a noise parameter determination condition preset in the system;

[0125] Supplementing the missing values ​​in the original time series data based on the missing value supplementation conditions preset in the system;

[0126] In response to the data format adjustment instruction, the format of the original time series data is adjusted;

[0127] In response to the frequency adjustment instruction, the frequency of the original time series data is adjusted.

[0128] It should be noted that in specific implementation, the above preprocessing operations are not in any particular order, and not every step is a must-perform operation. In specific applications, they can be adjusted according to actual needs.

[0129] Combine the following Figure 4 Taking the application of the time series data segmentation method for engineering testing in this embodiment to the operation of civil aircraft as an example, the method is further explained:

[0130] Figure 4IoTDB, MySQL, and MongoDB are all located on the server side, where:

[0131] That is, the time series data storage module in this embodiment can be composed of IoTDB time series database, which is a domestically developed native time series database for the Internet of Things). In other embodiments, other time series databases such as InfluxDB or OpenTSDB can also be used. InfluxDB is specially designed for time series data and provides high-performance query capabilities. OpenTSDB is an open source time series database suitable for large-scale data storage. IoTDB, as a data source, collects and stores all test flight data.

[0132] MySQL is used to store non-time series data such as user information, permission management, and export history records; MySQL is a relational database management system. In a specific embodiment, PostgreSQL can also be used. PostgreSQL and other databases can replace MySQL to perform related operations, providing the system with powerful transaction processing capabilities and scalability, and is not limited to the examples given in this embodiment.

[0133] MongoDB is used to store complex, unstructured data, including user interface configuration, export templates, parameter templates, and custom conditional logic algorithms. MongoDB is a database based on distributed file storage. In specific implementation, Cassandra or Couchbase databases may also be used instead, but are not limited to this embodiment. Cassandra is a distributed NoSQL database suitable for high-concurrency scenarios. Couchbase provides high-performance document storage capabilities, suitable for large-scale data processing.

[0134] The specific application process is as follows:

[0135] S1: The user enters the username and password on the front end to log in. The operation end sends a JSON request containing the user credentials to the server. The server receives the user's credential request for identity authentication and sends an SQL query request to the SQL database. The database returns the verification results (such as user roles, permissions, etc.). The operation end receives the server's response. If the identity authentication is successful, the user will be redirected to the data selection page. (Among them, JSON (JavaScript Object Notation) is designed based on a subset of ECMAScript and is an open standard file format and data exchange format; SQL (Structured Query Language) is a database language with multiple functions such as data manipulation and data definition. This language is interactive and can provide great convenience for users. In specific applications, other formats can also be used for application, but it is not limited to this);

[0136] S2: The user selects a data source or uploads test flight data on the front-end interface, the operator sends a data source selection request, the server receives the data source selection request, prepares query parameters according to the user's selection, sends a query request to IoTDB, obtains available time series data files, and returns the corresponding parameter list. If the user selects a parameter template, the server receives the selection request, sends a query request to MongoDB, obtains the template information and returns it to the server, and the server sends a query request to IoTDB again to obtain time series data.

[0137] S3: The operator sends a preprocessing instruction to the server. The server database executes the query and returns the matching time series data. The server receives the time series data returned by IoTDB, performs necessary data preprocessing (such as data smoothing, etc.), and transmits the processed data to the IoTDB database for storage. The stored data is then returned to the operator in the JSON format. The operator receives the time series data and displays it to the user for subsequent selection.

[0138] S4: The user selects a condition and clicks on splitting. The operation end sends a splitting instruction to the server end. The server end sends a data query request to IoTDB to obtain time series data, and then determines whether the condition in the splitting instruction is a system preset condition. If it is a system preset condition, the server end performs udf logic processing (where udf (User-Defined Function) is a user-defined function that allows users to create custom functions in the database for use in SQL queries), obtains the time series range, and stores it in IoTDB for splitting data, and then returns to the operation end; if the splitting condition is a custom condition that the user has uploaded and saved in the system, the server end requests MongoDB to query the algorithm and returns to the server end for logical processing. If the splitting condition is a custom condition that the user has not uploaded, the user needs to upload the algorithm file on the operation end, and the server end returns the algorithm file storage information. The user clicks Save, and the server end stores the algorithm file information persistently in MongoDB. After obtaining the time range data, the server end returns it to the operation end to display the segmentation result.

[0139] S5: The user selects the parameters to be exported, the format (such as CSV) and the merging method (such as 4-interpolateMerge. Suppose we have two test flight data, and the timestamps of each test flight data are not exactly the same. If we want to merge these two split test flight data into one set, and hope that each time point in the merged data set has complete data, then we need to interpolate the missing time points first, and then merge them.) Then the operation end sends an export request, including the selected parameters and conditions. The server end receives the export request, and according to the conditions set by the user, queries IoTDB for data according to the time range selected by the user. IoTDB returns the required time series data. The server converts the data into the format selected by the user (such as CSV) and stores the converted file in the IoTDB database. After successful storage, IoTDB sends a success instruction and data file to the server end. The server end returns the generated file as a byte stream to the operation end, and the user obtains the downloaded file.

[0140] The above process can be used Figure 5 The technical architecture described is implemented. Figure 5 It is a technical architecture diagram of a time series data segmentation method for engineering testing of the present invention in one embodiment.

[0141] like Figure 5 As shown, first, the original time series data uploaded by the user can be stored in the IoTDB time series database, which supports the connection and data query of mainstream relational databases and NoSQL database MongoDB.

[0142] The logic design layer includes the front-end interface and the back-end logic. The front-end includes the user page and the front-end framework, which provides an interactive graphical interface directly facing the user through web pages and browsers, is responsible for processing operations and requests from the user interface, and sends the request to the back-end server for processing (that is, the front-end is equivalent to the function implemented by the visualization operation module in the first aspect). Specifically, the front-end construction uses the Vue framework and the UI component Element UI to render pages and interactive elements, and calls the back-end through RESTful services to achieve communication and data interaction. Users can interact through interface controls, including but not limited to buttons, radio buttons, check boxes, input boxes, icons and other elements, select parameters to be exported through the file tree, and obtain the list of exported contents for display. The conditional export method is displayed and integrated in the interface in the form of a UI. Users click and select to define the merge method, time type and export conditions to improve ease of use and data export efficiency.

[0143] The backend receives the json data to be exported from the frontend through the API, and then uses the json parsing library in Java to parse the received json data into Java objects. File type conversion prepares the data structure according to the requirements, generates the file header, and converts the parsed data into the data structure required for the text format. The conditional logic performs udf logic processing according to the definition passed by the frontend, executes the configuration of the export SQL and exports the data. Exception handling considers various possible exceptions, takes corresponding exception handling measures, and records the exception information in the log file for subsequent problem troubleshooting.

[0144] In the logic processing layer, Vue determines the relationship between components by parsing HTML and uses the virtual DOM mechanism to improve page update efficiency. The front end exports data definitions based on user operations or other conditions, and encapsulates the data into JSON format and passes it to the back end. The back end processes the received JSON data accordingly, establishes a connection with the database, executes SQL queries or commands through the interface, processes the result set, and converts the data into a byte stream as a response to the front end.

[0145] In summary, the front-end and back-end architectures of the relevant systems need to implement the following functions respectively:

[0146] The front-end framework needs to implement the following functions:

[0147] (1) Dynamically display data, display the time series data and parameter list obtained from the backend in real time through data binding, support axios and other libraries to communicate with the backend, send user selected parameters and requests and obtain data responses

[0148] (2) Support the use of drop-down menus or file upload components, allowing users to select a data source or upload test flight data, and send the selected content to the backend (HTML5 File API and Vue components)

[0149] (3) Supports a variety of user interaction controls (such as buttons, radio buttons, check boxes, etc.), and provides users with customized segmentation conditions, export conditions, and formats through Vue event processing and conditional rendering.

[0150] (4) Support dynamic forms and parameter templates, display parameter templates obtained from MongoDB, and allow users to interactively select

[0151] (5) Receive the byte stream file returned by the backend and download the generated file in the browser

[0152] The backend framework needs to quickly build a backend API (Application Programming Interface) to process frontend requests and interact with the database. Java can be replaced by Node.js or Python. Node.js provides high-performance backend processing capabilities and is suitable for real-time applications. Python provides rich library support and is suitable for data processing and scientific computing.

[0153] Backend functionality:

[0154] (1) User authentication and permission management (Spring Security and MySQL)

[0155] (2) Support querying available time series data files from IoTDB and returning the required parameter list (MyBatis or JPA)

[0156] (3) Supports preprocessing operations such as time series database smoothing.

[0157] (4) Support time segmentation according to user instructions and return corresponding time series range data (UDF and custom algorithm)

[0158] (5) Support file generation tools (such as Apache POI) and file format conversion capabilities

[0159] The data segmentation process of this embodiment involves user interaction, allowing designers to select pending data segments and parameters manually or through conditional logic according to needs, supporting custom templates and conditional processing, so as to achieve accurate data segmentation. In this embodiment, a preset conditional processing library for civil aircraft time series data is set up, and a web-based civil aircraft time series data segmentation and export architecture is built to provide an automated conditional processing time series range segmentation method. Through the conditional processing module, users are allowed to automatically determine the data range according to specific parameters or logic, reduce the time and cost of data analysis preprocessing, reduce the subsequent calculation amount and increase the operation speed, and improve the speed and accuracy of data processing. The architecture of the Web side supports fast data segmentation and export, which can respond to user needs more quickly. The intuitive user interface enables designers to more conveniently select data and set parameters, so that data processing is not restricted by location, and improves flexibility and collaboration. A more efficient data segmentation and export process can be achieved, ensuring the consistency of exported data and the integrity of original data, reducing manual intervention, reducing the cost of repeated labor, and helping to reduce the complexity of data management.

[0160] For the sake of convenience, an example is given below to further illustrate the operation flow of performing time series segmentation on the method of the second aspect, for example:

[0161] The user's segmentation condition is "GPS ground speed>0 time to wheel speed 0 time". The two parameters of the segmentation condition, GPS ground speed and wheel speed, do not require algorithm calculation, and the segmentation logic is a simple greater than or less than judgment, which does not require complex algorithm processing.

[0162] 1. The user selects time series data in the system and wheel speed and ground speed parameters in the data source. If the database already has parameter templates for wheel speed and ground speed in the parameters previously saved by the user, the user does not need to find the parameters in the data source, but selects the time series data and then the template to obtain the parameter list.

[0163] 2. The user clicks the Remove Outliers button on the operation end, and the front end sends instructions and time series data parameters to the server. The server requests data from the IoTDB database and performs outlier removal logic processing on the server. After the operation is completed, it returns to the front end.

[0164] 3. The user creates a new segmentation rule on the operation page, clicks Save, and the segmentation rule is saved in MongoDB. The next time you segment, you can directly select this rule to segment the data. If the segmentation parameters and segmentation conditions in the user's segmentation conditions require complex algorithm calculations, and there is no corresponding algorithm support in the preset algorithm, the user needs to upload the algorithm file on the operation side, and the file is saved in MongoDB. Select the algorithm for segmentation. The segmentation instruction contains the segmentation condition and the current parameter file name information, which is sent to the server. The server requests IoTDB to query the data and performs UDF logic processing on the Java side. After processing, it sends a storage request to IoTDB. After IoTDB is stored, the server returns the segmentation result to the operation side, and the front end displays the segmentation time period.

[0165] 4. The user clicks the export menu button of the system, and the front-end displays the export page. The page contains the name and parameters of the exported data. The user can customize the exported file name, time format, data type (cs, txt), frequency of exported data, and merging method. After clicking the export button, the front-end sends the export instruction to the server. The server queries IoTDB for segmented data and converts it. After the conversion is successful, the export result is stored in IoTDB. After the storage is successful, IoTDB sends a success message to the server. The server generates a byte stream file from the data and sends it to the front-end for download.

[0166] The data formats, specific processing languages, architectures and databases mentioned above can also be adjusted according to user needs and are not limited to the above examples. For example:

[0167] The front-end framework Vue.js can be replaced by React.js or Angular.js. React.js provides similar functions, such as virtual DOM, component-based development, etc. Angular.js provides more powerful framework support, such as two-way data binding, etc. The front-end framework only needs to implement responsive interface and user interaction and support RESTful API calls.

[0168] The time series data segmentation method for engineering testing in this embodiment can bring the following beneficial effects:

[0169] 1. In this embodiment, a standard web-side time series data segmentation and export task processing flow is provided, which can help developers of different professions quickly obtain the required flight test time series data and parameter sets. At the same time, the standard task flow ensures that the exported data can be constrained by the framework semantics, maintains consistency with the original data, and avoids subsequent management difficulties.

[0170] 2. The web-based architecture supports fast data segmentation and export, which enables faster response to user needs. The intuitive user interface enables designers to more conveniently select data and set parameters, making data processing unrestricted by location and improving flexibility and collaboration.

[0171] 3. Conditional processing logic can help designers quickly determine the export timing range, allowing users to automatically determine the data range based on specific parameters or logic, reducing the time and cost of data analysis preprocessing, alleviating subsequent calculations and increasing the speed of operation, thereby improving the speed and accuracy of data processing.

[0172] In a third aspect, the present invention further provides a time series data segmentation device for engineering testing, comprising:

[0173] At least one processor; a memory coupled to the at least one processor, the memory storing executable instructions, wherein the executable instructions, when executed by the at least one processor, implement the method of the second aspect of the present invention.

[0174] This embodiment provides a video processing device, including: at least one processor; and a memory coupled to the at least one processor. The processor and the memory may be provided separately or integrated together.

[0175] For example, the memory may include a random access memory, a flash memory, a read-only memory, a programmable read-only memory, a non-volatile memory or a register, etc. The processor may be a central processing unit (CPU), etc., or a graphic processing unit (GPU). The memory may store executable instructions. The processor may execute the executable instructions stored in the memory to implement the various processes described herein.

[0176] It can be understood that the memory in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a ROM (Read-Only Memory), a PROM (Programmable ROM), an EPROM (Erasable PROM), an EEPROM (Electrically EPROM), or a flash memory. The volatile memory can be a RAM (Random Access Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), DDRSDRAM (Double Data Rate SDRAM), ESDRAM (Enhanced SDRAM), SLDRAM (Synchlink DRAM), and DRRAM (Direct Rambus RAM). The memory 42 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0177] In some embodiments, the memory stores the following elements, upgrade packages, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and an application program.

[0178] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program includes various application programs, which are used to implement various application services. The program for implementing the method of the embodiment of the present invention can be included in the application program.

[0179] In the embodiment of the present invention, the processor calls a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and the processor is used to execute the method steps provided in the second aspect.

[0180] In a fourth aspect, an embodiment of the present invention further provides a chip for executing the method in the second aspect. Specifically, the chip includes: a processor for calling and running a computer program from a memory, so that a device equipped with the chip is used to execute the method in the first aspect.

[0181] In a fifth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method of the second aspect of the present invention when the computer program is executed by a processor.

[0182] For example, machine-readable storage media may include, but are not limited to, various known and unknown types of non-volatile memory.

[0183] In a sixth aspect, an embodiment of the present invention further provides a computer program product, comprising computer program instructions, which enable a computer to execute the method in the second aspect.

[0184] The beneficial effects of the time series data segmentation system and method for engineering testing of the present invention are as follows:

[0185] The engineering test-oriented timing data segmentation system of the present invention is provided with a segmentation condition management module, in which parameter information corresponding to the target segmentation project and segmentation logic information corresponding to the target segmentation project are managed. Therefore, when the user needs to segment the timing data, the target segmentation condition information in the segmentation condition management module can be used to automatically segment the timing data, thereby reducing the tediousness of manual selection for timing data segmentation operations each time, improving the efficiency of data processing, and improving the accuracy of timing data segmentation to a certain extent.

[0186] The system and method for time series data segmentation for engineering testing of the present invention are easy to operate, effectively improve the efficiency of time series data segmentation, and reduce data management and operation costs.

[0187] It will be appreciated by those skilled in the art that the modules, units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0188] In the embodiments of the present application, the disclosed systems, devices and methods can be implemented in other ways. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system. In addition, the coupling between the various units can be direct coupling or indirect coupling. In addition, the various functional units in the embodiments of the present application can be integrated into a processing unit, or can be separate physical existences, etc.

[0189] It should be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0190] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a machine-readable storage medium. Therefore, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a machine-readable storage medium, which can include several instructions to enable an electronic device to perform all or part of the technical solution described in the embodiments of the present application. The above-mentioned storage medium may include various media that can store program codes, such as ROM, RAM, removable disk, hard disk, magnetic disk or optical disk.

[0191] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A time series data segmentation system for engineering testing, characterized in that: The system comprises: A time series data storage module, used for storing the time series data to be segmented; A segmentation condition management module, used to manage target segmentation condition information, wherein the target segmentation condition information includes parameter information corresponding to each target segmentation item and segmentation logic information corresponding to each target segmentation item; The time series data segmentation module is used to respond to the received target segmentation project information, segment the time series data to be segmented according to the parameter information corresponding to the target segmentation project and the segmentation logic information corresponding to the target segmentation project, so as to obtain the segmented target time series data corresponding to the target segmentation project.

2. The time series data segmentation system for engineering testing according to claim 1, characterized in that: The system further comprises: The data processing module is used to perform a data preprocessing operation on the original time series data to generate the time series data to be segmented, wherein the data preprocessing operation includes: Removing abnormal values ​​from the original time series data based on a numerical range determination condition preset in the system; Removing noise data from the original time series data based on a noise parameter determination condition preset in the system; The missing values ​​in the original time series data are supplemented based on the missing value supplementation conditions preset in the system.

3. The time series data segmentation system for engineering testing according to claim 2 is characterized in that: The data preprocessing operation also includes: In response to the data format adjustment instruction, the format of the original time series data is adjusted; In response to the frequency adjustment instruction, the frequency of the original time series data is adjusted.

4. The time series data segmentation system for engineering testing according to claim 1, characterized in that: The segmentation condition management module includes: A parameter information management unit, used to manage the parameter information corresponding to each of the target segmentation items, wherein the parameter information corresponding to each of the target segmentation items includes custom parameter information and pre-stored parameter information, wherein each of the custom parameter information is generated according to a received parameter setting instruction corresponding to the target segmentation item, and each of the pre-stored parameter information is composed of custom parameter information corresponding to the target segmentation item generated in the past; A segmentation logic information management unit is used to manage the segmentation logic information corresponding to each of the target segmentation items, each of the segmentation logic information includes custom segmentation logic information and pre-stored segmentation logic information, wherein each of the custom segmentation logic information is generated according to the received segmentation logic setting instructions corresponding to the target segmentation item, and each of the pre-stored segmentation logic information is composed of the custom segmentation logic information corresponding to the target segmentation item generated in the past.

5. The time series data segmentation system for engineering testing according to claim 4, characterized in that: The segmentation condition management module also includes: A custom parameter verification unit, used to verify whether the custom parameter information meets the parameter design conditions preset by the system, if the custom parameter information meets the parameter design conditions preset by the system, the custom parameter information is stored or applied, if the custom parameter information does not meet the parameter design conditions preset by the system, a parameter setting abnormality prompt is issued; The custom segmentation logic verification unit is used to verify whether the custom segmentation logic information meets the segmentation logic design conditions preset by the system. If the custom segmentation logic information meets the segmentation logic design conditions preset by the system, the custom segmentation logic information is stored or applied; if the custom segmentation logic information does not meet the segmentation logic design conditions preset by the system, a segmentation logic abnormality prompt is issued.

6. The time series data segmentation system for engineering testing according to claim 1, characterized in that: The system further comprises: A data merging module, configured to merge the target time series data after multiple segments are divided in response to a merging instruction, so as to generate a merged target time series data group; The visualization operation module is used to receive or modify the original time series data and target segmentation condition information, and to feedback and display system operation information, wherein the system operation information includes time series data processing progress information and operation instruction feedback information.

7. The time series data segmentation system for engineering testing according to claim 6, characterized in that: The system further comprises: The authority management module is used to manage the authority of the target user according to the authority management conditions preset by the system; The data recording module is used to record the timing segmentation operation process.

8. The time series data segmentation system for engineering testing according to claim 7, characterized in that: The system is constructed using a browser and server architecture model, wherein the time series data storage module, segmentation condition management module, time series data segmentation module, data processing module, data merging module, authority management module and data recording module in the system are located on the server side, while the visualization operation module in the system is located on the browser side, and the server interacts with the browser for information.

9. A time series data segmentation method for engineering testing, characterized in that: The method comprises: In response to the received target segmentation project information, the time series data to be segmented is segmented through the parameter information corresponding to the target segmentation project in the system and the segmentation logic information corresponding to the target segmentation project to obtain the segmented target time series data corresponding to the target segmentation project, wherein the target segmentation condition information includes the parameter information corresponding to each target segmentation project and the segmentation logic information corresponding to each target segmentation project.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method as described in claim 9 are implemented.

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