Web-based civil aircraft test flight time sequence data online calculation method and system

Abstract

The invention discloses a web-based civil aircraft test flight time sequence data online calculation method and system. The method comprises the following steps of S1, uploading data; s2, preprocessing the data; s3, selecting parameters; s4, editing a formula; s5, scientific calculation is carried out; and S6, data display and result visualization are carried out. According to the invention, efficient data management and analysis processing are realized, and the problems of information isolated island, complex operation, poor system expansibility and the like in the prior art are solved; the method has the advantages that the data management efficiency is improved, the operation process is simplified, the expandability of the system is enhanced, the large-scale time sequence data processing is optimized, the data visualization capability is improved, and the data security is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of civil aircraft flight test data processing, and particularly to an online calculation method and system for civil aircraft flight test time series data based on the web. Background Art

[0002] In recent years, during the development of civil aircraft, various professional subjects need to calculate and analyze a large amount of flight test data. However, aviation time series data faces many challenges, including difficult acquisition, many storage problems, complex data processing and analysis, high requirements for data quality and cleaning, and difficulties in visualization.

[0003] Currently, the existing technology mainly relies on local hard disks for data storage, which leads to scattered files and low management efficiency, and is not conducive to data retrieval and sharing. In terms of data analysis, researchers usually use data processing software such as MATLAB or dedicated software with single functions. These tools have limitations in applicability. Especially when performing professional calculations, it is often necessary to convert files into specific formats and master the corresponding programming languages, which not only increases the time and learning costs but also reduces work efficiency.

[0004] In addition, the existing technology also has the following significant defects:

[0005] (1) Information island problem: Since the data is stored locally, it is difficult to perform effective retrieval and management, resulting in data being unable to communicate with each other, and there are differences in identifier resolution between different systems, affecting data consistency and reliability.

[0006] (2) Operational complexity: High requirements for file format conversion and programming language learning, and difficult operation process. Researchers need to invest a lot of time and effort to adapt to the usage requirements of different software, which not only reduces work efficiency but also may introduce additional human errors.

[0007] (3) Poor system scalability: Difficult secondary development of existing systems, lack of autonomy and controllability, and difficult to customize and improve and expand functions according to the specific needs of civil aircraft flight test data analysis, restricting the adaptability of the system to new analysis requirements.

[0008] (4) Low data processing efficiency: When processing large-scale time series data, the existing technology is often inefficient and lacks optimization for specific needs in the aviation field.

[0009] (5) Insufficient data visualization ability: Limited data visualization ability, difficult to intuitively display the changing trends and patterns of complex time series data, affecting the depth and accuracy of data analysis.

[0010] (6) Data security issues: The local storage method is vulnerable to factors such as hardware failures and human operation errors, posing risks of data loss and leakage. At the same time, the lack of a unified data access control mechanism makes it difficult to ensure the security of sensitive data.

[0011] In summary, the existing technologies in the flight test data processing of civil aircraft urgently need to be improved to enhance the efficiency, accuracy, and security of data processing and meet the growing analysis requirements in the aviation field. Summary of the Invention

[0012] To solve at least one of the problems existing in the above-mentioned prior art, a first aspect of the present invention provides an online calculation method for civil aircraft flight test time-series data based on the web, which includes the following steps:

[0013] Step S1: Data upload; wherein, the time-series data is uploaded and imported in the form of a file, and the time-series data includes a timestamp and the numerical information corresponding to the timestamp.

[0014] Step S2: Data preprocessing; wherein, the missing values in the time-series data are filled, and the outliers in the time-series data are detected and processed.

[0015] Step S3: Parameter selection; wherein, the parameters to be analyzed are selected in the time-series dendrogram.

[0016] Step S4: Formula editing; wherein, the user can construct formula logic and edit operators to arrange them into advanced algorithms.

[0017] Step S5: Scientific calculation; wherein, mathematical operations, data analysis, or data processing tasks are performed based on the user-defined function (UDF) of the time-series database IoTDB.

[0018] Step S6: Data display and result visualization; wherein, the calculation results are presented in the form of a table or a time-series graph through the time-series graph, and the user can conduct in-depth analysis on data trends, patterns, or correlations.

[0019] In the online calculation method for civil aircraft flight test time-series data based on the web as described above, optionally, step S1 specifically includes the following steps:

[0020] Step S1.1: Use parallel job upload to upload the time-series data simultaneously from multiple sensors or data sources.

[0021] Step S1.2: Data identification and classification to distinguish the structured data and unstructured heterogeneous data in the time-series data.

[0022] Step S1.3: Synchronize the structured data to the time series database IoTDB, and construct the metadata model of the time series data based on the adaptive parameter types of the TsFile file format built into the time series database IoTDB.

[0023] Step S1.4: Synchronize the heterogeneous data to the document database MongoDB.

[0024] In the online calculation method of civil aircraft flight test time series data based on web as described above, optionally, the uploaded time series data is displayed in a tree-like form so that users can see the hierarchical structure of the time series data, and users can choose to view all the time series data or a subset of the time series data within a specified time period.

[0025] In the online calculation method of civil aircraft flight test time series data based on web as described above, optionally, step S2 specifically includes the following steps:

[0026] Step S2.1: According to the time period and specific requirements of the time series data selected by the user, draw a time series graph to visually display the change trend of the time series data over time.

[0027] Step S2.2: Conduct statistical analysis on the selected time series data interval, where the statistical indicators for statistical analysis include at least one of the following: mean, maximum value, minimum value, standard deviation, moving average.

[0028] In the online calculation method of civil aircraft flight test time series data based on web as described above, optionally, step S4 specifically includes the following steps:

[0029] Step S4.1: Pre-define calculation templates, where the analysis methods for time series include at least one of the following: moving average, exponential smoothing, autoregressive model.

[0030] Step S4.2: Conduct real-time formula verification, that is, immediately verify the syntax and semantics of the formula input by the user to ensure the correctness of the formula.

[0031] In the online calculation method of civil aircraft flight test time series data based on web as described above, optionally, in step S5, users can perform at least one of the following on the time series data through the user-defined function UDF: statistical analysis, signal processing, complex aggregation, prediction model application, time series interpolation, specific professional algorithms.

[0032] In the online calculation method of civil aircraft flight test time series data based on web as described above, optionally, in step S6, an interactive method controlled by dragging or a sliding bar is introduced in the time series graph, and an operation guide and help document are provided.

[0033] To achieve the above object, a second aspect of the present invention provides an online calculation system for civil aircraft flight test time series data based on the web. Among them, the online calculation method for civil aircraft flight test time series data based on the web described in any one of the embodiments of the first aspect above is used, including:

[0034] A client that uploads the collected civil aircraft flight test time series data to the server;

[0035] A server, including a data preprocessing module, a parameter selection module, an editing formula module, a scientific calculation module, a data display and result visualization module. Multiple core modules work together to achieve the full-process processing of the time series data from upload to result output;

[0036] A cloud database, including a time series database IoTDB and a document database MongoDB, which are respectively used to store structured time series data and unstructured heterogeneous data to support efficient data access and analysis;

[0037] A data channel is established between the server and the cloud database. The server receives the time series data uploaded from the client and performs access operations in the cloud database;

[0038] The front-end interface of the server is developed based on the v2.0 version of Vue.js. On the front-end page, Vue is used to display and operate the flight test time series data obtained from the back-end; the UI framework of Element Ui is introduced, and the uPlot chart library is built to provide rendering and charts for the time series data; Monaco Editor is used as a code editor to enable users to customize and edit calculation formulas; AntDesign X6 is used for algorithm arrangement to build a graphical algorithm design interface.

[0039] To achieve the above object, a third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. Among them, when the processor runs the program, it implements the online calculation method for civil aircraft flight test time series data based on the web described in any one of the embodiments of the first aspect above.

[0040] To achieve the above object, a fourth aspect of the present invention provides a computer-readable storage medium. Among them, the computer-readable storage medium stores computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are processed and executed, they implement the online calculation method for civil aircraft flight test time series data based on the web described in any one of the embodiments of the first aspect above.

[0041] The online calculation method and system for civil aircraft flight test time series data based on the web according to the present invention adopt an architecture of a client, a server, and a cloud database, and combine the time series database IoTDB and the document database MongoDB, realizing efficient data management and analysis processing, solving problems such as information islands, complex operations, and poor system scalability existing in the prior art, and having the advantages of improving data management efficiency, simplifying operation processes, enhancing system scalability, optimizing large-scale time series data processing, improving data visualization capabilities, and enhancing data security.

[0042] 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, features, and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic flowchart of an embodiment of an online calculation method for civil aircraft flight test time series data based on the web according to the present invention;

[0045] Figure 2 It is a schematic structural diagram of an embodiment of an online calculation system for civil aircraft flight test time series data based on the web according to the present invention;

[0046] Figure 3 is Figure 2 a schematic diagram of the technology tree of the system in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0048] Terms such as "including" and "comprising" indicate that in addition to the components directly and clearly described in the specification and claims, the technical solutions of the present invention do not exclude the situation of having other components not directly or clearly described.

[0049] To solve the problem of online calculation of civil aircraft flight test time series data, this application proposes an online calculation method for civil aircraft flight test time series data based on the web. Figure 1 It is a schematic flowchart of an embodiment of an online calculation method for civil aircraft flight test time series data based on the web according to the present invention.

[0050] Specifically, the method may include the following steps:

[0051] Step S1: Data upload.

[0052] Time series data refers to data points recorded in chronological order, which may include timestamps and numerical information corresponding to the timestamps. In this method, the time series data is uploaded in the form of a file and imported into the time series database IoTDB for storage and processing.

[0053] In an alternative embodiment, the source data can be stored in various formats, including but not limited to the following:

[0054] (1) csv (Comma-Separated Values) format: Used to store tabular data, where the data items are separated by commas, each row represents a data record, and each column represents a specific data field.

[0055] (2) txt (Text) format: Stores plain text data, where the source data is usually separated by a certain delimiter (such as a comma, tab, etc.), or each row represents a data record.

[0056] (3) Other formats: This includes but is not limited to the.xlsx format of Excel, database file formats, etc., for storing more complex or structured data.

[0057] The data upload step solves the problem of importing time series data. In this application, the uploaded data will be displayed in a tree form, which is achieved by sliding the time axis, entering a date range, etc., so that users can clearly see the data hierarchy, and users can choose to view all time series data or a subset of time series data within a specified time period. In this way, users can browse and manage time series data more conveniently, thereby improving the efficiency of data processing and analysis.

[0058] In an alternative embodiment, the tree-like display structure can be implemented through hierarchical nodes, and each node represents a time period or a data set. Users can view data at different levels by expanding or collapsing these nodes. Specifically, a front-end framework such as Vue.js can be used to build this tree structure and combined with the query function of the back-end database to dynamically load and display data. In addition, users can also specify the time period to be viewed through a time selection control, so as to filter out the corresponding subset of time series data.

[0059] For example, when viewing flight test data, the user can see a tree diagram of the time-series data for the entire flight process, or select a specific flight stage to further expand and view the detailed data within that stage. In this way, the user can not only view the overall data, but also focus on the data within a specific time period, thus enabling more efficient data analysis and processing.

[0060] Compared with the prior art, the present application enables the user to more intuitively understand the hierarchical structure of time-series data through a tree-like display, and can flexibly select to view all or part of the data. This method not only improves the convenience of data browsing, but also enhances the efficiency of data management, solving problems such as low efficiency of data management and difficult retrieval in the prior art. Thus, the present application has significant advantages in terms of user experience and data processing efficiency.

[0061] In an optional embodiment, step S1 may specifically include the following steps:

[0062] Step S1.1: Use parallel job uploading to upload time-series data simultaneously from multiple sensors or data sources.

[0063] Specifically, in step S1.1, parallel job uploading can be implemented through multi-threading or multi-processing. Further, a distributed computing framework such as Hadoop or Spark can be used to improve the efficiency of parallel processing.

[0064] Step S1.2: Data identification and classification to distinguish between structured data and unstructured heterogeneous data in the time-series data.

[0065] Specifically, in step S1.2, data identification and classification can be achieved through machine learning algorithms or rule engines, for example, using algorithms such as decision trees and support vector machines for classification.

[0066] Step S1.3: Synchronize the structured data to the time-series database IoTDB, and construct a metadata model for the time-series data based on the self-adaptive parameter types of the TsFile file format built into the time-series database IoTDB.

[0067] Specifically, in step S1.3, when constructing the metadata model, adjust the parameter types of the TsFile file format according to the specific application scenario to adapt to different types of time-series data.

[0068] Step S1.4: Synchronize the heterogeneous data to the document database MongoDB.

[0069] Specifically, in step S1.4, when synchronizing heterogeneous time-series data to MongoDB, the bulk write function of MongoDB can be used to improve the efficiency of data synchronization.

[0070] In this embodiment, these steps cooperate with each other to solve the problems of parallel processing of uploaded time-series data and classification and synchronization of structured and unstructured data. Compared with the prior art, the present application improves the efficiency of data upload, realizes efficient storage and management of data, and has significant technical advantages.

[0071] Step S2: Data preprocessing; wherein, in view of the possible missing values or outliers in the time-series data, a preprocessing function is provided: filling in the missing values in the time-series data, detecting and processing the outliers in the time-series data, etc.

[0072] In an alternative embodiment, step S2 may specifically include the following steps:

[0073] Step S2.1: According to the time period and specific requirements of the time-series data selected by the user, draw a time-series graph to visually display the changing trend of the time-series data over time.

[0074] Specifically, the steps of drawing the time-series graph can be implemented by using a graph drawing library or tool, such as Matplotlib, Plotly, etc. The user can select different time periods and specific requirements to generate the corresponding time-series graph, so as to visually display the changing trend of the data over time.

[0075] Step S2.2: Conduct statistical analysis on the selected time-series data interval, and at least one of the statistical indicators for the statistical analysis includes: mean, maximum value, minimum value, standard deviation, moving average. By conducting statistical analysis on the selected time-series data interval, statistical indicators such as mean, maximum value, minimum value, standard deviation, and moving average are calculated, so as to help the user identify the overall characteristics and trends of the data.

[0076] Compared with the prior art, through this intuitive and efficient time-series data display and analysis method of the present application, the user can more conveniently observe and analyze the changing trend of the data, and at the same time obtain the overall characteristics and trends of the data through statistical analysis, improving the efficiency and accuracy of data processing and analysis.

[0077] Step S3: Parameter selection; wherein, an interactive UI interface is provided by the online calculation software in the architecture of the online calculation system for civil aircraft flight test time-series data provided by the present application, and the parameters to be analyzed are selected in the time-series tree diagram.

[0078] Specifically, in this embodiment, the system displays the uploaded source data set in the form of a tree diagram, and each node corresponds to a parameter or a combination of parameters. The size, color, or shape of the node can represent certain attributes of the parameter, such as the amount of data, data quality, or importance level. The user can view the detailed information of the parameter, such as the parameter name, description, data type, time range, etc., by clicking on or hovering over the node. In addition, the interaction interface can provide interaction elements such as check boxes, radio buttons, or drag-and-drop operations, enabling the user to conveniently select one or more parameters. The user can select a single parameter for analysis or select multiple parameters for combined analysis to study the associations and impacts between different parameters. This application allows the user to filter according to the attributes or characteristics of the parameters, such as selecting parameters within a specific time period, parameters in a specific flight phase, or parameters collected by a specific sensor. This filtering function can help the user quickly locate the parameters of interest and improve the selection efficiency.

[0079] Step S4: Edit the formula; The formula editor of this application contains a rich library of scientific calculation symbols, such as basic arithmetic operators, comparison operators, functions (such as sin, cos, log, exp, etc.), statistical functions, and built-in matrix and vector operators. Among them, the user can also construct formula logic and edit operators to compile into advanced algorithms.

[0080] In an alternative embodiment, step S4 may specifically include the following steps:

[0081] Step S4.1: Pre-define calculation templates, where the analysis methods for time series include at least one of the following: moving average, exponential smoothing, autoregressive model. The user can directly use these templates and quickly apply them to their own data for analysis, reducing the complexity of manually writing formulas and the possibility of errors. Secondly, the system also allows the user to customize templates and save them for quick invocation in subsequent use.

[0082] Step S4.2: Real-time formula verification, which immediately verifies the syntax and semantics of the formula input by the user to ensure the correctness of the formula, such as checking whether the parentheses match and whether the function calls are incorrect. If an error is found, the system will immediately prompt the user to make modifications.

[0083] The real-time formula verification function in step S4.2 ensures that the user can immediately perform syntax and semantic checks when inputting the formula, avoiding the use of incorrect formulas, thereby improving the accuracy and effectiveness of the calculation.

[0084] This embodiment significantly reduces the probability of errors when users edit formulas through the combination of predefined calculation templates and real-time formula verification, improves the efficiency and accuracy of formula editing, and at the same time enhances the user experience and the reliability of calculation results. Compared with the prior art, the solution of this application does not require users to have profound programming knowledge, reduces the learning and usage threshold, and improves the usability of the system and the user experience. Thus, this application provides a more efficient, accurate, and convenient formula editing method, which is applicable to various complex time series data analysis scenarios.

[0085] Step S5: Scientific calculation; wherein, mathematical operations, data analysis, or data processing tasks are performed based on the user-defined function UDF of the time series database IoTDB.

[0086] In step S5, the user can perform at least one of the following on the time series data through the user-defined function UDF: statistical analysis, signal processing, complex aggregation, prediction model application, time series interpolation, and specific professional algorithms. Through the user-defined function UDF, the user can flexibly perform various complex analysis and processing tasks on the time series data, thereby improving the efficiency and accuracy of data processing and meeting the needs of different professional fields.

[0087] In an alternative embodiment, the user can use programming languages such as Python and Java to write UDFs to implement specific processing functions for time series data, as shown in Table 1 below:

[0088] Achievable computing functions Description Statistical analysis Such as calculating statistics such as the mean, median, standard deviation, etc. within a rolling window Signal processing Performing operations such as filtering, smoothing, and spectral analysis on time series data Complex aggregation Such as calculating the rate of change, slope, integral, or cumulative sum of consecutive measured values Prediction model application Applying machine learning or statistical models to data for predictive analysis Time series interpolation Filling in missing data points according to custom rules or algorithms Specific professional algorithms Using specific calculation formulas in the professional field

[0089] Table 1 Supported UDF calculation types

[0090] Specifically, the user can write a UDF function to calculate statistical metrics such as the average, median, standard deviation, maximum, and minimum of time series data; can also write UDFs to implement signal processing, such as filtering, smoothing, spectral analysis, denoising, etc.; can also write complex aggregation functions to aggregate the results of multiple time series data sources; in addition, the user can also use machine learning models for predictive analysis, write UDFs to implement time series interpolation, or apply specific professional algorithms to process time series data.

[0091] For example, the user can write a UDF function to implement the moving average calculation of time series data. This UDF function can receive a time window parameter, perform a sliding calculation on the time series data according to this time window, and output the moving average value. Further, the user can also write a UDF function to implement the Fourier transform and perform frequency domain analysis on the time series data. In this way, the user can customize various complex time series data processing and analysis functions according to specific needs.

[0092] Compared with the prior art, the solution of this embodiment has the following advantages: First, users can flexibly write UDFs according to their own needs to implement various complex time-series data processing and analysis functions, avoiding the cumbersome process in the prior art of converting data into a specific format and learning the corresponding programming language; Second, by using UDFs, the efficiency and accuracy of data processing can be improved to meet the needs of different professional fields; Finally, the solution of this application has high scalability, and users can continuously expand and optimize UDFs according to needs to achieve more functions.

[0093] Step S6: Data display and result visualization; wherein, the calculation results are presented in the form of a table or a time series graph through the time series graph, and users can conduct in-depth analysis on data trends or patterns or correlations.

[0094] Optionally, in step S6, interactive methods such as dragging or slider control are introduced in the time series graph, and an operation guide and help documents are provided.

[0095] Specifically, the interactive method of introducing dragging or slider control can be implemented in the following various ways: For example, draggable nodes are added to the time series graph, and users can adjust the time range by dragging the nodes to update the displayed data in real time; or a slider is added below the time series graph, and users can use the slider to zoom in and out and move the time axis to quickly locate and view data in a specific time period. In addition, the operation guide and help documents can be provided in various forms, such as embedded tutorials, video demonstrations, text descriptions, etc., to facilitate users to obtain help in different scenarios.

[0096] Thus, the solution provided by this embodiment enables users to analyze and operate data results more intuitively and efficiently by introducing the interactive method of dragging or slider control in the time series graph and providing an operation guide and help documents, significantly improving the usability and user experience of the system. Compared with the prior art, this application not only solves the technical problems of interactive operation and user guidance for calculation results, but also reduces the difficulty of using the system and improves user satisfaction through the support of various interactive methods and help documents.

[0097] The above steps can effectively solve the technical problems of data acquisition, storage, processing, analysis, and display existing in the prior art, achieve efficient data processing and analysis, and improve the convenience of data management and use. In practical applications, this application can provide a perfect solution for multiple technical problems that need to be solved in the online calculation of civil aircraft flight test time series data. First is the data upload problem. By uploading and importing time series data in the form of files, the integrity and consistency of the data can be ensured. Second is the data preprocessing problem. By filling in missing values in the time series data, detecting and processing outliers, the quality and reliability of the data can be improved. During the parameter selection process, by using a time series tree diagram to select the parameters to be analyzed, data analysis can be performed flexibly. During the formula editing process, users can construct formula logic, edit operators to compile into advanced algorithms, thereby realizing complex data processing and analysis. Scientific computing is based on the user-defined function UDF of the time series database IoTDB to execute mathematical operations, data analysis, or data processing tasks, and can achieve efficient data processing. Finally, through the time series diagram, the calculation results are presented in the form of a table or a time series diagram, and users can conduct in-depth analysis of data trends, patterns, or correlations.

[0098] To achieve the above object, the present invention also proposes an online calculation system for civil aircraft flight test time series data based on the web, including a client, a server, and a cloud database.

[0099] Figure 2 It is a schematic structural diagram of an embodiment of an online calculation system for civil aircraft flight test time series data based on the web of the present invention.

[0100] It should be noted that the online calculation architecture for algorithm arrangement of flight test time series data based on the web is an innovative web application development and deployment mode. This architecture effectively separates the application program logic, data storage, business logic, and user interface presentation layer, significantly improving the development efficiency, maintainability, and scalability of the system.

[0101] The following is Figure 2 a schematic diagram showing the main components of this architecture and their functions:

[0102] (1) Presentation layer: The presentation layer is mainly responsible for presenting information to users. It can be constructed using front-end technologies such as HTML, CSS, and JavaScript, presenting the data and logic of the application program to users in an intuitive manner. The presentation layer can adapt to multiple devices, including browsers, mobile devices, and desktop applications, ensuring that users can obtain a consistent interaction experience on different platforms.

[0103] Figure 3 is Figure 2 a schematic diagram of the technical tree of the system in. As Figure 3As shown, as a preferred embodiment, the front-end interface of the server is developed based on the v2.0 version of Vue.js, and Vue is used to display and operate the flight test time series data obtained from the back-end. The UI framework of ElementUi is introduced, and the uPlot chart library is built to provide rendering and charts for the time series data. MonacoEditor is used as the code editor, and users can customize and edit calculation formulas. Ant Design X6 is used for algorithm arrangement to build a graphical algorithm design interface.

[0104] Due to the separation of the logic and presentation layers, the system can improve the performance and scalability of the application by adding server resources or upgrading hardware to cope with the growing user demands and data volume.

[0105] (2) Application layer (server): The application layer is the core part of the system for processing business logic. It receives user requests from the presentation layer, performs corresponding business processing, and interacts with the data access layer to obtain or store data. The application layer can be implemented using programming languages such as Java and has powerful data processing capabilities and business logic implementation functions.

[0106] Specifically, the system uploads the collected time series data to the server through the client. The server includes modules such as data preprocessing (outlier repair, data filling, etc.), parameter selection (data management), formula editing, scientific calculation (frequency up / down conversion, etc.), data display, and result visualization. These modules work together to complete the full-process processing from data upload to result output.

[0107] (3) Data access layer: The data access layer is responsible for interacting with the database and performing data operations such as query, insert, update, and delete. This layer is implemented using an ORM (Object Relational Mapping) framework such as MyBatis to simplify the data access process and improve development efficiency and data operation flexibility.

[0108] (4) Data storage (cloud database): The data storage layer is responsible for storing the data of the application. It can include various storage solutions such as relational databases, NoSQL databases, cloud storage services, and time series databases to meet scenarios with different data types and storage requirements.

[0109] As a preferred embodiment, the time series database IoTDB and the document database MongoDB are used in the cloud database of the present invention to store structured and unstructured data respectively to support efficient data access and analysis. A data channel is established between the server and the cloud database to ensure timely data access and processing.

[0110] (5) Network transport layer: Responsible for transmitting data between the client and the server. It can use HTTP or HTTPS protocols to ensure the secure and reliable transmission of data. The design and optimization of the network layer directly affect the response speed of the application and the user experience.

[0111] (6) Client: The client is the entry point for users to interact with the application. It can be a browser, a mobile device, or a desktop application. The client is responsible for sending requests to the server and processing the response data returned by the server, providing users with a real-time and dynamic interaction experience.

[0112] In this embodiment, the separation between the above layers improves the reusability and maintainability of the code, enabling developers to more easily maintain, upgrade, and optimize the system, and reducing the long-term maintenance cost. In particular, the separation of the presentation layer and the data access layer enables the application to flexibly adapt to different front-end devices and database systems, enhancing the adaptability and flexibility of the system and facilitating a quick response to market changes and technological updates. Developers can focus on the development tasks of their respective layers, and the clear division of responsibilities improves the development efficiency and shortens the project development cycle.

[0113] Through the above solution, the system solves the problems in the prior art, such as the difficulty in collecting civil aircraft flight test timing data, many storage problems, and complex data processing and analysis. Compared with the prior art, the system of this application has the advantages of high data upload and processing efficiency, user-friendly interface, and powerful algorithm scheduling function, significantly improving the efficiency and accuracy of data processing and analysis.

[0114] To achieve the above object, the present invention also provides a computer device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor runs the program, it can implement the steps of an online calculation method for civil aircraft flight test timing data based on web as described in any one of the foregoing embodiments.

[0115] The processor and the memory can be set separately or integrated together. For example, they can be integrated on a system on chip (SOC) of a terminal device. It should be understood that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method embodiments can be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0116] To achieve the above object, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores executable instructions or programs. When the executable instructions or programs are processed and executed, an online calculation method for civil aircraft flight test timing data based on the web as described in any of the previous embodiments is implemented.

[0117] The readable storage medium is, for example, a memory. The memory can be a volatile memory or a non-volatile memory, or the memory can include both a volatile memory and a non-volatile memory at the same time. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0118] If the integrated unit in the above embodiments is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the above-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The software product is stored in the storage medium and includes several instructions for causing one or more devices (which can be a personal terminal, a client, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0119] The preferred specific embodiments of the present invention have been described in detail above. Only several implementation manners of the present invention are expressed, but it should not be construed as a limitation to the patent scope. The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification. It should be understood that those of ordinary skill in the art can make many modifications and variations according to the concept of the present invention without creative labor. Therefore, without departing from the concept of the present invention, all technical solutions that can be obtained by those skilled in the art in the technical field according to the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. A web-based online calculation method for civil aircraft flight test time series data, characterized in that: The steps include: Step S1: data upload; wherein, time series data is uploaded and imported in the form of a file, wherein the time series data includes a timestamp and numerical information corresponding to the timestamp; Step S2: data preprocessing, wherein missing values ​​in the time series data are filled, and abnormal values ​​in the time series data are detected and processed; Step S3: parameter selection; wherein the parameters to be analyzed are selected in the time series tree diagram; Step S4: Editing the formula; wherein the user can construct the formula logic and edit the operators to compile into an advanced algorithm; Step S5: scientific computing; wherein the user-defined function UDF based on the time series database IoTDB performs mathematical operations, data analysis or data processing tasks; Step S6: Data display and result visualization; wherein the calculation results are displayed in the form of a table or a time series diagram through a time series diagram, and the user can conduct in-depth analysis of data trends, patterns or associations.

2. The web-based online calculation method for civil aircraft flight test time series data according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S1.1: Uploading the time series data from multiple sensors or data sources simultaneously by using parallel job uploading; Step S1.2: data identification and classification, distinguishing structured data and unstructured heterogeneous data of the time series data; Step S1.3: synchronizing the structured data to the time series database IoTDB, and constructing a metadata model of the time series data based on the TsFile file format adaptive parameter type built into the time series database IoTDB; Step S1.4: Synchronize the heterogeneous data to the document database MongoDB.

3. The web-based online calculation method for civil aircraft flight test time series data according to claim 2, characterized in that: The uploaded time series data is displayed in a tree format so that the user can see the hierarchical structure of the time series data, and the user can choose to view all the time series data or a subset of the time series data within a specified time period.

4. The web-based online calculation method for civil aircraft flight test time series data according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S2.1: according to the time period and specific requirements of the time series data selected by the user, draw a time series diagram to intuitively display the change trend of the time series data over time; Step S2.2: Perform statistical analysis on the selected time series data interval, wherein the statistical indicators of the statistical analysis include at least one of the following: mean, maximum, minimum, standard deviation, and moving average.

5. The web-based online calculation method for civil aircraft flight test time series data according to claim 1, characterized in that: The step S4 specifically includes the following steps: Step S4.1: predefine a calculation template, wherein the analysis method for the time series includes at least one of: moving average, exponential smoothing, and autoregressive model; Step S4.2: Real-time formula verification, which instantly verifies the syntax and semantics of the formula entered by the user to ensure the correctness of the formula.

6. The web-based online calculation method for civil aircraft flight test time series data according to claim 1, characterized in that: In the step S5, the user can perform at least one of the following operations on the time series data through the user-defined function UDF: statistical analysis, signal processing, complex aggregation, prediction model application, time series interpolation, and specific professional algorithms.

7. The web-based online calculation method for civil aircraft flight test time series data according to claim 1, characterized in that: In the step S6, an interactive mode of dragging or sliding bar control is introduced into the timing diagram, and an operation guide and help document are provided.

8. A web-based online calculation system for civil aircraft flight test time series data, characterized in that: The online calculation method using web-based civil aircraft flight test time series data according to any one of claims 1 to 7 comprises: The client uploads the collected civil aircraft flight test time series data to the server; The server side includes a data preprocessing module, a parameter selection module, an editing formula module, a scientific computing module, a data display and result visualization module. Multiple core modules work together to realize the full process processing of the time series data from uploading to result output; Cloud databases, including the time series database IoTDB and the document database MongoDB, are used to store structured time series data and unstructured heterogeneous data, respectively, to support efficient data access and analysis; A data channel is established between the server and the cloud database, the server receives the time series data uploaded by the client and performs access operations in the cloud database; The front-end interface of the server is developed based on the v2.0 version of Vue.js. On the front-end page, Vue is used to display and operate the test flight timing data obtained from the back-end; the Element Ui UI framework is introduced, and the uPlot chart library is built to provide rendering and charts for timing data; Monaco Editor is used as the code editor to enable users to customize and edit calculation formulas; AntDesign X6 is used for algorithm arrangement to build a graphical algorithm design interface.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor runs the program, an online calculation method for civil aircraft flight test time series data based on a web is implemented as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions or computer programs, and when the computer-executable instructions or computer programs are processed and executed, the web-based online calculation method for civil aircraft flight test timing data according to any one of claims 1 to 7 is implemented.

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