Data analysis processing method and data analysis processing system based on b_s architecture

Abstract

The application provides a data analysis processing method and system based on a B_S architecture, belongs to the technical field of simulation test data processing, and comprises the following steps: obtaining a simulation test result file directory; selecting a to-be-processed result file in the simulation test result file directory and sending the to-be-processed result file to a client; the client generates a data configuration file, selects a data field in the to-be-processed result file as source data for data analysis processing, and imports the source data into the data configuration file; judging whether the source data needs to be configured with an algorithm; the server receives the source data, filters and encapsulates the source data to form preset structure data, sends the preset structure data to python for processing to obtain result data, and feeds back the result data to the client. The application does not need to download and install software such as matlab for data processing and analysis, and improves the convenience of use.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of simulation test data processing, and particularly relates to a data analysis processing method and system based on a B_S architecture. BACKGROUND

[0002] After the simulation test is completed, a user needs to process and analyze the results generated by the simulation test. In the traditional technology, most of the methods are to process and analyze the test results through software such as matlab. The software such as matlab needs to be downloaded first, then installed, and then the experimental data is processed and analyzed. The downloading and installation operation of the software such as matlab is very troublesome, which brings great inconvenience to the user, occupies a large storage space, requires a high storage space of the user's computer, cannot be applied to the process operation of data processing and algorithm configuration editing of multiple user terminals, and cannot be intuitively analyzed and displayed on multiple user terminals, resulting in very low flexibility and operation efficiency of data processing and analysis. SUMMARY

[0003] In view of the above problems in the process of data processing and analysis of the simulation test system, the software for analysis and processing cannot be applied to the process operation of data processing and algorithm configuration editing of multiple user terminals, and cannot be intuitively analyzed and displayed on multiple user terminals, resulting in very low flexibility and operation efficiency of data processing and analysis. The application provides a data analysis processing method and system based on a B_S architecture.

[0004] Compared with the traditional simulation test data processing method, the application can support multiple terminal users, does not need to be downloaded, and can directly process and analyze the test results through the access browser after the simulation test is completed, greatly improving the flexibility and efficiency of simulation test data processing and analysis. The specific technical scheme is as follows:

[0005] The data analysis processing method based on the B_S architecture comprises the following steps:

[0006] Obtaining a simulation test result file directory;

[0007] Selecting a to-be-processed result file in the simulation test result file directory and sending the to-be-processed result file to a client;

[0008] The client generates a data configuration file, selects a data field in the to-be-processed result file as source data for data analysis processing, and imports the source data into the data configuration file;

[0009] Judging whether the source data needs to be configured with an algorithm;

[0010] If needed, configure the algorithm and parameters, repeat the judgment step;

[0011] If not needed, send the source data and / or the source data configured by the algorithm to the server;

[0012] The server receives the source data and / or the source data configured by the algorithm, filters and encapsulates the source data and / or the source data configured by the algorithm, forms preset structure data, sends it to python for processing to obtain result data and feeds back to the client.

[0013] Further limitation, the server receives the source data, filters and encapsulates the source data, forms the preset structure data, sends it to python for processing to obtain the result data and feeds back to the client, specifically includes:

[0014] The server receives the source data and / or the source data configured by the algorithm, and filters and processes the source data and / or the source data configured by the algorithm to generate process data;

[0015] The server encapsulates the process data to form preset structure data;

[0016] The server sends the preset structure data to python for data processing to generate result data;

[0017] The server feeds back the result data to the client.

[0018] Further limitation, the server sends the preset structure data to python for data processing to generate result data specifically includes:

[0019] The server starts the python data processing process by controlling the cmd command, and sends the preset structure data to python for data processing to generate result data.

[0020] Further limitation, the server receives the source data, filters and encapsulates the source data, forms the preset structure data, sends it to python for processing to obtain the result data and feeds back to the client, which further includes storing the result data.

[0021] Further limitation, the data analysis and processing method based on B_S architecture further includes:

[0022] The client receives the result data fed back by the server, and performs visual analysis according to the result data.

[0023] Further limitation, if needed, configure the algorithm and parameters, repeat the judgment step, which further includes:

[0024] Store the source data configured by the algorithm.

[0025] Further limitation, the source data and / or the source data configured by the algorithm are sent to the server through the http request.

[0026] A data analysis processing system based on B_S architecture, comprising

[0027] The data acquisition module is configured to acquire a simulation test result file directory.

[0028] The data selection module is configured to select a to-be-processed result file in the simulation test result file directory and send the to-be-processed result file to the client.

[0029] The configuration file generation module is configured to generate a data configuration file by the client, select a data field in the to-be-processed result file as source data for data analysis processing, and import the source data into the data configuration file.

[0030] The configuration module is configured to determine whether the source data needs to be configured with an algorithm, configure the algorithm and parameters if the source data needs to be configured with the algorithm, and repeat the determination step, and send the source data and / or the source data configured with the algorithm to the server if the source data does not need to be configured with the algorithm.

[0031] The data processing module is configured to receive the source data and / or the source data configured with the algorithm by the server, filter and encapsulate the source data and / or the source data configured with the algorithm, form preset structure data, send the preset structure data to python for processing to obtain result data, and feed back the result data to the client.

[0032] Further limitation, the data processing module is configured to receive the source data and / or the source data configured with the algorithm by the server, filter and encapsulate the source data and / or the source data configured with the algorithm, form preset structure data, start a python data processing process by controlling a cmd command, send the preset structure data to python for data processing, generate result data after the data processing is completed, and feed back the result data to the client.

[0033] Further limitation, the data analysis processing system based on B_S architecture further comprises a visual analysis module,

[0034] The visual analysis module is configured to receive the result data fed back by the server by the client and perform visual analysis according to the result data.

[0035] Compared with the prior art, the present application has the following advantages:

[0036] 1. The data analysis processing method based on the B_S architecture, which acquires a simulation test result file directory first; selects data to be analyzed and processed and sends it to a client; configures an algorithm and parameters on the client; sends the configured algorithm to a server; analyzes and processes data through the server; feeds back to the client after processing; and displays through the client. The data analysis processing method can support multiple terminals to access quickly through a browser, and can process and analyze data without downloading and installing, thereby improving user convenience.

[0037] 2. The data analysis processing method based on the B_S architecture, which further comprises visualizing analysis on the client according to result data, so that users can directly perform visualized graphic analysis on the client, and can customize style and legends of various graphic analysis, so that the visual effect is more beautiful, data analysis and observation are very intuitive, and user convenience is further improved.

[0038] 3. To adapt to the B_S architecture, an algorithm is configured for source data on the client, and corresponding parameters are configured for the algorithm, and then sent to the server for processing. By filtering source data, algorithms and parameters without source data are filtered out to prevent errors in data processing and analysis. The preset structure data is formed to facilitate corresponding matching between source data, algorithms and parameters. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The process schematic diagram of the data analysis processing method based on the B_S architecture in Example 1;

[0040] Figure 2 The process schematic diagram of the data analysis processing method based on the B_S architecture in Example 2;

[0041] Figure 3 The schematic diagram of the data analysis processing system based on the B_S architecture in Example 3;

[0042] Figure 4 The schematic diagram of the data analysis processing system based on the B_S architecture in Example 4. DETAILED DESCRIPTION

[0043] The technical solutions of the present application will be further explained and described below in combination with the drawings and examples, but the present application is not limited to the following described embodiments.

[0044] Example 1

[0045] Referring to Figure 1 , the data analysis processing method based on the B_S architecture comprises the following steps:

[0046] S1: Obtain the simulation test result file directory to be processed; the simulation test result file directory is read from a disk or a database, and the obtained simulation test result file directory includes not only the result files generated by simulation tests, but also the result files generated after data processing. These files are stored on a disk and maintained in a database.

[0047] S2: Select one or more result files to be processed in the simulation test result file directory obtained in step S1, and send the selected one or more result files to a client; after being sent to the client, the result file directory is displayed in the form of a browser on the client; the result file directory can be added, deleted, modified, searched, viewed, etc. on the client, and the result file directory of a historical version can also be added, deleted, modified, searched, viewed, etc.

[0048] S3: The client generates a data configuration file, selects the data fields of the result files in step S2 as source data for data analysis processing, and imports the source data into the data configuration file;

[0049] S4: Determine whether the source data in step S3 needs to be configured with an algorithm, if the source data needs to be configured with an algorithm, configure the algorithm for the source data and configure the corresponding parameters for the algorithm; if the source data does not need to be configured with an algorithm, send the source data that does not need to be configured with an algorithm to the server; store the source data after the algorithm is configured, so as to be traced and viewed subsequently; the algorithm in this step includes trigonometric function algorithm (Pythagorean theorem, sine function, etc.), scientific calculation algorithm (addition, subtraction, square root, root mean square, etc.), statistical algorithm (peak value, sorting, etc.), and signal processing algorithm (filtering, Fourier transform), etc.

[0050] Determine whether the source data configured with the algorithm needs to be configured with an algorithm again, if the source data needs to be configured with an algorithm again, repeat the algorithm configuration process until the algorithm configuration is completed, and send the source data configured with the algorithm to the server; if the source data does not need to be configured with an algorithm again, send the source data configured with the algorithm to the server;

[0051] S5: The server receives the source data configured with the algorithm and the source data not needing to be configured with the algorithm, performs filtering processing on the source data configured with the algorithm and the source data not needing to be configured with the algorithm, encapsulates to form preset structure data, the server sends the preset structure data to python for data processing, generates result data after processing, feeds back the result data to the client, and stores the generated result data, preferably in a disk. Preferably, the source data configured with the algorithm or the source data not needing to be configured with the algorithm in this step is sent to the server through an http request. Further preferably, the server starts the python data processing process through a control cmd command in this step, and sends the preset structure data to python for data processing; wherein the filtering processing refers to filtering out the configuration data only with the algorithm but without the data source, and leaving the configuration data with the data source; the preset structure data refers to a two-dimensional array formed by the data source, the algorithm and the parameters configured for the algorithm.

[0052] This embodiment takes the aileron open-loop frequency response test data as an example for illustration:

[0053] S1: Obtain the simulation test result file directory of the aileron open-loop frequency response test data from the database;

[0054] S2: Select one or more result files of the aileron open-loop frequency response test data needing data analysis and processing from the simulation test result files of the aileron open-loop frequency response test data obtained in step S1, and select one or more result files to send to the client; and perform functions such as adding, deleting, modifying, searching and viewing on the simulation test result file directory of the aileron open-loop frequency response test data in the client;

[0055] S3: The client generates a data configuration file, selects the fields of the aileron open-loop frequency response test data in the result file in step S2 as source data for data analysis and processing, and imports the source data into the configuration data file;

[0056] S4: Determine whether the source data in step S3 needs to be configured with the algorithm, if the source data needs to be configured with the algorithm, configure the algorithm for the source data and configure the corresponding parameters for the algorithm, for example, configure the root mean square algorithm for the source data and configure the parameter of adding 2; if the source data does not need to be configured with the algorithm, send the source data not needing to be configured with the algorithm to the server through an http request, and store the source data after being configured with the algorithm for subsequent tracing and viewing;

[0057] Judging whether the source data configured with the algorithm needs to be configured with the algorithm again, if the source data configured with the algorithm needs to be configured with the algorithm again, repeating the configuration algorithm process until the algorithm configuration is completed, and sending the source data configured with the algorithm to the server through the http request mode; if the source data configured with the algorithm does not need to be configured with the algorithm again, sending the source data configured with the algorithm to the server through the http request mode.

[0058] S5: The server receives the source data configured with the algorithm in step S4, filters the source data, encapsulates the source data into a specified preset structure data, starts a python data processing process through a control cmd command, sends the preset structure data to the python for data processing, generates result data after the processing is completed, feeds back the result data to the client, and stores the generated result data.

[0059] Embodiment 2

[0060] Referring to Figure 2 , the embodiment is based on a data analysis processing method of the B_S architecture, and further includes S6,

[0061] S6: The client receives the result data of step S5, and performs visual analysis according to the result data. Specifically, the received result data is displayed on the client, and visual graphic analysis is performed according to the result data, including column chart analysis, pie chart analysis, bubble chart analysis, and the like. Specifically, after the data processing is completed, the result data after the processing is returned to the client for presentation, and the user can perform specified form visual graphic analysis on the browser interface according to the returned processing result, and intuitively view the test processing result.

[0062] Embodiment 3

[0063] Referring to Figure 3 , the embodiment is a data analysis processing system based on the B_S architecture, which is formed on the basis of the embodiment 1, and includes a data acquisition module, a data selection module, a configuration file generation module, a configuration module, an algorithm judgment module, and a data processing module,

[0064] The data acquisition module is used for acquiring a simulation test result file directory to be processed; the simulation test result file directory is read from a disk or a database; the acquired simulation test result file directory includes not only a result file generated by simulation test, but also a result file generated after data processing; and these files are stored on the disk and maintained in the database.

[0065] Data selection module: for selecting one or more result files that need to be analyzed and processed in the acquired simulation test result file directory, and sending the selected one or more result files to the client; after being sent to the client, the result file directory is displayed in the form of a browser on the client; the result file directory can also be added, deleted, modified, searched, viewed, etc. on the client, and the result file directory of the historical version can also be added, deleted, modified, searched, viewed, etc.

[0066] Configuration file generation module: for generating a data configuration file on the client, selecting the data field of the result file as the source data for data analysis and processing, and importing the source data into the data configuration file;

[0067] Configuration module: for determining whether the source data needs to be configured with an algorithm, if the source data needs to be configured with an algorithm, configuring the algorithm for the source data and configuring the corresponding parameters for the algorithm; if the source data does not need to be configured with an algorithm, sending the source data that does not need to be configured with an algorithm to the server; storing the source data after the algorithm is configured, so as to be traced and viewed later; wherein the algorithm includes trigonometric function algorithm (Pythagorean theorem, sine function, etc.), scientific calculation algorithm (addition, subtraction, square root, root mean square, etc.), statistical algorithm (peak value, sorting, etc.), and signal processing algorithm (filtering, Fourier transform) and the like;

[0068] Determining whether the source data configured with the algorithm needs to be configured with an algorithm again, if the source data needs to be configured with an algorithm again, repeatedly starting the configuration module until the algorithm configuration is completed, and sending the source data configured with the algorithm to the server; if the source data does not need to be configured with an algorithm again, sending the source data configured with the algorithm to the server;

[0069] Data processing module: for receiving the source data configured with the algorithm and the source data that does not need to be configured with an algorithm on the server, filtering the source data configured with the algorithm and the source data that does not need to be configured with an algorithm, and encapsulating to form a preset structure data, the server sends the preset structure data to python for data processing, generates result data after the processing is completed, and feeds back the result data to the client, and stores the generated result data, preferably in a disk. Preferably, the source data configured with the algorithm is sent to the server in the form of an http request. Further preferably, the server starts the python data processing process by controlling the cmd command, and sends the data of the specified data structure to python for data processing.

[0070] This embodiment takes aileron open-loop frequency response test data as an example for illustration:

[0071] Data acquisition module: for acquiring the simulation test result file directory of the aileron open-loop frequency response test data from the database;

[0072] Data selection module: for selecting one or more result files of aileron open-loop frequency response test data that need to be analyzed and processed in the simulation test result file of the acquired aileron open-loop frequency response test data, and selecting one or more result files to send to the client; and adding, deleting, modifying, searching, viewing, etc. Function to the directory of the simulation test result file of the aileron open-loop frequency response test data in the client;

[0073] Configuration file generation module: for the client to generate a data configuration file, select the fields of the aileron open-loop frequency response test data in the result file as the source data for data analysis and processing, and import the source data into the configuration data file;

[0074] Configuration module: for judging whether the source data needs to be configured with an algorithm, if the source data needs to be configured with an algorithm, configuring the algorithm for the source data and configuring the corresponding parameters for the algorithm, for example, configuring the root mean square algorithm for the source data and configuring the parameter of adding 2; if the source data does not need to be configured with an algorithm, sending the source data that does not need to be configured with an algorithm to the server through the http request mode, storing the source data after the algorithm is configured, so as to facilitate subsequent tracing and viewing;

[0075] Judging whether the source data configured with the algorithm needs to be configured with the algorithm again, if the source data needs to be configured with the algorithm again, repeating the configuration module until the algorithm configuration is completed, and sending the source data configured with the algorithm to the server; if the source data does not need to be configured with the algorithm again, sending the source data configured with the algorithm to the server through the http request mode;

[0076] Data processing module: for the server to receive the source data configured with the algorithm, filter and process the source data, and encapsulate the preset structure data, the server starts the python data processing process through the control cmd command, and sends the preset structure data to python for data processing, generates the result data after the processing is completed, and feeds back the result data to the client, and stores the generated result data.

[0077] Embodiment 4

[0078] Referring to Figure 4 , the data analysis and processing system based on the B_S architecture further includes a visual analysis module,

[0079] The visual analysis module is configured to receive the result data at the client and perform visual analysis according to the result data. Specifically, the received result data is displayed at the client, and visual graphical analysis is performed according to the result data, including column chart analysis, pie chart analysis, bubble chart analysis, and the like. Specifically, after the data processing is completed, the processed result data is returned to the client for presentation. The user can perform specified form visual graphical analysis on the browser interface according to the returned processing result, and intuitively view the test processing result.

Claims

1. A data analysis processing method based on a B_S architecture, characterized in that, The method comprises the following steps: obtaining a simulation test result file directory; selecting a to-be-processed result file in the simulation test result file directory and sending the to-be-processed result file to a client; generating a data configuration file by the client, selecting a data field in the to-be-processed result file as source data for data analysis and processing, and importing the source data into the data configuration file; judging whether the source data needs to be configured with an algorithm; if yes, configuring the algorithm and parameters and repeating the judging step; if no, sending the source data and / or the source data after the algorithm configuration to a server; receiving the source data and / or the source data after the algorithm configuration by the server, filtering and packaging the source data and / or the source data after the algorithm configuration to form preset structure data, sending the preset structure data to python for processing to obtain result data and feeding back the result data to the client; the server receiving the source data and filtering and packaging the source data to form preset structure data, sending the preset structure data to python for processing to obtain result data and feeding back the result data to the client specifically comprises: the server receiving the source data and / or the source data after the algorithm configuration and filtering and processing the source data and / or the source data after the algorithm configuration to generate process data, the filtering and processing being filtering out configuration data only with the algorithm but without the data source and leaving configuration data with the data source; the server packaging the process data to form preset structure data, the preset structure data being a two-dimensional array containing the data source, the algorithm and parameters configured for the algorithm; the server sending the preset structure data to python for data processing to generate result data; the server feeding back the result data to the client.

2. The data analysis processing method based on B_S architecture according to claim 1, wherein, the server sending the preset structure data to python for data processing to generate result data specifically comprises: the server starting a python data processing process by controlling a cmd command and sending the preset structure data to python for data processing to generate result data.

3. The data analysis processing method based on B_S architecture according to claim 2, wherein, the server receiving the source data and filtering and packaging the source data to form preset structure data, sending the preset structure data to python for processing to obtain result data and feeding back the result data to the client further comprises storing the result data.

4. The data analysis processing method based on B_S architecture according to any one of claims 1-3, characterized in that, The data analysis and processing method based on the B_S architecture further comprises: the client receiving the result data fed back by the server and performing visual analysis according to the result data.

5. The data analysis processing method based on B_S architecture according to any one of claims 1-3, characterized in that, the step of configuring the algorithm and parameters and repeating the judging step further comprises: storing the source data after the algorithm configuration.

6. The data analysis processing method based on B_S architecture according to any one of claims 1-3, characterized in that, The source data and / or the source data after the algorithm configuration are sent to the server through an http request.

7. A data analysis processing system based on a B_S architecture, characterized by, It comprises: a data acquisition module for obtaining a simulation test result file directory; a data selection module for selecting a to-be-processed result file in the simulation test result file directory and sending the to-be-processed result file to a client; a configuration file generation module for generating a data configuration file by the client, selecting a data field in the to-be-processed result file as source data for data analysis and processing, and importing the source data into the data configuration file; a configuration module for judging whether the source data needs to be configured with an algorithm; If needed, the algorithm and parameters are configured, the judgment step is repeated; if not, the source data and / or source data completed algorithm configuration are sent to the server; And the data processing module: for the server to receive source data and / or source data completed algorithm configuration, and filter and package the source data and / or source data completed algorithm configuration, form the preset structure data, send to python for processing to obtain result data and feedback to the client; The server receives the source data, filters and packages the source data, forms the preset structure data, sends to python for processing to obtain the result data and feeds back to the client, and the specific steps are as follows: The server receives the source data and / or source data completed algorithm configuration, and filters and packages the source data and / or source data completed algorithm configuration to generate process data, the filtering and packaging refers to filtering out the configuration data only with algorithm but without data source, and leaving the configuration data with data source; The server encapsulates the process data to form the preset structure data, and the preset structure data refers to a two-dimensional array formed by containing data source, algorithm and parameters configured for the algorithm; The server sends the preset structure data to python for data processing to generate result data; The server feeds back the result data to the client.

8. The data analytics processing system based on B_S architecture as claimed in claim 7, wherein, The data processing module specifically is: for the server to receive source data and / or source data completed algorithm configuration, and filter and package the source data and / or source data completed algorithm configuration to form the preset structure data, start the python data processing process through the control cmd command mode, and send the preset structure data to python for data processing, generate result data after processing, and feed back the result data to the client.

9. The data analytics processing system based on B_S architecture according to claim 7 or 8, characterized in that, The data analysis and processing system based on B_S architecture further comprises a visual analysis module, The visual analysis module: for the client to receive the result data fed back by the server, and perform visual analysis according to the result data.

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