A visual experimental flow field velocity distribution data automatic processing statistical analysis method, system, processing device and storage medium

Abstract

The application relates to a visual experimental flow field velocity distribution data automatic processing statistical analysis method, system, processing equipment and storage medium, which comprises the following steps: reading original experimental flow field velocity distribution data files in a target folder; establishing a standardized experimental flow field velocity distribution data matrix; performing coordinate compensation on measurement coordinates in the standardized experimental flow field velocity distribution data matrix; performing abnormal identification and automatic screening on velocity vectors in the coordinate-compensated standardized experimental flow field velocity distribution data matrix to obtain an effective experimental flow field velocity distribution data matrix; performing automatic clustering on the effective experimental flow field velocity distribution data matrix and establishing a space coordinate index; respectively calculating numerical simulation result data of the effective experimental flow field velocity distribution data matrix of the same measurement coordinate; and performing format unification on the calculated numerical simulation data to obtain a data file in a unified format. The application can be widely used in the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of experimental fluid mechanics data processing technology, and in particular to a method, system, processing equipment, and storage medium for automatic processing and statistical analysis of visual experimental flow field velocity distribution data. Background Technology

[0002] Particle Image Velocimetry (PIV) is a non-contact, full-field velocimetry method widely used in fluid mechanics, energy and power, aerospace, nuclear engineering, and combustion diagnostics. This method can instantaneously obtain the velocity vector distribution across the entire measurement cross-section, providing crucial experimental data for flow structure analysis, turbulence characteristic research, and numerical simulation verification. Currently, commercial PIV software (such as LaVision DaVis and TSI Insight 4G) can typically perform velocity vector calculations and save the experimental results in specialized data files such as Vec format. However, while these software programs primarily perform velocity vector calculations, the exported raw data still requires extensive post-processing by researchers. This includes extracting the necessary coordinates and velocity components, correcting the coordinates according to the experimental coordinate system, removing erroneous velocity vectors with low correlation or obvious anomalies, reorganizing the velocity data according to spatial location, calculating statistical parameters such as the average velocity, root mean square (RMS) velocity, and velocity fluctuation intensity, unifying the experimental and CFD simulation results into a unified format, and using post-processing software such as Tecplot to plot velocity contour maps, velocity vector maps, and statistical results.

[0003] Existing data processing workflows typically rely on manual switching between multiple software programs. For example, researchers first need to export Vec files from PIV software, then use software such as MATLAB and Excel to perform data extraction, data filtering, and statistical calculations, and finally import the data into Tecplot for plotting. This process involves a large number of repetitive manual operations, including file reading, variable selection, outlier deletion, operating condition identification, file renaming, color scale settings, and image export. Furthermore, for data analysis work involving numerous experimental operating conditions, a single experimental project often contains hundreds to thousands of velocity field data files, with different operating conditions requiring different clearances, Reynolds numbers, and measurement cross-sections. Existing methods usually rely on manually creating folders, manually inputting operating condition parameters, and manually setting plotting formats. This is not only inefficient but also prone to data omissions, naming errors, or inconsistent image styles across different operating conditions due to human error, affecting the comparative analysis of experimental results. Furthermore, when comparing experimental results with CFD simulation results, the two types of data usually come from different software platforms, and their variable names, coordinate formats, and data arrangement methods are significantly different. Researchers need to manually convert and unify these data, which further increases the workload of data processing.

[0004] Therefore, the existing technology has at least the following shortcomings: (1) the experimental data processing steps are cumbersome and the degree of automation is low; (2) a large number of repetitive manual operations lead to low processing efficiency; (3) abnormal velocity vectors mainly rely on manual screening, resulting in poor consistency of processing results; (4) experimental data and CFD data lack a unified data organization method; (5) the drawing process requires repeated setting of a large number of parameters, making it difficult to ensure the consistency of image styles under different working conditions; (6) it is difficult to achieve automatic statistical analysis and unified visualization output of large batches of experimental data. Therefore, it is urgent to propose a method that can realize automatic parsing of experimental flow field velocity distribution data, automatic screening of abnormal vectors, statistical analysis, unified processing of experimental and numerical simulation data, and batch automatic visualization output, so as to improve the efficiency of experimental data processing, reduce the degree of manual participation, and improve the consistency and reliability of experimental data analysis. Summary of the Invention

[0005] To address the aforementioned problems, the purpose of this invention is to provide an automatic processing and statistical analysis method, system, processing equipment, and storage medium for visual experimental flow field velocity distribution data, which can improve experimental data processing efficiency, reduce the degree of manual intervention, and improve the consistency and reliability of experimental data analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, it provides a method for automatic processing and statistical analysis of visualized experimental flow field velocity distribution data, comprising: Read the raw experimental flow field velocity distribution data files in the target folder, automatically identify raw experimental flow field velocity distribution data files that conform to the preset experimental data format, and form a queue of files to be processed; The original experimental flow field velocity distribution data files in the file queue to be processed are parsed sequentially to extract the measurement coordinates, velocity vectors and quality evaluation parameters of the corresponding original experimental flow field velocity distribution data, and to establish a standardized experimental flow field velocity distribution data matrix. According to the preset coordinate transformation rules, coordinate compensation is performed on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix to generate a new experimental flow field velocity distribution data file and its corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix. Anomaly identification and automatic filtering were performed on the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation to obtain an effective experimental flow field velocity distribution data matrix. Automatic clustering of the effective experimental flow field velocity distribution data matrix and establishment of spatial coordinate index; Based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix, the numerical simulation results of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate are calculated respectively. The numerical simulation data were standardized in format, and a unified format data file was obtained based on the new experimental flow field velocity distribution data file.

[0007] Further, the step of reading the raw experimental flow field velocity distribution data files in the target folder, automatically identifying raw experimental flow field velocity distribution data files that conform to the preset experimental data format, and forming a queue of files to be processed includes: Determine the target folder containing the experimental flow field velocity distribution data and establish an automatic retrieval mechanism for the experimental flow field velocity distribution data files; Based on the user-defined target path and the established automatic retrieval mechanism, all raw experimental flow field velocity distribution data files in the target folder are scanned, and raw experimental flow field velocity distribution data files that conform to the preset experimental data format are automatically identified to form a queue of files to be processed. For each original experimental flow field velocity distribution data file in the file queue to be processed, a corresponding data reading object is created.

[0008] Further, the original experimental flow field velocity distribution data files in the file queue to be processed are parsed sequentially to extract the measurement coordinates, velocity vectors, and quality evaluation parameters corresponding to the original experimental flow field velocity distribution data, and to establish a standardized experimental flow field velocity distribution data matrix, including: The raw experimental flow field velocity distribution data file in the file queue to be processed is read into the buffer area, and the character sequence of the raw experimental flow field velocity distribution data is established according to the preset data organization method; Based on the data structure of the original experimental flow field velocity distribution data file, the velocity field data region in the character sequence of the original experimental flow field velocity distribution data is automatically located, and an experimental data field index is established; Based on the established experimental data field index, the measurement coordinates, velocity components, and quality evaluation parameters of the original experimental flow field velocity distribution data corresponding to each original experimental flow field velocity distribution data file in the file queue to be processed are automatically extracted in sequence. Among them, the measurement coordinates include the spatial coordinates of the first direction of the experimental measurement section and the spatial coordinates of the second direction of the experimental measurement section, and the velocity vector includes the first velocity component and the second velocity component. The extracted measurement coordinates, velocity vectors, and quality evaluation parameters are rearranged in a unified order to establish a standardized experimental flow field velocity distribution data matrix, and the conversion of character variables to numerical variables is automatically completed. Check whether the standardized experimental flow field velocity distribution data matrix contains missing values, illegal characters, or abnormal data format.

[0009] Furthermore, a pre-established automatic screening model for abnormal velocity vectors is used to identify and automatically screen velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation. The construction process of the automatic screening model for abnormal velocity vectors is as follows: Set the standardized experimental flow field velocity distribution data matrix after coordinate compensation; For any velocity vector, if a preset validity criterion is met, the velocity vector is retained as a valid velocity vector; otherwise, it is determined to be an abnormal velocity vector and automatically deleted. The validity criteria include: ; ; ; in, For the first The quality evaluation parameters corresponding to each velocity vector; This is the quality evaluation threshold; For the first The first velocity component of the velocity vector; For the first The second velocity component of the velocity vector; This is the lower limit of axial velocity; , These are the minimum and maximum lateral velocities, respectively. The identified anomalous velocity vectors were classified, and the quality of the standardized experimental flow field velocity distribution data matrix after coordinate compensation was evaluated, including: The identified abnormal velocity vectors are classified and labeled according to the cause of the abnormality; For each original experimental flow field velocity distribution data file, the number of effective velocity vectors, the number of abnormal velocity vectors, and the number of different types of abnormal velocity vectors are counted. The effective data rate of the current standardized experimental flow field velocity distribution data matrix is ​​determined based on the ratio of the number of effective velocity vectors to the total number of velocity vectors. Based on the preset data quality evaluation rules, the effective data rate of the standardized experimental flow field velocity distribution data matrix is ​​compared with the data quality threshold. According to the preset data quality evaluation rules, the experimental velocity field data are marked as qualified or pending verification.

[0010] Furthermore, the automatic clustering of the effective experimental flow field velocity distribution data matrix and the establishment of a spatial coordinate index include: The velocity vectors are sorted according to the measured coordinates in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, and a spatial coordinate index is established according to the spatial coordinates of the first direction of the experimental measurement section in the measured coordinates. A caching mechanism is used to store the effective experimental flow field velocity distribution data matrix belonging to the same measurement coordinate in sequence. When a change in the measurement coordinate is detected, the current spatial coordinate index is statistically calculated, and the spatial coordinate index of the next set of measurement coordinates is automatically established.

[0011] Furthermore, the automatic clustering of the effective experimental flow field velocity distribution data matrix and the establishment of a spatial coordinate index include: The velocity vectors are sorted according to the measured coordinates in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, and a spatial coordinate index is established according to the spatial coordinates of the first direction of the experimental measurement section in the measured coordinates. A caching mechanism is used to store the effective experimental flow field velocity distribution data matrix belonging to the same measurement coordinate in sequence. When a change in the measurement coordinate is detected, the current spatial coordinate index is statistically calculated, and the spatial coordinate index of the next set of measurement coordinates is automatically established.

[0012] Furthermore, the process of standardizing the format of the calculated numerical simulation data and obtaining a unified format data file based on the new experimental flow field velocity distribution data file includes: Standardize the format of the calculated numerical simulation data; Based on the name of the new experimental flow field velocity distribution data file, the operating condition information is automatically identified, and statistical result files corresponding to the numerical simulation data with a unified naming rule are generated. Output statistical results files and numerical simulation data in a unified format as a data file.

[0013] Furthermore, the method also includes: The obtained data files in a unified format are batch visualized to generate corresponding experimental velocity cloud maps.

[0014] Furthermore, the step of batch visualizing the obtained uniformly formatted data files to generate corresponding experimental velocity cloud maps includes: Automatically retrieve all data files of the same format according to the preset directory and read them in sequence; Based on the name of the experimental flow field velocity distribution data file, the experimental conditions are automatically identified, and the image title, output file name, and batch save path of the experimental velocity cloud map are automatically generated. Generate experimental velocity cloud plots based on Tecplot variable mapping relationships; Configure a color scale for the generated experimental velocity cloud map; Configure the graphical parameters for the generated experimental velocity contour plot; Based on the identified experimental conditions, generate the name of the experimental velocity cloud map and export the final experimental velocity cloud map.

[0015] Furthermore, the step of automatically identifying experimental conditions based on the name of the read data file and automatically creating the image title, output file name, and batch save path of the experimental speed cloud map includes: According to the preset naming rules, the name of the read data file is automatically parsed. The name of the data file includes the measurement gap, Reynolds number, measurement section and data type. Establish a mapping relationship between experimental conditions; Based on the established mapping relationship, the data files are automatically checked for operational integrity. By using the established mapping relationship and based on the name of the parsed data file, the image title, output file name, and batch save path of the experimental velocity cloud map are automatically generated to distinguish experimental velocity cloud maps of different planes, shooting positions, and Reynolds numbers, to correspond the experimental velocity cloud map with its experimental conditions, and to save experimental velocity cloud maps of different shooting positions to different folders.

[0016] Furthermore, the automatic check of the operational integrity of the data file based on the established mapping relationship includes: Based on the pre-defined set of experimental conditions, establish a set of experimental conditions that should exist, and match the experimental conditions corresponding to the actual retrieved data files with the set of experimental conditions item by item; When a data file exists in the actual data file that matches the pre-set set of experimental conditions, the experimental condition corresponding to the data file is marked as completed; when no data file exists in the actual data file that matches the pre-set set of experimental conditions, the experimental condition is marked as missing, and the experimental condition information is recorded; when the number of data files corresponding to the same experimental condition does not meet the number requirement of the pre-set set of experimental conditions, the corresponding experimental condition is marked as pending review, the experimental condition integrity check result of the data file is generated, and the automatic check of the condition integrity of the data file is completed.

[0017] Secondly, a system for automatically processing and statistically analyzing visualized experimental flow field velocity distribution data is provided, including: The file reading module is used to read the original experimental flow field velocity distribution data files in the target folder, automatically identify the original experimental flow field velocity distribution data files that conform to the preset experimental data format, and form a queue of files to be processed. The parsing module is used to parse the original experimental flow field velocity distribution data files in the file queue to be processed in turn, extract the measurement coordinates, velocity vectors and quality evaluation parameters of the corresponding original experimental flow field velocity distribution data, and establish a standardized experimental flow field velocity distribution data matrix. The coordinate compensation module is used to perform coordinate compensation on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix according to the preset coordinate transformation rules, and generate a new experimental flow field velocity distribution data file and its corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix. The anomaly identification module is used to identify and automatically filter anomalies in the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, so as to obtain an effective experimental flow field velocity distribution data matrix. The automatic clustering module is used to automatically cluster the effective experimental flow field velocity distribution data matrix and establish a spatial coordinate index. The numerical simulation calculation module is used to calculate the numerical simulation results of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate, based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix. The format unification module is used to unify the format of the calculated numerical simulation data and obtain a unified format data file based on the new experimental flow field velocity distribution data file.

[0018] Thirdly, a processing device is provided, including a computer program, wherein when the computer program is executed by the processing device, it is used to implement the steps corresponding to the above-mentioned method for automatic processing and statistical analysis of visual experimental flow field velocity distribution data.

[0019] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, is used to implement the steps corresponding to the above-mentioned method for automatic processing and statistical analysis of velocity distribution data in a visualized experimental flow field.

[0020] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention can automatically parse the raw velocity field data of PIV experiments without the need for manual reading of data files one by one, and realize the batch automatic import of experimental flow field velocity distribution data, which greatly improves the processing efficiency of experimental flow field velocity distribution data.

[0021] 2. This invention establishes an automatic screening mechanism for abnormal velocity vectors based on a combination of velocity range, velocity direction, and data quality evaluation parameters. It can automatically identify and remove erroneous velocity vectors, improve the quality of experimental flow field velocity distribution data, and reduce errors caused by human judgment.

[0022] 3. This invention can automatically classify experimental flow field velocity distribution data according to spatial coordinates, and automatically calculate statistical parameters such as average velocity, root mean square velocity, and velocity fluctuation intensity, thereby automating the experimental velocity field statistical analysis process.

[0023] 4. This invention can automatically unify the data format and variable correspondence between experimental flow field velocity distribution data and CFD numerical simulation data, enabling rapid comparison and analysis of experimental results and numerical simulation results, and improving experimental verification efficiency.

[0024] 5. This invention can automatically identify experimental parameters based on the experimental velocity field file name and establish a unified data naming rule to achieve automatic classification and management of data under different experimental conditions, avoiding manual naming errors.

[0025] 6. This invention can automatically call a visualization program to batch draw experimental speed cloud maps, and automatically complete color scale settings, image export and file saving, so that different experimental conditions have a unified drawing style and improve the comparability and standardization of experimental results.

[0026] 7. This invention establishes a standardized experimental flow field velocity distribution data matrix, avoiding data reading errors caused by different variable arrangement orders between different experimental flow field velocity distribution data files. This ensures that all experimental flow field velocity distribution data have a consistent data structure, forming a unified format of experimental flow field velocity distribution data. This improves the stability and reliability of the experimental flow field velocity distribution data parsing process, reduces the complexity of subsequent algorithms, improves data compatibility between different experimental conditions, and provides a standardized data foundation for subsequent coordinate correction and anomaly vector screening.

[0027] 8. This invention automates the entire process of experimental flow field velocity distribution data analysis, anomaly screening, statistical analysis, unified processing of experimental and numerical simulation data, and batch visualization output. It significantly reduces manual operation, improves experimental data processing efficiency and consistency of analysis results, and is particularly suitable for flow field measurement data processing tasks with a large number of experimental conditions.

[0028] In summary, this invention can be widely applied in the field of experimental fluid dynamics data processing technology. Attached Figure Description

[0029] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a schematic diagram of a method flow provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an experimental velocity cloud provided in an embodiment of the present invention. Detailed Implementation

[0030] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0031] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0032] Currently, existing methods for analyzing experimental flow field velocity distribution data suffer from cumbersome data processing steps, low processing efficiency, poor consistency of results, lack of unified data organization, difficulty in ensuring consistent image styles across different operating conditions, and challenges in achieving automated statistical analysis and unified visualization output of large volumes of experimental data. Therefore, this invention provides an automated processing and statistical analysis method for visualized experimental flow field velocity distribution data, comprising: reading raw experimental flow field velocity distribution data files from a target folder; automatically identifying raw experimental flow field velocity distribution data files conforming to a preset experimental data format to form a queue of files to be processed; sequentially parsing the raw experimental flow field velocity distribution data files in the queue to extract the corresponding measurement coordinates, velocity vectors, and quality evaluation parameters to establish a standardized experimental flow field velocity distribution data matrix; and performing coordinate compensation on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix according to preset coordinate transformation rules to generate a new experimental flow field. This invention relates to a method for automatically parsing, filtering out anomalies, statistically analyzing, uniformly managing, and batch visualizing velocity distribution data of particle image velocimetry (PIV) experimental flow fields and their corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrices. The method involves identifying and automatically filtering velocity vectors in the coordinate-compensated standardized experimental flow field velocity distribution data matrix to obtain a valid experimental flow field velocity distribution data matrix. The valid experimental flow field velocity distribution data matrix is ​​then automatically clustered, and a spatial coordinate index is established. Based on the established spatial coordinate index and the clustered valid experimental flow field velocity distribution data matrix, numerical simulation results for the same measurement coordinate are calculated. The calculated numerical simulation data is then formatted uniformly, and a unified format data file is obtained based on the new experimental flow field velocity distribution data file. Specifically, this invention relates to a method for automatically parsing, filtering out anomalies, statistically analyzing, uniformly managing, and batch visualizing velocity distribution data of particle image velocimetry (PIV) experimental flow fields and numerical simulation (CFD) results.

[0033] Correspondingly, other embodiments of the present invention provide a system, a processing device, and a storage medium.

[0034] Example 1 like Figure 1 As shown in the figure, this embodiment provides an automatic processing and statistical analysis method for visual experimental flow field velocity distribution data, including the following steps: 1) Read the raw experimental flow field velocity distribution data files in the target folder, automatically identify raw experimental flow field velocity distribution data files that conform to the preset experimental data format, and form a queue of files to be processed. In this embodiment, experimental velocity field data is used as an example, specifically: 1.1) Determine the target folder containing the experimental flow field velocity distribution data and establish an automatic retrieval mechanism for the experimental flow field velocity distribution data files.

[0035] Specifically, the automatic retrieval mechanism for experimental flow field velocity distribution data files includes: establishing a mapping between data files and corresponding locations and operating conditions based on the naming rules of data files (during the experiment, fields including shooting location and operating conditions are entered in a specific position of the file name).

[0036] 1.2) Based on the target path set by the user and the established automatic retrieval mechanism, scan all original experimental flow field velocity distribution data files in the target folder, and automatically identify original experimental flow field velocity distribution data files that conform to the preset experimental data format to form a queue of files to be processed.

[0037] Specifically, the experimental data is read by batch file traversal, eliminating the need for manual specification of each experimental file, thus enabling the automatic import of all velocity field data under the same experimental condition at once.

[0038] 1.3) For each original experimental flow field velocity distribution data file in the file queue to be processed, a corresponding data reading object is created, that is, a corresponding Excel file is created for each experimental flow field velocity distribution data file.

[0039] The above method enables batch automatic reading of experimental flow field velocity distribution data, avoiding the repetitive operations caused by manually opening each experimental flow field velocity distribution data file one by one, and improving the processing efficiency of experimental flow field velocity distribution data.

[0040] 2) The raw experimental flow field velocity distribution data files in the file queue to be processed are parsed sequentially to extract the measurement coordinates, velocity vectors, and quality evaluation parameters corresponding to the raw experimental flow field velocity distribution data. A standardized experimental flow field velocity distribution data matrix is ​​established to form experimental flow field velocity distribution data in a unified format, specifically as follows: 2.1) Since a large number of files generated during the experiment have a unified data structure, this step reads the original experimental flow field velocity distribution data files in the file queue to be processed into the buffer area, and establishes the character sequence of the original experimental flow field velocity distribution data according to the preset data organization method.

[0041] Specifically, the raw experimental flow field velocity distribution data files output by the PIV software, such as Vec files, store the raw experimental flow field velocity distribution data in a fixed format. The data content is arranged in a character sequence, which includes file header information, experimental parameter information, and velocity field data area.

[0042] 2.2) Based on the data structure of the original experimental flow field velocity distribution data file, the velocity field data region in the character sequence of the original experimental flow field velocity distribution data is automatically located, and an experimental data field index is established.

[0043] Specifically, at a specific position in the first line of the original experimental flow field velocity distribution data file, such as the Vec file, the header of the corresponding experimental flow field velocity distribution data, such as X, Z, U, W, etc., is read, and the corresponding experimental data field index is established.

[0044] 2.3) Based on the established experimental data field index, the measurement coordinates, velocity components and quality evaluation parameters of the original experimental flow field velocity distribution data corresponding to each original experimental flow field velocity distribution data file in the file queue to be processed are automatically extracted in sequence, without the need to manually specify the correspondence of each column of variables.

[0045] Specifically, the measurement coordinates include the spatial coordinates of the first direction (X-axis direction) and the spatial coordinates of the second direction (Y-axis direction) of the experimental measurement section. The velocity vector includes the first velocity component (X-axis velocity component) and the second velocity component (Z-axis velocity component). The quality evaluation parameter is the velocity vector quality evaluation parameter. The velocity vector quality evaluation parameter is used to reflect the reliability of the current velocity vector correlation and can serve as an important basis for subsequent automatic screening of abnormal velocity vectors.

[0046] Specifically, to improve the program's ability to process batch experimental flow field velocity distribution data, this embodiment automatically completes the above variable extraction based on fixed field intervals in the experimental flow field velocity distribution data file, without requiring manual specification of variable positions.

[0047] The above method can effectively avoid data arrangement differences caused by changes in experimental conditions and improve the consistency of subsequent algorithm processing.

[0048] 2.4) The extracted measurement coordinates, velocity vectors and quality evaluation parameters are rearranged in a unified order to establish a standardized experimental flow field velocity distribution data matrix. The conversion of character variables to numerical variables is automatically completed so that all experimental flow field velocity distribution data have a consistent data structure and form experimental flow field velocity distribution data in a unified format.

[0049] Specifically, the measured coordinates, velocity vectors, and quality evaluation parameters are stored in the same standardized experimental flow field velocity distribution data matrix according to a preset field order. Each row of the standardized experimental flow field velocity distribution data matrix corresponds to a velocity vector, and each column corresponds to an experimental variable. The experimental variable includes the spatial coordinates of the experimental measurement section in the first direction, the experimental measurement section in the second direction, the first velocity component, the second velocity component, and the quality evaluation parameters corresponding to the velocity vector.

[0050] 2.5) Check whether there are missing values, illegal characters or abnormal data format in the standardized experimental flow field velocity distribution data matrix.

[0051] Specifically, when abnormal data is detected in the standardized experimental flow field velocity distribution data matrix, it is automatically ignored, zeroed out, or reread according to preset rules to ensure the integrity of the entire standardized experimental flow field velocity distribution data matrix.

[0052] By adopting the above method, data reading errors caused by different variable arrangement orders between different experimental flow field velocity distribution data files can be avoided, forming experimental flow field velocity distribution data in a unified format. This improves the stability and reliability of the experimental flow field velocity distribution data parsing process, reduces the complexity of subsequent algorithms, improves data compatibility between different experimental conditions, and provides a standardized data foundation for subsequent coordinate correction and anomaly vector screening.

[0053] 3) Based on the preset coordinate transformation rules, coordinate compensation is performed on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix to generate a new experimental flow field velocity distribution data file and its corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix.

[0054] Specifically, since the experimental coordinate system established during the experiment usually uses the local position of the experimental setup as the reference coordinate, while subsequent experimental statistical analysis and comparison between experiments and numerical simulations all use a unified analysis coordinate system, it is necessary to uniformly transform the extracted measurement coordinates. The specific process is as follows: 3.1) Based on the relative positional relationship between the experimental coordinate system and the unified analysis coordinate system, coordinate compensation is performed on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix.

[0055] Specifically, based on preset coordinate transformation parameters, the second-direction spatial coordinates of the experimental measurement section in each measurement coordinate of the standardized experimental flow field velocity distribution data matrix are translated as a whole, so that the entire experimental velocity field is transformed to a unified coordinate reference.

[0056] Specifically, in other embodiments, coordinate scaling, coordinate rotation, mirror transformation, or multi-coordinate system transformation models can be established based on the actual experimental setup, and the specific process is not limited thereto.

[0057] After adopting a unified coordinate transformation, the experimental flow field velocity distribution data obtained under different experimental conditions, measurement sections, and experimental dates can all establish a unified spatial reference, providing a consistent spatial coordinate basis for subsequent statistical analysis and comparison of experimental and numerical simulation results.

[0058] 3.2) Based on the coordinate-compensated standardized experimental flow field velocity distribution data matrix, generate a new experimental flow field velocity distribution data file and automatically write the variable name, variable unit and the corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix.

[0059] Specifically, the output of the new experimental flow field velocity distribution data file is saved in a common text format, so that the experimental flow field velocity distribution data can be directly read by commonly used data analysis software such as MATLAB, Tecplot, Origin, and Python.

[0060] Specifically, the output standardized experimental flow field velocity distribution data maintains a consistent field arrangement order, ensuring that subsequent statistical analysis programs can read it directly without needing to perform variable mapping again.

[0061] Specifically, a new experimental flow field velocity distribution data file name is automatically generated based on the original experimental flow field velocity distribution data file name, while maintaining a one-to-one correspondence between the standardized experimental flow field velocity distribution data and the original experimental flow field velocity distribution data file.

[0062] This invention establishes a standardized data output mechanism, which enables the automatic conversion of raw experimental flow field velocity distribution data into experimental flow field velocity distribution data in a unified data format. This significantly reduces the time required for manual processing of experimental flow field velocity distribution data and improves the efficiency of experimental flow field velocity distribution data management and subsequent batch analysis capabilities.

[0063] 4) To ensure the accuracy of subsequent statistical analysis results, anomaly identification and automatic filtering were performed on the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, based on the velocity range, velocity direction, and quality evaluation parameters, to obtain the effective experimental flow field velocity distribution data matrix, specifically: 4.1) Due to the influence of factors such as particle concentration, lighting conditions, image matching accuracy, and boundary occlusion during PIV velocities measurement, the experimental results usually contain a small number of incorrectly matched velocity vectors. If these are directly used in statistical calculations, they will lead to significant errors in statistical parameters such as root mean square velocity and velocity pulsation. Therefore, this invention establishes an automatic screening model for abnormal velocity vectors: 4.1.1) Set the standardized experimental flow field velocity distribution data matrix after coordinate compensation. for: (1) in, , Indicates the first The measured coordinates of each velocity vector; , Indicates the first The first velocity component and the second velocity component of the velocity vector; Indicates the first The quality evaluation parameters corresponding to each velocity vector.

[0064] 4.1.2) For any velocity vector, if the preset validity criteria are met, the velocity vector is retained as a valid velocity vector; otherwise, it is judged as an abnormal velocity vector and automatically deleted.

[0065] Specifically, this embodiment comprehensively employs quality evaluation parameter constraints, axial velocity constraints, and lateral velocity constraints to construct validity condition criteria. The validity condition criteria include: (2) (3) (4) in, This is the quality evaluation threshold; This is the lower limit of the axial (i.e., y-direction) velocity; , These represent the minimum and maximum lateral velocities, respectively. Only data that simultaneously meet the above validity conditions will be considered valid velocity vectors for subsequent statistical analysis.

[0066] Furthermore, the present invention allows the above threshold parameters to be adjusted according to different experimental objects, so it is applicable not only to the experimental platform in this embodiment, but also to other PIV experimental systems.

[0067] 4.1.3) Classify the identified abnormal velocity vectors and evaluate the quality of the standardized experimental flow field velocity distribution data matrix after coordinate compensation: A) Classify and label the abnormal velocity vectors according to the validity conditions that the abnormal velocity vectors fail to meet.

[0068] Specifically, when the quality evaluation parameter (CHC) of the velocity vector is an abnormal value (negative number), the corresponding velocity vector is marked as having an abnormal quality evaluation; when the first velocity component is lower than the preset lower limit of lateral velocity, the corresponding velocity vector is marked as having an abnormal lateral velocity; when the second velocity component exceeds the preset axial velocity range, the corresponding velocity vector is marked as having an abnormal axial velocity.

[0069] B) For each original experimental flow field velocity distribution data file, count the number of effective velocity vectors, the number of abnormal velocity vectors, and the number of different types of abnormal velocity vectors. Determine the effective data rate of the current standardized experimental flow field velocity distribution data matrix based on the ratio of the number of effective velocity vectors to the total number of velocity vectors, so as to characterize the quality of the current standardized experimental flow field velocity distribution data matrix.

[0070] C) According to the preset data quality evaluation rules, mark the standardized experimental flow field velocity distribution data matrix as qualified or pending review.

[0071] Specifically, based on preset data quality evaluation rules, the effective data rate of the standardized experimental flow field velocity distribution data matrix is ​​compared with a data quality threshold. When the effective data rate meets the preset requirements, the current standardized experimental flow field velocity distribution data matrix is ​​marked as qualified data; otherwise, it is marked as data to be reviewed, and the corresponding data file and anomaly type are recorded for subsequent manual inspection or reprocessing.

[0072] By adopting the above method, abnormal velocity vectors are automatically removed while retaining the type and quantity information of abnormal data. This enables quantitative evaluation of the data quality of velocity distribution data in different experimental flow fields, avoids the inability to trace the source of abnormality after abnormal data is directly deleted, and can promptly detect experimental conditions with a high proportion of abnormal velocity vectors, thereby improving the reliability of subsequent statistical analysis results.

[0073] 4.2) The established abnormal velocity vector automatic screening model is used to identify and automatically screen the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation. This effectively removes abnormal velocity vectors with poor correlation, incorrect direction, and obvious deviation from the real flow field, thus obtaining an effective experimental flow field velocity distribution data matrix and improving the stability of subsequent statistical analysis results.

[0074] 5) Since the PIV experiment yields a two-dimensional velocity field, with each spatial location corresponding to multiple velocity measurement points, it needs to be reorganized according to the same spatial coordinates. This step automatically clusters the effective experimental flow field velocity distribution data matrix and establishes a spatial coordinate index, specifically: 5.1) Sort the velocity vectors according to the measured coordinates in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, and establish a spatial coordinate index according to the spatial coordinates of the first direction of the experimental measurement section in the measured coordinates.

[0075] Specifically, set all velocity vectors for: (5) in, Indicates the first A velocity vector, and , ; This represents the number of velocity vectors. For any measured coordinate value... , Indicates a non-specific one Coordinates, for example, as needed, such as Figure 2 As shown, The coordinates range from -50 to 50 mm, in 0.1 mm increments. They are -50, -49.9 up to 50 respectively, the same Under the same coordinates (Below) All velocity components (i.e., the following) Perform root mean square calculation.

[0076] Establish spatial coordinate index : (6) in, Indicates the first Spatial coordinate index of group measurement coordinates, spatial coordinate index All velocity vectors within the same experimental measurement section have the same spatial coordinates in the first direction. The measurement coordinates are grouped in intervals of 0.1 mm between -50 and 50 mm. For the first The first velocity component; This indicates the number of speed measurement points corresponding to the current measurement coordinates.

[0077] 5.2) A caching mechanism is used to store the effective experimental flow field velocity distribution data matrix belonging to the same measurement coordinate in sequence. When a change in the measurement coordinate is detected, the current spatial coordinate index is statistically calculated immediately, and the spatial coordinate index of the next set of measurement coordinates is automatically established.

[0078] Compared with traditional point-by-point statistics, this invention eliminates the need for manual division of coordinate regions and can automatically complete velocity clustering across the entire experimental cross section.

[0079] 6) Based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix, calculate the numerical simulation results of the effective experimental flow field velocity distribution data matrix for the same measurement coordinate, including average velocity, root mean square velocity, and root mean square velocity fluctuation, specifically: 6.1) Based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix, calculate the average velocity of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate. : (7) Using absolute value average velocity can avoid the cancellation of positive and negative lateral velocities, and more accurately reflect the amplitude of lateral velocity in the local flow field.

[0080] 6.2) Based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix, calculate the root mean square velocity of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate. : (8) The root mean square velocity (RMS velocity) comprehensively reflects the local velocity energy distribution and is an important statistical parameter for evaluating complex flow fields. Since this invention automatically calculates the RMS velocity for all spatial locations, manual statistical analysis of each coordinate is unnecessary, significantly improving the efficiency of experimental data processing.

[0081] 6.3) Based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix, calculate the root mean square of velocity fluctuations in the effective experimental flow field velocity distribution data matrix at the same measurement coordinate. : (9) in, It is a velocity pulsation, and , The average speed, and The root mean square of velocity fluctuations can accurately reflect the intensity of local turbulent fluctuations, providing an important basis for experimental flow field analysis.

[0082] 7) Standardize the format of the calculated numerical simulation data, and based on the new experimental flow field velocity distribution data file, obtain a data file with a unified format, specifically: 7.1) Standardize the format of the calculated numerical simulation data.

[0083] The final output of the numerical simulation data for each measured coordinate is set to a uniform format as follows: (10) in, Indicates the spatial coordinates of the first direction of the measured section; This represents the average velocity. All numerical simulation data are automatically output using a unified data format, maintaining a consistent variable order to facilitate direct comparison and analysis between subsequent experimental and CFD data.

[0084] 7.2) Based on the name of the new experimental flow field velocity distribution data file, the system automatically identifies operating condition information such as measurement gap, measurement section and Reynolds number, and generates statistical result files corresponding to numerical simulation data with a unified format according to a unified naming rule, thereby realizing automatic classification and management of batch experimental data.

[0085] 7.3) Output the statistical results file and the numerical simulation data in a uniform format as a data file (Excel file or TXT file).

[0086] Specifically, in the actual processing, the original experimental flow field velocity distribution data file is processed to obtain Excel files named according to the measurement cross-section (XZ, XY, and YZ planes), measurement height (105 refers to 1-1.5H), measurement gap (1GAP to 5GAP), and Reynolds number (1250-5000). Each file contains one column for the X-coordinate, and the other three columns are... , , wait.

[0087] Compared to the traditional method of manually exporting data one by one using commercial software and then manually organizing it, this embodiment can significantly reduce the degree of human involvement in the experimental data processing process, improve the efficiency of experimental data processing, and ensure that the data structure remains consistent across different experimental conditions, providing a reliable data foundation for the unified analysis of subsequent experimental data and numerical simulation data.

[0088] Furthermore, the method in this embodiment also includes: 8) Perform batch visualization of the obtained uniformly formatted data files to generate corresponding experimental velocity cloud plots, including: 8.1) Automatically retrieve all data files in the same format according to the preset directory and read them sequentially according to the experimental conditions.

[0089] Specifically, let the set of all experimental conditions be defined. for: (11) in, Indicates the first Group experimental conditions; Indicates the number of experimental conditions.

[0090] For any experimental condition, the corresponding experimental flow field velocity distribution data for: (12) in, Indicates the spatial coordinates of the first direction of the measured section; Indicates the spatial coordinates of the experimental measurement section in the second direction; Indicates the first velocity component; Indicates the second velocity component; This represents the quality evaluation parameters.

[0091] Specifically, this invention employs a cyclic reading mechanism to establish a queue of experimental flow field velocity distribution data files, enabling continuous automatic processing of a large number of experimental conditions.

[0092] This invention sequentially imports data for all experimental conditions without requiring manual selection of each experimental flow field velocity distribution data file.

[0093] 8.2) Based on the name of the read data file, automatically identify the experimental conditions and automatically create the image title, output file name, and batch save path for the experimental velocity cloud map, specifically: 8.2.1) Automatically parse the name of the read data file according to the preset naming rules. The name of the data file includes the measurement gap, Reynolds number, measurement section and data type.

[0094] Specifically, the name of the data file for: (13) in, Indicates the measurement gap; Represents the Reynolds number; Indicates the measurement section.

[0095] 8.2.2) Establish the mapping relationship between experimental conditions: (14) in, Indicates the corresponding experimental conditions. Indicates based on the name of the data file Automatically parse and obtain experimental operating condition information The mapping function.

[0096] 8.2.3) Based on the established mapping relationship, perform automatic integrity checks on the data files: A) Pre-set a set of experimental conditions and match the experimental conditions corresponding to the actual retrieved data files with the pre-set set of experimental conditions item by item. The experimental conditions include one or more of the following: measurement gap, Reynolds number, measurement cross section, and measurement height.

[0097] B) When a data file exists in the actual data file that matches the pre-defined set of experimental conditions, the experimental condition corresponding to the data file is marked as completed (the specific results can be output in the MATLAB output window); when no data file exists in the actual data file that matches the pre-defined set of experimental conditions, the experimental condition is marked as missing, and the missing experimental condition information such as measurement gap, Reynolds number, measurement cross section, or measurement height is recorded; when the number of data files corresponding to the same experimental condition does not meet the number requirement of the pre-defined set of experimental conditions, the corresponding experimental condition is marked as pending verification, the experimental condition integrity check result of the data file is generated, and the automatic check of the condition integrity of the data file is completed.

[0098] Specifically, the system determines whether a data file exists in the actual data file that matches a pre-defined set of experimental conditions. For example, it pre-defines experimental conditions with Reynolds numbers of 2500, 3750, and 5000. The system reads the data file, and if an experimental condition with a Reynolds number of 2500 is found, it marks the corresponding experimental condition as completed. After traversing all data, if an experimental condition with a Reynolds number of 5000 is found to be missing, the corresponding experimental condition for that data file is marked as missing and displayed.

[0099] Specifically, in this embodiment, an experimental condition set is established based on the measurement cross section, measurement height, measurement gap, and Reynolds number. The actual data files are automatically traversed and matched with the pre-set experimental condition set to automatically generate an experimental condition integrity check result. The experimental condition integrity check result includes at least existing conditions, missing conditions, and conditions with abnormal data file numbers.

[0100] Using the above method, problems such as missing experimental data or abnormal number of working condition files can be automatically detected before batch processing of experimental data. This avoids incomplete subsequent statistical analysis and velocity cloud map output due to missing experimental working conditions, while also reducing the workload of manually checking each experimental file.

[0101] 8.2.4) Using the established mapping relationship, based on the name of the parsed data file, the image title, output file name and batch save path of the experimental velocity cloud map are automatically generated to distinguish experimental velocity cloud maps of different planes, shooting positions and Reynolds numbers, correspond the experimental velocity cloud map with its experimental conditions, and save experimental velocity cloud maps of different shooting positions to different folders, without the need for manual input of experimental parameters.

[0102] 8.3) Establish Tecplot variable mapping relationships.

[0103] Specifically, based on the variable type, Tecplot variable mapping relationships are automatically established. These relationships include spatial coordinate variables (used to determine the measurement coordinates in the experimental velocity cloud plot), velocity vector variables (not involved in the experimental velocity cloud plot but used to draw the velocity vector plot), color mapping variables (used to determine the color scale of the experimental velocity cloud plot), and vector direction variables (the first velocity component corresponds to the X-axis, and the second velocity component corresponds to the Z-axis to draw the correct velocity vector plot).

[0104] Specifically, the first velocity component is set as the cloud plot color variable, and the two-dimensional velocity vector is set as the velocity vector variable. Therefore, Tecplot does not require manual reassignment of variables.

[0105] Using automatic variable mapping ensures that velocity distribution data from different experimental flow fields all use uniform variable definitions, improving plotting efficiency. Figure 1 To the point of being responsive.

[0106] 8.4) Based on the established Tecplot variable mapping relationship, generate the experimental velocity contour plot, specifically: 8.4.1) Define the color mapping function: (15) in, This indicates the color value. Different speeds are displayed as different colors in the cloud map.

[0107] 8.4.2) Based on the velocity distribution, establish a set of color contour lines. : (16) in, For the first There are 3 color contour lines, each corresponding to a speed level pre-set according to the Reynolds number in the experimental conditions.

[0108] 8.4.3) Calculate the color range based on the identified experimental conditions to ensure that different experimental conditions can maintain a unified color level division, so as to ensure that all experimental velocity cloud maps use a unified velocity level division (divided into 11 levels).

[0109] Compared to Tecplot's default automatic grading method, this invention can avoid the problem of inconsistent color ranges between different images, which makes it difficult to directly compare experimental results.

[0110] 8.4.4) Based on the set color mapping function, the established set of color contour lines and the calculated color range, an experimental velocity cloud map is generated according to a data file in a unified format.

[0111] 8.5) Configure a color scale for the generated experimental velocity contour plot, specifically: 8.5.1) Set the range of the color scale : (17) in, Indicates the lower limit of the color; Indicates the upper limit of the color range.

[0112] 8.5.2) Establish color levels based on the identified experimental conditions.

[0113] Specifically, color levels for: (18) in, Indicates the first There are several color levels, and the color levels are divided by equal spacing or preset spacing.

[0114] 8.5.3) Based on the established color levels, automatically adjust the upper and lower limits of the color scale to configure the color scale of the experimental velocity cloud map. Different experimental conditions adopt color distributions suitable for the velocity range of the flow field.

[0115] Using the above method can significantly improve the readability of velocity cloud maps.

[0116] 8.6) Configure the graphic parameters for the generated experimental velocity cloud map.

[0117] Specifically, the graphical parameters include the two-dimensional coordinate range, coordinate axis titles, coordinate scales, velocity vector display method, legend position, font size, background color, and image title. The legend position is uniformly located at the lower right of the experimental velocity cloud map, and the font size and background color are uniformly set to ensure a consistent style.

[0118] Furthermore, all experimental conditions used the same template, thus the velocity distribution data of the entire experimental flow field had a unified format.

[0119] Compared to traditional manual settings, the velocity cloud map of each experiment in this invention does not require any parameter readjustment.

[0120] 8.7) Based on the identified experimental conditions, generate a name for the experimental velocity contour map and export the final experimental velocity contour map, such as... Figure 2 As shown, specifically: 8.7.1) Generate the experimental velocity cloud map name based on the identified experimental conditions.

[0121] Specifically, the experimental velocity cloud map is named as follows: .

[0122] 8.7.2) Establish the output path .

[0123] 8.7.3) Based on the established output path, derive the final experimental velocity contour map.

[0124] Specifically, the experimental velocity contour plots are exported as TIFF images and saved as Tecplot layout files, with the plots categorized and stored accordingly. The entire export process requires no manual intervention. Therefore, hundreds of experimental velocity contour plots can be automatically generated with just a single program launch.

[0125] Step 8) Compared to traditional Tecplot manual plotting methods, this step eliminates the need for manual import of experimental data frame by frame, improving the efficiency of post-processing experimental data; it can automatically identify experimental conditions and name images, avoiding manual input errors; it can automatically establish a unified color scale, enabling direct comparison of velocity fields between different experimental conditions; it can automatically unify coordinate ranges, legend positions, fonts, and page layouts, improving the consistency and standardization of experimental velocity cloud maps; it can automatically export high-resolution images and layout files, facilitating the archiving of scientific research papers, technical reports, and experimental data; and it is suitable for batch processing tasks of a large number of experimental conditions, significantly reducing repetitive manual operations in the process of visualizing experimental data, improving the efficiency of post-processing and the reproducibility of results.

[0126] Example 2 This embodiment provides an automatic processing and statistical analysis system for visualized experimental flow field velocity distribution data, including: The file reading module is used to read the original experimental flow field velocity distribution data files in the target folder, automatically identify the original experimental flow field velocity distribution data files that conform to the preset experimental data format, and form a queue of files to be processed. The parsing module is used to parse the original experimental flow field velocity distribution data files in the file queue to be processed in turn, extract the measurement coordinates, velocity vectors and quality evaluation parameters of the corresponding original experimental flow field velocity distribution data, and establish a standardized experimental flow field velocity distribution data matrix. The coordinate compensation module is used to perform coordinate compensation on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix according to the preset coordinate transformation rules, and generate a new experimental flow field velocity distribution data file and its corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix. The anomaly identification module is used to identify and automatically filter anomalies in the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, so as to obtain an effective experimental flow field velocity distribution data matrix. The automatic clustering module is used to automatically cluster the effective experimental flow field velocity distribution data matrix and establish a spatial coordinate index. The numerical simulation calculation module is used to calculate the numerical simulation results of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate, based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix. The format unification module is used to unify the format of the calculated numerical simulation data and obtain a unified format data file based on the new experimental flow field velocity distribution data file.

[0127] In a preferred embodiment, the system further includes: The batch visualization module is used to batch visualize the obtained data files in a uniform format and generate corresponding experimental velocity cloud maps.

[0128] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0129] Example 3 This embodiment provides a processing device corresponding to the automatic processing and statistical analysis method for visualized experimental flow field velocity distribution data provided in Embodiment 1. The processing device can be applied to client processing devices, such as mobile phones, laptops, tablets, desktop computers, etc., to execute the method of Embodiment 1.

[0130] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores computer programs that can run on the processing device. When the processing device runs the computer programs, it executes the automatic processing and statistical analysis method for visualized experimental flow field velocity distribution data provided in Embodiment 1.

[0131] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0132] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0133] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] Those skilled in the art will understand that the structure of the above-described computing device is only a partial structure related to the present invention and does not constitute a limitation on the computing device to which the present invention is applied. A specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0135] Example 4 This embodiment provides a computer program product corresponding to the automatic processing and statistical analysis method for visual experimental flow field velocity distribution data provided in Embodiment 1. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the automatic processing and statistical analysis method for visual experimental flow field velocity distribution data described in Embodiment 1 are loaded.

[0136] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0137] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0138] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by a computer program. These computer programs can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that instructions executable by the processor of the computer or other programmable data processing apparatus generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer programs may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer programs may also be loaded onto a computer or other programmable data processing equipment, causing a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] The above embodiments are only used to illustrate the present invention. The structure, connection method and manufacturing process of each component can be varied. All equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A method for automatic processing and statistical analysis of velocity distribution data in visualized experimental flow fields, characterized in that, include: Read the raw experimental flow field velocity distribution data files in the target folder, automatically identify raw experimental flow field velocity distribution data files that conform to the preset experimental data format, and form a queue of files to be processed; The original experimental flow field velocity distribution data files in the file queue to be processed are parsed sequentially to extract the measurement coordinates, velocity vectors and quality evaluation parameters of the corresponding original experimental flow field velocity distribution data, and to establish a standardized experimental flow field velocity distribution data matrix. According to the preset coordinate transformation rules, coordinate compensation is performed on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix to generate a new experimental flow field velocity distribution data file and its corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix. Anomaly identification and automatic filtering were performed on the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation to obtain an effective experimental flow field velocity distribution data matrix. Automatic clustering of the effective experimental flow field velocity distribution data matrix and establishment of spatial coordinate index; Based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix, the numerical simulation results of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate are calculated respectively. The numerical simulation data were standardized in format, and a unified format data file was obtained based on the new experimental flow field velocity distribution data file.

2. The method for automatic processing and statistical analysis of visual experimental flow field velocity distribution data as described in claim 1, characterized in that, The process involves reading raw experimental flow field velocity distribution data files from the target folder, automatically identifying raw experimental flow field velocity distribution data files that conform to a preset experimental data format, and forming a queue of files to be processed, including: Determine the target folder containing the experimental flow field velocity distribution data and establish an automatic retrieval mechanism for the experimental flow field velocity distribution data files; Based on the user-defined target path and the established automatic retrieval mechanism, all raw experimental flow field velocity distribution data files in the target folder are scanned, and raw experimental flow field velocity distribution data files that conform to the preset experimental data format are automatically identified to form a queue of files to be processed. For each original experimental flow field velocity distribution data file in the file queue to be processed, a corresponding data reading object is created.

3. The method for automatic processing and statistical analysis of visual experimental flow field velocity distribution data as described in claim 1, characterized in that, The original experimental flow field velocity distribution data files in the file queue to be processed are parsed sequentially to extract the measurement coordinates, velocity vectors, and quality evaluation parameters corresponding to the original experimental flow field velocity distribution data, and to establish a standardized experimental flow field velocity distribution data matrix, including: The raw experimental flow field velocity distribution data file in the file queue to be processed is read into the buffer area, and the character sequence of the raw experimental flow field velocity distribution data is established according to the preset data organization method; Based on the data structure of the original experimental flow field velocity distribution data file, the velocity field data region in the character sequence of the original experimental flow field velocity distribution data is automatically located, and an experimental data field index is established. Based on the established experimental data field index, the measurement coordinates, velocity components, and quality evaluation parameters of the original experimental flow field velocity distribution data corresponding to each original experimental flow field velocity distribution data file in the file queue to be processed are automatically extracted in sequence. Among them, the measurement coordinates include the spatial coordinates of the first direction of the experimental measurement section and the spatial coordinates of the second direction of the experimental measurement section, and the velocity vector includes the first velocity component and the second velocity component. The extracted measurement coordinates, velocity vectors, and quality evaluation parameters are rearranged in a unified order to establish a standardized experimental flow field velocity distribution data matrix, and the conversion of character variables to numerical variables is automatically completed. Check whether the standardized experimental flow field velocity distribution data matrix contains missing values, illegal characters, or abnormal data format.

4. The method for automatic processing and statistical analysis of visual experimental flow field velocity distribution data as described in claim 1, characterized in that, A pre-established automatic screening model for abnormal velocity vectors is used to identify and automatically screen velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation. The construction process of the automatic screening model for abnormal velocity vectors is as follows: Set the standardized experimental flow field velocity distribution data matrix after coordinate compensation; For any velocity vector, if a preset validity criterion is met, the velocity vector is retained as a valid velocity vector. Otherwise, it is identified as an abnormal velocity vector and automatically deleted; The validity criteria include: ； ； ； in, For the first The quality evaluation parameters corresponding to each velocity vector; This is the quality evaluation threshold; For the first The first velocity component of the velocity vector; For the first The second velocity component of the velocity vector; This is the lower limit of axial velocity; , These are the minimum and maximum lateral velocities, respectively. The identified anomalous velocity vectors were classified, and the quality of the standardized experimental flow field velocity distribution data matrix after coordinate compensation was evaluated, including: The identified abnormal velocity vectors are classified and labeled according to the cause of the abnormality; For each original experimental flow field velocity distribution data file, the number of effective velocity vectors, the number of abnormal velocity vectors, and the number of different types of abnormal velocity vectors are counted. The effective data rate of the current standardized experimental flow field velocity distribution data matrix is ​​determined based on the ratio of the number of effective velocity vectors to the total number of velocity vectors. Based on the preset data quality evaluation rules, the effective data rate of the standardized experimental flow field velocity distribution data matrix is ​​compared with the data quality threshold. According to the preset data quality evaluation rules, the experimental velocity field data are marked as qualified or pending verification.

5. The method for automatic processing and statistical analysis of visual experimental flow field velocity distribution data as described in claim 1, characterized in that, The automatic clustering of the effective experimental flow field velocity distribution data matrix and the establishment of a spatial coordinate index include: The velocity vectors are sorted according to the measured coordinates in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, and a spatial coordinate index is established according to the spatial coordinates of the first direction of the experimental measurement section in the measured coordinates. A caching mechanism is used to sequentially store the effective experimental flow field velocity distribution data matrix belonging to the same measurement coordinate. When a change in the measurement coordinate is detected, the current spatial coordinate index is statistically calculated, and the spatial coordinate index of the next set of measurement coordinates is automatically established.

6. The automatic processing and statistical analysis method for visualized experimental flow field velocity distribution data as described in claim 1, characterized in that, The method also includes: The obtained data files in a unified format are batch visualized to generate corresponding experimental velocity cloud maps.

7. The method for automatic processing and statistical analysis of visual experimental flow field velocity distribution data as described in claim 6, characterized in that, The step of batch visualizing the obtained uniformly formatted data files to generate corresponding experimental velocity cloud maps includes: Automatically retrieve all data files of the same format according to the preset directory and read them in sequence; Based on the name of the read data file, the experimental conditions are automatically identified, and the image title, output file name, and batch save path of the experimental velocity cloud map are automatically generated. Generate experimental velocity cloud plots based on Tecplot variable mapping relationships; Configure a color scale for the generated experimental velocity cloud map; Configure the graphical parameters for the generated experimental velocity contour plot; Based on the identified experimental conditions, generate the name of the experimental velocity cloud map and export the final experimental velocity cloud map.

8. A system for automatic processing and statistical analysis of visual experimental flow field velocity distribution data, characterized in that, include: The file reading module is used to read the original experimental flow field velocity distribution data files in the target folder, automatically identify the original experimental flow field velocity distribution data files that conform to the preset experimental data format, and form a queue of files to be processed. The parsing module is used to parse the original experimental flow field velocity distribution data files in the file queue to be processed in turn, extract the measurement coordinates, velocity vectors and quality evaluation parameters of the corresponding original experimental flow field velocity distribution data, and establish a standardized experimental flow field velocity distribution data matrix. The coordinate compensation module is used to perform coordinate compensation on the measurement coordinates in the standardized experimental flow field velocity distribution data matrix according to the preset coordinate transformation rules, and generate a new experimental flow field velocity distribution data file and its corresponding coordinate-compensated standardized experimental flow field velocity distribution data matrix. The anomaly identification module is used to identify and automatically filter anomalies in the velocity vectors in the standardized experimental flow field velocity distribution data matrix after coordinate compensation, so as to obtain an effective experimental flow field velocity distribution data matrix. The automatic clustering module is used to automatically cluster the effective experimental flow field velocity distribution data matrix and establish a spatial coordinate index. The numerical simulation calculation module is used to calculate the numerical simulation results of the effective experimental flow field velocity distribution data matrix at the same measurement coordinate, based on the established spatial coordinate index and the clustered effective experimental flow field velocity distribution data matrix. The format unification module is used to unify the format of the calculated numerical simulation data and obtain a unified format data file based on the new experimental flow field velocity distribution data file.

9. A processing device, characterized in that, The method includes a computer program, wherein when the computer program is executed by a processing device, it is used to implement the steps corresponding to the automatic processing and statistical analysis method for visual experimental flow field velocity distribution data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it is used to implement the steps corresponding to the automatic processing and statistical analysis method for visualized experimental flow field velocity distribution data as described in any one of claims 1-7.

Images