A system and method for analyzing the validity of test data from a transonic continuous wind tunnel

Abstract

The application provides a test data validity analysis system and method of a transonic continuous wind tunnel, and belongs to the technical field of test data validity analysis.The system comprises a central control unit, a balance acquisition and calculation unit, a pressure acquisition unit, an attitude angle control unit, a data monitoring unit and a data analysis unit.The central control unit is connected with the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit and the data analysis unit.The balance acquisition and calculation unit is connected with the data monitoring unit and the data analysis unit.The pressure acquisition unit is connected with the data monitoring unit and the data analysis unit.The attitude angle control unit is connected with the data monitoring unit and the data analysis unit.The application solves the problems of low analysis efficiency and deviation between analysis results and actual results in the data validity analysis of a continuous transonic wind tunnel test.

Description

Technical Field

[0001] This application relates to a method for analyzing the validity of test data, and more particularly to a system and method for analyzing the validity of test data from a transonic continuous wind tunnel, belonging to the field of test data validity analysis technology. Background Technology

[0002] Continuous wind tunnel testing, a widely adopted method in aerodynamics research, provides essential technical support for the development of fields such as aviation, aerospace, automotive, and construction. The effectiveness of wind tunnel data acquisition is a crucial factor in ensuring efficient and high-quality operation of the test, directly impacting wind tunnel operational efficiency and the automation of the test. Its main functions include continuously monitoring various flow field parameters in the wind tunnel and data collected by key sensors (wind tunnel balance, pressure scanning valve, angle sensor, etc.) mounted on the wind tunnel test model; classifying, processing, and analyzing the data according to its intended use; issuing alarms to alert wind tunnel operators to potential problems when abnormal data is detected; and automatically generating a data validity analysis report after each test, facilitating the traceability of data anomalies by operators.

[0003] Experimental data validity analysis is a method to ensure the automated operation of wind tunnel tests. Automated analysis of data validity can effectively improve the efficiency of aerodynamic data analysis and reduce the impact of experimental data acquisition errors or wind tunnel accidents caused by operator mistakes. However, experimental data validity analysis also has certain limitations during the test process. For example, in the analysis of the correctness of experimental data curves, different experimental models may not produce the same experimental results under the same experimental conditions. Models based on experimental conditions as a machine learning training set may not be able to accurately predict all experimental results, leading to a certain deviation between the correctness analysis results and the actual results. Summary of the Invention

[0004] A brief overview of the invention is given below to provide a basic understanding of certain aspects of it. It should be understood that this overview is not an exhaustive summary of the invention. It is not intended to identify key or essential parts of the invention, nor is it intended to limit the scope of the invention. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0005] In view of this, in order to solve the technical problems of low efficiency in data validity analysis and certain deviation between the correctness analysis results and the actual results in the existing technology when conducting continuous transonic wind tunnel tests, the present invention provides a test data validity analysis system and method for continuous transonic wind tunnels.

[0006] Option 1: A test data validity analysis system for a transonic continuous wind tunnel, comprising a central control unit, a balance acquisition and calculation unit, a pressure acquisition unit, an attitude angle control unit, a data monitoring unit, and a data analysis unit; The central control unit is connected to the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit, and the data analysis unit, respectively. The input end of the balance acquisition and calculation unit is connected to the balance and the angle sensor, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The input end of the pressure acquisition unit is connected to the pressure scanning valve, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The input end of the attitude angle control unit is connected to the motor of the model attitude angle, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The central control unit includes a process control module and a flow field data acquisition module; The process control module and the flow field data acquisition module are respectively connected to the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit, and the data analysis unit; The balance acquisition and calculation unit includes a data acquisition module, a data processing module, and an angle sensor acquisition module; The data acquisition module and the angle sensor acquisition module are respectively connected to the data processing module; The attitude angle control unit is a positioning judgment module; The data monitoring unit includes a data over-limit monitoring module for balance strain and balance force, a balance feedback voltage status monitoring module, a pressure measurement hardware status monitoring module, a balance temperature change status monitoring module, and an angle sensor data status monitoring module. The balance strain and balance force data over-limit monitoring module, the balance feedback voltage status monitoring module, and the balance temperature change status monitoring module are respectively connected to the balance acquisition and calculation unit; The pressure measurement hardware status monitoring module is connected to the pressure acquisition unit; The angle sensor data status monitoring module is connected to both the angle sensor acquisition module and the attitude angle control unit. The data analysis unit includes a flow field information plotting and analysis module, a balance strain and balance force data plotting and prediction analysis module, a raw data analysis and plotting module, and a balance temperature change slope analysis and plotting module. The flow field information plotting and analysis module, the balance strain and balance force data plotting and prediction analysis module, the raw data analysis and plotting module, and the balance temperature change slope analysis and plotting module are connected in sequence.

[0007] Option 2: A method for analyzing the validity of test data from a transonic continuous wind tunnel, comprising the following steps: S1. The central control unit starts the test, and the control data monitoring unit monitors the balance acquisition and calculation unit, pressure acquisition unit and attitude angle control unit feedback balance strain and balance force data, angle sensor data status, balance temperature change status, balance feedback voltage status and pressure measuring device status. S2. Test run begins; S3. After the test run ends, the central control unit controls the data analysis unit to analyze whether the flow field data of each train run exceeds the tolerance, analyze the spectrum of the original strain data collected for the entire train run, analyze the slope of the balance temperature change for the entire train run, and analyze the reliability of the balance strain and balance force data for the entire train run. S4. Continue testing the train, repeating S2 to S3 until the test ends.

[0008] Preferred method for monitoring the over-limit of balance strain and balance force data: determine whether the balance strain and balance force exceed the upper and lower limit thresholds; if they exceed the thresholds, an alarm is triggered. Angle sensor data status monitoring method: Compare the mechanism angle data with the model attitude angle data. If the difference is greater than the error threshold, an alarm will be triggered. The method for converting mechanism angle data into model attitude angle data is as follows: ; in, and These represent the pitch angle and roll angle of the angle mechanism, respectively. Indicates the actual angle of attack. Indicates the actual sideslip angle. Indicates the pre-deflection angle of the support rod; Balance temperature change monitoring method: The data monitoring unit stores the balance temperature data within 5 seconds, performs polynomial fitting on the stored temperature data, and calculates the slope of the temperature change within 5 seconds; when the slope of the temperature change exceeds 0.5 seconds, an alarm is triggered. Balance feedback voltage status monitoring method: The balance feedback voltage is compared with the balance excitation voltage. When the difference between the balance feedback voltage and the excitation voltage is greater than the error threshold, an alarm is triggered. Pressure measurement device status monitoring method: Compare the Pct data collected on the scanning valve with the Pct data in the flow field information. When the difference between the scanning valve Pct data and the flow field Pct data is greater than the error threshold, an alarm is triggered.

[0009] A preferred method for polynomial fitting of temperature data is: Suppose the fitted polynomial is: ; Where a is the coefficient of the multinomial term, x is the multinomial term, and y is the polynomial; The sum of the distances from each temperature point to the curve, i.e., the sum of squared deviations, is given by the following formula: ; To find the 'a' that satisfies the conditions, we take the partial derivative of ai on the right side of the equation, and we get: ; ; ……………… ; Simplifying the left side of the equation, we get: ; ; ; Rewriting the equation in matrix form, we obtain the following matrix: ; Simplifying the matrix further, we get: ; Right now: ; Minimize the mean square error as follows: ; ; ; ; Differentiating the above equation, we get: ; Therefore, the unbiased estimate of a is: Find the a of each term with a power in the polynomial.

[0010] The preferred method for analyzing whether the flow field data of each train route exceeds the tolerance is as follows: the collected test flow field data is compared with the test information. When the difference between the test flow field data and the test information is greater than the error threshold, the difference is recorded in the report, and the curves of each flow field information are plotted in the report.

[0011] Preferably, the method for analyzing the spectrum of the original strain data collected for the entire train journey is as follows: perform FFT on the original data of the balance strain signal generated in the experiment, and plot the frequency domain graph generated by FFT in the data validity report.

[0012] The method for analyzing the reliability of balance strain and balance force data throughout the entire train journey includes the following steps: S32. Obtain all data files in the folder containing the data files containing force measurement data and flow field information; S33. Use all data as the training dataset and extract the Mach number, total pressure, Reynolds number, angle of attack, and sideslip angle parameters from the data as feature vectors; S34. Extract the balance resistance Nx, normal force Ny, lateral force Nz, roll moment Mx, yaw moment My, and normal moment Mz in sequence as output; S35. Constructing a flow field tool using eigenvectors and outputs; S36. Based on the least squares method, perform polynomial fitting to establish a linear regression model; S37. Train the model using the training dataset described in S33, input the training dataset data into the model, and output the experimental results of the balance force; S38. At the end of each test run, using Mach number, total pressure, Reynolds number, angle of attack and sideslip angle as prediction data, curve predictions are made for the test results of balance resistance Nx, normal force Ny, lateral force Nz, rolling moment Mx, yaw moment My and normal moment Mz. S39. Plot the experimental results and the predicted results together in the validity report.

[0013] Option 3: An electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for analyzing the validity of test data in a transonic continuous wind tunnel as described in Option 2.

[0014] Option 4: A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for analyzing the validity of test data in a transonic continuous wind tunnel as described in Option 2.

[0015] The beneficial effects of this invention are as follows: Based on the current system status of the wind tunnel, this invention enables full-process and full-critical-node data validity analysis and judgment of the test data, which can reduce the steps of manual data analysis, reduce the probability of wind tunnel accidents, and improve the operating efficiency of the wind tunnel. At the same time, it solves the technical problem in the prior art that there is a certain deviation between the correctness analysis results and the actual results when conducting continuous transonic wind tunnel tests. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A schematic diagram of the structure of a transonic continuous wind tunnel test data validity analysis system; Figure 2 This is a schematic diagram of the central control unit structure; Figure 3 This is a schematic diagram of the data unit structure; Figure 4 This is a schematic diagram of the data analysis unit structure. Detailed Implementation

[0017] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0018] Example 1, Reference Figures 1-4 This embodiment describes a test data validity analysis system for a transonic continuous wind tunnel, comprising a central control unit, a balance acquisition and calculation unit, a pressure acquisition unit, an attitude angle control unit, a data monitoring unit, and a data analysis unit. The central control unit is connected to the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit, and the data analysis unit, respectively. The input end of the balance acquisition and calculation unit is connected to the balance and the angle sensor, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The input end of the pressure acquisition unit is connected to the pressure scanning valve, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The input end of the attitude angle control unit is connected to the motor of the model attitude angle, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The central control unit includes a process control module and a flow field data acquisition module; The process control module and the flow field data acquisition module are respectively connected to the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit, and the data analysis unit; The process control module is used to coordinate the actions of the balance acquisition and calculation unit, pressure acquisition unit, attitude angle control unit, data monitoring unit, and data analysis unit; the flow field data acquisition module is used to acquire flow field data. Specifically, after the test is started, the process control module sends a monitoring start command to the data monitoring unit. Then, after clicking the start test button, the process control module first reads the attitude angle sequence in the test schedule, and starts the attitude angle control unit according to the target angle value to make the bending mechanism in the wind tunnel move to the target position. Then, it controls the balance acquisition and calculation unit and the pressure acquisition unit to collect data. After that, the process control module continues to control the attitude angle control unit, the balance acquisition and calculation unit and the pressure acquisition unit according to the test schedule until the entire attitude angle sequence is traversed, and the test is completed. When the test is completed, it sends a command to the data analysis unit to analyze the validity of the data collected.

[0019] The flow field data acquisition module is responsible for collecting flow field information, including key flow field information such as total wind tunnel pressure, static pressure, total temperature, Reynolds number, stagnation chamber Mach number, and Mach number. After the central control unit is activated, the flow field data acquisition module simultaneously starts and sends data to the balance acquisition and calculation unit, pressure acquisition unit, data monitoring unit, and data analysis unit.

[0020] The balance acquisition and calculation unit includes a data acquisition module, a data processing module, and an angle sensor acquisition module; it is used to acquire and process balance strain and balance force data, balance temperature data, balance feedback voltage data, scanning valve data, flow field data, and mechanism angle data; The data acquisition module and the angle sensor acquisition module are respectively connected to the data processing module; The pressure acquisition unit is used to acquire the pressure of force and pressure measurement tests. The attitude angle control unit is a positioning judgment module; it is used to control and collect the real-time angle of the mechanism. The data monitoring unit includes a data over-limit monitoring module for balance strain and balance force, a balance feedback voltage status monitoring module, a pressure measurement hardware status monitoring module, a balance temperature change status monitoring module, and an angle sensor data status monitoring module. The balance strain and balance force data over-limit monitoring module, the balance feedback voltage status monitoring module, and the balance temperature change status monitoring module are respectively connected to the balance acquisition and calculation unit; The pressure measurement hardware status monitoring module is connected to the pressure acquisition unit; The angle sensor data status monitoring module is connected to both the angle sensor acquisition module and the attitude angle control unit. Specifically, the balance strain and balance force data over-limit monitoring module, the balance feedback voltage status monitoring module, the pressure measurement hardware status monitoring module, the balance temperature change status monitoring module, and the angle sensor data status monitoring module all operate simultaneously after the monitoring unit starts running.

[0021] Specifically, the data monitoring unit interacts with the central control unit and the balance acquisition and calculation unit through a communication protocol to exchange data and commands; The balance strain and balance force data over-limit monitoring module is used to read balance strain and balance force data and determine in real time whether the data exceeds the limits; by determining whether the data exceeds the limits in real time, it can be determined whether the data exceeds the limits in real time. The balance feedback voltage status monitoring module is used to read the balance strain and balance force data, and monitor the difference between the balance feedback voltage and the balance excitation voltage in real time. By monitoring the difference between the balance feedback voltage and the balance excitation voltage in real time, it can be determined whether the data exceeds the limit. The pressure measurement hardware status monitoring module is used to read the pressure of the pressure measurement test and monitor the difference between the Pct point in the data and the Pct data in the flow field data in real time. By monitoring the difference between the Pct point in the data and the Pct data in the flow field data in real time, it can be determined whether a large difference has occurred between the Pct point and the Pct in the flow field data. The balance temperature change monitoring module is used to read the balance temperature data and monitor the temperature slope change; by monitoring the temperature slope change, it can be determined whether an excessive temperature slope change has occurred.

[0022] The angle sensor data status monitoring module is used to read angle sensor data and the real-time angle of the mechanism, and monitor the difference between the angle sensor data and the real-time angle of the mechanism in real time. By monitoring the difference between the angle sensor data and the real-time angle of the mechanism in real time, it can be determined whether a situation has occurred where the angle data differs significantly from the actual angle data. The data analysis unit is used to perform secondary analysis and judgment on the data collected in the experiment, including a flow field information drawing and analysis module, a data drawing and prediction analysis module for balance strain and balance force, a raw data analysis and drawing module, and a balance temperature change slope analysis and drawing module. The flow field information plotting and analysis module, the balance strain and balance force data plotting and prediction analysis module, the raw data analysis and plotting module, and the balance temperature change slope analysis and plotting module are connected in sequence; Specifically, the balance, angle sensor, pressure scanning valve, and motor described in this embodiment are internal devices of the cavity and are not part of the hardware structure of this analysis system.

[0023] Example 2, Reference Figure 2 This embodiment describes a method for analyzing the validity of test data from a transonic continuous wind tunnel, comprising the following steps: S1. The central control unit starts the test, and the control data monitoring unit monitors the balance acquisition and calculation unit, pressure acquisition unit and attitude angle control unit feedback balance strain and balance force data, angle sensor data status, balance temperature change status, balance feedback voltage status and pressure measuring device status. Specifically, the balance temperature change monitoring module monitors the balance strain and balance force data for exceeding limits. The specific monitoring method is to determine whether the balance strain and balance force exceed the upper and lower limit thresholds. If they exceed the thresholds, an alarm is triggered. Specifically, the angle sensor data status monitoring module monitors the angle sensor data status. The specific monitoring method is to compare the mechanism angle data with the model attitude angle data. When the difference is greater than the error threshold, an alarm is triggered. Specifically, the error threshold is set to within 3 seconds by default; this threshold can be set according to the actual situation. Specifically, mechanism angle data and model attitude angle data cannot be directly compared. Therefore, it is necessary to convert mechanism angle data into model attitude angle data. The specific method is as follows: ; in, and These represent the pitch angle and roll angle of the angle mechanism, respectively. Indicates the actual angle of attack. Indicates the actual sideslip angle. Indicates the pre-deflection angle of the support rod; Specifically, the balance temperature change monitoring module monitors the balance temperature change status. The specific monitoring method is as follows: the data monitoring unit stores the balance temperature data within 5 seconds, performs polynomial fitting on the stored temperature data, and calculates the temperature change slope within 5 seconds; when the temperature change slope exceeds 0.5 seconds, an alarm is triggered. Specifically, the default error threshold is within 0.5 seconds; this threshold can be set according to the actual situation. Specifically, the method for polynomial fitting of temperature data is as follows: Suppose the fitted polynomial is: ; Where a is the coefficient of the multinomial term, x is the multinomial term, and y is the polynomial; The sum of the distances from each temperature point to the curve, i.e., the sum of squared deviations, is given by the following formula: ; To find the 'a' that satisfies the conditions, we take the partial derivative of ai on the right side of the equation, and we get: ; ; ……………… ; Simplifying the left side of the equation, we get: ; ; ; Rewriting the equation in matrix form, we obtain the following matrix: ; Simplifying the matrix further, we get: ; Right now: ; Minimize the mean square error as follows: ; ; ; ; Differentiating the above equation, we get: ; Therefore, the unbiased estimate of a is: Find the a of each term with a power in the polynomial.

[0024] Specifically, the balance feedback voltage status monitoring module monitors the balance feedback voltage status. The specific monitoring method is as follows: the balance feedback voltage is compared with the balance excitation voltage. When the difference between the balance feedback voltage and the excitation voltage is greater than the error threshold, an alarm is triggered. Specifically, the pressure measurement hardware status monitoring module is used to monitor the status of the pressure measurement device. The specific monitoring method is to compare the Pct data collected on the scanning valve with the Pct data in the flow field information. When the difference between the scanning valve Pct data and the flow field Pct data is greater than the error threshold, an alarm is triggered.

[0025] Specifically, the error threshold is 100 Pa. S2. Test run begins; S3. After the test run ends, the central control unit controls the data analysis unit to analyze whether the flow field data of each train run exceeds the tolerance, analyze the spectrum of the original strain data collected for the entire train run, analyze the slope of the balance temperature change for the entire train run, and analyze the reliability of the balance strain and balance force data for the entire train run. Specifically, the flow field information drawing and analysis module analyzes whether the flow field data of each train in the whole train is out of tolerance. The specific method is to compare the collected test flow field data with the test information. When the difference between the test flow field data and the test information is greater than the error threshold, the difference is recorded in the report and the flow field information curves are drawn in the report.

[0026] Specifically, the raw data analysis and plotting module analyzes the spectrum of the raw strain data collected for the entire train journey. The specific method is to perform FFT on the raw data of the balance strain signal generated in the experiment and plot the frequency domain graph generated by FFT in the data validity report.

[0027] Specifically, FFT stands for Fast Fourier Transform; Specifically, the balance temperature change slope analysis and plotting module analyzes the balance temperature change slope throughout the entire train journey; The slope of the temperature change on the balance can be calculated using Python tools. The data plotting and predictive analysis module for balance strain and balance force analyzes the reliability of balance strain and balance force data throughout the entire train journey. The specific method includes the following steps: S32. Obtain all data files in the folder containing the data files containing force measurement data and flow field information; S33. Use all data as the training dataset and extract the Mach number, total pressure, Reynolds number, angle of attack, and sideslip angle parameters from the data as feature vectors; S34. Extract the balance resistance Nx, normal force Ny, lateral force Nz, roll moment Mx, yaw moment My, and normal moment Mz in sequence as output; S35. Constructing a flow field tool using eigenvectors and outputs; Specifically, the flow field tool is a Python tool that uses a large amount of training data to train the regression model and improve the accuracy of the model's predictions; S36. Based on the least squares method, perform polynomial fitting to establish a linear regression model; S37. Train the model using the training dataset described in S33, input the training dataset data into the model, and output the experimental results of the balance force; S38. At the end of each test run, using Mach number, total pressure, Reynolds number, angle of attack and sideslip angle as prediction data, curve predictions are made for the test results of balance resistance Nx, normal force Ny, lateral force Nz, rolling moment Mx, yaw moment My and normal moment Mz. S39. Plot the experimental results and the predicted results together in the validity report.

[0028] S4. Continue testing the train, repeating S2 to S3 until the test ends.

[0029] Example 3: The computer device of the present invention may include a processor and a memory, such as a microcontroller containing a central processing unit. Furthermore, the processor executes the computer program stored in the memory to implement the steps of the above-described method for analyzing the validity of test data in a transonic continuous wind tunnel.

[0030] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0031] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0032] Example 4: Computer-readable storage medium example The computer-readable storage medium of the present invention can be any form of storage medium that can be read by the processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. The computer-readable storage medium stores a computer program. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-described method for analyzing the validity of test data in a transonic continuous wind tunnel can be implemented.

[0033] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0034] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A system for analyzing the validity of test data from a transonic continuous wind tunnel, characterized in that, It includes a central control unit, a balance acquisition and calculation unit, a pressure acquisition unit, an attitude angle control unit, a data monitoring unit, and a data analysis unit; The central control unit is connected to the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit, and the data analysis unit, respectively. The input end of the balance acquisition and calculation unit is connected to the balance and the angle sensor, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The input end of the pressure acquisition unit is connected to the pressure scanning valve, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The input end of the attitude angle control unit is connected to the motor of the model attitude angle, and the output end is connected to the data monitoring unit and the data analysis unit respectively. The central control unit includes a process control module and a flow field data acquisition module; The process control module and the flow field data acquisition module are respectively connected to the balance acquisition and calculation unit, the pressure acquisition unit, the attitude angle control unit, the data monitoring unit, and the data analysis unit; The balance acquisition and calculation unit includes a data acquisition module, a data processing module, and an angle sensor acquisition module; The data acquisition module and the angle sensor acquisition module are respectively connected to the data processing module; The attitude angle control unit is a positioning judgment module; The data monitoring unit includes a data over-limit monitoring module for balance strain and balance force, a balance feedback voltage status monitoring module, a pressure measurement hardware status monitoring module, a balance temperature change status monitoring module, and an angle sensor data status monitoring module. The balance strain and balance force data over-limit monitoring module, the balance feedback voltage status monitoring module, and the balance temperature change status monitoring module are respectively connected to the balance acquisition and calculation unit; The pressure measurement hardware status monitoring module is connected to the pressure acquisition unit; The angle sensor data status monitoring module is connected to both the angle sensor acquisition module and the attitude angle control unit. The data analysis unit includes a flow field information plotting and analysis module, a balance strain and balance force data plotting and prediction analysis module, a raw data analysis and plotting module, and a balance temperature change slope analysis and plotting module. The flow field information drawing and analysis module, the balance strain and balance force data drawing and prediction analysis module, the raw data analysis and drawing module, and the balance temperature change slope analysis and drawing module are connected in sequence. The data plotting and predictive analysis module for balance strain and balance force analyzes the reliability of balance strain and balance force data for the entire train route. The analysis method includes the following steps: S32. Obtain all data files in the folder containing the data files containing force measurement data and flow field information; S33. Use all data as the training dataset and extract the Mach number, total pressure, Reynolds number, angle of attack, and sideslip angle parameters from the data as feature vectors; S34. Extract the balance resistance Nx, normal force Ny, lateral force Nz, roll moment Mx, yaw moment My, and normal moment Mz in sequence as output; S35. Constructing a flow field tool using eigenvectors and outputs; S36. Based on the least squares method, perform polynomial fitting to establish a linear regression model; S37. Train the model using the training dataset described in S33, input the training dataset data into the model, and output the experimental results of the balance force; S38. At the end of each test run, using Mach number, total pressure, Reynolds number, angle of attack and sideslip angle as prediction data, curve predictions are made for the test results of balance resistance Nx, normal force Ny, lateral force Nz, rolling moment Mx, yaw moment My and normal moment Mz. S39. Plot the experimental results and the predicted results together in the validity report.

2. A method for analyzing the validity of test data from a transonic continuous wind tunnel, which is implemented based on the test data validity analysis system for a transonic continuous wind tunnel as described in claim 1, characterized in that, Includes the following steps: S1. The central control unit starts the test and controls the data monitoring unit to monitor the balance strain and balance force data, angle sensor data status, balance temperature change status, balance feedback voltage status and pressure measuring device status. S2. Test run begins; S3. After the test run ends, the central control unit controls the data analysis unit to analyze whether the flow field data of each train run exceeds the tolerance, analyze the spectrum of the original strain data collected for the entire train run, analyze the slope of the balance temperature change for the entire train run, and analyze the reliability of the balance strain and balance force data for the entire train run. S4. Continue testing the train, repeating S2 to S3 until the test ends.

3. The method for analyzing the validity of test data from a transonic continuous wind tunnel according to claim 2, characterized in that, Method for monitoring the over-limit of balance strain and balance force data: Determine whether the balance strain and balance force exceed the upper and lower limit thresholds. If they exceed the thresholds, an alarm will be triggered. Angle sensor data status monitoring method: Compare the mechanism angle data with the model attitude angle data. If the difference is greater than the error threshold, an alarm will be triggered. Balance temperature change monitoring method: The data monitoring unit stores the balance temperature data within 5 seconds, performs polynomial fitting on the stored temperature data, and calculates the slope of the temperature change within 5 seconds; when the slope of the temperature change exceeds 0.5 seconds, an alarm is triggered. Balance feedback voltage status monitoring method: The balance feedback voltage is compared with the balance excitation voltage. When the difference between the balance feedback voltage and the excitation voltage is greater than the error threshold, an alarm is triggered. Pressure measurement device status monitoring method: Compare the Pct data collected on the scanning valve with the Pct data in the flow field information. When the difference between the scanning valve Pct data and the flow field Pct data is greater than the error threshold, an alarm is triggered.

4. The method for analyzing the validity of test data from a transonic continuous wind tunnel according to claim 3, characterized in that, The method for polynomial fitting of temperature data is: Let the fitted polynomial be: Where a is the coefficient of the multinomial term, x is the multinomial term, and y is the polynomial; The sum of the distances from each temperature point to the curve, i.e., the sum of squared deviations, is given by the following formula: To find the 'a' that satisfies the conditions, we need to find 'a' on the right side of the equation. i The partial derivatives yield the following: ……………… Simplifying the left side of the equation, we get: Rewriting the equation in matrix form, we obtain the following matrix: Simplifying the matrix further, we get: Right now: Minimize the mean square error as follows: Differentiating the above equation, we get: Therefore, the unbiased estimate of a is: Find the a of each term with a power in the polynomial.

5. The method for analyzing the validity of test data from a transonic continuous wind tunnel according to claim 4, characterized in that, Method for analyzing whether the flow field data of each train route exceeds the tolerance: compare the collected test flow field data with the test information. When the difference between the test flow field data and the test information is greater than the error threshold, the difference is recorded in the report and the flow field information curves are plotted in the report.

6. The method for analyzing the validity of test data from a transonic continuous wind tunnel according to claim 4, characterized in that, Method for analyzing the spectrum of raw strain acquisition data for the entire train: Perform FFT on the raw data of the balance strain signal generated in the experiment, and plot the frequency domain graph generated by FFT in the data validity report.

7. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method for analyzing the validity of test data of a transonic continuous wind tunnel as described in any one of claims 2-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for analyzing the validity of test data in a transonic continuous wind tunnel as described in any one of claims 2-6.

Images