Cone motion test data processing method based on parallel solution and harmonic wavelet filtering

Abstract

The invention discloses a cone motion test data processing method based on parallel calculation and harmonic wavelet filtering, and the method comprises the steps: collecting an original voltage signal matrix measured by a wind tunnel force measurement six-component balance through a data collection system when a test model carries out the cone motion in a cone motion wind tunnel test; a principal coefficient matrix and a secondary coefficient matrix are constructed by using a six-component balance formula matrix, and a parallel resolving matrix is constructed by using the principal coefficient matrix and an original voltage signal matrix; iteratively solving a time-domain balance load matrix at each moment in the conical motion wind tunnel test based on the original voltage signal matrix, the principal coefficient matrix, the secondary coefficient matrix and the parallel solution matrix; the method comprises the following steps: determining the center frequency of a harmonic wavelet according to the prior information of a conical motion wind tunnel test, constructing a generalized harmonic wavelet, constructing a comb filter in a frequency domain based on the generalized harmonic wavelet, filtering a frequency domain balance load matrix by using the constructed comb filter, and then converting the frequency domain balance load matrix into a time domain, thereby completing the extraction and reconstruction of load data.

Description

Technical Field

[0001] The present invention relates to the technical fields of model cone motion wind tunnel tests, and in particular to a cone motion test data processing method based on parallel solution and harmonic wavelet filtering. Background Art

[0002] The conical motion of a rotating missile is a significant factor affecting its range and accuracy. In severe cases, it can even cause flight instability and lead to missile failure. Obtaining aerodynamic parameters of missile conical motion using model conical motion wind tunnel tests can provide data support for aerodynamic design and analysis. Research on conical motion test data processing methods and obtaining accurate aerodynamic data from conical motion wind tunnel tests are of great significance to missile aerodynamic design and research. Model conical motion wind tunnel tests are dynamic wind tunnel tests, resulting in a significantly larger data volume than conventional wind tunnel force measurement tests. Furthermore, the conical motion wind tunnel test system incorporates a rotating mechanical structure and a motor drive system. During signal acquisition, the force balance signal inevitably introduces vibration noise and electromagnetic interference, resulting in a poor signal-to-noise ratio (SNR) of the resulting raw voltage signal, making it difficult to effectively reflect actual aerodynamic loads. Consequently, the aerodynamic parameters of the test model's conical motion cannot be efficiently and accurately acquired, seriously hindering the analysis and evaluation of aircraft coning issues. Summary of the Invention

[0003] The purpose of the present invention is to provide a cone motion test data processing method based on parallel solution and harmonic wavelet filtering, so as to provide a reliable data processing means for the cone motion test of high-speed wind tunnel models.

[0004] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0005] The cone motion test data processing method based on parallel solution and harmonic wavelet filtering includes:

[0006] A data acquisition system is used to collect the original voltage signal matrix measured by the six-component balance of the wind tunnel force measurement when the test model performs conical motion in the conical motion wind tunnel test; the six-component balance formula matrix is ​​used to construct the main coefficient matrix and the secondary coefficient matrix, and the main coefficient matrix and the original voltage signal matrix are used to construct the parallel solution matrix. Based on the original voltage signal matrix, the main coefficient matrix, the secondary coefficient matrix and the parallel solution matrix, the time domain balance load matrix at each moment in the conical motion wind tunnel test is iteratively solved; according to the prior information of the conical motion wind tunnel test, the center frequency of the harmonic wavelet is determined and a generalized harmonic wavelet is constructed. Based on the generalized harmonic wavelet, a comb filter is constructed in the frequency domain. The constructed comb filter is used to filter the frequency domain balance load matrix and then convert it into the time domain to complete the extraction and reconstruction of the load data.

[0007] Furthermore, according to the sampling frequency f of the voltage signal of the six-component balance of wind tunnel force measurement during the cone motion wind tunnel test, s and sampling time Ts Determine the number of rows N=f of the original voltage signal matrix X(t) composed of the original voltage signals of the cone motion wind tunnel test s ·T s .

[0008] Furthermore, the six-component balance formula matrix is ​​used to construct the main coefficient matrix and the secondary coefficient matrix, including:

[0009] Before the conical motion wind tunnel test, the six-component balance of the wind tunnel force measurement was calibrated to obtain the six-component balance formula matrix C 27×6 ; Extract the six-component balance formula matrix C 27×6 The principal coefficient matrix C is constructed based on the number of rows N of the original voltage signal M , each row of the matrix is ​​the principal element coefficient;

[0010] The six-component balance formula matrix C 27×6 The principal element coefficient value in is taken as 0, and the secondary coefficient matrix C is constructed S .

[0011] Furthermore, the main coefficient matrix and the original voltage signal matrix are used to construct a parallel solution matrix, including:

[0012] Using the original voltage signal matrix X(t) and the main coefficient matrix C M Calculate the initial value matrix of the balance load Among them, the symbol It is defined as the multiplication of corresponding elements in two matrices;

[0013] Use the balance load initial value matrix G0 to construct the parallel solution matrix G = [G0, G1, G2]; Each row of elements in G2 is obtained by cross-multiplying the corresponding row elements in G0.

[0014] Furthermore, the time domain balance load matrix at each moment in the cone motion wind tunnel test is iteratively solved based on the original voltage signal matrix, the main coefficient matrix, the secondary coefficient matrix and the parallel solution matrix, including:

[0015] Based on the original voltage signal matrix X(t), the main coefficient matrix C M , secondary coefficient matrix C S And parallel solution matrix G calculation to solve the balance load matrix

[0016] Calculate the balance load matrix G * The difference between the initial value matrix G0 of the balance load is compared with the preset threshold e. If the difference is greater than e, G0 = G * , and iteratively re-solve the balance load matrix G * , until G *The difference between G0 and G1 is less than the preset threshold value e. * This is the time domain balance load matrix obtained by solution.

[0017] Furthermore, the prior information of the conical motion wind tunnel test includes the cone frequency, the second frequency of the cone frequency, and the fourth frequency of the cone frequency, and the cone frequency, the second frequency of the cone frequency, and the fourth frequency of the cone frequency are used as the center frequency.

[0018] Furthermore, based on the center frequency f k Constructing generalized harmonic wavelet ψ k (ω),ψ k The frequency domain expression of (ω) satisfies the following formula:

[0019]

[0020] Where ω represents the angular frequency and m is f k -f c , n is f k +f c , f c is the half-passband filter.

[0021] Furthermore, using the constructed k generalized harmonic wavelets ψ k (ω) constructs a comb filter Ψ(ω) in the frequency domain; transforms the time domain balance load matrix G * Perform fast Fourier transform to the frequency domain to obtain the frequency domain balance load matrix G * (ω); The frequency domain balance load matrix G is calculated by constructing the generalized harmonic wavelet comb filter Ψ(ω) * (ω) is filtered to obtain the filtered balance load matrix G + (ω)=G * (ω)Ψ(ω); Finally, the filtered balance load matrix G + (ω) Perform inverse fast Fourier transform to obtain the reconstructed time domain balance load matrix G + (t), complete the extraction and reconstruction of load data.

[0022] Furthermore, the conical motion test system includes a test model, a six-component wind tunnel force measurement balance, a support rod mechanism, a counterweight, a middle bracket, a servo motor, a data acquisition system, a servo motor control system, and a wind tunnel measurement and control system; the servo motor is fixed in the test section of the wind tunnel main structure through the middle bracket, the test model is installed at the output end of the servo motor through the support rod mechanism, and a counterweight is provided on the support rod mechanism; the test model is provided with a six-component wind tunnel force measurement balance, and the six-component wind tunnel force measurement balance is connected to the data acquisition system; the data acquisition system and the servo motor control system are both connected to the wind tunnel measurement and control system;

[0023] When the test model undergoes a conical motion wind tunnel test, the wind tunnel measurement and control system controls the wind tunnel body to establish a stable flow field in the test section. The servo motor control system controls the servo motor to drive the support rod mechanism to move, thereby driving the test model to rotate in a circular motion around the wind tunnel axis, namely conical motion. The six-component wind tunnel force balance connected to the test model senses the load on the test model and outputs a corresponding voltage signal. The data acquisition system collects the voltage signal to obtain the original voltage signal matrix.

[0024] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor executes the computer program, the cone motion test data processing method based on parallel solution and harmonic wavelet filtering is implemented.

[0025] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the method for processing cone motion test data based on parallel solution and harmonic wavelet filtering is implemented.

[0026] Compared with the prior art, the present invention has the following technical features:

[0027] This invention uses a parallel solution method to calculate the load measured by a six-component balance during a conical motion wind tunnel test. It uses harmonic wavelets to construct a comb filter to extract and reconstruct the balance load, providing an effective data processing method for conical motion wind tunnel tests. Its advantages are that in terms of balance load calculation (converting the original voltage signal into force / torque data), the six-component balance formula and the original voltage signal are used to construct the primary coefficient matrix, secondary coefficient matrix, and parallel solution matrix, allowing for rapid and efficient solution of the balance load. Compared to conventional force measurement test serial data processing methods, the computational efficiency is significantly improved. In terms of balance load extraction and reconstruction, a harmonic wavelet comb filter is designed based on prior information such as the cone frequency of the conical motion wind tunnel test to extract valid data from the load measured by the balance, thus providing reliable test data results for conical motion wind tunnel test data analysis. In summary, the present invention utilizes the model conical motion wind tunnel test signal collected by a six-component balance, and realizes the solution and extraction of the original voltage signal of the conical motion wind tunnel test based on parallel solution and harmonic wavelet filtering. It has the characteristics of simplicity, efficiency, ease of implementation, and high accuracy. It can provide reliable test data for the conical motion wind tunnel test of aircraft models and has practical engineering value. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the data processing of the cone motion wind tunnel test based on parallel solution and harmonic wavelet filtering of the present invention;

[0029] Figure 2 Schematic diagram of the model cone motion wind tunnel test system;

[0030] Figure 3 for the reason Figure 2 Time domain diagram of the original voltage signal collected by the six-component balance in the conical motion wind tunnel test system; among them, (a) to (f) correspond to the time domain waveform diagram of the original voltage signal of the Y, Mz, X, Mx, Z, and My components of the six-component balance, respectively. The horizontal axis represents time in seconds, and the vertical axis represents signal amplitude in mV.

[0031] Figure 4 for Figure 3 Time domain waveforms of the balance load signal obtained by parallel solution of the original voltage signal; (a), (c), and (e) are the loads in the Y, X, and Z directions of the six-component balance, respectively. The horizontal axis represents time in seconds, and the vertical axis represents the load magnitude in Newtons; (b), (d), and (f) are the loads in the Mz, Mx, and My directions of the six-component balance, respectively. The horizontal axis represents time in seconds, and the vertical axis represents the load magnitude in Newton-meters.

[0032] Figure 5 Figure 3. Time domain waveforms of the effective balance load obtained after data extraction and reconstruction using a comb filter constructed using harmonic wavelets. (a), (c), and (e) are the loads in the Y, X, and Z directions of the six-component balance, respectively. The horizontal axis represents time in seconds, and the vertical axis represents the load size in Newtons. (b), (d), and (f) are the loads in the Mz, Mx, and My directions of the six-component balance, respectively. The horizontal axis represents time in seconds, and the vertical axis represents the load size in Newton-meters.

[0033] Explanation of the numbers in the figure: 1 test model, 2 six-component balance for wind tunnel force measurement, 3 support rod mechanism, 4 counterweight, 5 middle bracket, 6 servo motor, 7 data acquisition system, 8 servo motor control system, 9 wind tunnel measurement and control system. DETAILED DESCRIPTION

[0034] Reference Figure 1 As shown, a flow chart of a method for processing cone motion test data based on parallel solution and harmonic wavelet filtering is provided. The method comprises: using a data acquisition system to collect the original voltage signal matrix measured by a six-component balance of wind tunnel force measurement when the test model performs cone motion in a cone motion wind tunnel test; using the six-component balance formula matrix to construct a main coefficient matrix and a secondary coefficient matrix, using the main coefficient matrix and the original voltage signal matrix to construct a parallel solution matrix, iteratively solving the time domain balance load matrix at each moment in the cone motion wind tunnel test based on the original voltage signal matrix, the main coefficient matrix, the secondary coefficient matrix and the parallel solution matrix; determining the center frequency of the harmonic wavelet according to the prior information of the cone motion wind tunnel test and constructing a generalized harmonic wavelet, constructing a comb filter in the frequency domain based on the generalized harmonic wavelet, using the constructed comb filter to filter the frequency domain balance load matrix and then convert it to the time domain, thereby completing the extraction and reconstruction of the load data.

[0035] The load data obtained by this solution is important data for cone motion wind tunnel tests and can provide a basis for the analysis of aircraft aerodynamic performance.

[0036] The specific implementation process of the present invention is described in detail below with reference to the accompanying drawings.

[0037] (1) Acquisition of cone motion wind tunnel test signals.

[0038] refer to Figure 2 The conical motion test system includes a test model, a six-component wind tunnel force measurement balance, a support rod mechanism, a counterweight, a middle bracket, a servo motor, a data acquisition system, a servo motor control system and a wind tunnel measurement and control system, wherein the test object is the test model; the servo motor is fixed in the test section of the wind tunnel main structure through the middle bracket, the test model is installed at the output end of the servo motor through the support rod mechanism, and a counterweight is provided on the support rod mechanism; a six-component wind tunnel force measurement balance is provided on the test model, and the six-component wind tunnel force measurement balance is connected to the data acquisition system; the data acquisition system and the servo motor control system are both connected to the wind tunnel measurement and control system.

[0039] When the test model undergoes a conical motion wind tunnel test, the wind tunnel measurement and control system controls the wind tunnel body to establish a stable flow field with a certain wind speed in the test section. The servo motor control system controls the servo motor to drive the support rod mechanism to move, thereby driving the test model to rotate and draw a circular motion around the wind tunnel axis, that is, conical motion. The six-component wind tunnel force balance connected to the test model senses the load on the test model and outputs a corresponding voltage signal. The data acquisition system collects the voltage signal to obtain the original voltage signal matrix, which is expressed as follows:

[0040] X(t)=[x1(t),x2(t),x3(t),x4(t),x5(t),x6(t)] T

[0041] Among them, x i (t)(i=1,2,3,4,5,6) is the voltage signal output by the i-th component of the six-component balance of wind tunnel force measurement at time t.

[0042] (2) Calculate the load measured by the six-component balance during the conical motion wind tunnel test based on the parallel solution method.

[0043] The six-component balance formula is used to construct the main coefficient matrix and the secondary coefficient matrix. The main coefficient matrix and the original voltage signal are used to construct the parallel solution matrix. Based on the original voltage signal, the main coefficient matrix, the secondary coefficient matrix and the parallel solution matrix, the load measured by the six-component balance at each moment in the conical motion wind tunnel test is iteratively solved.

[0044] (2.1) According to the sampling frequency f of the voltage signal of the six-component balance of wind tunnel force measurement during the cone motion wind tunnel tests and sampling time T s Determine the number of rows N=f of the matrix X(t) composed of the original voltage signal of the cone motion wind tunnel test s ·T s .

[0045] (2.2) Before the conical motion wind tunnel test, the six-component balance of the wind tunnel force measurement is calibrated to obtain the six-component balance formula matrix C 27×6 ;(Six-component balance formula matrix C 27×6 The number of rows in the equation corresponds to 1 main coefficient, 5 first-order interference coefficients, 6 second-order square interference coefficients, and 15 second-order cross-term interference coefficients, for a total of 27 items. For a detailed introduction, see Wind Tunnel Balance, edited by He Dexin, published by National Defense Industry Press, ISBN: 9787118023558). Extract the six-component balance formula matrix C 27×6 The principal component coefficient c in 1,1 ,c 2,2 ,c 3,3 ,c 4,4 ,c 5,5 ,c 6,6 (diagonal elements of the first six rows of the matrix), construct the main coefficient matrix C based on the number of rows N of the original voltage signal M :

[0046]

[0047] Among them, C M It is a matrix with N rows and 6 columns, where the elements in the i-th column are all composed of the i-th principal element coefficient c i,i constitute.

[0048] (2.3) The six-component balance formula matrix C 27×6 The principal component coefficient c in 1,1 ,c 2,2 ,c 3,3 ,c 4,4 ,c 5,5 ,c 6,6 The value is set to 0, and the secondary coefficient matrix C is constructed. S :

[0049]

[0050] (2.4) Using the original voltage signal matrix X(t) and the main coefficient matrix C M Calculate the initial value matrix of the balance load Among them, the symbol It is defined as the multiplication of corresponding elements in two matrices, where G0 is a matrix with N rows and 6 columns.

[0051] (2.5) Use the balance load initial value matrix G0 to construct the parallel solution matrix G:

[0052] G=[G0,G1,G2]

[0053] Among them, G is a matrix with N rows and 27 columns. Each row element in G2 is obtained by cross-multiplying the corresponding row elements in G0. Let the i-th row element of G0 be [g i,1 ,g i,2 ,g i,3 ,g i,4 ,g i,5 ,g i,6 ], then the element in row i of G2 is:

[0054] [g i,1 ·g i,2 ,g i,1 ·g i,3 ,g i,1 ·g i,4 ,g i,1 ·g i,5 ,g i,1 ·g i,6 ,g i,2 ·g i,3 ,g i,2 ·g i,4 ,

[0055] g i,2 ·g i,5 ,g i,2 ·g i,6 ,g i,3 ·g i,4 ,g i,3 ·g i,5 ,g i,3 ·g i,6 ,g i,4 ·g i,5 ,g i,4 ·g i,6 ,g i,5 ·g i,6 ] 1×15

[0056] (2.6) Based on the original voltage signal matrix X(t), the main coefficient matrix C M , secondary coefficient matrix C S And parallel solution matrix G calculation to solve the balance load matrix

[0057] (2.7) Calculate the balance load matrix G * The difference between the initial value matrix G0 of the balance load is compared with the preset threshold e. If the difference is greater than e, G0 = G * Repeat the above steps (2.5) to (2.7) until G * The difference between G0 and G1 is less than the preset threshold value e.* This is the time domain balance load matrix obtained by solution.

[0058] (3) A comb filter is constructed based on harmonic wavelet to realize balance load extraction and reconstruction.

[0059] According to the prior information such as the cone frequency of the conical motion wind tunnel test, harmonic wavelets with different center frequencies are designed. A comb filter is constructed in the frequency domain based on the designed harmonic wavelets. The constructed harmonic wavelet comb filter is used to extract the valid data from the load measured by the balance, thereby realizing the extraction and reconstruction of the balance load in the conical motion wind tunnel test.

[0060] (3.1) Based on the prior information of the test model's cone frequency, the cone frequency, its second harmonic frequency, fourth harmonic frequency, etc. (determined by the load data to be extracted in the end) are used as one or more center frequencies f of the harmonic wavelet k , subscript k is the kth load data to be extracted. Assuming that the data to be extracted consists of K frequencies, then k = 1, 2, ... K.

[0061] (3.2) After the center frequency is determined, based on the center frequency f k Constructing generalized harmonic wavelet ψ k (ω),ψ k The frequency domain expression of (ω) satisfies the following formula:

[0062]

[0063] Where ω represents the angular frequency and m is f k -f c , n is f k +f c , f c The local bandwidth B is determined based on the load data (cone frequency, or its double or quadruple frequency) extracted from the test. c =B / 2.

[0064] (3.3) Using the constructed k generalized harmonic wavelets ψ k (ω) Construct a comb filter Ψ(ω) in the frequency domain.

[0065] (3.4) The time domain balance load matrix G * Perform Fast Fourier Transform (FFT) to transform to the frequency domain to obtain the frequency domain balance load matrix G * (ω); The frequency domain balance load matrix G is calculated by constructing the generalized harmonic wavelet comb filter Ψ(ω) * (ω) is filtered to obtain the filtered balance load matrix G + (ω)=G *(ω)Ψ(ω); Finally, the filtered balance load matrix G + (ω) Perform inverse fast Fourier transform (IFFT) to obtain the reconstructed time domain balance load matrix G + (t), complete the extraction and reconstruction of the balance load of the cone motion wind tunnel test.

[0066] The following is a specific application example process to verify the effectiveness of the present invention in engineering applications:

[0067] Model cone motion wind tunnel test system Figure 2 As shown, the test object is the test model 1; in the wind tunnel test, the servo motor 6 is fixed in the test section of the wind tunnel main structure through the middle bracket 5, and the test model 1 is installed on the output end of the servo motor 6 through the support rod mechanism 3, and a counterweight 4 is provided on the support rod mechanism 3; a wind tunnel force measurement six-component balance 2 is provided on the test model 1, and the wind tunnel force measurement six-component balance 2 is connected to the data acquisition system 7; the data acquisition system 7 and the servo motor control system 8 are both connected to the wind tunnel measurement and control system 9.

[0068] The wind tunnel measurement and control system 9 controls the wind tunnel to establish a stable flow field with a Mach number of 0.4. The servo motor control system 8 controls the servo motor 6 to drive the support rod mechanism 3 and the counterweight 4, causing the test model 1 to perform conical motion around the wind tunnel axis within the flow field at conical speeds of 1Hz, 2Hz, and 3Hz, respectively, with a conical angle of 4°. During the conical motion, the load on the test model 1 is sensed by the wind tunnel's six-component force balance 2, which outputs a corresponding voltage signal. The data acquisition system 7 collects this voltage signal and generates a time-domain waveform diagram of the six-component raw voltage signal, as shown in Figure 1. Figure 3 As shown; the time domain waveforms of the six component balance load signals calculated using the parallel solution method are shown in Figure 4 As shown; the six-component effective balance load time domain waveform obtained after data extraction and reconstruction using harmonic wavelet to construct comb filter is shown as follows Figure 5 shown.

[0069] Depend on Figures 3 to 5 It can be seen that this method can effectively process the noisy voltage signal collected by the data acquisition system in the cone motion wind tunnel test into a payload signal with the required cone motion frequency, providing a basis for data analysis.

[0070] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A cone motion test data processing method based on parallel solution and harmonic wavelet filtering is characterized in that: include: The data acquisition system is used to collect the original voltage signal matrix measured by the six-component balance of the wind tunnel force measurement when the test model performs conical motion in the conical motion wind tunnel test. The six-component balance formula matrix is ​​used to construct the main coefficient matrix and the secondary coefficient matrix. The main coefficient matrix and the original voltage signal matrix are used to construct the parallel solution matrix. Based on the original voltage signal matrix, the main coefficient matrix, the secondary coefficient matrix and the parallel solution matrix, the time domain balance load matrix at each moment in the cone motion wind tunnel test is iteratively solved. According to the prior information of the conical motion wind tunnel test, the center frequency of the harmonic wavelet is determined and a generalized harmonic wavelet is constructed. A comb filter is constructed in the frequency domain based on the generalized harmonic wavelet. The constructed comb filter is used to filter the frequency domain balance load matrix and then converted to the time domain to complete the extraction and reconstruction of the load data.

2. The cone motion test data processing method based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: According to the sampling frequency f of the voltage signal of the six-component balance of wind tunnel force measurement during the cone motion wind tunnel test s and sampling time T s Determine the number of rows N=f of the original voltage signal matrix X(t) composed of the original voltage signals of the cone motion wind tunnel test s ·T s .

3. The cone motion test data processing method based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: The six-component balance formula matrix is ​​used to construct the main coefficient matrix and the secondary coefficient matrix, including: Before the conical motion wind tunnel test, the six-component balance of the wind tunnel force measurement was calibrated to obtain the six-component balance formula matrix C 27×6 ; Extract the six-component balance formula matrix C 27×6 The principal coefficient matrix C is constructed based on the number of rows N of the original voltage signal M , each row of the matrix is ​​the principal element coefficient; The six-component balance formula matrix C 27×6 The principal element coefficient value in is taken as 0, and the secondary coefficient matrix C is constructed S .

4. The cone motion test data processing method based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: The main coefficient matrix and the original voltage signal matrix are used to construct a parallel solution matrix, including: Using the original voltage signal matrix X(t) and the main coefficient matrix C M Calculate the initial value matrix of the balance load Among them, the symbol It is defined as the multiplication of corresponding elements in two matrices; Use the balance load initial value matrix G0 to construct the parallel solution matrix G = [G0, G1, G2]; Each row of elements in G2 is obtained by cross-multiplying the corresponding row elements in G0.

5. The cone motion test data processing method based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: The time domain balance load matrix at each moment in the cone motion wind tunnel test is iteratively solved based on the original voltage signal matrix, the main coefficient matrix, the secondary coefficient matrix and the parallel solution matrix, including: Based on the original voltage signal matrix X(t), the main coefficient matrix C M , secondary coefficient matrix C S And parallel solution matrix G calculation to solve the balance load matrix Calculate the balance load matrix G * The difference between the initial value matrix G0 of the balance load is compared with the preset threshold e. If the difference is greater than e, G0 = G * , and iteratively re-solve the balance load matrix G * , until G * The difference between G0 and G1 is less than the preset threshold value e. * This is the time domain balance load matrix obtained by solution.

6. The cone motion test data processing method based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: The prior information of the conical motion wind tunnel test includes the cone frequency, the second frequency of the cone frequency, and the fourth frequency of the cone frequency. The cone frequency, the second frequency of the cone frequency, and the fourth frequency of the cone frequency are used as the center frequency.

7. The cone motion test data processing method based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: Based on the center frequency f k Constructing generalized harmonic wavelet ψ k (ω),ψ k The frequency domain expression of (ω) satisfies the following formula: Where ω represents the angular frequency and m is f k -f c , n is f k +f c , f c is the half-passband filter.

8. The method for processing cone motion test data based on parallel solution and harmonic wavelet filtering according to claim 1 is characterized in that: Using the constructed k generalized harmonic wavelets ψ k (ω) constructs a comb filter Ψ(ω) in the frequency domain; transforms the time domain balance load matrix G * Perform fast Fourier transform to the frequency domain to obtain the frequency domain balance load matrix G * (ω); The frequency domain balance load matrix G is calculated by constructing the generalized harmonic wavelet comb filter Ψ(ω) * (ω) is filtered to obtain the filtered balance load matrix G + (ω)=G * (ω)Ψ(ω); Finally, the filtered balance load matrix G + (ω) Perform inverse fast Fourier transform to obtain the reconstructed time domain balance load matrix G + (t), complete the extraction and reconstruction of load data.

9. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the method for processing cone motion test data based on parallel solution and harmonic wavelet filtering according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the method for processing cone motion test data based on parallel solution and harmonic wavelet filtering according to any one of claims 1 to 8 is implemented.

Images