Method and device for measuring time of flight of guided waves based on multi-resolution singular value decomposition

Abstract

The present application belongs to the field of signal processing technology, and specifically discloses a method and device for determining acoustic timing of guided waves based on multi-resolution singular value decomposition, wherein the method comprises: preprocessing a guided wave signal S, extracting a direct wave signal SD from the preprocessed guided wave signal SP, and intercepting a reference signal Y from the direct wave signal SD; performing sliding window processing on the preprocessed guided wave signal SP, and based on the signal X in the i-th window, performing sliding window processing on the preprocessed guided wave signal SP, and ... i and Y, construct the bipartite recursive matrix H 1,i , based on H 1,i To X i Perform j-times multi-resolution singular value decomposition to obtain j-times singular correlation values, and then construct j-times singular correlation value sequences. In the waveguide singular correlation value spectrum corresponding to the j-times singular correlation value sequence, the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the waveguide acoustic timing, which can realize waveguide acoustic timing detection of complex structures.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of signal processing, and more particularly relates to a guided wave sound time measurement method and device based on multi-resolution singular value decomposition. BACKGROUND

[0002] Due to the advantages of wide coverage, long propagation distance and parallel propagation to the medium boundary, ultrasonic guided waves based on acoustic effect can not only detect defects in the medium, but also truly reflect the overall stress of the medium, and are a very potential nondestructive testing stress identification method. However, in the process of stress detection by using ultrasonic guided waves, the end echo is affected by noise and multi-modal aliasing, and the echo arrival time is difficult to accurately obtain.

[0003] When ultrasonic guided waves propagate in waveguide bodies under different stress levels, the propagation speed will change, that is, the acoustic elastoplastic effect of the guided wave. The key to applying the acoustic elastoplastic effect to stress detection is to obtain the guided wave sound time and calculate the guided wave speed according to the obtained sound time, and reflect the overall stress change of the medium through the change of the guided wave speed. In a complex waveguide structure, the guided wave is affected by multi-modal and noise, and the echo signal is seriously mixed and attenuated, which makes it difficult to measure the echo sound time and poses a great challenge to stress detection. There are many methods for sound time measurement, such as amplitude method, zero-crossing detection method, cross-correlation method, characteristic structure method and time-frequency measurement method. The cross-correlation method, amplitude method and zero-crossing detection method are easily affected by noise, the time-frequency measurement method has a problem of time-frequency resolution, and the characteristic structure method needs multiple groups of mutually independent signals. For the problem of guided wave sound time measurement of complex structure, the above methods all have their own limitations. SUMMARY

[0004] In view of the defects in the related art, the embodiments of the present application provide a guided wave sound time measurement method and device based on multi-resolution singular value decomposition, aiming to solve the problem of guided wave sound time detection of complex structure.

[0005] In a first aspect, the embodiments of the present application provide a guided wave sound time measurement method based on multi-resolution singular value decomposition, comprising:

[0006] The guided wave signal S is preprocessed, the direct wave signal SD is extracted from the preprocessed guided wave signal SP, and the reference signal Y is intercepted from the direct wave signal SD;

[0007] The preprocessed guided wave signal SP is subjected to sliding window processing, and based on the signal X i and the reference signal Y in the i-th window in the sliding window processing process, a two-part recursive matrix H 1，i is constructed, and based on the two-part recursive matrix H 1,i the signal X i in the window in the sliding window processing process is subjected to j times of multi-resolution singular value decomposition, j times of singular correlation values are obtained, and based on all the signals Xi The j-th singular correlation value is calculated respectively, and a j-th singular correlation value sequence is constructed;

[0008] In the waveguide singular correlation value spectrum corresponding to the sub-singular correlation value sequence, the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the acoustic time of the waveguide.

[0009] In some embodiments, extracting the reference signal Y from the direct wave signal SD includes:

[0010] The direct wave signal SD is processed by sliding window, and the signal Y in the xth window during the sliding window processing is x , construct the bipartite recursive matrix H x , based on the bisection recursive matrix H x Perform singular value decomposition to obtain a singular correlation value; based on all window signals Y in the sliding window processing process x The first-order singular correlation values ​​calculated respectively are used to construct a first-order singular correlation value sequence ψ1;

[0011] The first-order singular correlation value sequence ψ1 is normalized, and the normalized first-order singular correlation value sequence is plotted into a direct wave singular correlation value spectrum. The signal intercepted when the center of the sliding window is located at the peak of the direct wave singular correlation value spectrum during the sliding window processing is determined as the reference signal Y.

[0012] In some embodiments, constructing a singular correlation value sequence ψ1 includes:

[0013] Perform sliding window processing on the direct wave signal SD to obtain the window signal Y during the sliding window processing process x =(y x,1 ,y x,2 ,…,y x,L ), L is the sliding window length;

[0014] Based on Y x =(y x,1 ,y x,2 ,…,y x,L ), construct the binary recursive matrix H x And perform singular value decomposition to obtain Get the first singular value correlation value ψ 1，x =ρσ x,1 (σ x，1 -σ x，2 ); ρ is the zero point correction coefficient, ρ=a max -|a m |, a max is the maximum value in the time series, a m is the value at the middle position of the time series; U x and V xis the orthogonal matrix generated during the singular value decomposition process;

[0015] Based on all window signals Y in the sliding window processing process x The first singular correlation values ​​are calculated respectively, and the first singular correlation value sequence ψ1=(ψ 1，1 , ψ 1，2 ,…,ψ 1，M ), M is the total number of sliding windows for sliding window processing of the direct wave signal SD.

[0016] In some embodiments, constructing a sub-singular correlation value sequence includes:

[0017] The pre-processed waveguide signal SP is subjected to sliding window processing to obtain the window signal X in the sliding window processing process. i =(x i，1 , x i，2 ,…,x i,Q ); Q is the signal X in the window i The signal length is consistent with the length of the reference signal Y;

[0018] Based on X i =(x i，1 , x i，2 ,…,x i,Q ) and the reference signal Y=(y1,y2,…,y Q ), construct a binary recursive matrix

[0019] Based on the bipartite recursive matrix H 1，i , perform j times multi-resolution singular value decomposition to obtain j times singular correlation value sequence; among which, in the j times multi-resolution singular value decomposition, the first multi-resolution singular value decomposition is to X i Decomposed into approximate signal A 1，i and detail signal D 1，i The nth multi-resolution singular value decomposition is the approximate signal A obtained by the n-1th multi-resolution singular value decomposition n-1，i Further decomposed into approximate signal A n，i and detail signal D n,i ; 1 <n≤j,且n为正整数。

[0020] In some embodiments, the process of the first multi-resolution singular value decomposition includes:

[0021] Bipartite recursive matrix H 1，i Perform multi-resolution singular value decomposition to obtain Among them, the singular values ​​include σ 1，i，1 and σ 1，i，2 ,σ 1，i，1 ≥σ1，i，2 ;U 1,i and V 1，i is the orthogonal matrix generated in the process of multi-resolution singular value decomposition;

[0022] H 1,i Expand to get U 1,i =(u 1,1，i ,u 1,2，i ), U 1，i ∈R 2×2 , V j，i =(v 1，1，i ;v 1，2，i ;…;v 1,Q,i ), V j，i ∈R Q×Q ;H 1，a，i is an approximate matrix, H 1，d，i is the detail matrix,

[0023] Based on the approximation matrix and the detail matrix, the approximate signal A is obtained 1，i =L 1，a，1 , and detail signal D 1，i =L 1，d，1 , where X i =L 1，a，1 +L 1，d，1 =A 1，i +D 1，i .

[0024] In some embodiments, based on the bisection recursion matrix H 1，i , perform j times multi-resolution singular value decomposition to obtain j times singular correlation value sequence, including:

[0025] Based on the bipartite recursive matrix H 1，i , perform j times of multi-resolution singular value decomposition, and obtain the approximate signal A obtained by the j-th multi-resolution singular value decomposition j，i =(a j，1，i , a j，2，i ,…,a j，Q，i );

[0026] Based on A j，i Construct a bipartite recursive matrix

[0027] For H j，i Perform singular value decomposition and get H j，i =U j，i Λ j，i V j，i T Among them, U j，i =(u j，1，i ,uj，2，i ), U j，i ∈R 2×2 , V j，i = (v j，1，i ; v j，2，i ;... ; v j，Q，i ), V j，i ∈R Q×Q , Λ j，i = (diag (σ j，1，i , σ j，2，i ), O), Λ j，i ∈R Q×Q ;

[0028] The j-th singular correlation value ψ j，i = ρσ j，1，i (σ j，1，i - σ j，2，i ) is obtained, wherein ρ is a zero point correction coefficient, ρ = a max - |a m |, a max is the maximum value in the time series, and a m is the value at the middle position in the time series.

[0029] Based on the j-th singular correlation values respectively calculated from all the window signals in the sliding window processing of the preprocessed guided wave signal SP, a j-th singular correlation value sequence ψ j = (ψ 1，1 , ψ 1，2 ,..., ψ 1，K ) is obtained, and K is the total number of sliding windows in the sliding window processing of the preprocessed guided wave signal SP.

[0030] In some embodiments, the guided wave signal S of the guided wave is preprocessed, including:

[0031] The SG filtering method is used to remove the noise in the guided wave signal S.

[0032] The linear interpolation method is used to process the guided wave signal after removing the noise.

[0033] In a second aspect, the embodiments of the present application also provide a guided wave acoustic time measuring device based on multi-resolution singular value decomposition, comprising:

[0034] A preprocessing module is configured to preprocess the guided wave signal S, extract the direct wave signal SD from the preprocessed guided wave signal SP, and intercept the reference signal Y from the direct wave signal SD.

[0035] A sliding window processing module is configured to perform sliding window processing on the preprocessed guided wave signal SP, construct a two-part recursive matrix H i based on the i-th window signal X 1，i, based on a binary recursive matrix H 1，i The signal X in the window in the sliding window processing process i j times of singular value decomposition are performed on the signal X in the window in the sliding window processing process to obtain j times of singular correlation values i The j times of singular correlation values obtained are respectively calculated to construct a sequence of j times of singular correlation values.

[0036] The time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the sound time of the guided wave.

[0037] In a third aspect, the embodiments of the present application also provide an electronic device, including: at least one memory for storing a program; at least one processor for executing the program stored in the memory, and when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0038] In a fourth aspect, the embodiments of the present application also provide a computer readable storage medium, and the computer readable storage medium stores a computer program, and when the computer program runs on the processor, the processor executes the method described in the first aspect or any possible implementation manner of the first aspect.

[0039] In a fifth aspect, the embodiments of the present application also provide a computer program product, and when the computer program product runs on the processor, the processor executes the method described in the first aspect or any possible implementation manner of the first aspect.

[0040] The method and device for measuring the sound time of the guided wave based on the multi-resolution singular value decomposition provided by the embodiments of the present application are based on the binary recursive principle, a new binary recursive matrix is proposed, a new echo sound time positioning characteristic quantity, i.e., the singular correlation value, is extracted by combining the multi-resolution singular value decomposition, the singular correlation spectrum of the guided wave signal is calculated, and the singular correlation spectrum is used for measuring the sound time of the guided wave, and the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is extracted as the sound time. Compared with the correlation technology, the signal-to-noise ratio of the echo signal is low in the complex waveguide structure, which makes it difficult to measure the echo sound time. The sound time can be measured from the guided wave signal with low signal-to-noise ratio, and the sound time of the guided wave in the complex structure can be detected. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present application or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or the related art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings without creative labor.

[0042] Figure 1 is one of flowcharts of a guided wave sound time measurement method based on multi-resolution singular value decomposition provided by an embodiment of the present application;

[0043] Figure 2 is another one of flowcharts of a guided wave sound time measurement method based on multi-resolution singular value decomposition provided by an embodiment of the present application;

[0044] Figure 3 is a schematic diagram of a cable guided wave signal to be analyzed provided by an embodiment of the present application;

[0045] Figure 4 is a schematic diagram of a preprocessed guided wave signal provided by an embodiment of the present application;

[0046] Figure 5 is a direct wave signal extracted from the preprocessed guided wave signal and a first singular correlation value spectrum calculated therefrom provided by an embodiment of the present application;

[0047] Figure 6 is a schematic diagram of a multi-resolution singular value decomposition process provided by an embodiment of the present application;

[0048] Figure 7 is a schematic diagram of a fifth singular correlation value spectrum of a guided wave signal provided by an embodiment of the present application;

[0049] Figure 8 is a structural schematic diagram of a guided wave sound time measurement device based on multi-resolution singular value decomposition provided by an embodiment of the present application;

[0050] Figure 9 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0052] Figure 1 is one of flowcharts of a guided wave sound time measurement method based on multi-resolution singular value decomposition provided by an embodiment of the present application, as shown in the figure, the method comprises the following steps 101-103: Figure 1

[0053] Step 101, preprocessing a guided wave signal S of a guided wave, extracting a direct wave signal SD from the preprocessed guided wave signal SP, and intercepting a reference signal Y from the direct wave signal SD.

[0054] ​In some embodiments, the guided wave signal S of the guided wave in step 101 is preprocessed, specifically including:

[0055] The SG filtering method is used to remove the noise in the guided wave signal S;

[0056] The linear interpolation method is used to process the guided wave signal after removing the noise.

[0057] Specifically, the SG filtering method is first used to remove the high-frequency noise on the signal surface, which has little effect on the echo sound time; Then the linear interpolation method is used to generate more sampling points by linear fitting between two sampling points to increase the sampling rate, thereby improving the accuracy of sound time measurement, and the calculation complexity will also increase with the increase of sampling points.

[0058] In some embodiments, the reference signal Y is intercepted from the direct wave signal SD in step 101, specifically including the following steps (1a)-(2a):

[0059] Step (1a), the direct wave signal SD is processed by sliding window, and the signal Y x in the xth window in the sliding window processing process is used to construct the bisection recursive matrix H x , and the singular value decomposition is performed based on the bisection recursive matrix H x to obtain the first singular correlation value, and the first singular correlation value is calculated based on all signals Y x in the sliding window processing process, and the first singular correlation value sequence ψ1 is constructed.

[0060] Where, the bisection recursive matrix H x is introduced: according to the signal Y x =(y x，1 , y x，2 , …, y x，L ) in the sliding window of the direct wave signal SD, the matrix H x is constructed as:

[0061]

[0062] The matrix H x is decomposed by SVD, where the singular values are λ1 and λ2, λ1≥λ2, U x and V x are orthogonal matrices generated in the singular value decomposition process. The guided wave signal is a kind of central symmetric multi-periodic sinusoidal signal. When the sliding window center is located at the central symmetric point of the guided wave signal, the smaller singular value λ2 tends to 0.

[0063] Step (2a), normalizing the first singular correlation value sequence ψ1, and drawing the normalized first singular correlation value sequence into a direct wave singular correlation value spectrum to determine the signal intercepted when the sliding window center is located at the peak of the direct wave singular correlation value spectrum in the sliding window processing process as the reference signal Y.

[0064] In some embodiments, step (1a) specifically includes steps (1a.1)-(1a.3) as follows:

[0065] Step (1a.1), performing sliding window processing on the direct wave signal SD to obtain the in-window signal Y x x,1 x,2 x,L in the sliding window processing process, L being the length of the sliding window.

[0066] Step (1a.2), constructing a two-division recursive matrix H x x,1 x,2 x,L based on Y x and performing singular value decomposition (SVD decomposition) to obtain:

[0067]

[0068] Further obtaining the first singular correlation value ψ 1，x = ρσ x,1 (σ x，1 -σ x，2 ); U x and V x are orthogonal matrices generated in the singular value decomposition process, ρ is a zero point correction coefficient, ρ = a max - |a m |, a max is the maximum value in the time series, and a m is the value at the middle position of the time series.

[0069] Step (1a.3), based on the first singular correlation values respectively calculated from all the in-window signals Y x in the sliding window processing process, obtaining the first singular correlation value sequence ψ1 = (ψ 1，1 , ψ 1，2 , …, ψ 1，M ), M being the total number of sliding windows for the sliding window processing on the direct wave signal SD.

[0070] Step 102, performing sliding window processing on the preprocessed guided wave signal SP, and based on the i-th in-window signal X i in the sliding window processing process and the reference signal Y, constructing a two-division recursive matrix H 1，i ​​​​​​, based on the bisection recursive matrix H 1,i The window-in signal X i is subjected to j times of multi-resolution singular value decomposition to obtain j singular correlation values, based on all window-in signals X i in the sliding window processing process.

[0071] In some embodiments, step 102 specifically comprises steps (1b)-(3b) as follows:

[0072] Step (1b), the preprocessed guided wave signal SP is subjected to sliding window processing to obtain window-in signals X i = (x i，1 , x i，2 , …, x i,Q ); Q is the signal length of the window-in signal X i , which is consistent with the length of the reference signal Y.

[0073] Step (2b), based on X i = (x i，1 , x i，2 , …, x i,Q ) and the reference signal Y = (y1, y2, …, y Q ), a bisection recursive matrix H

[0074] Step (3b), based on the bisection recursive matrix H 1，i , j times of multi-resolution singular value decomposition is performed to obtain a singular correlation value sequence; wherein, in the j times of multi-resolution singular value decomposition, the first time of multi-resolution singular value decomposition is to decompose X i into an approximate signal A 1，i and a detail signal D 1，i , the nth time of multi-resolution singular value decomposition is to further decompose the approximate signal A n-1，i obtained by the (n-1)th time of multi-resolution singular value decomposition into an approximate signal A n，i and a detail signal D n，i ; 1

[0075] Wherein, the j times of multi-resolution singular value decomposition is introduced as follows: according to the original signal A0 = (a 0，1 , a 0，2 , …, a 0，N ) (i.e. each window-in signal X i in the sliding window processing process in the embodiments of the present application) and the reference signal Y = (y1, y2, …, y N ), the following matrix is constructed:

[0076]

[0077] H = UΛV 2×N

[0078] H = UΛV T

[0079] where U = (u1, u2), U ∈ R 2×2 , V = (v1; v2;...; v N ), V ∈ R N×N , Λ = (diag(σ1, σ2), O), Λ ∈ R 2×N .

[0080] σ1 is an approximate singular value, corresponding to the larger of the two singular values. σ2 is a detail singular value, corresponding to the smaller of the two singular values. By reconstructing the signal through the approximate and detail singular values, respectively, two component signals can be obtained, which are the approximate signal A1 and the detail signal D1, respectively. Similarly, the approximate signal A1 can be further constructed into the above matrix form for SVD decomposition to obtain the approximate signal A2 and the detail signal D2. Therefore, by continuously constructing the approximate signal into the above matrix form for decomposition, different levels of detail signals and approximate signals can be obtained, i.e.,

[0081] A j-1 = A j + D j

[0082]

[0083] where A j-1 is the approximate signal after j-1 times of decomposition, A j is the approximate signal after j times of decomposition, and D j is the detail signal after j times of decomposition. By decomposing the approximate signal for N times, the original signal A0 can be decomposed into the approximate signal A N and a series of detail signals.

[0084] In some embodiments, the process of the first multi-resolution singular value decomposition in step (3b) can include the following steps (3b.1)-(3b.3):

[0085] Step (3b.1), performing multi-resolution singular value decomposition on the dyadic recursive matrix H 1，i to obtain:

[0086]

[0087] where the singular values include σ 1，i，1 and σ 1，i，2 , σ 1，i，1 ≥ σ​1，i，2 ; when the center of the sliding window is located at the center of the echo signal, the smaller singular value σ 1，i，2 tends to 0.

[0088] Step (3b.2), unfolding H1,i to obtain:

[0089]

[0090] U 1，i = (u 1,1，i , u 1,2，i ), U 1，i ∈ R 2×2 , V 1，i = (v 1，1 , i; v 1，2，i ; …; v 1,Q,i ), V 1，i ∈ R Q×Q ; H 1,a，i is an approximation matrix:

[0091]

[0092] H 1,d,i is a detail matrix:

[0093]

[0094] Step (3b.3), based on the approximation matrix and the detail matrix, obtaining an approximation signal A 1，i = L 1，a，1 , and a detail signal D 1,i = L 1，d，1 , wherein X i = L 1，a，1 + L 1，d，1 = A 1，i + D 1,i .

[0095] In some embodiments, after the first multi-resolution singular value decomposition in step (3b) is performed, it can further include:

[0096] Step (3b.4), unfolding the approximation signal A 1，i Continue to be constructed into the above-mentioned matrix form to carry out SVD decomposition, to obtain an approximation signal A 2，i and a detail signal D 2,i , and so on. By continuously constructing the approximation signal into the above-mentioned matrix form to carry out decomposition, different levels of detail signals and approximation signals can be obtained, until j times of multi-resolution singular value decomposition is completed, and the signal after j-1 times of decomposition is:

[0097] A j-1，i = (a j-1,1,i , a j-1,2,i , …, aj-1,Q,i )

[0098] In the jth multi-resolution singular value decomposition, A j-1，i The construction matrix is:

[0099]

[0100] Perform SVD decomposition on it and get:

[0101] H j，i =U j，i Λ j，i V j，i T

[0102] Where U j，i =(u j，1，i ,u j，2，i ), V j，i ∈R 2×2 , V j，i =(v j，1，i ;v j，2，i ;…;v j，Q，i ), V j，i ∈R (Q-1) × (Q-1) , Λ j，i =(diag(σ j，1，i ,σ j，2，i ), O), Λ j，i ∈R Q×Q ;

[0103] A j-1，i Decomposed into:

[0104] A j-1，i =A j，i +D j，i

[0105] Get the approximate signal A obtained by the j-th multi-resolution singular value decomposition j，i =(a j，1，i , a j，2，i ,…,a j，Q，i ); perform multi-resolution singular value decomposition (MSVD) j times.

[0106] In some embodiments, in step (3b), based on the binary recursive matrix H 1,i , perform j times of multi-resolution singular value decomposition, and obtain the approximate signal A obtained by the j-th multi-resolution singular value decomposition j，i =(a j，1，i , a j，2，i ,…,a j，Q，i ) after which the following steps (3b.5) to (3b.8) may also be included:

[0107] Step (3b.5), based on the approximate signal A obtained by the jth multi-resolution singular value decomposition j，i Construct the binary recursive matrix:

[0108]

[0109] Step (3b.6), singular value decomposition is performed on H j，i to obtain:

[0110] H j，i = U j，i Λ j，i V j，i T

[0111] wherein, U j，i = (u j，1，i , u j，2，i ), U j，i ∈ R 2×2 , V j，i = (v j，1，i ; v j，2，i ; …; v j，Q，i ), V j，i ∈ R Q×Q , Λ j，i = (diag(σ j，1，i , σ j，2，i ), O), Λ j，i ∈ R Q×Q .

[0112] Step (3b.7), obtain the secondary singular correlation value:

[0113] ψ j，i = ρσ j,1,i (σ j，1，i - σ j,2,i )

[0114] wherein, the approximate singular value σ j，1，i mainly reflects the signal energy size, the difference between the approximate singular value σ j，1，i and the detail singular value σ j，2，i reflects the correlation degree between the echo signal and the reference signal, when the sliding window center is located at the center of the echo signal, the smaller singular value σ 1，i，2 tends to 0, ρ is a zero correction coefficient, ρ = a max -|a m |, a max is the maximum value in the time series, and a m is the value at the middle position of the time series.

[0115] Step (3b.8), based on all the signals X iThe j-th singular correlation value calculated respectively is obtained as a j-th singular correlation value sequence:

[0116] Ψ j = (Ψ 1，1 , Ψ 1，2 , …, Ψ 1，K )

[0117] K is the total number of sliding windows for performing sliding window processing on the preprocessed guided wave signal SP, that is, the singular correlation values Ψ i，j calculated in all sliding windows are integrated to obtain a singular value sequence Ψ j = (Ψ j，1 , Ψ j，2 , …, Ψ j，K ).

[0118] In the j-th singular correlation value sequence corresponding to the guided wave singular correlation value spectrum, the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the acoustic time of the guided wave.

[0119] Specifically, according to the j-th singular correlation value sequence Ψ j = (Ψ 1，1 , Ψ 1，2 , …, Ψ 1，K ) and the time position passed by the center of the sliding window, a guided wave singular correlation value spectrum is drawn. For the echo signal and the direct wave signal with a high degree of correlation with the reference signal, according to the singular value decomposition principle, the approximate singular value σ j，1 is larger and the detail singular value σ j，2 tends to 0 in the two singular values. When the center of the sliding window is located at the center of the echo or direct wave signal, the singular correlation value spectrum will have a peak value at this position. Therefore, from the singular value correlation spectrum, the time difference between the singular correlation spectrum peaks at the direct wave and the guided wave echo can be extracted as the acoustic time of the guided wave.

[0120] The guided wave acoustic time measurement method based on multi-resolution singular value decomposition provided in the embodiments of the present application is based on the principle of binary recursion, proposes a new type of binary recursion matrix, combines multi-resolution singular value decomposition, extracts a new echo acoustic time positioning characteristic quantity, that is, a singular correlation value, calculates the j-th singular correlation value spectrum of the guided wave signal, and uses the singular correlation value spectrum to measure the acoustic time of the guided wave. The time difference between the singular correlation spectrum peaks at the direct wave and the end echo is extracted as the acoustic time. Compared with the correlation technology, in a complex waveguide structure, the signal-to-noise ratio of the echo signal is low, which makes it difficult to measure the echo acoustic time. The present application can measure the acoustic time from the guided wave signal with a low signal-to-noise ratio, and can realize detection of the guided wave acoustic time of a complex structure.

[0121] The technical solutions provided in the embodiments of the present application are further described below through specific examples.

[0122] In one embodiment, Figure 2This is a second flow chart of the method for determining acoustic timing of guided waves based on multi-resolution singular value decomposition provided in an embodiment of the present application, as shown in FIG. Figure 2 As shown, the main steps include:

[0123] Step 1: Preprocess the original waveguide signal to be analyzed. The preprocessing process mainly includes SG filtering method (Savitzy-Golay filter) to remove high-frequency noise and using linear interpolation method to increase the signal sampling rate, thereby obtaining the preprocessed waveguide signal.

[0124] Step 2: Extract the direct wave signal from the pre-processed waveguide signal; then intercept the reference signal from the direct wave signal.

[0125] Step 3: Perform sliding window processing on the entire processed waveguide signal, calculate the singular correlation values ​​of the reference signal and the signal in each window, and obtain a singular correlation value sequence composed of a series of singular correlation values.

[0126] Step 4: Plot the singular correlation value sequence into a singular correlation value spectrum (also known as the waveguide singular correlation value spectrum). The time difference between the peaks of the singular correlation spectrum at the direct wave and the end echo is used as the acoustic time. In complex waveguide structures, waveguides are affected by multimodality and noise, resulting in a low signal-to-noise ratio (SNR) in their echo signals, making it difficult to measure the acoustic time of the echoes. This embodiment of the present application enables acoustic time measurement in waveguide signals with low SNRs.

[0127] For example, you can first load the waveguide signal to be analyzed and preprocess it. Then, use SG filtering to reduce high-frequency noise. Then, perform linear interpolation on the filtered signal to increase the signal sampling rate.

[0128] Furthermore, the direct wave signal in the waveguide to be analyzed is extracted as the reference signal. The waveguide signal is a center-symmetrical multi-period sinusoidal signal. The SVD decomposition can be used to extract the center-symmetrical point position of the waveguide signal. The direct wave signal is extracted from the received signal, and the intercepted signal is subjected to sliding window processing with a window length of L and a step length of 1. The signal in each window is detrended. Let the processed signal in the xth sliding window be Y x =(y x,1 ,y x,2 ,…,y x,L ), and construct the matrix H x Perform SVD decomposition:

[0129]

[0130] Calculate a singular correlation value:

[0131] ψ 1，x =ρσ x,1 (σ x，1 -σx,2 )

[0132] Among them, ρ is the zero point correction coefficient, ρ = a max -|a m |, a max is the maximum value in the time series, a m is the value at the middle position of the time series. Combining the first singular correlation values ​​in all windows, we get the first singular correlation value sequence ψ1=(ψ 1，1 , ψ 1，2 ,…,ψ 1，M ), where M is the total number of sliding windows. Normalize the singular correlation value sequence and plot the normalized singular correlation value spectrum. Select the signal captured when the sliding window center is at the peak of the singular correlation spectrum as the reference signal Y.

[0133] Furthermore, a sliding window process is performed on the entire received signal, with a window length of Q, and a step is performed according to the unit length, and a detrending term is performed on the signal in each window. Let the processed signal in the i-th sliding window be X i =(x i，1 , x i，2 ,…,x i,Q ), combined with the reference signal Y, construct the matrix H 1,i :

[0134]

[0135] Where U 1，i and V 1，i is the orthogonal matrix generated in the process of multi-resolution singular value decomposition, U 1,i =(u 1,1，i ,u 1,2，i ), U 1，i ∈R 2×2 , V 1，i =(v 1，1，i ;v 1，2，i ;…;v 1,Q,i ), V 1，i ∈R Q×Q . The matrix H 1,i Expand and decompose into approximate matrix H 1,a，i and the detail matrix H 1,d,i ,get:

[0136]

[0137]

[0138]

[0139] Let L 1，a，1 =(a 1，1，i , a 1，2，i..., a 1,Q,i ), L 1，a，2 = (a 2，1，i , a 2，2，i ,..., a 2,Q,i ), L 1，a，1 is a subvector corresponding to the first row of the approximation matrix, L 1，a，2 is a subvector corresponding to the second row of the approximation matrix. L 1，d，1 = (d 1，1，i , d 1，2，i ,..., d 1,Q,i ), L 1，d，2 = (d 2，1，i , d 2，2，i ,..., d 2，Q，i ), L 1，d，1 is a subvector corresponding to the first row vector of the detail matrix, L d,2 is a subvector corresponding to the second row vector of the detail matrix. Let the approximation signal A 1，i and the detail signal D 1,i be:

[0140]

[0141] Therefore, the signal X i within the window can be decomposed into the approximation signal A 1，i and the detail signal D 1，i :

[0142] X i = L 1，a，1 + L 1,d,1 = A 1,i + D 1,i

[0143] Similarly, the approximation signal A 1，i can be further constructed into the above matrix form for SVD decomposition to obtain the approximation signal A 2，i and the detail signal D 2，i . Therefore, by continuously constructing the approximation signal into the above matrix form for decomposition, different levels of detail signals and approximation signals can be obtained, i.e.:

[0144] A j-1，i = A j，i + D j，i

[0145] Let the signal after j times of decomposition be A j，i = (a j，1，i , a j,2,i ,..., a j,Q,i ), and the 2-row matrix constructed therefrom be:

[0146]

[0147] SVD decomposition is performed on it to obtain:

[0148] H j，i = U j，i Λ j，i V j，i T

[0149] wherein, U j，i = (u j，1，i , u j,2,i ), U j，i ∈R 2×2 , V j，i = (v j，1，i ; v j,2,i ; …; v j，Q，i ), V j，i ∈R Q×Q , Λ j，i = (diag(σ j，1，i , σ j，2，i ), O), Λ j，i ∈R Q×Q .

[0150] The j-th singular correlation value is calculated:

[0151] ψ j，i = ρσ j，1，i (σ j，1，i - σ j，2，i )

[0152] wherein, ρ is a zero point correction coefficient, ρ = a max - |a m |, a max is the maximum value in the time series, and a m is the value at the middle position of the time series. The j-th singular correlation value of all windows is integrated to obtain the j-th singular correlation value sequence ψ j = (ψ 1，1 , ψ 1，2 , …, ψ 1，K ), and K is the total number of sliding windows.

[0153] Further, according to the j-th singular correlation value sequence, a singular correlation value spectrum is drawn. The time difference between the singular correlation spectrum peaks at the direct wave and the end echo is extracted as the acoustic time.

[0154] In one embodiment, the cable guided wave is used as the measured acoustic time, Figure 3 is a schematic diagram of a measured cable guided wave signal to be analyzed provided by an embodiment of the present application, Figure 4 is a schematic diagram of a preprocessed guided wave signal provided by an embodiment of the present application, which is loaded into, for example, Figure 3The measured cable waveguide signal to be analyzed is shown, and the waveguide signal is preprocessed; first, the SG filtering method is used to reduce high-frequency noise; then the linear interpolation method is used to improve the signal sampling rate. The preprocessed waveguide signal is shown in Figure 4 The direct wave signal extracted from the preprocessed waveguide signal is shown in the black line in

[0155] Figure 5 The direct wave signal extracted from the preprocessed waveguide signal is shown in the black line in Figure 5 The intercepted signal is subjected to sliding window processing, the window length is L, the step length of the sliding window is 1, and the trend item of the signal in each window is removed. Let the processed signal in the xth sliding window be Y x =(y x,1 ,y x,2 ,…,y x,L ), and construct a matrix H x SVD decomposition is performed:

[0156] The signal in each window is only decomposed once, and the first singular correlation value is calculated:

[0157] ψ 1，i =ρσ i,1 (σ i,1 -σ i,2 )

[0158] Wherein, ρ is the zero point correction coefficient, ρ=a max -|a m |, a max is the maximum value in the time series, and a m is the value at the middle position of the time series. The singular correlation value sequence ψ1=(ψ 1，1 ,ψ 1，2 ,…,ψ 1，M ) is obtained, and M is the total number of sliding windows. The singular correlation value sequence is normalized, and the normalized singular correlation spectrum is shown in the red line in Figure 5 The singular correlation spectrum peak value is taken as the center of the sliding window, and the signal in the sliding window is selected as the reference signal Y, as shown in the blue thick solid line in Figure 5

[0159] The sliding window processing is performed on the whole received signal, the window length is Q, and the step length is unit length, and the trend item of the signal in each window is removed. Let the processed signal in the ith sliding window be X i =(x i，1 ,x i，2 ,…,x i,Q ), and construct a matrix H 1,i :​

[0160]

[0161] Figure 6 This is a schematic diagram of the multi-resolution singular value decomposition process provided by the embodiment of the present application. Figure 6 The multi-resolution singular value decomposition process shown in the figure is used to decompose the signal X i Perform multi-resolution singular value decomposition and obtain the signal after j decompositions as A j，i =(a j，1，i , a j,2,i ,…,a j,Q,i ), construct it as a 2-row matrix:

[0162]

[0163] Perform SVD decomposition on it and get:

[0164]

[0165] Calculate the singular correlation value ψ after j decompositions j，i :

[0166] ψ j,i =ρσ j,1,i (σ j,1,i -σ j，2，i )

[0167] Combine all the calculated ψ in the sliding window i，j , get the j-th singular correlation value sequence ψ j =(ψ j，1 , ψ j，2 ,…,ψ j，K ), where K is the total number of sliding windows on the received signal.

[0168] Figure 7 Schematic diagram of the 5th singular correlation value spectrum of the waveguide signal provided in the embodiment of the present application. According to the 5th singular correlation value sequence, the following is drawn: Figure 7 The 5th order singular correlation value spectrum is shown. The peak point of the direct wave singular correlation value spectrum is located at 0.1611ms, the peak point of the echo signal singular correlation value spectrum is located at 0.4575ms, and the extracted sound time is 0.2964ms.

[0169] Figure 8 : is a structural diagram of a guided wave acoustic timing measurement device based on multi-resolution singular value decomposition provided in an embodiment of the present application, such as Figure 8 As shown, the device includes:

[0170] A preprocessing module 801 is used to preprocess the waveguide signal S, extract the direct wave signal SD from the preprocessed waveguide signal SP, and intercept the reference signal Y from the direct wave signal SD;

[0171] The sliding window processing module 802 is used to perform sliding window processing on the pre-processed waveguide signal SP, based on the i-th window signal X i And the reference signal Y, construct the binary recursive matrix H 1，i , based on the bisection recursive matrix H 1，i The signal X in the window during the sliding window processing i Perform j times of multi-resolution singular value decomposition to obtain j times of singular correlation values, based on all window signals X in the sliding window processing process i The j-th singular correlation value is calculated respectively, and a j-th singular correlation value sequence is constructed;

[0172] The acoustic time determination module 803 is used to determine the time difference between the singular correlation spectrum peaks at the direct wave and the end echo in the waveguide singular correlation value spectrum corresponding to the sub-singular correlation value sequence as the acoustic time of the waveguide.

[0173] In some embodiments, extracting the reference signal Y from the direct wave signal SD includes:

[0174] The direct wave signal SD is processed by sliding window, and the signal Y in the xth window during the sliding window processing is x , construct the bipartite recursive matrix H x , based on the bisection recursive matrix H x Perform singular value decomposition to obtain a singular correlation value; based on all window signals Y in the sliding window processing process x The first-order singular correlation values ​​calculated respectively are used to construct a first-order singular correlation value sequence ψ1;

[0175] The first-order singular correlation value sequence ψ1 is normalized, and the normalized first-order singular correlation value sequence is plotted into a direct wave singular correlation value spectrum. The signal intercepted when the center of the sliding window is located at the peak of the direct wave singular correlation value spectrum during the sliding window processing is determined as the reference signal Y.

[0176] In some embodiments, constructing a singular correlation value sequence ψ1 includes:

[0177] Perform sliding window processing on the direct wave signal SD to obtain the window signal Y during the sliding window processing process x =(y x,1 ,y x,2 ,…,y x,L ), L is the sliding window length;

[0178] Based on Y x =(y x,1 ,y x,2 ,…,y x,L ), construct the binary recursive matrix H xand singular value decomposition is performed to obtain to obtain a first singular correlation value ψ 1，x = pσ x,1 (σ x，1 -σ x，2 ); p is a zero correction coefficient, p = a max |a m |, a max is the maximum value in the time series, a m is the value at the middle position in the time series; U x and V x are orthogonal matrices generated in the singular value decomposition process;

[0179] Based on the singular correlation values respectively calculated from the signals in all windows in the sliding window processing of the direct wave signal SD, a first singular correlation value sequence ψ1 = (ψ 1，1 , ψ 1，2 , …, ψ 1，M ) is obtained, and M is the total number of sliding windows in the sliding window processing of the direct wave signal SD.

[0180] In some embodiments, the sliding window processing module 802 is specifically configured to:

[0181] The preprocessed guided wave signal SP is subjected to sliding window processing to obtain the window-in signals X i = (x i，1 , x i，2 , …, x i,Q ) in the sliding window processing; Q is the signal length of the window-in signals X i , which is consistent with the length of the reference signal Y;

[0182] Based on X i = (x i，1 , x i，2 , …, x i,Q ) and the reference signal Y = (y1, y2, …, y Q ), a two-part recursive matrix H 1,i is constructed.

[0183] Based on the two-part recursive matrix H 1,i , j times of multi-resolution singular value decomposition is performed to obtain a j-time singular correlation value sequence; wherein, in the j times of multi-resolution singular value decomposition, the first time of multi-resolution singular value decomposition is to decompose X i into an approximate signal A 1，i and a detail signal D 1,i , and the n-time of multi-resolution singular value decomposition is to further decompose the approximate signal A n-1，i obtained from the (n-1)-time of multi-resolution singular value decomposition into an approximate signal A n，i and a detail signal D n,i1 < n < j, and n is a positive integer.

[0184] In some embodiments, the process of the first multi-resolution singular value decomposition includes:

[0185] The binary recursive matrix H 1,i is decomposed by the multi-resolution singular value decomposition to obtain wherein the singular values include σ 1，i，1 and σ 1，i，2 , σ 1，i，1 > σ 1，i，2 ; U 1,i and V 1，i are orthogonal matrices generated in the process of the multi-resolution singular value decomposition;

[0186] H 1,i is expanded to obtain U 1,i = (u 1,1，i , u 1,2，i ), U 1，i ∈ R 2×2 , V 1，i = (v 1，1，i ; v 1，2，i ; …; v 1,Q,i ), V 1，i ∈ R Q×Q ; H 1,a,i is an approximation matrix, H 1,d,i is a detail matrix,

[0187] Based on the approximation matrix and the detail matrix, the approximation signal A 1，i = L 1，a，1 and the detail signal D 1,i = L 1,d,1 are obtained, wherein X i = L 1，a，1 + L 1,a,1 = A 1,i + D 1,i .

[0188] In some embodiments, based on the binary recursive matrix H 1,i , j times of multi-resolution singular value decomposition are performed to obtain a sequence of j times of singular values, including:

[0189] Based on the binary recursive matrix H 1,i , j times of multi-resolution singular value decomposition are performed to obtain an approximation signal A j，i = (a j，1，i , a j，2，i , …, a j，Q，i ) obtained by the jth multi-resolution singular value decomposition.

[0190] Based on A j，i Constructing a two-part recursive matrix

[0191] To H j，i Perform singular value decomposition to obtain H j，i = U j，i Λ j，i V j，i T ; wherein U j，i = (u j，1，i , u j，2，i ), U j，i ∈R 2×2 , V j，i = (v j，1，i ; v j，2，i ; …; v j，Q，i ), V j，i ∈R Q×Q , Λ j，i = (diag(σ j，1，i , σ j，2，i ), O), Λ j，i ∈R Q×Q ;

[0192] Obtain the j-th singular correlation value ψ j，i = ρσ j，1，i (σ j，1，i - σ j，2，i ); wherein ρ is a zero point correction coefficient, ρ = a max - |a m |, a max is the maximum value in the time series, and a m is the value at the middle position of the time series;

[0193] Based on the j-th singular correlation values respectively calculated from all windows in the sliding window processing of the preprocessed guided wave signal SP, obtain the j-th singular correlation value sequence ψ j = (ψ 1，1 , ψ 1，2 , …, ψ 1，K ), K is the total number of sliding windows in the sliding window processing of the preprocessed guided wave signal SP.

[0194] In some embodiments, the guided wave signal S of the guided wave is preprocessed, including:

[0195] The SG filtering method is used to remove the noise in the guided wave signal S;

[0196] The linear interpolation method is used to process the guided wave signal after removing the noise.

[0197] The device for measuring the acoustic time of the guided wave based on the multi-resolution singular value decomposition provided by the embodiment of the application is based on the principle of bisection recursion, proposes a new bisection recursion matrix, extracts a new echo acoustic time positioning characteristic quantity, i.e., a singular correlation value, by combining the multi-resolution singular value decomposition, calculates the j-th singular correlation value spectrum of the guided wave signal, and uses the singular correlation value spectrum to measure the acoustic time of the guided wave, and extracts the time difference between the singular correlation spectrum peaks at the direct wave and the end echo as the acoustic time. Compared with the correlation technology, in a complex waveguide structure, the signal-to-noise ratio of the echo signal is low, which makes it difficult to measure the echo acoustic time. The application can measure the acoustic time from the guided wave signal with a low signal-to-noise ratio, and can realize the detection of the guided wave acoustic time of a complex structure.

[0198] It can be understood that the detailed function implementation of each unit / module described above can refer to the description in the foregoing method embodiments, and will not be described herein.

[0199] It should be understood that the above device is used to execute the method in the above embodiment, and the corresponding program module in the device has similar implementation principles and technical effects to the description in the above method. The working process of the device can refer to the corresponding process in the above method, and will not be described herein.

[0200] Based on the method in the above embodiment, the embodiment of the application provides an electronic device. The device can include at least one memory for storing programs and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the method described in the above embodiment.

[0201] Figure 9 is a structural schematic diagram of an electronic device provided by the embodiment of the application, as Figure 9 shown, the electronic device can include a processor 901, a communication interface 902, a memory 903 and a communication bus 904, wherein the processor 901, the communication interface 902 and the memory 903 complete mutual communication through the communication bus 904. The processor 901 can call the software instructions in the memory 903 to execute the method described in the above embodiment.

[0202] In addition, the logic instructions in the memory 903 described above can be implemented in the form of software function units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the related art or parts of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application.

[0203] Based on the method in the above embodiments, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and when the computer program runs on a processor, the processor executes the method in the above embodiments.

[0204] Based on the method in the above embodiments, the embodiments of the present application provide a computer program product, and when the computer program product runs on a processor, the processor executes the method in the above embodiments.

[0205] It can be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0206] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0207] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0208] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0209] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present application, and are not used to limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A guided wave acoustic timing determination method based on multi-resolution singular value decomposition, characterized in that: include: For waveguide signal S Perform preprocessing and obtain the preprocessed waveguide signal SP Extract the direct wave signal SD , from the direct wave signal SD Intercept the reference signal Y ; The pre-processed waveguide signal SP Perform sliding window processing, based on the first Signals within the window and the reference signal Y , construct a bipartite recursive matrix , based on the bisection recursive matrix The signal in the window during the sliding window processing conduct Multi-resolution singular value decomposition, we get The subsingular correlation value is based on all the signals in the window during the sliding window processing. Calculated separately Secondary singular correlation value, construct subsingular correlation value sequence; In the In the waveguide singular correlation value spectrum corresponding to the sub-singular correlation value sequence, the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the acoustic time of the waveguide; The construction Sub-singular correlation value sequence, including: The pre-processed waveguide signal SP Perform sliding window processing to obtain the signal within the window during the sliding window processing ; The signal within the window The signal length, and the reference signal Y The length is consistent; based on and reference signal , construct a bipartite recursive matrix ; Based on the bisection recursive matrix ,conduct Multi-resolution singular value decomposition, we get sub-singular correlation value sequence; wherein, in the In the multi-resolution singular value decomposition, the first multi-resolution singular value decomposition is to Decomposition into approximate signals and detail signals , No. The second multi-resolution singular value decomposition is to Approximate signal obtained by multi-resolution singular value decomposition Further decomposed into approximate signals and detail signals ; ,and Is a positive integer.

2. The guided wave acoustic timing determination method based on multi-resolution singular value decomposition according to claim 1, characterized in that: The direct wave signal SD Intercept the reference signal Y ,include: The direct wave signal SD Perform sliding window processing, based on the first Signals within the window , construct a bipartite recursive matrix , based on the bisection recursive matrix Perform singular value decomposition to obtain a singular correlation value; based on all window signals in the sliding window processing process Calculate the singular correlation values ​​of each time and construct a singular correlation value sequence ; For the first singular correlation value sequence Perform normalization processing, draw the normalized first-order singular correlation value sequence into a direct wave singular correlation value spectrum, and determine the signal intercepted when the sliding window center is located at the peak of the direct wave singular correlation value spectrum during the sliding window processing as the reference signal Y .

3. The guided wave acoustic timing determination method based on multi-resolution singular value decomposition according to claim 2, characterized in that: The construction of a singular correlation value sequence ,include: The direct wave signal SD Perform sliding window processing to obtain the signal within the window during the sliding window processing , is the sliding window length; based on , construct a bipartite recursive matrix And perform singular value decomposition to obtain Get a singular value correlation value ; is the zero point correction coefficient, , is the maximum value in the time series, is the value at the middle position of the time series; and is the orthogonal matrix generated during the singular value decomposition process; Based on all window signals in the sliding window processing process The first singular correlation values ​​are calculated separately to obtain a first singular correlation value sequence , For the direct wave signal SD The total number of sliding windows processed by the sliding window.

4. The guided wave acoustic timing determination method based on multi-resolution singular value decomposition according to claim 1, characterized in that: The process of the first multi-resolution singular value decomposition includes: Bipartite recursive matrix Perform multi-resolution singular value decomposition to obtain ; Among them, the singular values ​​include and , ; and is an orthogonal matrix generated in the process of multi-resolution singular value decomposition; Will Expand to get ; ； is an approximate matrix, ; is the detail matrix, ; Based on the approximation matrix and the detail matrix, the approximation signal is obtained , and the detail signal ,in, .

5. The guided wave acoustic timing determination method based on multi-resolution singular value decomposition according to claim 4, characterized in that: The binary recursive matrix ,conduct Multi-resolution singular value decomposition, we get Sub-singular correlation value sequence, including: Based on the bisection recursive matrix ,conduct The multi-resolution singular value decomposition is performed and the Approximate signal obtained by multi-resolution singular value decomposition ; based on Construct a bipartite recursive matrix ; right Perform singular value decomposition and get ; in, , ； get subsingular correlation value ;in, is the zero point correction coefficient, , is the maximum value in the time series, is the value at the middle position of the time series; Based on the pre-processed waveguide signal SP All window signals in the sliding window processing process are calculated separately The second singular correlation value is obtained subsingular correlation value sequence , The pre-processed waveguide signal SP The total number of sliding windows used for sliding window processing.

6. The guided wave acoustic timing determination method based on multi-resolution singular value decomposition according to claim 1, characterized in that: The waveguide signal of the waveguide S Perform preprocessing, including: Use SG filtering method to remove guided wave signals S Noise in The linear interpolation method is used to process the waveguide signal after noise removal.

7. A guided wave acoustic timing measurement device based on multi-resolution singular value decomposition, characterized in that: include: Preprocessing module for waveguide signal S Perform preprocessing and obtain the preprocessed waveguide signal SP Extract the direct wave signal SD , from the direct wave signal SD Intercept the reference signal Y ; Sliding window processing module, used for processing the pre-processed waveguide signal SP Perform sliding window processing, based on the first Signals within the window and the reference signal Y , construct a bipartite recursive matrix , based on the bisection recursive matrix The signal in the window during the sliding window processing conduct Multi-resolution singular value decomposition, we get The subsingular correlation value is based on all the signals in the window during the sliding window processing. Calculated separately Secondary singular correlation value, construct subsingular correlation value sequence; The sound time determination module is used in the In the waveguide singular correlation value spectrum corresponding to the sub-singular correlation value sequence, the time difference between the singular correlation spectrum peaks at the direct wave and the end echo is determined as the acoustic time of the waveguide; The sliding window processing module is used to process the pre-processed waveguide signal SP Perform sliding window processing to obtain the signal within the window during the sliding window processing ; The signal within the window The signal length, and the reference signal Y The length is consistent; based on and reference signal , construct a bipartite recursive matrix ; Based on the bisection recursive matrix ,conduct Multi-resolution singular value decomposition, we get sub-singular correlation value sequence; wherein, in the In the multi-resolution singular value decomposition, the first multi-resolution singular value decomposition is to Decomposition into approximate signals and detail signals , No. The second multi-resolution singular value decomposition is to Approximate signal obtained by multi-resolution singular value decomposition Further decomposed into approximate signals and detail signals ; ,and Is a positive integer.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.

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