Adaptive matrix block weighted distributed optical fiber strain measurement method

Abstract

The invention relates to a distributed optical fiber strain measurement method, in particular to an adaptive matrix block weighted distributed optical fiber strain measurement method, which comprises the following steps: S1, respectively acquiring a reference signal and a test signal; s2, performing frequency domain windowing processing on the reference signal and the test signal, and performing local cross-correlation analysis on each windowing signal to obtain a one-dimensional cross-correlation result; s3, integrating one-dimensional cross-correlation results of all sub-windows, and reconstructing a two-dimensional image signal on the optical fiber distance; and S4, carrying out denoising processing on the image through a self-adaptive matrix block weighting processing method. And S5, outputting a strain measurement result. According to the invention, noise interference can be suppressed in long-distance high-spatial-resolution and large-range distributed strain sensing measurement signals, and the precision and reliability of the signals are improved. And the optical fiber sensor has wide application potential in the fields of intelligent optical fiber sensing, structural health monitoring, temperature sensing and the like, and the measurement precision and the data quality can be improved.

Description

Technical Field

[0001] The invention relates to a distributed optical fiber strain measurement method, and in particular to an adaptive matrix block weighted distributed optical fiber strain measurement method. Background Art

[0002] In distributed fiber optic sensing technology, optical fiber not only acts as a sensing medium, but also as a transmission medium for measuring signals. By utilizing the transmission characteristics of light waves in optical fibers, such as Raman scattering, Rayleigh scattering, and Brillouin scattering, real-time monitoring of the external environment can be achieved, and the monitoring range extends along the length of the optical fiber. This technology has significant advantages, including strong anti-electromagnetic interference ability, simple structure, high spatial resolution, and long sensing distance. With these characteristics, distributed fiber optic sensing technology has been gradually applied to many fields, such as bridge safety monitoring, civil engineering detection, underground tunnel fire alarm, and geological survey, and has produced important social benefits. As a representative of this technology, optical frequency domain reflectometry (OFDR) can continuously measure changes in external physical quantities such as strain, vibration, and temperature along the optical fiber due to its advantages such as lightness, small size, high sensitivity, strong anti-electromagnetic interference, and excellent spatial resolution. In recent years, with the development of OFDR technology, its application fields have continued to expand and have expanded to emerging fields such as shape sensing and acoustic sensing.

[0003] The working principle of OFDR is as follows: the linear frequency sweep light emitted by the tunable laser light source is divided into two beams through a coupler, one of which enters the optical fiber to be tested, and after the back Rayleigh scattered light of the optical fiber to be tested returns, it interferes with the other reference light. By collecting the beat signal and performing fast Fourier transform processing, the distance domain information along the optical fiber can be obtained. In the measurement process of OFDR, it is necessary to first collect a reference signal without external interference, then collect the test signal affected by the outside world, and calculate the external changes through cross-correlation operation. A prominent feature of the OFDR system is its high spatial resolution, which can reach the millimeter level, which makes it widely used in high-precision monitoring fields such as aerospace. However, as the sensing distance and spatial resolution increase, the stretching of large strains will cause multiple peaks or false peaks in the cross-correlation results, which in turn affects the accuracy of the measurement results. This is mainly due to the accumulation of long-distance noise and the reduction of correlation caused by strain stretching. Therefore, how to effectively achieve long-distance and high-spatial-resolution large strain measurement has become a key research problem that needs to be solved urgently.

[0004] Based on this, the present invention provides an adaptive matrix block weighted distributed optical fiber strain measurement method to solve the above problems. Summary of the invention

[0005] To make up for the deficiencies in the prior art, the present invention proposes an adaptive matrix block weighting method for improving the signal quality of long-distance high-spatial-resolution - large-range distributed strain sensing measurements. By adaptively adjusting the size of the matrix blocks and the weighting parameters, the local regions of the signal are adaptively weighted, while dealing with the influence of noise and the cross-correlation error problem caused by strain stretching.

[0006] The technical solution of the present invention is as follows: An adaptive matrix block weighted distributed optical fiber strain measurement method, comprising the following steps: S1, respectively collect a reference signal and a test signal; S2, perform frequency-domain windowing processing on the reference signal and the test signal, and perform local cross-correlation analysis on each windowed signal to obtain a one-dimensional cross-correlation result; S3, integrate the one-dimensional cross-correlation results of all windows and reconstruct them into a two-dimensional image signal in the fiber distance; S4, perform denoising processing on the image through an adaptive matrix block weighting processing method; S5, output the strain measurement result.

[0007] To better implement the present invention, a further technical solution is: S1 is specifically: The reference signal is a signal without strain information, and the test signal is a signal containing strain information.

[0008] S2 is specifically: S21, map the reference signal and the test signal to the distance domain through a fast Fourier transform, and divide the signal into N equal parts with a window size C, where the window size C determines the spatial resolution of the system; S22, perform an inverse fast Fourier transform on the local distance domain information of the first reference signal and the measurement signal; S23, obtain a one-dimensional cross-correlation result through cross-correlation calculation of the reference signal and the measurement signal after the inverse fast Fourier transform.

[0009] S3 is specifically: Repeat S22 - S23 to obtain the cross-correlation results of each corresponding position of the optical fiber, and reconstruct all the obtained one-dimensional cross-correlation results into a two-dimensional image signal in the fiber distance.

[0010] S4 is specifically: S41, perform local variance calculation on each two-dimensional data using a 3×3 matrix block: , judge the noise intensity of the local matrix block according to the variance size. This operation is used to preliminarily estimate the degree of change in the surrounding area of each pixel, so as to adaptively judge the size of the matrix block participating in the weighting calculation; Among them, is the pixel value of the k-th pixel point within the window, is the average value of all pixels within the window, and n is the total number of pixel points within the window. is the variance of the current window, representing the local change intensity; S42. Calculate the average value of the variances of all matrix blocks in the entire two-dimensional image: ; Among them, is the average value of the variances of all matrix blocks, M is the total number of rows of the image, and N is the total number of columns of the image; S43. Since the variance can reflect the strength of noise, introduce an exponential mapping function to convert the local variance of each pixel into the size of a weighted calculation matrix block to facilitate adaptive adjustment of the weighted matrix block size: ; Among them, represents the radius size of the matrix block participating in the weight calculation, , are the set minimum and maximum matrix block radius values, k is the slope coefficient of the mapping function, and the larger it is, the more sensitive the matrix block size is to the variance response. is the variance of the local matrix block, is the average value of the variances of all matrix blocks; S44. Calculate the similarity weight between the current pixel and each pixel in the neighborhood, and use an adaptive weighting function: ; Normalize the obtained weights: ; Among them, is the pixel value of the center point of the matrix block, is the pixel value of other points in the matrix block, is the value controlling the weight attenuation degree, and the weight is adaptively and dynamically adjusted according to the similarity between pixels. is the normalized weight value.

[0011] S45. Perform weighted average processing on the pixels within the matrix block through the calculated similarity weights to obtain the denoised pixel value. The weighted average formula is: ; Among them, is the normalized weight value, is the pixel value of other points in the matrix block, is the new pixel value after weighted average processing. It is the result obtained by weighting the pixel values in the neighborhood. This new pixel value can effectively remove noise while retaining the details of the signal.

[0012] Specifically, S4 is In S43, the adaptively determined matrix block size, and the pixel values of the matrix block are: ; where r is the radius of the matrix block, and i and j are the row and column coordinates of the pixels in the image, and is the pixel value of other points in the matrix block.

[0013] Specifically, S5 is Determine the appropriate spectral shift between the reference signal and the measurement signal through cross-correlation analysis, and analyze the strain information along the sensing optical fiber.

[0014] The beneficial effects of the present invention are: 1. The present invention adopts an adaptive matrix block weighted denoising method, which can adaptively adjust the size of the matrix block participating in the weighted calculation and the weight of each data in the matrix block according to the characteristics of the local area of the image. Compared with the traditional denoising method, it can better remove noise while effectively retaining the details of the signal, avoiding the phenomenon of over-smoothing, and is particularly suitable for retaining the details of large-range distributed strain sensing measurement signals.

[0015] 2. Compared with the traditional denoising method, the algorithm of the present invention only depends on the local matrix block information, has a lower computational complexity, and is particularly suitable for denoising processing of large data volume signals. Its calculation efficiency is relatively high and is suitable for application in real-time or high-efficiency processing scenarios.

[0016] 3. By introducing an adaptive weight calculation mechanism, the algorithm can dynamically adjust the denoising strategy according to the local changes of the image, so as to adapt to different types of noise, including Gaussian noise and non-Gaussian noise. This makes the method have strong adaptability and flexibility in practical applications.

[0017] 4. The present invention can suppress noise interference in long-distance high-spatial resolution - large-range distributed strain sensing measurement signals, improve the accuracy and reliability of the signal. And it also has broad application potential in the fields of intelligent optical fiber sensing, structural health monitoring, temperature sensing, etc., and can improve the measurement accuracy and data quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the device of the adaptive matrix block weighted distributed optical fiber strain measurement system used in the present invention.

[0019] Figure 2 It is a processing flow chart of the present invention.

[0020] Figure 3It is a graph of the spectral shift results of the sensing optical fiber obtained by the existing technology measurement method. The sensing optical fiber is subjected to strain information at 54.4 - 55.4 m. The spatial resolution is 2 mm, the strain range is 500 με - 5500 με, and the strain interval range is 500 με.

[0021] Figure 4 It is a graph of the spectral shift results of the sensing optical fiber obtained by the measurement method of the present invention. The sensing optical fiber is subjected to strain information at 54.4 - 55.4 m. The spatial resolution is 2 mm, the strain range is 500 με - 5500 με, and the strain interval range is 500 με. Specific embodiments

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

[0024] Reference Figure 2 , in this embodiment, the adaptive matrix block weighted distributed optical fiber strain measurement method solves the problems of common signal detail loss and insufficient peak misalignment removal in the traditional processing method by dynamically adjusting the weighting coefficient of the neighborhood block, and can retain more signal details during the processing, solves the problems of multiple peaks and false peaks in the traditional OFDR system during the measurement, and significantly improves the accuracy and signal quality of long-distance high-spatial-resolution - large-range distributed strain sensing measurement. The specific steps are as follows: 1. Collect two signals respectively. One is the signal without strain information, which is the reference signal; the other is the signal with strain information, which is the test signal; 2. Map the reference signal and the test signal to the distance domain through fast Fourier transform, and divide the signal into N equal parts by taking the window size C, where the window size C determines the spatial resolution of the system; 3. Perform fast inverse Fourier transform on the local distance domain information of the first reference signal and the measurement signal; 4. Obtain the one-dimensional cross-correlation result by performing cross-correlation calculation on the reference signal and the measurement signal after the fast inverse Fourier transform; 5. Repeat steps 3 - 4 to obtain the cross - correlation results at each corresponding position of the optical fiber, and reconstruct all the obtained one - dimensional cross - correlation results into a two - dimensional image signal in terms of the optical fiber distance.

[0025] 6. Calculate the local variance for each two - dimensional data using a 3×3 matrix block: , and judge the noise intensity of the local matrix block according to the variance size. This operation is used to preliminarily estimate the degree of change in the area around each pixel, so as to adaptively judge the size of the matrix block participating in the weighted calculation.

[0026] Among them, is the pixel value of the k - th pixel point in the window, is the average value of all pixels in the window, n is the total number of pixel points in the window, is the variance of the current window, representing the local change intensity.

[0027] 7. Calculate the average value of the variances of all matrix blocks in the entire two - dimensional image: . Among them, is the average value of the variances of all matrix blocks, M is the total number of rows of the image, and N is the total number of columns of the image.

[0028] Since the variance can reflect the noise strength, introduce an exponential mapping function to convert the local variance of each pixel into the size of the matrix block for weighted calculation, so as to adaptively adjust the size of the weighted matrix block: ; Among them, represents the radius size of the matrix block participating in the weight calculation, , are the set minimum and maximum matrix block radius values, k is the slope coefficient of the mapping function, and the larger it is, the more sensitive the matrix block size is to the variance response, is the variance of the local matrix block, is the average value of the variances of all matrix blocks.

[0029] The adaptively determined matrix block size, and the pixel value of the matrix block is: . Calculate the similarity weight between the current pixel and each pixel in the neighborhood, and adopt an adaptive weighting function: . Normalize the obtained weights: .

[0030] Among them, r is the radius of the matrix block, i and j are the row and column coordinates of the pixels in the image, is the pixel value of the center point of the matrix block, is the pixel value of other points in the matrix block, is the value controlling the weight attenuation degree, and the weight is adaptively and dynamically adjusted according to the similarity between pixels, is the normalized weight value.

[0031] 8. The pixels within the matrix block are weighted and averaged using the calculated similarity weights to obtain the denoised pixel values. The weighted average formula is:

[0032] where, is the normalized weight value, are the pixel values of other points in the matrix block, is the pixel value after weighted average processing, which is the result obtained by weighting the pixel values within the neighborhood. This new pixel value can effectively remove noise while retaining the details of the signal.

[0033] 9. Determine the appropriate spectral shift between the reference signal and the measurement signal through cross-correlation analysis to analyze the strain information along the sensing optical fiber.

[0034] Figure 1 The adaptive matrix block weighted distributed optical fiber strain measurement system shown in the figure includes: The continuous laser output of the tunable laser source is split into two parts by coupler 1 (10 / 90 optical coupler). 10% of the light is incident on an unbalanced Mach-Zehnder trigger interferometer to provide a trigger signal for the acquisition card, and the remaining light enters coupler 2; then coupler 2 (1 / 99 optical coupler) is split into two parts, where 1% of the output is adjusted by polarization controller 1 to make the "p" and "s" light components have the same power, and 99% enters the sensing optical fiber for detection through the circulator and polarization controller 2. The sensing optical fiber is a standard single-mode optical fiber; then the Rayleigh scattering signal and the interference signal obtained by combining 1% of the laser output from coupler 3 (50 / 50 optical coupler) are decomposed into "p" and "s" components by the polarization beam splitter; finally, the "p" and "s" light are collected by the acquisition card.

[0035] Measure the same sensing optical fiber using the existing measurement method and the measurement method of the present invention. The sensing optical fiber is subjected to strain information at 54.4 - 55.4 m, the spatial resolution is 2 mm, the strain range is 500 με - 5500 με, and the strain interval range is 500 με. Respectively obtain Figure 3 , Figure 4 of the sensing optical fiber spectral shift result diagram. Figure 3 represents the original measurement result image directly generated from the collected cross-correlation result image by the traditional measurement method. Signals of different colors represent different strain signals. It can be seen that without using any image processing means, the specific measurement results of the signals cannot be observed. Figure 4 is the strain distribution result diagram along the optical fiber obtained using the measurement method of the present invention. Signals of different colors represent different strain signals. It can be seen that compared with Figure 3In terms of this, the present invention solves the problems of common signal detail loss and insufficient noise removal in traditional denoising methods during measurement, enabling different strain information to be clearly distinguished. Compared with traditional measurement methods, the measurement effect of the present invention has been significantly improved.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Any other modifications or equivalent replacements made by those of ordinary skill in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive matrix block weighted distributed optical fiber strain measurement method, characterized in that: It includes the following steps: S1. Collect the reference signal and the test signal respectively; S2. Perform frequency-domain windowing processing on the reference signal and the test signal, and perform local cross-correlation analysis on each windowed signal to obtain a one-dimensional cross-correlation result; S3. Integrate the one-dimensional cross-correlation results of all windows and reconstruct them into a two-dimensional image signal in terms of the optical fiber distance; S4. Denoise the image through an adaptive matrix block weighting processing method; S5. Output the strain measurement result.

2. The adaptive matrix block weighted distributed optical fiber strain measurement method according to claim 1, wherein: Specifically, S1 is The reference signal is a signal without strain information, and the test signal is a signal with strain information.

3. The adaptive matrix block weighted distributed optical fiber strain measurement method according to claim 1, wherein: Specifically, S2 is S21. Map the reference signal and the test signal to the distance domain through fast Fourier transform, and divide the signal into N equal parts with a window size C, where the window size C determines the spatial resolution of the system; S22. Perform inverse fast Fourier transform on the local distance domain information of the first reference signal and the measurement signal; S23. Obtain a one-dimensional cross-correlation result through cross-correlation calculation of the reference signal and the measurement signal after the inverse fast Fourier transform.

4. The adaptive matrix block weighted distributed optical fiber strain measurement method according to claim 1, wherein: Specifically, S3 is Repeat S22 - S23 to obtain the cross-correlation results at each corresponding position of the optical fiber, and reconstruct all the obtained one-dimensional cross-correlation results into a two-dimensional image signal in terms of the optical fiber distance.

5. The adaptive matrix block weighted distributed optical fiber strain measurement method according to claim 1, wherein: Specifically, S4 is S41, perform local variance calculation on each two-dimensional data using a 3×3 matrix block: , determine the noise intensity of the local matrix block according to the variance magnitude. This operation is used to preliminarily estimate the degree of change in the area around each pixel, so as to adaptively determine the size of the matrix block participating in the weighted calculation; wherein, is the pixel value of the k-th pixel in the window, is the average value of all pixels in the window, and n is the total number of pixel points in the window, is the variance of the current window, representing the local change intensity; S42, calculate the average value of the variances of all matrix blocks in the entire two-dimensional image: ; Among them, is the average value of the variances of all matrix blocks, M is the total number of rows of the image, and N is the total number of columns of the image; S43. Introduce an exponential mapping function to convert the local variance of each pixel into the size of the matrix block for weighted calculation, so as to facilitate the adaptive adjustment of the weighted matrix block size: ; Among them, represents the radius size of the matrix block participating in the weight calculation, , are the set minimum and maximum matrix block radius values, k is the slope coefficient of the mapping function, and the larger it is, the more sensitive the matrix block size is to the variance response, is the variance of the local matrix block, is the average value of the variances of all matrix blocks; S44. Calculate the similarity weights between the current pixel and each pixel in the neighborhood using an adaptive weighting function: ; Normalize the obtained weights: ; Among them, is the pixel value of the center point of the matrix block, is the pixel value of other points of the matrix block, is the value controlling the degree of weight decay, and the weight is adaptively and dynamically adjusted according to the similarity between pixels, is the normalized weight value; S45, perform weighted average processing on the pixels within the matrix block using the calculated similarity weights to obtain the denoised pixel values. The weighted average formula is: ; Among them, is the normalized weight value, is the pixel value of other points in the matrix block, is the new pixel value after weighted average processing.

6. The adaptive matrix block weighted distributed optical fiber strain measurement method according to claim 1, wherein: Specifically, S4 is In S43, the adaptively determined matrix block size, and the pixel values of the matrix block are: ; where r is the radius of the matrix block, and i and j are the row and column coordinates of the pixels in the image, and it is the pixel value of other points in the matrix block.

7. The adaptive matrix block weighted distributed optical fiber strain measurement method according to claim 1, characterized in that: Specifically, S5 is Determine the appropriate spectral shift between the reference signal and the measurement signal through cross-correlation analysis, and analyze the strain information along the sensing optical fiber.

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