Phase-sensitive optical time domain reflectometer based on image processing and signal demodulation method and system

Abstract

The invention discloses a phase-sensitive optical time domain reflectometer based on image processing and a signal demodulation method and system. Comprising a laser, an electro-optical modulator, an arbitrary function generator, a first circulator, a filter, a first erbium-doped optical fiber amplifier, a band-pass filter, a semiconductor optical amplifier, a second erbium-doped optical fiber amplifier, a second circulator, an optical fiber to be measured, a photoelectric detector and a data acquisition card. The method disclosed by the invention is favorable for realizing high-precision demodulation of strain or temperature in a large range and under high spatial resolution. According to the method, image block matching and image denoising are combined, multiple pieces of frequency information and multiple pieces of spatial position information are utilized in the image block matching mode, the demodulation precision when the strain or temperature variation is large is improved, and the measurement range can be expanded; according to the image denoising method, denoising processing is carried out on the matching function image, the random noise of the system is reduced, and the demodulation precision at high spatial resolution is improved.

Description

Technical Field

[0001] The present invention relates to the field of optical fiber sensing technology, and in particular to a phase-sensitive optical time domain reflectometer based on image processing and a signal demodulation method and system. Background Art

[0002] The development of distributed optical fiber sensing has made significant contributions to many applications in the fields of structural health monitoring, safety monitoring, energy industry, etc. Among various distributed optical fiber sensors, phase-sensitive optical time-domain reflectometer is well-known for its ultra-high sensitivity and measurement resolution. Thanks to its extremely sensitive characteristics to external changes, phase-sensitive optical time-domain reflectometer has been applied to high-sensitivity temperature and strain measurement.

[0003] In the demodulation process of phase-sensitive optical time-domain reflectometer, the offset of the optical frequency is usually determined by calculating the cross-correlation of the backward Rayleigh scattering spectra before and after the strain or temperature change, thereby obtaining the change in strain or temperature. However, when the strain or temperature change is large, the similarity between the two spectra decreases, and false peaks appear in the cross-correlation spectrum, resulting in errors in the demodulation results. This phenomenon reduces the measurement accuracy and increases the cumulative error in the large-scale measurement process, limiting the measurable strain and temperature range. In addition, as the spatial resolution of the system increases, the influence of random noise in the system becomes increasingly greater, resulting in reduced measurement accuracy. Therefore, there is an urgent need for a high-precision signal demodulation method for phase-sensitive optical time-domain reflectometers. Summary of the invention

[0004] The present invention aims at solving the problems in the prior art and proposes a phase-sensitive optical time domain reflectometer and a signal demodulation method based on image processing. The present invention is implemented by the following technical solutions:

[0005] The present invention discloses a phase-sensitive optical time domain reflectometer based on image processing, comprising: a laser (1-1), an electro-optical modulator (1-2), an arbitrary function generator (1-3), a first circulator (1-4), a filter (1-5), a first erbium-doped fiber amplifier (1-6), a bandpass filter (1-7), a semiconductor optical amplifier (1-8), a second erbium-doped fiber amplifier (1-9), a second circulator (1-10), an optical fiber to be tested (1-11), a photoelectric detector (1-12) and a data acquisition card (1-13).

[0006] Furthermore, the laser (1-1) is connected to the electro-optic modulator (1-2), the arbitrary function generator (1-3) is connected to the electro-optic modulator (1-2), the light output by the laser (1-1) is modulated by the electro-optic modulator (1-2) into a double-sideband modulation signal with suppressed carrier; the double-sideband modulation signal with suppressed carrier passes through the first circulator (1-4) and enters the filter (1-5) to filter out one of the sidebands, and the output signal obtained is a single-sideband modulation signal; the single-sideband modulation signal enters the first erbium-doped fiber amplifier ( The optical fiber (1-6) is amplified and then filtered out by the bandpass filter (1-7) to remove the spontaneous radiation noise; the semiconductor optical amplifier (1-8) is used to modulate the filtered single-sideband modulation signal into a pulse light sequence with different frequencies; the second erbium-doped fiber amplifier (1-9) amplifies the pulse light sequence, and the pulse light sequence is injected into the optical fiber (1-11) to be tested through the second circulator (1-10); the backward Rayleigh scattering signal in the optical fiber to be tested is converted into an electrical signal by the photoelectric detector (1-12) and then collected by the data acquisition card (1-13).

[0007] Furthermore, the filter (1-5) is a reflective fiber Bragg grating; the semiconductor optical amplifier (1-8) has a high extinction ratio of 50 dB; the first erbium-doped fiber amplifier (1-6) is a continuous light amplifier, and the second erbium-doped fiber amplifier (1-9) is a pulse light amplifier.

[0008] The present invention also relates to a signal demodulation method using the above-mentioned phase-sensitive optical time domain reflectometer based on image processing, and the signal demodulation method comprises the following steps:

[0009] Step S1, respectively collecting a first characteristic graph of the Rayleigh backscattering spectrum before strain and a second characteristic graph of the Rayleigh backscattering spectrum after strain;

[0010] Step S2, according to the influence of the image block size on the matching degree of the image blocks in the first feature map and the second feature map, calculate the change of the matching degree between the image block in the first feature map and the matching image block in the second feature map with the image block size under different strain changes, and select the image block size a when the matching degree is the largest to perform the image block matching process;

[0011] Step S3: The difference between the second-dimensional coordinates of the image block of the first feature map and the second-dimensional coordinates of the matching image block of the second feature map is the frequency offset. The spatial domain and the frequency offset domain are the first-dimensional coordinates and the second-dimensional coordinates, and the matching function value is the third-dimensional coordinate. The matching function curves of all spatial positions are constructed into a three-dimensional matching function graph.

[0012] Step S4, projecting the three-dimensional matching function graph into a two-dimensional image in the space domain-frequency offset domain, and performing denoising on the image using the BM3D image denoising method;

[0013] Step S5, extracting the second dimensional coordinates corresponding to the maximum values ​​of the matching functions at each spatial position in the denoised image, and determining the frequency offset at each position of the optical fiber to be tested according to the coordinate values; solving the temperature according to the relationship between the frequency offset and the temperature change, and solving the strain according to the relationship between the frequency offset and the strain change.

[0014] Further, step S1 is specifically as follows:

[0015] The data acquisition card collects the back Rayleigh scattering spectra of the pulse sequence at N frequencies at the first moment, takes the space domain and the frequency domain as the first dimension coordinate and the second dimension coordinate respectively, and the signal intensity as the third dimension coordinate, obtains the three-dimensional back Rayleigh scattering spectrum and projects it to the space domain-frequency domain as the first characteristic graph; collects the back Rayleigh scattering spectra of the pulse sequence at N frequencies at the second moment, takes the space domain and the frequency domain as the first dimension coordinate and the second dimension coordinate respectively, and the signal intensity as the third dimension coordinate, obtains the three-dimensional back Rayleigh scattering spectrum and projects it to the space domain-frequency domain as the second characteristic graph.

[0016] Furthermore, in step S2, the matching function for measuring the matching degree is:

[0017]

[0018] Wherein, Δf is the frequency offset, f is the laser frequency, A is the image block in the first feature map, B is the image block in the second feature map, x is the spatial position of the image block, M is the number of positions in the optical fiber to be tested, and N is the number of modulation signals generated by the arbitrary function generator.

[0019] Further, in step S3, the two feature maps are subjected to image block matching, an image block of size a×a is selected at the spatial domain coordinate x in the first feature map, and the coordinates of the lower left corner of the image block are used as the coordinates of the image block. In the second feature map, an image block with an initial position and an initial frequency as coordinates is selected, and a matching function between the image block and the image block at the spatial domain coordinate x in the first feature map is calculated; the image block in the second feature map is moved along the frequency domain, and a matching function is calculated for each step; after the image block in the second feature map is moved to the maximum value in the frequency domain, it returns to the initial frequency and moves the image block to the next position along the spatial domain, and the image block in the second feature map is moved again along the frequency domain, and a matching function is calculated for each step; the entire second feature map is looped through in this way, and a matching function is calculated between the image block a×a at each spatial position in the second feature map and the image block at the spatial domain coordinate x in the first feature map, and the position coordinates with the maximum matching function are obtained; the difference of the second dimension coordinates of the coordinates is the frequency offset under the spatial coordinates, and the spatial domain curve where the coordinates are located is the matching function curve at the spatial domain coordinate x;

[0020] Move the image block in the first feature map to the next position along the spatial domain, and calculate the matching function between each a×a image block in the second feature map and the image block at the position, so as to obtain the matching function between the image block at each spatial position in the first feature map and the image block in the second feature map; the difference between the second dimensional coordinate of the image block in the first feature map and the second dimensional coordinate of the matching image block in the second feature map is the frequency offset, and the spatial domain and the frequency offset domain are the first dimensional coordinate and the second dimensional coordinate, and the matching function value is the third dimensional coordinate, and the matching function curves of all spatial positions are constructed into a three-dimensional matching function graph.

[0021] Furthermore, in step S4, the BM3D process is divided into two stages: basic estimation and final estimation:

[0022] Basic estimation stage: Divide the matching function graph into a series of reference blocks of size k×k, and traverse all points of the image through a fixed-step raster scanning sequence; for the current reference block, search for similar blocks in its surrounding neighborhood (n×n, n>k), compare the reference block with all blocks in the window, and stack all similar blocks to form a three-dimensional matrix according to the Euclidean distance. Perform hard threshold collaborative filtering on the three-dimensional matrix, use 2D-Bior1.5 wavelet to perform two-dimensional transformation on the two-dimensional data block in each three-dimensional matrix, and use 1D-Sym8 wavelet transform to perform one-dimensional transformation on the third dimension of the matrix, and jointly realize three-dimensional transformation. Obtain the processed image block through three-dimensional inverse transformation, and return the block estimate to their original position. The basic estimate of the image is calculated by weighted average of all the obtained overlapping estimation blocks.

[0023] Final estimation stage: The matching function graph and the basic estimate are block matched and stacked to form a three-dimensional matrix. Both three-dimensional matrices are transformed in three dimensions. The two-dimensional transformation adopts discrete cosine transform, and the one-dimensional transformation of the third dimension adopts 1D-Sym8 wavelet transform. The three-dimensional matrix of the matching function graph is scaled by Wiener filtering. The coefficients are obtained by the value of the three-dimensional matrix of the basic estimate and the noise intensity. The estimated values ​​of all image blocks are generated by three-dimensional inverse transformation, and the estimated values ​​of the blocks are returned to their original positions. The weighted average of all the overlapping estimated blocks obtained is used to calculate the final estimate of the image.

[0024] Furthermore, in step S5, the relationship between the frequency offset and the temperature change is: Wherein, Δf is the frequency offset, f is the laser frequency, ζ is the thermo-optic coefficient, α is the thermal expansion coefficient, and ΔT is the temperature change; the relationship between the frequency offset and the strain change is: Where Δf is the frequency offset, f is the laser frequency, and p ε is the elastic-optic coefficient, and Δε is the strain change.

[0025] The present invention also relates to a signal demodulation system of a phase-sensitive optical time domain reflectometer based on image processing, comprising a computer module, wherein the computer module runs the signal demodulation method of the phase-sensitive optical time domain reflectometer based on image processing.

[0026] Beneficial Effects

[0027] The phase-sensitive optical time-domain reflectometer and signal demodulation method based on image processing of the present invention are helpful to achieve high-precision demodulation under a large range of strain or temperature and high spatial resolution. This method combines image block matching and image denoising. The image block matching method utilizes multiple frequency information and multiple spatial position information, improves the demodulation accuracy when the strain or temperature changes greatly, and helps to expand the measurement range; the image denoising method denoises the matching function image, reduces the random noise of the system, and improves the demodulation accuracy at high spatial resolution. The matching function in the signal demodulation method of the phase-sensitive optical time-domain reflectometer based on image processing of the present invention is different from the cross-correlation function, which avoids the error caused by the cross-correlation calculation itself and improves the measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic structural diagram of the phase-sensitive optical time-domain reflectometer system provided by the present invention.

[0029] Figure 2 The present invention provides a flow chart of a signal demodulation method of a phase-sensitive optical time domain reflectometer based on image processing.

[0030] Figure 3 This is a relationship diagram between the image block size and the matching degree when the strain takes different values ​​in a specific embodiment of the present invention.

[0031] Figure 4 It is a three-dimensional matching function diagram in a specific embodiment of the present invention.

[0032] Description of Figure Numbers

[0033] Laser 1-1, electro-optic modulator 1-2, arbitrary function generator 1-3, first circulator 1-4, filter 1-5, first erbium-doped fiber amplifier 1-6, bandpass filter 1-7, semiconductor optical amplifier 1-8, second erbium-doped fiber amplifier 1-9, second circulator 1-10, optical fiber to be tested 1-11, photodetector 1-12, data acquisition card 1-13. DETAILED DESCRIPTION

[0034] The following is combined with Figures 1 to 4 The phase-sensitive optical time domain reflectometer and signal demodulation method based on image processing of the present invention are further described in detail.

[0035] See also Figure 1The phase-sensitive optical time domain reflectometer based on image processing of the present invention comprises, structurally, a laser 1-1, an electro-optic modulator 1-2, an arbitrary function generator 1-3, a first circulator 1-4, a filter 1-5, a first erbium-doped fiber amplifier 1-6, a bandpass filter 1-7, a semiconductor optical amplifier 1-8, an erbium-doped fiber amplifier 1-9, a second circulator 1-10, an optical fiber to be tested 1-11, a photodetector 1-12 and a data acquisition card 1-13;

[0036] The connection relationship and principle of each component are as follows:

[0037] The laser 1-1 is connected to the electro-optical modulator 1-2, and the arbitrary function generator 1-3 is connected to the electro-optical modulator 1-2. The light output by the laser 1-1 is modulated into a double-sideband modulation signal with a suppressed carrier by the electro-optical modulator 1-2; the double-sideband modulation signal with a suppressed carrier enters the filter 1-5 through the first circulator 1-4 to filter out one of the sidebands, and the output signal obtained is a single-sideband modulation signal; the single-sideband modulation signal enters the first erbium-doped fiber amplifier 1-6 for amplification, and then passes through the bandpass The filter 1-7 filters out spontaneous radiation noise; the semiconductor optical amplifier 1-8 is used to modulate the filtered single-sideband modulated signal into a pulse light sequence with N different frequencies; the second erbium-doped fiber amplifier 1-9 amplifies the pulse light sequence, and the pulse light sequence is injected into the optical fiber to be tested 1-11 through the second circulator 1-10; the optical fiber to be tested 1-11 includes M position points, and the backward Rayleigh scattering signal in the optical fiber to be tested is converted into an electrical signal by the photoelectric detector 1-12 and then collected by the data acquisition card 1-13.

[0038] The filter 1-5 is realized by a reflective fiber Bragg grating; the semiconductor optical amplifier 1-8 has a high extinction ratio.

[0039] Preferably, the first erbium-doped fiber amplifier 1-6 is a continuous light amplifier, and the second erbium-doped fiber amplifier 1-9 is a pulse light amplifier.

[0040] Example

[0041] In this embodiment, the laser 1-1 in the phase-sensitive optical time domain reflectometer based on image processing has an output light wavelength of 1550nm and a line width of 1kHz; the electro-optic modulator 1-2 has a bandwidth of 20GHz; the modulation signal generated by the arbitrary function generator 1-3 has a frequency range of 9GHz-10 GHz, and a frequency change interval of 20MHz; the semiconductor optical amplifier 1-8 modulates the continuous light into a pulse sequence with a pulse width of 10ns.

[0042] It should be noted that the phase-sensitive optical time-domain reflectometer based on image processing of the present invention is not limited to the above parameter design, and any parameters within a feasible range are acceptable.

[0043] See also Figure 2 The present invention also relates to a signal demodulation method of a high-precision phase-sensitive optical time domain reflectometer based on image processing, the method comprising the following steps:

[0044] S1. Collecting a first characteristic graph of the Rayleigh backscattering spectrum before strain and a second characteristic graph of the Rayleigh backscattering spectrum after strain respectively.

[0045] S2. Analyze the influence of the image block size on the matching degree of the image blocks in the first feature map and the second feature map, calculate the change of the matching degree between the image block in the first feature map and the matching image block in the second feature map under different strain changes as the image block size changes, and select the image block size a when the matching degree is the largest to perform the image block matching process.

[0046] Among them, the matching function to measure the matching degree is:

[0047]

[0048] S3, perform image block matching on the two feature maps, select an image block of size a×a at the spatial domain coordinate x in the first feature map, and use the coordinates of the lower left corner of the image block as the coordinates of the image block, and select an image block with the initial position and initial frequency as coordinates in the second feature map, and calculate the matching function between the image block and the image block at the spatial domain coordinate x in the first feature map; move the image block in the second feature map along the frequency domain, and calculate the matching function for each step; after the image block in the second feature map moves to the maximum value in the frequency domain, return to the initial frequency and move the image block to the next position along the spatial domain, and move the image block in the second feature map again along the frequency domain, and calculate the matching function for each step; loop through the entire second feature map in this way, and calculate the matching function between the image block of a×a at each spatial position in the second feature map and the image block at the spatial domain coordinate x in the first feature map. The matching function of the image block is obtained, and the position coordinates with the maximum matching function are obtained; the difference of the second-dimensional coordinates of the coordinates is the frequency offset under the spatial coordinates, and the spatial domain curve where the coordinates are located is the matching function curve at the spatial domain coordinate x; the image block in the first feature map is moved to the next position along the spatial domain, and the matching function of each a×a image block in the second feature map and the image block at the position is calculated, so as to obtain the matching function of the image block at each spatial position in the first feature map and the image block in the second feature map in this way; the difference of the second-dimensional coordinates of the image block in the first feature map and the second-dimensional coordinates of the matching image block in the second feature map is the frequency offset, with the spatial domain and the frequency offset domain as the first-dimensional coordinate and the second-dimensional coordinate, and the matching function value as the third-dimensional coordinate, and the matching function curves of all spatial positions are constructed into a three-dimensional matching function graph.

[0049] S4. De-noise the matching image, project the three-dimensional matching function graph into a two-dimensional image in the space domain-frequency offset domain; use BM3D to denoise the image, and BM3D is divided into two stages: basic estimation and final estimation.

[0050] In the basic estimation stage, the matching function graph is divided into a series of reference blocks of size k×k, and all points of the image are traversed through a fixed-step raster scanning sequence. For the current reference block, a similar block search is performed in its surrounding neighborhood (n×n, n>k), and the reference block is compared with all blocks in the window. All similar blocks are stacked to form a three-dimensional matrix according to the Euclidean distance. The three-dimensional matrix is ​​subjected to hard threshold collaborative filtering. The two-dimensional data blocks in each three-dimensional matrix are transformed into two dimensions using 2D-Bior1.5 wavelet, and the third dimension of the matrix is ​​transformed into one dimension using 1D-Sym8 wavelet transform. The three-dimensional transformation is jointly realized, and the processed image blocks are obtained through the three-dimensional inverse transformation, and the estimates of the blocks are returned to their original positions. The basic estimate of the image is calculated by weighted averaging all the obtained overlapping estimation blocks.

[0051] In the final estimation stage, the matching function graph and the basic estimate are block matched and stacked to form a three-dimensional matrix. The two three-dimensional matrices are transformed in three dimensions. The two-dimensional transformation adopts discrete cosine transform, and the one-dimensional transformation of the third dimension adopts 1D-Sym8 wavelet transform. The three-dimensional matrix of the matching function graph is scaled by Wiener filtering. The coefficients are obtained by the value of the three-dimensional matrix of the basic estimate and the noise intensity. The estimated values ​​of all image blocks are generated by three-dimensional inverse transformation, and the estimated values ​​of the blocks are returned to their original positions. The weighted average of all the overlapping estimated blocks obtained is used to calculate the final estimate of the image.

[0052] S5, extracting the second-dimensional coordinates corresponding to the maximum values ​​of the matching functions at each spatial position in the denoised image, and determining the frequency offset at each position of the optical fiber to be tested according to the coordinate values. Calculate the strain change, where Δf is the frequency offset, f is the laser frequency, and p ε is the elastic-optic coefficient, and Δε is the strain change.

[0053] Figure 3 The figure shows the relationship between the image block size and the matching degree when the strain changes are 0.5με, 1με, 2με, and 3με respectively.

[0054] Figure 4 Shown is the three-dimensional matching function diagram of each spatial position when the strain change at the 4-10m position is 3με and there is no change at other positions.

[0055] The above is only one embodiment of the present invention, not all or the only embodiment. Any equivalent changes made to the technical solution of the present invention by ordinary technicians in this field after reading the specification of the present invention are covered by the claims of the present invention.

Claims

1. A phase-sensitive optical time-domain reflectometer based on image processing, characterized in that: include: A laser (1-1), an electro-optic modulator (1-2), an arbitrary function generator (1-3), a first circulator (1-4), a filter (1-5), a first erbium-doped fiber amplifier (1-6), a bandpass filter (1-7), a semiconductor optical amplifier (1-8), a second erbium-doped fiber amplifier (1-9), a second circulator (1-10), an optical fiber to be tested (1-11), a photodetector (1-12) and a data acquisition card (1-13).

2. The phase-sensitive optical time domain reflectometer based on image processing according to claim 1, characterized in that: The laser (1-1) is connected to the electro-optic modulator (1-2), and the arbitrary function generator (1-3) is connected to the electro-optic modulator (1-2). The light output by the laser (1-1) is modulated into a double-sideband modulation signal with suppressed carrier by the electro-optic modulator (1-2); the double-sideband modulation signal with suppressed carrier passes through the first circulator (1-4) and enters the filter (1-5) to filter out one sideband, and the output signal obtained is a single-sideband modulation signal; the single-sideband modulation signal enters the first erbium-doped fiber amplifier (1- 6) after amplification, the spontaneous radiation noise is filtered out by the bandpass filter (1-7); the semiconductor optical amplifier (1-8) is used to modulate the filtered single-sideband modulation signal into a pulse light sequence with different frequencies; the second erbium-doped fiber amplifier (1-9) amplifies the pulse light sequence, and the pulse light sequence is injected into the optical fiber to be tested (1-11) through the second circulator (1-10); the backward Rayleigh scattering signal in the optical fiber to be tested is converted into an electrical signal by the photoelectric detector (1-12) and then collected by the data acquisition card (1-13).

3. The phase-sensitive optical time domain reflectometer based on image processing according to claim 1, characterized in that: The filter (1-5) is a reflective fiber Bragg grating; the semiconductor optical amplifier (1-8) has a high extinction ratio of 50 dB; the first erbium-doped fiber amplifier (1-6) is a continuous light amplifier, and the second erbium-doped fiber amplifier (1-9) is a pulse light amplifier.

4. A signal demodulation method using the phase-sensitive optical time domain reflectometer based on image processing as described in any one of claims 1 to 3, characterized in that: The signal demodulation method comprises the following steps: Step S1, respectively collecting a first characteristic graph of the Rayleigh backscattering spectrum before strain and a second characteristic graph of the Rayleigh backscattering spectrum after strain; Step S2, according to the influence of the image block size on the matching degree of the image blocks in the first feature map and the second feature map, calculate the change of the matching degree between the image block in the first feature map and the matching image block in the second feature map with the image block size under different strain changes, and select the image block size a when the matching degree is the largest to perform the image block matching process; Step S3: The difference between the second-dimensional coordinates of the image block of the first feature map and the second-dimensional coordinates of the matching image block of the second feature map is the frequency offset. The spatial domain and the frequency offset domain are the first-dimensional coordinates and the second-dimensional coordinates, and the matching function value is the third-dimensional coordinate. The matching function curves of all spatial positions are constructed into a three-dimensional matching function graph. Step S4, projecting the three-dimensional matching function graph into a two-dimensional image in the space domain-frequency offset domain, and performing denoising on the image using the BM3D image denoising method; Step S5, extracting the second dimensional coordinates corresponding to the maximum values ​​of the matching functions at each spatial position in the denoised image, and determining the frequency offset at each position of the optical fiber to be tested according to the coordinate values; solving the temperature according to the relationship between the frequency offset and the temperature change, and solving the strain according to the relationship between the frequency offset and the strain change.

5. The signal demodulation method of phase-sensitive optical time domain reflectometer based on image processing according to claim 4, characterized in that: Step S1 is specifically as follows: The backscattering spectrum of the pulse sequence at N frequencies is collected by the data acquisition card at the first moment, and the spatial domain and the frequency domain are respectively the first-dimensional coordinate and the second-dimensional coordinate, and the signal intensity is the third-dimensional coordinate, so as to obtain a three-dimensional backscattering spectrum and project it into the spatial domain-frequency domain as a first feature map; At the second moment, the back Rayleigh scattering spectrum of the pulse sequence at N frequencies is collected, and the spatial domain and the frequency domain are respectively the first and second dimensional coordinates and the signal intensity is the third dimensional coordinate. The three-dimensional back Rayleigh scattering spectrum is obtained and projected into the spatial domain-frequency domain as the second feature map.

6. The signal demodulation method of phase-sensitive optical time domain reflectometer based on image processing according to claim 4, characterized in that: In step S2, the matching function for measuring the matching degree is: Wherein, Δf is the frequency offset, f is the laser frequency, A is the image block in the first feature map, B is the image block in the second feature map, x is the spatial position of the image block, M is the number of positions in the optical fiber to be tested, and N is the number of modulation signals generated by the arbitrary function generator.

7. The signal demodulation method of phase-sensitive optical time domain reflectometry based on image processing according to claim 4, characterized in that: In step S3, the two feature maps are subjected to image block matching. An image block of size a×a is selected at the spatial domain coordinate x in the first feature map, and the coordinates of the lower left corner of the image block are used as the coordinates of the image block. In the second feature map, an image block with an initial position and an initial frequency as coordinates is selected, and a matching function between the image block and the image block at the spatial domain coordinate x in the first feature map is calculated; the image block in the second feature map is moved along the frequency domain, and a matching function is calculated for each step; after the image block in the second feature map is moved to the maximum value in the frequency domain, it returns to the initial frequency and moves the image block to the next position along the spatial domain, and the image block in the second feature map is moved again along the frequency domain, and a matching function is calculated for each step; the entire second feature map is looped through in this way, and a matching function is calculated between the image block a×a at each spatial position in the second feature map and the image block at the spatial domain coordinate x in the first feature map, and the position coordinates with the maximum matching function are obtained; the difference of the second dimension coordinates of the coordinates is the frequency offset under the spatial coordinates, and the spatial domain curve where the coordinates are located is the matching function curve at the spatial domain coordinate x; Move the image block in the first feature map to the next position along the spatial domain, and calculate the matching function between each a×a image block in the second feature map and the image block at the position, so as to obtain the matching function between the image block at each spatial position in the first feature map and the image block in the second feature map; the difference between the second dimensional coordinate of the image block in the first feature map and the second dimensional coordinate of the matching image block in the second feature map is the frequency offset, and the spatial domain and the frequency offset domain are the first dimensional coordinate and the second dimensional coordinate, and the matching function value is the third dimensional coordinate, and the matching function curves of all spatial positions are constructed into a three-dimensional matching function graph.

8. The signal demodulation method of phase-sensitive optical time domain reflectometer based on image processing according to claim 4, characterized in that: In step S4, the BM3D process is divided into two stages: basic estimation and final estimation: Basic estimation stage: Divide the matching function graph into a series of reference blocks of size k×k, and traverse all points of the image through a fixed-step raster scanning sequence; for the current reference block, search for similar blocks in its surrounding neighborhood (n×n, n>k), compare the reference block with all blocks in the window, and stack all similar blocks to form a three-dimensional matrix according to the Euclidean distance. Perform hard threshold collaborative filtering on the three-dimensional matrix, use 2D-Bior1.5 wavelet to perform two-dimensional transformation on the two-dimensional data block in each three-dimensional matrix, and use 1D-Sym8 wavelet transform to perform one-dimensional transformation on the third dimension of the matrix, and jointly realize three-dimensional transformation. Obtain the processed image block through three-dimensional inverse transformation, and return the block estimate to their original position. The basic estimate of the image is calculated by weighted average of all the obtained overlapping estimation blocks. Final estimation stage: The matching function graph and the basic estimate are block matched and stacked to form a three-dimensional matrix. Both three-dimensional matrices are transformed in three dimensions. The two-dimensional transformation adopts discrete cosine transform, and the one-dimensional transformation of the third dimension adopts 1D-Sym8 wavelet transform. The three-dimensional matrix of the matching function graph is scaled by Wiener filtering. The coefficients are obtained by the value of the three-dimensional matrix of the basic estimate and the noise intensity. The estimated values ​​of all image blocks are generated by three-dimensional inverse transformation, and the estimated values ​​of the blocks are returned to their original positions. The weighted average of all the overlapping estimated blocks obtained is used to calculate the final estimate of the image.

9. The signal demodulation method of phase-sensitive optical time domain reflectometer based on image processing according to claim 4, characterized in that: In step S5, the relationship between the frequency offset and the temperature change is: Wherein, Δf is the frequency offset, f is the laser frequency, ζ is the thermo-optic coefficient, α is the thermal expansion coefficient, and ΔT is the temperature change; the relationship between the frequency offset and the strain change is: Where Δf is the frequency offset, f is the laser frequency, and p ε is the elastic-optic coefficient, and Δε is the strain change.

10. A signal demodulation system of a phase-sensitive optical time domain reflectometer based on image processing, comprising a computer module, wherein the computer module runs the signal demodulation method of a phase-sensitive optical time domain reflectometer based on image processing according to any one of claims 1 to 9.

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