Adaptive denoising of OFDR shape sensing spatial resolution enhancement method

Abstract

The application belongs to the technical field of distributed optical fiber shape sensing, and particularly relates to an OFDR shape sensing spatial resolution improvement method with self-adaptive denoising. The method arranges the cross-correlation results of local Rayleigh scattering spectra at different optical fiber positions in a spatial order, constructs a two-dimensional cross-correlation image, adopts a sparrow search algorithm (SSA) to perform self-adaptive optimization on key parameters of two-dimensional variational mode decomposition (2D-VMD), and performs mode decomposition, effective mode screening and image reconstruction on the two-dimensional cross-correlation image according to the optimized parameters, so as to suppress random noise, false peaks and abnormal points. The method solves the problems that in the existing OFDR shape sensing system in a high spatial resolution measurement scene, the main peak is blurred, false peaks occur frequently, the strain demodulation accuracy is insufficient, and the traditional 2D-VMD parameters rely on artificial experience and the denoising effect is unstable.

Description

Technical Field

[0001] This application belongs to the technical field of distributed optical fiber shape sensing, specifically relating to an adaptive noise reduction method for improving the spatial resolution of OFDR shape sensing. Background Technology

[0002] Fiber optic shape sensing technology typically uses multi-core optical fibers or fiber bundles arranged in a specific spatial structure as sensing units. When the shape of the fiber changes, different cores produce different strain responses. By measuring the strain distribution of each core individually or synchronously, the bending curvature and bending direction at each cross-section of the fiber can be calculated. This data is then combined with shape reconstruction algorithms to reconstruct the three-dimensional shape of the fiber. This technology offers advantages such as high spatial resolution, resistance to electromagnetic interference, tolerance to harsh environments, and distributed sensing, and has broad application prospects in fields such as precision interventional diagnosis and treatment, aerospace, structural health monitoring, and intelligent robotics.

[0003] Distributed strain measurement is the core foundation for fiber optic shape sensing, and its measurement accuracy and stability directly determine the effect of three-dimensional shape reconstruction. Optical frequency domain reflectometers (OFDRs) have advantages such as high spatial resolution, high sensitivity, resistance to electromagnetic interference, and distributed strain measurement, making them a core technology for achieving high-precision fiber optic shape sensing.

[0004] The working principle of an OFDR system is as follows: A tunable laser source emits linearly swept light, one beam of which enters the fiber under test. The backscattered Rayleigh light from the fiber returns, forming a signal beam, which beats with a reference beam. After processing the beat signal using a Fast Fourier Transform (FFT), distance domain information constructed along the sensing fiber can be obtained. In actual measurements, the system acquires both a reference signal without external disturbances and a measurement signal subject to strain disturbances. Through frequency domain transformation, segmentation, inverse Fourier transform, and cross-correlation calculations, the external strain change information is calculated. However, in practical applications, system noise, environmental interference, and speckle noise can degrade the quality of the cross-correlation signal, leading to blurred main peaks, raised side lobes, and an increase in spurious peaks. As spatial resolution increases, the effective spectral information participating in local cross-correlation calculations decreases, exacerbating these problems and easily causing insufficient strain demodulation accuracy, thus affecting the accuracy and stability of fiber shape reconstruction. Therefore, researching how to effectively improve the spatial resolution of strain measurement in OFDR shape sensing systems has significant theoretical and engineering value. Summary of the Invention

[0005] This invention aims to address the problems of existing OFDR shape sensing systems in high spatial resolution measurement scenarios, such as noise interference affecting cross-correlation signals leading to blurred main peaks, frequent spurious peaks, and insufficient strain demodulation accuracy, as well as the reliance on manual experience and unstable denoising effects of traditional two-dimensional variational mode decomposition (2D-VMD) parameters. This invention provides an adaptive denoising method for improving the spatial resolution of OFDR shape sensing, the technical solution of which is as follows:

[0006] An adaptive denoising method for improving the spatial resolution of OFDR shape sensing includes the following steps:

[0007] S1: Acquire two signals separately. One signal does not contain strain information and serves as the reference signal. The other signal contains strain information and serves as the measurement signal. S2: Map the reference signal and the measured signal onto the distance domain through a fast Fourier transform, and divide the signal into B equal parts by taking a window size D, where the window size D determines the spatial resolution of the system; S3: Perform a fast inverse Fourier transform on the range domain information of the reference signal and the measured signal within the first local window; S4: Perform cross-correlation calculation on the reference signal and the measured signal after fast inverse Fourier transform to obtain the one-dimensional cross-correlation result; S5: Repeat steps S3-S4 to obtain the one-dimensional cross-correlation results at each location along the fiber optic line, and reconstruct all the results into a two-dimensional image cross-correlation information L over the fiber optic distance; S6: Input the cross-correlation information L of the original 2D image into the SSA model to optimize the 2D-VMD parameters. After iterative optimization, output the optimal parameter combination for 2D-VMD. ; S7: Based on optimal parameter combination Perform 2D-VMD decomposition on the mutual correlation information L of the original two-dimensional image to obtain several high-fidelity two-dimensional intrinsic mode components. 2D-VMD employs a strict convergence threshold and a large number of iterations for final high-precision denoising, outputting the cross-correlation information L' of the denoised two-dimensional image; S8: Perform peak search on the cross-correlation information L' of the denoised 2D image at each position along the fiber distance direction to extract the peak information at each distance position. The cross-correlation peak at the position is determined, and the corresponding wavelength shift is calculated based on the shift of the peak relative to the zero-offset position. This allows for the acquisition of spectral offset measurement results along the fiber under high spatial resolution conditions. S9: Based on the linear correspondence between the wavelength shift of the Rayleigh scattering spectrum in optical fiber and the axial strain This allows for the demodulation of strain information at various locations along the optical fiber, enabling high spatial resolution strain measurement in the OFDR shape sensing system; among which... This is the wavelength offset-strain calculation factor for the optical fiber. For each distance position The strain value at that point.

[0008] The preferred formula for calculating spatial resolution D is: ; = ; In the formula, M is the number of data points contained in the window. denoted as the sampling distance between two adjacent sampling points, c is the speed of light in vacuum, n is the refractive index of the optical fiber, and Δf is the sweep frequency range of the tunable laser.

[0009] Preferably, the subroutine for SSA to optimize 2D-VMD parameters includes the following steps: S6.1: Input the original two-dimensional image cross-correlation information L, and initialize the SSA parameters, including the population size. Maximum number of iterations The optimization parameters are the upper and lower bounds, where the parameters to be optimized are the number of modes K and the penalty factor α in 2D-VMD, and their value ranges are as follows: , ; Randomly initialize the sparrow population positions, and represent the position of the j-th individual sparrow as follows: ; in, This represents the current iteration number. and These correspond to the number of modes and the penalty factor, respectively. S6.2: Based on the parameter combination corresponding to the current sparrow position, perform fast 2D-VMD decomposition on the mutual information L of the two-dimensional image to obtain several two-dimensional intrinsic mode components. ; S6.3: According to the formula Calculate the Pearson correlation coefficient of the cross-correlation L between each modal component obtained from the decomposition and the original two-dimensional image; S6.4: Constructing a composite fitness function based on the main peak signal-to-noise ratio, main peak continuity, and main peak smoothness. ; ; The weighting coefficients in the formula satisfy The normalized main peak signal-to-noise ratio represents the noise suppression capability. To normalize the continuity of the main peak, representing the stability of the peak position, Normalized smoothness represents the ability to suppress spurious peaks or spikes. S6.5: After calculating the fitness of all parameter combinations in the current population, distinguish the best and worst individuals based on their fitness values, and update the sparrow population position according to the SSA "discoverer-follower-guardian" mechanism. The update process is denoted as: ; In the formula: , The first Only the position of the sparrow before and after the iteration update; , The first The best and worst individual positions within the population in the next iteration; The population position update operator represents the sparrow search algorithm; S6.6: After each position update, the modality number is... Perform rounding and set the modal number. With penalty factor Constrain to a preset value range; if the current iteration number has not reached the maximum iteration number. Proceed to step S6.2 to begin the next round of parameter optimization; if the number of iterations reaches... The optimization iteration is terminated, and the parameter combination with the largest fitness value during the iteration is selected as the optimal parameter combination: ; In the formula: To be the optimal number of modes, This is the optimal penalty factor.

[0010] Preferably, when If the threshold is less than the preset threshold, the modality is removed. If the value is greater than or equal to a preset threshold, the mode is retained; according to the formula... The effective modes are superimposed and reconstructed to obtain the initial denoised signal L0; where For the first Each intrinsic mode component The Pearson correlation coefficient L between the cross-correlation information L and the original 2D image, where Cov is the covariance and Var is the variance. This is the initial denoised image for effective modal superposition reconstruction. For the effective modal set, These are the effective modal components after being filtered by correlation coefficient.

[0011] Preferably, in step S8, the fiber optic location... The sampling sequence number corresponding to the main cross-correlation peak satisfies: ; In the formula The cross-correlation information of the denoised 2D image; For the first sensing fiber One measurement location; This refers to the sampling point number along the spectral offset direction; Distance position First The cross-correlation amplitude corresponding to each sampling point; This indicates the sampling number that maximizes the cross-correlation amplitude; For position The sampling sequence number corresponding to the main cross-correlation peak; This is the reference sampling number corresponding to the zero-spectral offset.

[0012] Preferably, the reference sampling number corresponding to the zero-spectral offset is . The optical frequency interval corresponding to adjacent sampling points in the spectral dimension is Then the position The optical frequency offset at that point is: ; The corresponding wavelength offset is: ; In the formula: For position The optical frequency offset at that location. The frequency interval between adjacent spectrum sampling points; Let λ be the center wavelength of the tunable laser, and c be the speed of light in a vacuum. For position The wavelength shift of the Rayleigh scattering spectrum.

[0013] Compared with the prior art, the beneficial effects of this application are as follows:

[0014] The proposed method for improving the spatial resolution of OFDR shape sensing through adaptive denoising can effectively suppress noise, spurious peaks, and sidelobe interference in cross-correlation images, significantly improving the spatial resolution of the system. This gives it greater advantages and broader applications in high-precision, high-spatial-resolution monitoring fields such as aerospace, precision interventional diagnosis and treatment, and structural health monitoring.

[0015] The present invention proposes an adaptive denoising OFDR shape sensing spatial resolution improvement method, which adaptively optimizes the core parameters of 2D-VMD through the SSA algorithm. This solves the problems of traditional 2D-VMD parameters relying on human experience and unstable denoising effect. It can still accurately achieve mode separation and signal fidelity in complex noise environments, and can significantly improve the accuracy and stability of strain demodulation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the main process of an adaptive noise reduction method for improving the spatial resolution of OFDR shape sensing. Figure 2 A schematic diagram of the sub-process flow for optimizing 2D-VMD parameters for SSA; Figure 3 This is a schematic diagram of an OFDR strain measurement system for shape sensing; where 1- is a tunable laser; 2- is coupler one; 3- is coupler two; 4- is a circulator; 5- is a Mach-Zehnder interferometer; 6- is polarization controller one; 7- is polarization controller two; 8- is coupler three; 9- is a polarization beam splitter; 10- is a balanced detector; 11- is a data acquisition card; 12- is a sensing fiber; and 13- is a Fresnel ring. Figure 4 (a) shows the results of not using this method when the sensing fiber is subjected to 200με, 400με, 600με, 800με and 1000με at a spatial resolution of 2mm at a distance of 55m. It can be seen that there are many outliers in the results, and the correct strain distribution along the fiber length cannot be obtained. Figure 4 (b) shows the results of using this method at a sensing fiber distance of 55m and a spatial resolution of 2mm, when subjected to 200με, 400με, 600με, 800με and 1000με respectively. It can be seen that the outlier was well eliminated and the spatial resolution of the system was effectively improved after using this method. Figure 5 The linear relationship between the demodulated spectral offset and the applied strain is shown at a sensing fiber of 55m and a spatial resolution of 2mm. Figure 5 (a) shows the linear fitting results of the average spectral shift within the strain loading region as a function of the applied strain. Figure 5 (b) shows the distribution of the linear fitting determination coefficient R² at different locations within the strain loading region. Detailed Implementation

[0017] The technology will be further described below with reference to the accompanying drawings and specific embodiments to help understand the content of the present invention.

[0018] An adaptive denoising method for improving the spatial resolution of OFDR shape sensing includes the following steps: S1: Acquire two signals separately. One signal does not contain strain information and serves as the reference signal. The other signal contains strain information and serves as the measurement signal. S2: Map the reference signal and the measured signal onto the range domain using a Fast Fourier Transform. Divide the signal into B equal parts using a local window containing M range domain data points. The spatial resolution D of the system is calculated using the following formula: D = M , = ; In the formula, M is the number of data points contained in the window. denoted as the sampling distance between two adjacent sampling points, c is the speed of light in vacuum, n is the refractive index of the optical fiber, and Δf is the sweep frequency range of the tunable laser.

[0019] S3: Perform a fast inverse Fourier transform on the range domain information of the reference signal and the measured signal within the first local window; S4: Perform cross-correlation calculation on the reference signal and the measured signal after fast inverse Fourier transform to obtain the one-dimensional cross-correlation result; S5: Repeat steps S3-S4 to obtain the one-dimensional cross-correlation results at each location along the fiber optic line, and reconstruct all the results into a two-dimensional image cross-correlation information L over the fiber optic distance; S6: Input the cross-correlation information L of the original 2D image into the subroutine of SSA to optimize the 2D-VMD parameters. After completing the iterative optimization, output the optimal parameter combination of 2D-VMD. ; The subroutine for optimizing 2D-VMD parameters using SSA includes the following steps: S6.1: Input the original two-dimensional image cross-correlation information L, and initialize the SSA parameters, including the population size. Maximum number of iterations The optimization parameters are the upper and lower bounds, where the parameters to be optimized are the number of modes K and the penalty factor α in 2D-VMD, and their value ranges are as follows: , ; Randomly initialize the sparrow population positions, and represent the position of the j-th individual sparrow as follows: ; in, This represents the current iteration number. and These correspond to the number of modes and the penalty factor, respectively.

[0020] S6.2: Based on the parameter combination corresponding to the current sparrow position, perform fast 2D-VMD decomposition on the mutual information L of the two-dimensional image to obtain several two-dimensional intrinsic mode components. Among them, Fast 2D-VMD is used to improve optimization efficiency by using fewer iterations and lower convergence accuracy in the parameter search stage, in order to quickly evaluate the denoising effect of the current parameter combination.

[0021] S6.3: According to the formula Calculate the Pearson correlation coefficient of the cross-correlation L between each modal component obtained from the decomposition and the original two-dimensional image. When If the threshold is less than the preset threshold, the modality is removed. If the value is greater than or equal to a preset threshold, retain the mode. Then, according to the formula... The effective modes are superimposed and reconstructed to obtain the preliminary denoised signal L0. For the first Each intrinsic mode component The Pearson correlation coefficient L between the cross-correlation information L and the original 2D image, where Cov is the covariance and Var is the variance. This is the initial denoised image for effective modal superposition reconstruction. For the effective modal set, These are the effective modal components after being filtered by correlation coefficient.

[0022] S6.4: Constructing a composite fitness function based on the main peak signal-to-noise ratio, main peak continuity, and main peak smoothness: ; The composite fitness value corresponding to the current parameter combination is calculated using this formula. A higher fitness value indicates better denoising performance. The weighting coefficients in the formula satisfy... The normalized main peak signal-to-noise ratio represents the noise suppression capability. To normalize the continuity of the main peak, representing the stability of the peak position, Normalized smoothness represents the ability to suppress spurious peaks or spikes. S6.5: After calculating the fitness of all parameter combinations in the current population, distinguish the best and worst individuals based on their fitness values, and update the sparrow population position according to the SSA "discoverer-follower-guardian" mechanism. The update process is denoted as:

[0023] In the formula: , The first Only the position of the sparrow before and after the iteration update; , The first The best and worst individual positions within the population in the next iteration; The population position update operator represents the sparrow search algorithm.

[0024] S6.6: After each position update, the modality number is... Perform rounding and set the modal number. With penalty factor Constrain to a preset value range. If the current iteration count has not reached the maximum iteration count. Proceed to step S6.2 to begin the next round of parameter optimization; if the number of iterations reaches... The optimization iteration is terminated, and the parameter combination with the largest fitness value during the iteration is selected as the optimal parameter combination: ; In the formula: To be the optimal number of modes, This is the optimal penalty factor.

[0025] Finally, the optimal parameter combination Output and pass it to step S7 of the main program.

[0026] S7: Based on optimal parameter combination High-precision 2D-VMD decomposition is performed on the mutual correlation information L of the original two-dimensional image to obtain several high-fidelity two-dimensional intrinsic mode components. The high-precision 2D-VMD employs a strict convergence threshold and a large number of iterations for final high-precision denoising. Then, effective modes are selected according to the Pearson correlation coefficient formula in S6.3, and noisy modes are removed according to the preset correlation threshold. The remaining effective mode components are then superimposed and reconstructed to output the denoised two-dimensional image mutual information L'. S8: Perform peak search along the fiber optic distance direction on the cross-correlation information L' of the denoised 2D image, extracting the peak values ​​at each distance position. The cross-correlation peak at the position is determined, and the corresponding wavelength shift is calculated based on the shift of the peak relative to the zero-offset position. This allows for the acquisition of spectral offset measurements along the fiber under high spatial resolution conditions.

[0027] Fiber optic location The sampling sequence number corresponding to the main cross-correlation peak satisfies: ; In the formula The cross-correlation information of the denoised 2D image; For the first sensing fiber One measurement location; This refers to the sampling point number along the spectral offset direction; Distance position First The cross-correlation amplitude corresponding to each sampling point; This indicates the sampling number that maximizes the cross-correlation amplitude; For position The sampling sequence number corresponding to the main cross-correlation peak; This is the reference sampling number corresponding to the zero-spectral offset.

[0028] Location The optical frequency offset at that point is: ; The corresponding wavelength offset is: ; In the formula: For position The optical frequency offset at that location. The frequency interval between adjacent spectrum sampling points; Let λ be the center wavelength of the tunable laser, and c be the speed of light in a vacuum. For position The wavelength shift of the Rayleigh scattering spectrum.

[0029] S9: Based on the linear correspondence between the wavelength shift of the Rayleigh scattering spectrum in optical fiber and the axial strain This allows for the demodulation of strain information at various locations along the optical fiber, enabling high spatial resolution strain measurement in the OFDR shape sensing system. This is the wavelength offset-strain calculation factor for the optical fiber. For each distance position The strain value at that point.

[0030] Figure 3 This is a schematic diagram of an OFDR system. An OFDR strain measurement system for shape sensing includes: light emitted from a tunable laser 1 is split into two parts by coupler 2 (a 10 / 90 optical coupler). 10% of the light is incident on an unbalanced Mach-Zehnder interferometer 5, providing a trigger signal for the acquisition card 11. The remaining light enters coupler 3. Coupler 3 (a 1 / 99 optical coupler) then splits the light into two parts. 1% of the output is adjusted by polarization controller 6 to ensure that the "p" and "s" light components have the same power. The remaining 99% passes through circulator 4 and polarization controller 7 and enters the sensing fiber 12 for detection. A Fresnel ring 13 is used to suppress Fresnel reflection at the fiber end. The Rayleigh scattering signal is then combined with the 1% laser output from coupler 8 (a 50 / 50 optical coupler) to obtain an interference signal, which is then decomposed into "p" and "s" components by a polarization beam splitter. Finally, the "p" and "s" light are acquired by the acquisition card.

[0031] Figure 4 (a) shows the results of not using this method when the sensing fiber is subjected to 200με, 400με, 600με, 800με and 1000με at a spatial resolution of 2mm at a distance of 55m. It can be seen that there are many outliers in the results, and the correct strain distribution along the fiber length cannot be obtained. Figure 4(b) shows the results of using this method at a sensing fiber distance of 55m and a spatial resolution of 2mm, when subjected to 200με, 400με, 600με, 800με and 1000με respectively. It can be seen that the outlier was well eliminated after using this method, and the spatial resolution of the system was effectively improved. Figure 5 The linear relationship between the demodulated spectral shift and the applied strain is shown at a sensing fiber distance of 55m and a spatial resolution of 2mm after using this method. Figure 5 (a) shows the linear fitting results of the average spectral shift within the strain loading region as a function of the applied strain. Figure 5 (b) shows the distribution of the linear fitting determination coefficient R² at different locations within the strain loading region.

[0032] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for improving the spatial resolution of OFDR shape sensing with adaptive denoising, characterized in that, Includes the following steps: S1: Acquire two signals separately. One signal does not contain strain information and serves as the reference signal. The other signal contains strain information and serves as the measurement signal. S2: Map the reference signal and the measured signal onto the distance domain through a fast Fourier transform, and divide the signal into B equal parts by taking a window size D, where the window size D determines the spatial resolution of the system; S3: Perform a fast inverse Fourier transform on the range domain information of the reference signal and the measured signal within the first local window; S4: Perform cross-correlation calculation on the reference signal and the measured signal after fast inverse Fourier transform to obtain the one-dimensional cross-correlation result; S5: Repeat steps S3-S4 to obtain the one-dimensional cross-correlation results at each location along the fiber optic line, and reconstruct all the results into a two-dimensional image cross-correlation information L over the fiber optic distance; S6: Input the cross-correlation information L of the original 2D image into the SSA model to optimize the 2D-VMD parameters. After iterative optimization, output the optimal parameter combination for 2D-VMD. ; S7: Based on optimal parameter combination Perform 2D-VMD decomposition on the mutual correlation information L of the original two-dimensional image to obtain several high-fidelity two-dimensional intrinsic mode components. 2D-VMD employs a strict convergence threshold and a large number of iterations for final high-precision denoising, outputting the cross-correlation information L' of the denoised two-dimensional image; S8: Perform peak search on the cross-correlation information L' of the denoised 2D image at each position along the fiber distance direction to extract the peak information at each distance position. The cross-correlation peak at the position is determined, and the corresponding wavelength shift is calculated based on the shift of the peak relative to the zero-offset position. This allows for the acquisition of spectral offset measurement results along the fiber under high spatial resolution conditions. S9: Based on the linear correspondence between the wavelength shift of the Rayleigh scattering spectrum in optical fiber and the axial strain This allows for the demodulation of strain information at various locations along the optical fiber, enabling high spatial resolution strain measurement in the OFDR shape sensing system; among which... This is the wavelength offset-strain calculation factor for the optical fiber. For each distance position The strain value at that point.

2. The adaptive denoising OFDR shape sensing spatial resolution enhancement method according to claim 1, characterized in that, Formula for calculating spatial resolution D: D = M ; = ； In the formula, M is the number of data points contained in the window. denoted as the sampling distance between two adjacent sampling points, c is the speed of light in vacuum, n is the refractive index of the optical fiber, and Δf is the sweep frequency range of the tunable laser.

3. The adaptive denoising method for improving the spatial resolution of OFDR shape sensing according to claim 1, characterized in that, The subroutine for optimizing 2D-VMD parameters using SSA includes the following steps: S6.1: Input the original two-dimensional image cross-correlation information L, and initialize the SSA parameters, including the population size. Maximum number of iterations The optimization parameters are the upper and lower bounds, where the parameters to be optimized are the number of modes K and the penalty factor α in 2D-VMD, and their value ranges are as follows: ， ； Randomly initialize the sparrow population positions, and represent the position of the j-th individual sparrow as follows: ； in, This represents the current iteration number. and These correspond to the number of modes and the penalty factor, respectively. S6.2: Based on the parameter combination corresponding to the current sparrow position, perform fast 2D-VMD decomposition on the mutual information L of the two-dimensional image to obtain several two-dimensional intrinsic mode components. ; S6.3: According to the formula Calculate the Pearson correlation coefficient of the cross-correlation L between each modal component obtained from the decomposition and the original two-dimensional image; S6.4: Constructing a composite fitness function based on the main peak signal-to-noise ratio, main peak continuity, and main peak smoothness. ; ； The weighting coefficients in the formula satisfy The normalized main peak signal-to-noise ratio represents the noise suppression capability. To normalize the continuity of the main peak, representing the stability of the peak position, Normalized smoothness represents the ability to suppress spurious peaks or spikes. S6.5: After calculating the fitness of all parameter combinations in the current population, distinguish the best and worst individuals based on their fitness values, and update the sparrow population position according to the SSA "Discoverer-Follower-Watcher" mechanism. The update process is denoted as: ； In the formula: , The first Only the position of the sparrow before and after the iteration update; , The first The best and worst individual positions within the population in the next iteration; The population position update operator represents the sparrow search algorithm; S6.6: After each position update, the modality number is... Perform rounding and set the modal number. With penalty factor Constrain to a preset value range; if the current iteration number has not reached the maximum iteration number. Proceed to step S6.2 to begin the next round of parameter optimization; if the number of iterations reaches... The optimization iteration is terminated, and the parameter combination with the largest fitness value during the iteration is selected as the optimal parameter combination: ； In the formula: To be the optimal number of modes, This is the optimal penalty factor.

4. The adaptive denoising OFDR shape sensing spatial resolution enhancement method according to claim 3, characterized in that, when If the threshold is less than the preset threshold, the modality is removed. If the value is greater than or equal to a preset threshold, the mode is retained; according to the formula... The effective modes are superimposed and reconstructed to obtain the initial denoised signal L0; where For the first Each intrinsic mode component The Pearson correlation coefficient L between the cross-correlation information L and the original 2D image, where Cov is the covariance and Var is the variance. This is the initial denoised image for effective modal superposition reconstruction. For the effective modal set, These are the effective modal components after being filtered by correlation coefficient.

5. The adaptive denoising OFDR shape sensing spatial resolution enhancement method according to claim 3, characterized in that, Step S8 Fiber location The sampling sequence number corresponding to the main cross-correlation peak satisfies: ； In the formula The cross-correlation information of the denoised 2D image; For the first sensing fiber One measurement location; This refers to the sampling point number along the spectral offset direction; Distance position First The cross-correlation amplitude corresponding to each sampling point; This indicates the sampling number that maximizes the cross-correlation amplitude; For position The sampling sequence number corresponding to the main cross-correlation peak; This is the reference sampling number corresponding to the zero-spectral offset.

6. The adaptive denoising method for improving the spatial resolution of OFDR shape sensing according to claim 5, characterized in that, Let the reference sampling number corresponding to the zero-spectral offset be . The optical frequency interval corresponding to adjacent sampling points in the spectral dimension is Then the position The optical frequency offset at that point is: ； The corresponding wavelength offset is: ； In the formula: For position The optical frequency offset at that location. The frequency interval between adjacent spectrum sampling points; Let λ be the center wavelength of the tunable laser, and c be the speed of light in a vacuum. For position The wavelength shift of the Rayleigh scattering spectrum.

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