CEEMDAN-PCC-based phase sensitive optical time domain reflectometer signal processing method and system

Abstract

The invention discloses a CEEMDAN-PCC-based phase-sensitive optical time-domain reflectometer signal processing method and system. The method comprises the following steps: decomposing a backward Rayleigh scattering light signal output by a phase-sensitive optical time-domain reflectometer into a plurality of IMF components and residual components by adopting a CEEMDAN algorithm; and screening the decomposed components according to the calculated Pearson correlation coefficient between the backward Rayleigh scattering light signal and each IMF component, and determining the position of the disturbance point based on the screened components. The method solves the problems that in an existing signal processing method of the phase-sensitive optical time-domain reflectometer depending on wavelet decomposition and EMD, the adaptability of wavelet decomposition is weak, and modal aliasing and endpoint effect are prone to occurring in the decomposition process of EMD.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology for phase-sensitive optical time-domain reflectometers, specifically to a signal processing method and system for phase-sensitive optical time-domain reflectometers based on the CEEMDAN-PCC. Background Technology

[0002] Phase-sensitive optical time-domain reflectometry (OTDR) is a classic distributed fiber optic sensing system that offers advantages such as high sensitivity, fast response speed, and long-distance sensing for detecting minor disturbances. Specifically, a phase-sensitive OTDR injects a highly coherent pulse of light into the sensing fiber. When a weak disturbance occurs in the fiber, the refractive index at the disturbance location changes, causing a corresponding change in the optical phase. This results in a change in the amplitude of the backscattered Rayleigh interference signal, allowing the determination of the location of the disturbance and its related parameters.

[0003] Phase-sensitive optical time-domain reflectometers (OTDRs) require the reception and acquisition of scattered light signals from the sensing fiber during the sensing process. However, the acquired signals often carry a large amount of noise, such as interference fading noise from Rayleigh scattering and phase noise caused by laser frequency drift. Backscattered Rayleigh light is very weak and easily drowned out by this noise, making it unrecognizable. Furthermore, the high sensitivity of phase-sensitive OTDRs means that the external environment can easily affect the system signal, necessitating signal processing to obtain valid information. Therefore, signal processing is an indispensable part of phase-sensitive OTDRs.

[0004] In existing signal processing methods for phase-sensitive optical time-domain reflectometers, common approaches for signal decomposition include wavelet decomposition and empirical mode decomposition (EMD). However, wavelet decomposition has weak adaptability and is difficult to flexibly match scattering signals with different characteristics. On the other hand, EMD is prone to mode aliasing during decomposition, and the interference of endpoint effects also reduces the reliability of the decomposition results. Summary of the Invention

[0005] To address the aforementioned shortcomings, this invention proposes a signal processing method and system for phase-sensitive optical time-domain reflectometers based on CEEMDAN-PCC. The aim is to solve the problems of weak adaptability of wavelet decomposition and easy mode mixing and endpoint effects in existing signal processing methods for phase-sensitive optical time-domain reflectometers that rely on wavelet decomposition and EMD.

[0006] To achieve this objective, the present invention adopts the following technical solution: The signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC includes the following steps: Step S1: Input pulse signals to the phase-sensitive optical time-domain reflectometer at a preset frequency for processing, and output and collect several backscattered Rayleigh light signals; Step S2: The fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm is used to decompose each backscattered Rayleigh light signal to obtain the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. Step S3: Calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF component obtained from the decomposition. Step S4: Set the maximum threshold for the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient ; Step S5: Traverse all backscattered Rayleigh light signals. During the traversal, if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component is... Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered component. Step S6: Determine the location of the disturbance point based on the filtered components, and filter out the components with non-disturbance point locations.

[0007] Preferably, in step S2, the CEEMDAN algorithm is used to decompose the i-th backscattered Rayleigh light signal to obtain the IMF component and residual component corresponding to the i-th backscattered Rayleigh light signal, specifically including the following sub-steps: Step S21: For the i-th backscattered Rayleigh light signal M independent Gaussian white noise groups are added sequentially to obtain M groups of noisy i-th backscattered Rayleigh light signals, where the j-th group of noisy i-th backscattered Rayleigh light signals... The mathematical expression is as follows: ; in, express The j-th group of Gaussian white noise was added; M represents the total number of Gaussian white noises. Step S22: Use the EMD decomposition algorithm to decompose the i-th noisy backscattered Rayleigh light signal in each group, and extract the corresponding first-layer IMF component from the decomposition results of each group. ; Step S23: ... All corresponding first-layer IMF components Perform a summation and average operation to obtain The corresponding final first IMF component ,in, The specific calculation formula is as follows: ; Step S24: minus ,get The corresponding residual components; Step S25: Determine Check if the corresponding residual component is a monotonic function. If so, output... And the residual component; if not, proceed to step S26; Step S26: First, add M sets of independent Gaussian white noise to the current residual component in sequence to obtain M sets of new noisy signals; Subsequently, the EMD decomposition algorithm was used to decompose each group of new noisy signals and extract the first-layer IMF component corresponding to each group of new noisy signals. Next, the first-layer IMF components corresponding to the M new noisy signals are summed and averaged to obtain... The corresponding final next IMF component; Then subtract the current residual component The corresponding final IMF component yields a new residual component; Finally, determine whether the new residual component is a monotonic function. If yes, output the final IMF component and the new residual component; otherwise, use the new residual component as the current residual component and repeat step S26 until the decomposed residual component is a monotonic function, and output it. This corresponds to all final IMF components and final residual components.

[0008] Preferably, in step S3, the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component obtained from the decomposition is calculated, specifically including the following sub-steps: Step S31: Extract the data points from the i-th backward Rayleigh scattering signal. and the data points in the k-th IMF component obtained by decomposing the i-th backscattered Rayleigh light signal. ; Step S32: Calculate the average value of all data points in the i-th backscattered Rayleigh light signal. and the average value of all data points in the k-th IMF component obtained from the decomposition of the i-th backscattered Rayleigh light signal. ; Step S33: According to , , and Calculate the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition. ,in, The specific calculation formula is as follows: ; in, This represents the coordinates of the l-th data point in the i-th backward Rayleigh scattering signal; The coordinates of the l-th data point in the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal are represented; N represents the total number of data points in the i-th backward Rayleigh scattering light signal or the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal.

[0009] Another aspect of this application provides a signal processing system for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC, the system comprising: The input module is used to process pulse signals input to the phase-sensitive optical time-domain reflectometer according to a preset frequency. The output and acquisition module is used to output and acquire several backscattered Rayleigh light signals; The decomposition module is used to decompose each backscattered Rayleigh light signal using the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm, and obtain the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. The calculation module is used to calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF components obtained from the decomposition. The settings module is used to set the maximum threshold value for the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient ; The traversal module is used to traverse all backscattered Rayleigh light signals. During the traversal process, the judgment and filtering module is executed. The judgment and filtering module is used to determine the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component. Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered component. The determination module is used to determine the location of the disturbance point based on the filtered components; The filtering module is used to filter out components at non-disturbance point locations.

[0010] Preferably, the decomposition module includes: The first noise-adding submodule is used to process the i-th backscattered Rayleigh light signal. M independent Gaussian white noise groups are added sequentially to obtain M groups of noisy i-th backscattered Rayleigh light signals, where the j-th group of noisy i-th backscattered Rayleigh light signals... The mathematical expression is as follows: ; in, express The j-th group of Gaussian white noise was added; M represents the total number of Gaussian white noises. The first decomposition submodule is used to decompose the i-th noisy backscattered Rayleigh light signal in each group using the EMD decomposition algorithm; The first extraction submodule is used to extract the corresponding first-layer IMF components from each group of decomposition results. ; The first submodule for summation and averaging is used to calculate... All corresponding first-layer IMF components Perform a summation and average operation to obtain The corresponding final first IMF component ,in, The specific calculation formula is as follows: ; The first difference operation submodule is used to perform the operation. minus ,get The corresponding residual components; The first judgment submodule is used for judgment. If the corresponding residual component is a monotonic function, then execute the first output submodule; otherwise, execute the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging submodule, the second difference operation submodule, and the second judgment submodule in sequence. The first output submodule is used for output. as well as The corresponding residual components; The second noise-adding submodule is used to sequentially add M sets of independent Gaussian white noise to the current residual component to obtain M sets of new noisy signals. The second decomposition submodule is used to decompose each group of new noisy signals using the EMD decomposition algorithm. The second extraction submodule is used to extract the first-layer IMF components corresponding to each group of new noisy signals; The second summation and averaging submodule is used to perform summation and averaging operations on the first-layer IMF components corresponding to the M new noisy signals to obtain... The corresponding final next IMF component; The second difference operation submodule is used to subtract the current residual component. The corresponding final IMF component yields a new residual component; The second judgment submodule is used to determine whether the new residual component is a monotonic function. If it is, the second output submodule is executed. If not, the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging submodule, the second difference operation submodule, and the second judgment submodule are executed in sequence until the residual component obtained by decomposition is a monotonic function, and the third output submodule is executed. The second output submodule is used to output the obtained final IMF components and the new residual components; The third output submodule is used for output. This corresponds to all final IMF components and final residual components.

[0011] Preferably, the computing module includes: The third extraction submodule is used to extract data points from the i-th backward Rayleigh scattering light signal. and the data points in the k-th IMF component obtained by decomposing the i-th backscattered Rayleigh light signal. ; The first calculation submodule is used to calculate the average value of all data points in the i-th backscattered Rayleigh light signal. and the average value of all data points in the k-th IMF component obtained from the decomposition of the i-th backscattered Rayleigh light signal. ; The second calculation submodule is used to calculate based on... , , and Calculate the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition. ,in, The specific calculation formula is as follows: ; in, This represents the coordinates of the l-th data point in the i-th backward Rayleigh scattering signal; The coordinates of the l-th data point in the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal are represented; N represents the total number of data points in the i-th backward Rayleigh scattering light signal or the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal.

[0012] The technical solution provided by this invention may include the following beneficial effects: This scheme first uses the CEEMDAN algorithm to decompose the backscattered Rayleigh light signal output by the phase-sensitive optical time-domain reflectometer into several IMF components and residual components. Then, based on the calculated Pearson correlation coefficient between the backscattered Rayleigh light signal and each IMF component, the decomposed components are filtered, and the perturbation points are accurately located based on the filtered components. Compared with existing signal processing methods that rely on wavelet decomposition and EMD, the CEEMDAN algorithm used in this scheme, on the one hand, does not rely on the preset basis functions of wavelet decomposition and can achieve adaptive decomposition based on the characteristics of the backscattered Rayleigh light signal, thereby improving the adaptability to scattering signals with different characteristics; on the other hand, it can effectively suppress the mode mixing problem commonly found in EMD decomposition and weaken the interference of endpoint effects on the decomposition results, thereby improving the reliability of signal decomposition. Attached Figure Description

[0013] Figure 1 This is a flowchart of the signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC. Detailed Implementation

[0014] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0015] The signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC includes the following steps: Step S1: Input pulse signals to the phase-sensitive optical time-domain reflectometer at a preset frequency for processing, and output and collect several backscattered Rayleigh light signals; Step S2: The fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm is used to decompose each backscattered Rayleigh light signal to obtain the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. Step S3: Calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF component obtained from the decomposition. Step S4: Set the maximum threshold for the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient ; Step S5: Traverse all backscattered Rayleigh light signals. During the traversal, if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component is... Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered component. Step S6: Determine the location of the disturbance point based on the filtered components, and filter out the components with non-disturbance point locations.

[0016] The signal processing method of this scheme based on the CEEMDAN-PCC phase-sensitive optical time-domain reflectometer, such as... Figure 1 As shown, the first step is to input pulse signals to the phase-sensitive optical time-domain reflectometer at a preset frequency for processing, outputting and acquiring several backscattered Rayleigh light signals. In this embodiment, by inputting pulse signals to the phase-sensitive optical time-domain reflectometer at a preset frequency for processing, the backscattered Rayleigh light signals can be stably and continuously acquired, providing reliable basic data support for subsequent signal decomposition. The second step is to use the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm to decompose each backscattered Rayleigh light signal, obtaining the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. In this embodiment, by using the CEEMDAN algorithm to decompose the backscattered Rayleigh light signals, the stability of the signal decomposition process is effectively improved by leveraging the "adaptive noise injection and ensemble averaging" characteristics of the CEEMDAN algorithm. The third step is to calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF component obtained from the decomposition. In this embodiment, by calculating the Pearson correlation coefficient between the backscattered Rayleigh light signal and each IMF component, the strength of the correlation between the backscattered Rayleigh light signal and each IMF component can be quantified, providing a reliable basis for subsequent component selection. The fourth step is to set the maximum threshold value of the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient In this embodiment, the criteria for subsequent component selection are defined by setting a maximum and minimum threshold for the Pearson correlation coefficient. The fifth step is to iterate through all backscattered Rayleigh light signals. During this iteration, if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component is... Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered components. In this embodiment, the Pearson correlation coefficient between each backward Rayleigh scattering light signal and the corresponding IMF component obtained by decomposition is respectively compared with... and The comparison process accurately filters and retains effective feature components strongly correlated with the backscattered Rayleigh light signal, while eliminating feature components weakly correlated with the backscattered Rayleigh light signal, providing cleaner feature data support for subsequent disturbance point localization. The sixth step is to determine the disturbance point location based on the filtered components and filter out components that do not correspond to the disturbance point location. In this embodiment, by locating the disturbance point based on the filtered components and filtering out components that do not correspond to the disturbance point, interference from redundant data is effectively reduced, and the accuracy of disturbance point localization is improved, thereby enhancing the ability of the phase-sensitive optical time-domain reflectometer to identify minor disturbance events.

[0017] This scheme first uses the CEEMDAN algorithm to decompose the backscattered Rayleigh light signal output by the phase-sensitive optical time-domain reflectometer into several IMF components and residual components. Then, based on the calculated Pearson correlation coefficient between the backscattered Rayleigh light signal and each IMF component, the decomposed components are filtered, and the perturbation points are accurately located based on the filtered components. Compared with existing signal processing methods that rely on wavelet decomposition and EMD, the CEEMDAN algorithm used in this scheme, on the one hand, does not rely on the preset basis functions of wavelet decomposition and can achieve adaptive decomposition based on the characteristics of the backscattered Rayleigh light signal, thereby improving the adaptability to scattering signals with different characteristics; on the other hand, it can effectively suppress the mode mixing problem commonly found in EMD decomposition and weaken the interference of endpoint effects on the decomposition results, thereby improving the reliability of signal decomposition.

[0018] Preferably, in step S2, the CEEMDAN algorithm is used to decompose the i-th backscattered Rayleigh light signal to obtain the IMF component and residual component corresponding to the i-th backscattered Rayleigh light signal, specifically including the following sub-steps: Step S21: For the i-th backscattered Rayleigh light signal M independent Gaussian white noise groups are added sequentially to obtain M groups of noisy i-th backscattered Rayleigh light signals, where the j-th group of noisy i-th backscattered Rayleigh light signals... The mathematical expression is as follows: ; in, express The j-th group of Gaussian white noise was added; M represents the total number of Gaussian white noises. Step S22: Use the EMD decomposition algorithm to decompose the i-th noisy backscattered Rayleigh light signal in each group, and extract the corresponding first-layer IMF component from the decomposition results of each group. ; Step S23: ... All corresponding first-layer IMF components Perform a summation and average operation to obtain The corresponding final first IMF component ,in, The specific calculation formula is as follows: ; Step S24: minus ,get The corresponding residual components; Step S25: Determine Check if the corresponding residual component is a monotonic function. If so, output... And the residual component; if not, proceed to step S26; Step S26: First, add M sets of independent Gaussian white noise to the current residual component in sequence to obtain M sets of new noisy signals; Subsequently, the EMD decomposition algorithm was used to decompose each group of new noisy signals and extract the first-layer IMF component corresponding to each group of new noisy signals. Next, the first-layer IMF components corresponding to the M new noisy signals are summed and averaged to obtain... The corresponding final next IMF component; Then subtract the current residual component The corresponding final IMF component yields a new residual component; Finally, determine whether the new residual component is a monotonic function. If yes, output the final IMF component and the new residual component; otherwise, use the new residual component as the current residual component and repeat step S26 until the decomposed residual component is a monotonic function, and output it. This corresponds to all final IMF components and final residual components.

[0019] In this embodiment, in step S21, by adding multiple sets of independent Gaussian white noise to the i-th backscattered Rayleigh light signal, diverse analysis conditions can be created for subsequent EMD decomposition, avoiding the problem of mode aliasing in the backscattered Rayleigh light signal due to its single feature during EMD decomposition. In step S22, by using the EMD decomposition algorithm to decompose each group of noisy i-th backscattered Rayleigh light signals and extracting the corresponding first-layer IMF components from each group's decomposition results, the high-frequency effective feature components superimposed with noise in the noisy backscattered Rayleigh light signal can be initially separated. In step S23, by summing and averaging all the first-layer IMF components corresponding to the noisy backscattered Rayleigh light signal, the random error of a single IMF component can be effectively reduced, making the final IMF component more stable and more in line with the high-frequency characteristics of the real signal. In step S24, by calculating the difference between the original backscattered Rayleigh light signal and the corresponding IMF component, the extracted high-frequency effective features can be separated from the original signal, realizing the layered stripping of the original signal according to the frequency dimension. In step S25, by determining whether the residual component is a monotonic function, it can be determined whether the current decomposition has reached the trend term of the signal, thus avoiding over-decomposition. In step S26, by repeating the process of "adding noise - EMD decomposition - component averaging - updating residual components - monotonicity judgment" for the non-monotonic residual components, the effective low- and mid-frequency IMF components can be continuously separated from the residual components until the residual components become monotonic functions, thereby achieving adaptive hierarchical decomposition of the signal.

[0020] Preferably, in step S3, the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component obtained from the decomposition is calculated, specifically including the following sub-steps: Step S31: Extract the data points from the i-th backward Rayleigh scattering signal. and the data points in the k-th IMF component obtained by decomposing the i-th backscattered Rayleigh light signal. ; Step S32: Calculate the average value of all data points in the i-th backscattered Rayleigh light signal. and the average value of all data points in the k-th IMF component obtained from the decomposition of the i-th backscattered Rayleigh light signal. ; Step S33: According to , , and Calculate the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition. ,in, The specific calculation formula is as follows: ; in, This represents the coordinates of the l-th data point in the i-th backward Rayleigh scattering signal; The coordinates of the l-th data point in the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal are represented; N represents the total number of data points in the i-th backward Rayleigh scattering light signal or the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal.

[0021] In this embodiment, in step S31, the data points of the i-th backscattered Rayleigh light signal and its corresponding k-th IMF component are extracted to provide a data basis for the subsequent calculation of the Pearson correlation coefficient. In step S32, the average value of all data points in the i-th backscattered Rayleigh light signal and its corresponding k-th IMF component is calculated to provide a data basis for the subsequent calculation of the Pearson correlation coefficient. In step S33, the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component is calculated by substituting into the formula, which can quantify the degree of linear correlation between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component.

[0022] Another aspect of this application provides a signal processing system for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC, the system comprising: The input module is used to process pulse signals input to the phase-sensitive optical time-domain reflectometer according to a preset frequency. The output and acquisition module is used to output and acquire several backscattered Rayleigh light signals; The decomposition module is used to decompose each backscattered Rayleigh light signal using the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm, and obtain the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. The calculation module is used to calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF components obtained from the decomposition. The settings module is used to set the maximum threshold value for the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient ; The traversal module is used to traverse all backscattered Rayleigh light signals. During the traversal process, the judgment and filtering module is executed. The judgment and filtering module is used to determine the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component. Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered component. The determination module is used to determine the location of the disturbance point based on the filtered components; The filtering module is used to filter out components at non-disturbance point locations.

[0023] The signal processing system of the phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC in this scheme realizes the processing of backscattered Rayleigh light signals and the location of disturbance points through the cooperation of input module, output and acquisition module, decomposition module, calculation module, setting module, traversal module, judgment and filtering module, determination module and filtering module.

[0024] Preferably, the decomposition module includes: The first noise-adding submodule is used to process the i-th backscattered Rayleigh light signal. M independent Gaussian white noise groups are added sequentially to obtain M groups of noisy i-th backscattered Rayleigh light signals, where the j-th group of noisy i-th backscattered Rayleigh light signals... The mathematical expression is as follows: ; in, express The j-th group of Gaussian white noise was added; M represents the total number of Gaussian white noises. The first decomposition submodule is used to decompose the i-th noisy backscattered Rayleigh light signal in each group using the EMD decomposition algorithm; The first extraction submodule is used to extract the corresponding first-layer IMF components from each group of decomposition results. ; The first submodule for summation and averaging is used to calculate... All corresponding first-layer IMF components Perform a summation and average operation to obtain The corresponding final first IMF component ,in, The specific calculation formula is as follows: ; The first difference operation submodule is used to perform the operation. minus ,get The corresponding residual components; The first judgment submodule is used for judgment. If the corresponding residual component is a monotonic function, then execute the first output submodule; otherwise, execute the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging submodule, the second difference operation submodule, and the second judgment submodule in sequence. The first output submodule is used for output. as well as The corresponding residual components; The second noise-adding submodule is used to sequentially add M sets of independent Gaussian white noise to the current residual component to obtain M sets of new noisy signals. The second decomposition submodule is used to decompose each group of new noisy signals using the EMD decomposition algorithm. The second extraction submodule is used to extract the first-layer IMF components corresponding to each group of new noisy signals; The second summation and averaging submodule is used to perform summation and averaging operations on the first-layer IMF components corresponding to the M new noisy signals to obtain... The corresponding final next IMF component; The second difference operation submodule is used to subtract the current residual component. The corresponding final IMF component yields a new residual component; The second judgment submodule is used to determine whether the new residual component is a monotonic function. If it is, the second output submodule is executed. If not, the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging submodule, the second difference operation submodule, and the second judgment submodule are executed in sequence until the residual component obtained by decomposition is a monotonic function, and the third output submodule is executed. The second output submodule is used to output the obtained final IMF components and the new residual components; The third output submodule is used for output. This corresponds to all final IMF components and final residual components.

[0025] In this embodiment, by setting a first noise-adding submodule, diverse analysis conditions can be created for subsequent EMD decomposition, avoiding the problem of mode aliasing in the backscattered Rayleigh light signal due to its single feature during EMD decomposition. By setting a first decomposition submodule, high-frequency effective feature components with superimposed noise in the noisy backscattered Rayleigh light signal can be initially separated. By setting a first summation and averaging operation submodule, the random error of a single IMF component can be effectively reduced, making the final IMF component more stable and closer to the high-frequency characteristics of the real signal. By setting a first difference operation submodule, the extracted high-frequency effective features can be separated from the original signal, realizing the layered stripping of the original signal according to the frequency dimension. By setting a first judgment submodule, it can be determined whether the current decomposition has reached the trend term of the signal, avoiding over-decomposition. By repeatedly executing the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging operation submodule, the second difference operation submodule, and the second judgment submodule sequentially on the residual components of the non-monotonic function, the effective IMF components of medium and low frequencies can be continuously separated from the residual components.

[0026] Preferably, the computing module includes: The third extraction submodule is used to extract data points from the i-th backward Rayleigh scattering light signal. and the data points in the k-th IMF component obtained by decomposing the i-th backscattered Rayleigh light signal. ; The first calculation submodule is used to calculate the average value of all data points in the i-th backscattered Rayleigh light signal. and the average value of all data points in the k-th IMF component obtained from the decomposition of the i-th backscattered Rayleigh light signal. ; The second calculation submodule is used to calculate based on... , , and Calculate the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition. ,in, The specific calculation formula is as follows: ; in, This represents the coordinates of the l-th data point in the i-th backward Rayleigh scattering signal; The coordinates of the l-th data point in the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal are represented; N represents the total number of data points in the i-th backward Rayleigh scattering light signal or the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal.

[0027] In this embodiment, a third extraction submodule and a first calculation submodule are set up to provide a data foundation for the subsequent calculation of the Pearson correlation coefficient. By setting up a second calculation submodule, the linear correlation between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition can be quantified.

[0028] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0029] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC, characterized in that: Includes the following steps: Step S1: Input pulse signals to the phase-sensitive optical time-domain reflectometer at a preset frequency for processing, and output and collect several backscattered Rayleigh light signals; Step S2: The fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm is used to decompose each backscattered Rayleigh light signal to obtain the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. Step S3: Calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF component obtained from the decomposition. Step S4: Set the maximum threshold for the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient ; Step S5: Traverse all backscattered Rayleigh light signals. During the traversal, if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component is... Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered component. Step S6: Determine the location of the disturbance point based on the filtered components, and filter out the components with non-disturbance point locations.

2. The signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC according to claim 1, characterized in that: In step S2, the CEEMDAN algorithm is used to decompose the i-th backscattered Rayleigh light signal to obtain the IMF component and residual component corresponding to the i-th backscattered Rayleigh light signal. This specifically includes the following sub-steps: Step S21: For the i-th backscattered Rayleigh light signal M independent Gaussian white noise groups are added sequentially to obtain M groups of noisy i-th backscattered Rayleigh light signals, where the j-th group of noisy i-th backscattered Rayleigh light signals... The mathematical expression is as follows: ； in, express The j-th group of Gaussian white noise was added; M represents the total number of Gaussian white noises. Step S22: Use the EMD decomposition algorithm to decompose the i-th noisy backscattered Rayleigh light signal in each group, and extract the corresponding first-layer IMF component from the decomposition results of each group. ; Step S23: All corresponding first-layer IMF components Perform a summation and average operation to obtain The corresponding final first IMF component ,in, The specific calculation formula is as follows: ； Step S24: minus ,get The corresponding residual components; Step S25: Determine Check if the corresponding residual component is a monotonic function. If so, output... And the residual component; if not, proceed to step S26; Step S26: First, add M sets of independent Gaussian white noise to the current residual component in sequence to obtain M sets of new noisy signals; Subsequently, the EMD decomposition algorithm was used to decompose each group of new noisy signals and extract the first-layer IMF component corresponding to each group of new noisy signals. Next, the first-layer IMF components corresponding to the M new noisy signals are summed and averaged to obtain... The corresponding final next IMF component; Then subtract the current residual component The corresponding final IMF component yields a new residual component; Finally, determine whether the new residual component is a monotonic function. If yes, output the final IMF component and the new residual component; otherwise, use the new residual component as the current residual component and repeat step S26 until the decomposed residual component is a monotonic function, and output it. This corresponds to all final IMF components and final residual components.

3. The signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC according to claim 1, characterized in that: In step S3, the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition is calculated, which specifically includes the following sub-steps: Step S31: Extract the data points from the i-th backward Rayleigh scattering signal. and the data points in the k-th IMF component obtained by decomposing the i-th backscattered Rayleigh light signal. ; Step S32: Calculate the average value of all data points in the i-th backscattered Rayleigh light signal. and the average value of all data points in the k-th IMF component obtained from the decomposition of the i-th backscattered Rayleigh light signal. ; Step S33: According to , , and Calculate the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition. ,in, The specific calculation formula is as follows: ； in, This represents the coordinates of the l-th data point in the i-th backward Rayleigh scattering signal; The coordinates of the l-th data point in the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal are represented; N represents the total number of data points in the i-th backward Rayleigh scattering light signal or the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal.

4. A signal processing system for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC, using the signal processing method for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC as described in any one of claims 1-3, characterized in that: The system includes: The input module is used to process pulse signals input to the phase-sensitive optical time-domain reflectometer according to a preset frequency. The output and acquisition module is used to output and acquire several backscattered Rayleigh light signals; The decomposition module is used to decompose each backscattered Rayleigh light signal using the fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) algorithm, and obtain the intrinsic mode function (IMF) components and residual components corresponding to each backscattered Rayleigh light signal. The calculation module is used to calculate the Pearson correlation coefficient between each backscattered Rayleigh light signal and the corresponding IMF components obtained from the decomposition. The settings module is used to set the maximum threshold value for the Pearson correlation coefficient. and the minimum threshold of the Pearson correlation coefficient ; The traversal module is used to traverse all backscattered Rayleigh light signals. During the traversal process, the judgment and filtering module is executed. The judgment and filtering module is used to determine the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the corresponding k-th IMF component. Greater than When, then retain all Pearson correlation coefficients greater than 1 in the i-th backscattered Rayleigh light signal. The IMF components and residual components; if the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and all corresponding IMF components is less than or equal to When the i-th backward Rayleigh scattering light signal is obtained, the residual component is retained to obtain the filtered component. The determination module is used to determine the location of the disturbance point based on the filtered components; The filtering module is used to filter out components at non-disturbance point locations.

5. The signal processing system for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC according to claim 4, characterized in that: The decomposition module includes: The first noise-adding submodule is used to process the i-th backscattered Rayleigh light signal. M independent Gaussian white noise groups are added sequentially to obtain M groups of noisy i-th backscattered Rayleigh light signals, where the j-th group of noisy i-th backscattered Rayleigh light signals... The mathematical expression is as follows: ； in, express The j-th group of Gaussian white noise was added; M represents the total number of Gaussian white noises. The first decomposition submodule is used to decompose the i-th noisy backscattered Rayleigh light signal in each group using the EMD decomposition algorithm; The first extraction submodule is used to extract the corresponding first-layer IMF components from each group of decomposition results. ; The first submodule for summation and averaging is used to calculate... All corresponding first-layer IMF components Perform a summation and average operation to obtain The corresponding final first IMF component ,in, The specific calculation formula is as follows: ； The first difference operation submodule is used to perform the operation. minus ,get The corresponding residual components; The first judgment submodule is used for judgment. If the corresponding residual component is a monotonic function, then execute the first output submodule; otherwise, execute the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging submodule, the second difference operation submodule, and the second judgment submodule in sequence. The first output submodule is used for output. as well as The corresponding residual components; The second noise-adding submodule is used to sequentially add M sets of independent Gaussian white noise to the current residual component to obtain M sets of new noisy signals. The second decomposition submodule is used to decompose each group of new noisy signals using the EMD decomposition algorithm. The second extraction submodule is used to extract the first-layer IMF components corresponding to each group of new noisy signals; The second summation and averaging submodule is used to perform summation and averaging operations on the first-layer IMF components corresponding to the M new noisy signals to obtain... The corresponding final next IMF component; The second difference operation submodule is used to subtract the current residual component. The corresponding final IMF component yields a new residual component; The second judgment submodule is used to determine whether the new residual component is a monotonic function. If it is, the second output submodule is executed. If not, the second noise-adding submodule, the second decomposition submodule, the second extraction submodule, the second summation and averaging submodule, the second difference operation submodule, and the second judgment submodule are executed in sequence until the residual component obtained by decomposition is a monotonic function, and the third output submodule is executed. The second output submodule is used to output the obtained final IMF components and the new residual components; The third output submodule is used for output. This corresponds to all final IMF components and final residual components.

6. The signal processing system for a phase-sensitive optical time-domain reflectometer based on CEEMDAN-PCC according to claim 4, characterized in that: The computing module includes: The third extraction submodule is used to extract data points from the i-th backward Rayleigh scattering light signal. and the data points in the k-th IMF component obtained by decomposing the i-th backscattered Rayleigh light signal. ; The first calculation submodule is used to calculate the average value of all data points in the i-th backscattered Rayleigh light signal. and the average value of all data points in the k-th IMF component obtained from the decomposition of the i-th backscattered Rayleigh light signal. ; The second calculation submodule is used to calculate based on... , , and Calculate the Pearson correlation coefficient between the i-th backscattered Rayleigh light signal and the k-th IMF component obtained from the corresponding decomposition. ,in, The specific calculation formula is as follows: ； in, This represents the coordinates of the l-th data point in the i-th backward Rayleigh scattering signal; The coordinates of the l-th data point in the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal are represented; N represents the total number of data points in the i-th backward Rayleigh scattering light signal or the k-th IMF component obtained by decomposing the i-th backward Rayleigh scattering light signal.

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