DAS-based water pipe detection signal denoising method and system

Abstract

The invention relates to a DAS-based water pipe detection signal denoising method and system, and the method comprises the steps: enabling a modal decomposition number and a filter length to serve as a parameter group, enabling the parameter group to serve as an individual of a population in a TTAO algorithm, enabling the envelope entropy of a water pipe detection signal to serve as the fitness, carrying out the optimization solution, and obtaining a denoising result; obtaining an optimal modal decomposition number and an optimal filter length; processing the water pipe detection signal by using an FMD algorithm based on the optimal modal decomposition number and the optimal filter length to obtain a plurality of IMF components, and calculating a correlation kurtosis value of each IMF component; modal mixing or redundant modals are eliminated based on the correlation kurtosis value, and a characteristic modal set is obtained; calculating Pearson's correlation coefficients of elements in the characteristic mode set, screening effective IMF components based on the Pearson's correlation coefficients, and summing the effective IMF components to obtain a denoised water pipe detection signal, and the system is used for implementing the method. Compared with the prior art, the method can be applied to non-periodic or weak characteristic signals and is high in adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed acoustic sensing, and in particular to a method and system for denoising water pipe detection signals based on DAS. Background Technique

[0002] Traditional Φ-OTDR uses a direct detection method to detect and locate signals by monitoring the intensity change of Rayleigh backscattered light (RBS) of a sensing optical fiber. Subsequently, coherent detection improves the signal-to-noise ratio and sensing range of Φ-OTDR. DAS is a new type of distributed optical fiber sensing technology using coherent detection developed on the basis of Φ-OTDR. It uses an optical fiber as a transmission medium and senses by detecting changes in the external environment, such as acoustic waves and vibration signals, and converting them into minute changes in the sensing optical fiber. It has the characteristics of anti-electromagnetic interference, high sensitivity, long-distance measurement, real-time monitoring, and resistance to harsh environments. It can be applied to pipeline leakage, oil exploration, perimeter security, seismic exploration, traffic safety, etc. However, in the actual environment of event recognition, noise is inevitable, which will more or less increase the false alarm rate and thus cause a decrease in the recognition rate. Therefore, the decomposition and feature extraction of signals have a great impact on event recognition. The main idea of the earlier wavelet transform is to decompose a signal into a superposition of a series of wavelet functions. According to different decomposition levels and methods, it can be divided into continuous wavelet transform (CWT) and discrete wavelet transform (DWT), but this method requires a long time for recognition. Therefore, an empirical mode decomposition (EMD) method is proposed. Although it does not require the selection of a window function or wavelet basis function and can reduce the recognition time, for discontinuous signals and signals with sudden change properties, EMD is prone to mode mixing and noise residue phenomena. Subsequently, methods that appeared later improved EMD. For example, ensemble empirical mode decomposition (EEMD) introduced the strategies of noise assistance and ensemble decomposition, and complete ensemble empirical mode decomposition (CEEMD) improved the noise tolerance and robustness of decomposition by adding white noise sequences. However, their effects are not obvious when dealing with complex signals.

[0003] For better signal decomposition, a Feature Mode Decomposition (FMD) is proposed, which utilizes the superiority of Correlation Kurtosis (CK), taking into account both the impulsiveness and periodicity of the signal. Inspired by the deconvolution principle, this method constructs an adaptive Finite Impulse Response (FIR) filter by iteratively updating the filter coefficients, making the filtered signal infinitely approximate the deconvolution objective function. The signal is decomposed into different modes using an adaptive FIR filter bank, and the correlation kurtosis can evaluate the impulsiveness and periodicity of the signal simultaneously. For example, in Chinese Patent Application 《CN114742111A》, a swarm intelligence optimization method based on the maximum signal cyclic kurtosis ratio is provided to automatically determine the feature mode decomposition parameters and calculate the square envelope spectrum feature energy ratio. Although it solves the problem of insufficient parameter adaptability in the prior art, it only relies on a single primary IMF component for fault diagnosis and may have insufficient adaptability to non-periodic or weak feature signals.

[0004] Therefore, providing a method with wide adaptability is a technical problem to be solved. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a DAS-based water pipe detection signal denoising method and system to improve the denoising effect of the water pipe vibration event signal in a Distributed Acoustic Sensing (DAS) system of a Phase-Sensitive Optical Time Domain Reflectometer (Φ-OTDR). A new denoising method based on Triangular Topology Aggregation Optimization (TTAO) Feature Mode Decomposition (FMD) is proposed. First, the FMD is used to decompose the collected vibration signal into k-order Intrinsic Mode Functions (IMFs), where k and the FMD filter length are iteratively and adaptively obtained by the TTAO algorithm. Then, the Pearson Correlation Coefficient (PCC) is used to select effective signal components and sum them to reconstruct a new denoised signal.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] According to the first aspect of the present invention, a DAS-based water pipe detection signal denoising method is provided, and the method includes:

[0008] Taking the number of mode decompositions and the filter length as a parameter group, and using the parameter group as an individual in the population of the TTAO algorithm, and taking the envelope entropy of the water pipe detection signal as the fitness, and performing optimization to obtain the optimal number of mode decompositions and the optimal filter length;

[0009] Based on the optimal number of mode decompositions and the optimal filter length, using the FMD algorithm to process the water pipe detection signal to obtain multiple IMF components, and calculating the correlation kurtosis value of each IMF component;

[0010] Eliminate modal mixing or redundant modes based on the aforementioned kurtosis values to obtain a set of characteristic modes;

[0011] Calculate the Pearson correlation coefficients of the elements in the set of characteristic modes, screen the effective IMF components based on the Pearson correlation coefficients, and sum the effective IMF components to obtain the denoised water pipe detection signal.

[0012] As a preferred technical solution, the method for obtaining the optimal modal decomposition number and the optimal filter length includes:

[0013] Randomly generate an initial population within the value range of the parameter group. The initial population contains multiple initial individuals. Calculate the fitness values of each individual in the initial population, and obtain the initial best fitness value and the initial optimal solution;

[0014] Iteratively execute the following steps to obtain the optimal modal decomposition number and the optimal filter length:

[0015] Take each individual in the current population as the first individual respectively, generate the second individual and the third individual based on the first individual, update the current population, calculate the fitness values of the second individual and the third individual, and compare them with the current best fitness value. If the current best fitness value is not the minimum fitness value, then take the minimum fitness value as the best fitness value, and the corresponding individual as the optimal solution; otherwise, do not perform any operation;

[0016] Generate candidate solutions based on the individuals in the current population, update the current population, and calculate the candidate fitness values of the candidate solutions. If the candidate fitness value is the minimum fitness value, then update the best fitness value to the candidate fitness value, and update the optimal solution to the candidate solution; otherwise, do not operate;

[0017] Randomly select an individual in the current population as the random individual, linearly cross the random individual and the optimal solution to generate a crossed individual, update the current population, and calculate the crossed fitness value of the crossed individual. If the crossed fitness value is the minimum fitness value, then update the best fitness value to the crossed fitness value, and update the optimal solution to the crossed individual; otherwise, compare it with the second-best fitness value. If it is less than the second-best fitness value, then update the second-best fitness value to the crossed fitness value, and update the second-best solution to the crossed individual. If it is greater than or equal to the second-best fitness value, then do not operate;

[0018] Generate new individuals based on the optimal solution and the second-best solution and calculate the fitness values. If the fitness value is the minimum fitness value, then update the best fitness value to the fitness value, and the solution to the new individual; otherwise, do not operate;

[0019] Determine whether the iteration end condition is satisfied. If so, end the iteration; otherwise, continue the next iteration.

[0020] As a preferred technical solution, the method for generating the initial individual is as follows:

[0021] X 0i = LB + (UB - LB) × rand(0, 1),

[0022] where LB represents the lower limit of the value of the parameter group; UB represents the upper limit of the value of the parameter group; rand(0, 1) represents a random number between [0, 1].

[0023] As a preferred technical solution, the method for generating the second individual and the third individual includes:

[0024] X 2i = X 1i + l × f(θ),

[0025]

[0026] where X 2i represents the second individual corresponding to the i-th first individual; X 3i represents the third individual; X 1i represents the i-th first individual; l represents the size of the triangular topological unit, and t represents the current iteration number, T represents the total number of iterations; f(θ) and both represent the direction vector.

[0027] As a preferred technical solution, the method for generating the candidate solution includes:

[0028] X 4i = r1 × X 1i + r2 × X 2i + r3 × X 3i ,

[0029] where X 4i represents the candidate solution; r1, r2, and r3 all represent random numbers between [0, 1]; X 2i represents the second individual corresponding to the i-th first individual; X 3i represents the third individual corresponding to the i-th first individual; X 1i represents the i-th first individual.

[0030] As a preferred technical solution, the method for generating the new individual is as follows:

[0031] X n2i = X bi + α(X bi - X sbi ),

[0032] where X n2i represents the new individual; X biDenote the solution; X sbi Denote the sub - optimal solution; α represents the aggregation range size, and T represents the total number of iterations, and t represents the t - th iteration.

[0033] As a preferred technical solution, the method for obtaining the plurality of IMF components includes:

[0034] Construct a mode decomposition constraint function of the water pipe detection signal based on the optimal mode decomposition number and the optimal filter length;

[0035] Transform the mode decomposition constraint function by iterative eigenvalue decomposition to obtain a generalized eigenfunction;

[0036] Iteratively solve the generalized eigenfunction to obtain the filter coefficients of the mode;

[0037] Obtain the IMF component corresponding to the mode based on the filter coefficients.

[0038] As a preferred technical solution, the mode decomposition constraint function includes a correlation kurtosis calculation formula and a mode - filter relationship formula, and its expression is:

[0039]

[0040]

[0041] Among them, CK M (u k ) represents the correlation kurtosis of the k - th mode u k , and the maximum value of k is the optimal mode decomposition number; f k (l) represents the filter coefficient corresponding to the k - th mode, and the filter length is l, and the maximum value of l is the optimal filter length; n represents the length of the water pipe detection signal, and its total length is N; m represents the displacement order, and its total order is M; T s represents the sampling period.

[0042] As a preferred technical solution, the method for obtaining the generalized eigenfunction includes:

[0043] Convert the mode - filter relationship formula into a first matrix, and its expression is: u k =Xf x , u k represents the k - th mode; f x represents the filter coefficient corresponding to the k - th mode; X represents the signal construction matrix, and its expression is: N represents the total length of the water pipe detection signal, and L represents the optimal filter length;

[0044] Convert the above-mentioned relevant kurtosis calculation formula into a second matrix, and its expression is: represents the conjugate transpose of the k-th mode, W M represents the weighted correlation matrix, which is determined by the sampling period and the displacement order;

[0045] Obtain an intermediate function based on the first matrix and the second matrix, and its expression is:

[0046]

[0047] where, represents the conjugate transpose of the filter coefficient corresponding to the k-th mode; X (H) represents the conjugate transpose of the signal construction matrix; W M represents the weighted correlation matrix; f k represents the filter coefficient corresponding to the k-th mode; R XWX represents the weighted correlation matrix; R XX represents the correlation matrix;

[0048] Maximize the filter coefficient in the intermediate function to obtain a generalized eigenfunction, and its expression is:

[0049] R XWX f k = R XX f k λ,

[0050] where λ represents the maximum eigenvalue.

[0051] According to the second aspect of the present invention, a DAS-based water pipe detection signal denoising system is provided, and this system is used to implement the above method.

[0052] Compared with the prior art, the present invention provides a two-layer screening mechanism and a multi-component fusion strategy, which effectively improve the complex signal processing ability. Specifically, the first-layer screening mechanism is to eliminate redundant modes based on the relevant kurtosis value, retain the components with periodic impact characteristics, and specifically filter out noise; the second-layer screening mechanism is to use the Pearson correlation coefficient to screen out the effective components strongly correlated with the original signal, avoid the interference of pseudo-features, and ensure that the screened components have both fault characteristics and signal relevance; fuse the multiple modal components after screening the signals to obtain the denoised signal; compared with the prior art, the present invention can capture weak features and non-periodic fault signals scattered in multiple components, avoid information omission, and has stronger robustness and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the Φ-OTDR structure diagram of the present invention;

[0054] Figure 2 It is the signal denoising flowchart of the present invention;

[0055] Figure 3 It is the schematic diagram of the signal acquisition device of the present invention;

[0056] Figure 4 It is the schematic diagram of various vibration signals in the embodiment of the present invention;

[0057] Figure 5 It is the schematic diagram of the effects of various denoising methods in the embodiment of the present invention;

[0058] Figure 6 It is the method flowchart of the present invention;

[0059] Figure 7 It is the optimized curve graph of the iterative calculation of the TTAO algorithm of the present invention;

[0060] Figure 8 It is the time domain graph of the modal components of the FMD algorithm of the present invention;

[0061] Figure 9 It is the frequency domain graph of the modal components of the FMD algorithm of the present invention;

[0062] Figure 10 It is the comparison graph before and after denoising of the steel pipe knocking signal of the present invention;

[0063] Figure 11 It is the comparison graph before and after denoising of the pipeline leakage signal of the present invention. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those with ordinary skills in the technical field to which this application belongs. The similar words such as "a", "an", "one kind", "the" and the like involved in this application do not indicate a limitation in quantity and may represent singular or plural. The terms "include", "comprise", "have" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The similar words such as "connect", "be connected", "couple" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates an "or" relationship between the associated objects before and after. The terms "first", "second", "third" and the like involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0066] When the environmental conditions of the external physical field change, or when the loss of the optical fiber line acts on any position of the sensing optical fiber, the elasto-optic effect and the thermo-optic effect will occur, resulting in changes in the refractive index and the length of the scattering unit of the sensing optical fiber at that position, causing a change in the phase of the backward Rayleigh scattered light at that position, further leading to a change in the phase difference of the Rayleigh scattered light transmitted to the detector, and causing a change in the light intensity of the backward Rayleigh scattered light. Therefore, by reverse derivation, detecting the light intensity of the backward Rayleigh scattered light can deduce various information about the external environment, and using the time difference between the incident pulsed light and the backward Rayleigh scattered light detected at the incident end can also measure the information of the optical fiber position. In the present invention, the Φ-OTDR technology is used for water pipe detection. Specifically, the light source of Φ-OTDR is a narrow linewidth laser, which can provide a very narrow spectral range, so that it is easier to distinguish the refractive index changes at different positions in the optical fiber, because the change in refractive index will cause an optical path difference, thus changing the phase. At the same time, in a long-distance optical fiber, non-linear effects (such as dispersion and self-phase modulation) may interfere with the signal, and the narrow linewidth laser can reduce this influence. Detecting the interference signal of the backward Rayleigh scattered light between scattering points within the detection optical pulse width is a new type of distributed optical fiber sensing technology different from OTDR. Specifically, this technology obtains the distribution information of the refractive index or stress in the optical fiber by analyzing the interference phenomenon between the Rayleigh scattered lights generated by different scattering points within the optical pulse. This interference signal provides detailed information about the minute refractive index changes inside the optical fiber, so it can be used for high-precision distributed sensing.

[0067] Figure 1 This is the schematic diagram of Φ-OTDR. The pulsed light entering the sensing optical fiber through the circulator has strong coherence. When the external environment changes or the optical fiber line condition changes, resulting in a certain interference signal generated on the sensing optical fiber, the phase and refractive index of the backward Rayleigh scattering in the optical fiber in this area will change accordingly, thus causing a change in the intensity of the interference signal of the backward Rayleigh scattering. Specifically, the position of the interference signal is determined by the time delay difference between the signal received by the detector and the input optical pulse signal.

[0068] In the present invention, the above-mentioned Φ-OTDR technology is used for water pipe detection. When collecting signals, such as Figure 2The device shown is composed of an external protective tube, four 160-meter inner tubes, two steel tubes and two wooden tubes. Each inner tube is wound with optical fibers, and square holes with a side length of 1 cm are drilled at random positions on the inner tube to simulate water leakage points. When water flows through the pipeline, leakage will occur at these holes, and the resulting abnormal vibrations will be captured by the optical fibers wrapped around the pipeline, so that vibration events can be detected and an alarm can be issued. The device uses a narrow linewidth laser with a linewidth of 3 kHz and an output power of 10 mW. Through a 90:10 optical coupler, the laser beam is split into two beams. 90% of the light is used as the probe light, and 10% of the light is used as the local oscillator light. The probe light is modulated by an acousto-optic modulator with a bandwidth of 150 MHz to form pulsed light. Then, the modulated pulsed light is amplified by an erbium-doped fiber amplifier with a gain of 27 dB. The amplified pulsed light enters the sensing fiber through an optical circulator. The Rayleigh backscattered light from the SF interferes with the local oscillator light at the second optical coupler to generate a beat signal. The beat signal is detected by a balanced photodetector with a bandwidth of 200 MHz and collected by a data acquisition card. The trigger pulse is provided by a pulse generator with a pulse repetition frequency of 20 kHz.

[0069] When conducting water pipe detection, simply focusing on water leakage events and noise does not provide a comprehensive solution because practical applications may be affected by other abnormal vibrations and human interferences. Therefore, two additional events are added: steel pipe knocking and mallet knocking. The knocking positions are at the same points as the water leakage holes of the pipeline, approximately at 78 meters of the pipeline. The data acquisition system divides the 160-meter-long pipeline into 40 channels, and each channel covers 4 meters. Therefore, the required signals can be extracted from the 20th channel. Four vibration signals, each containing 3,840 sampling points (with a length of 0.48 seconds), are as Figure 3 shown, and as can be seen from Figure 3 the noise signal shows a sinusoidal waveform and has a certain periodicity. The steel pipe knocking signal is more concentrated at the knocking point, while the mallet knocking signal is more dispersed around the knocking point. The reason for this difference may be that the deformation of the pipeline caused by the steel pipe results in higher-frequency vibrations compared to the mallet, and the leakage signal has no fixed shape and is relatively chaotic, making it difficult to find a regularity.

[0070] Regarding the characteristics of the above various signals, the existing technologies adopt three denoising methods including FMD, VMD and EMD for denoising. In order to select the best denoising method, in this embodiment, a denoising effect comparison experiment is first conducted for the above three methods, and the mallet knocking is selected as the original signal for denoising. The results are as Figure 4As shown, it can be seen that compared with VMD and EMD, the denoising effect of FMD is better. However, in the existing technology, when using FMD for denoising, the filter length and the number of modal components involved are often set artificially. Therefore, the denoising result is strongly affected by subjective factors.

[0071] Based on this, an optimization algorithm is proposed to optimize the parameters of the filter length and the number of modal components in the FMD algorithm. However, in the existing technology, when performing parameter optimization, the algorithm has low adaptability and is not conducive to wide application. Therefore, the present invention proposes a method for denoising water pipe detection signals based on DAS. The entire process is carried out according to Figure 5 the process shown, and the TTAO algorithm is selected to optimize and adjust the parameters of the FMD algorithm. The detailed algorithm process is as Figure 6 shown.

[0072] Specifically, the method includes:

[0073] S1. Take the number of modal decompositions and the filter length as a parameter group, and use the parameter group as an individual in the population of the TTAO algorithm. Use the envelope entropy of the water pipe detection signal as the fitness, and perform optimization to obtain the optimal number of modal decompositions and the optimal filter length.

[0074] Since the envelope entropy mainly measures the degree of envelope change of the signal and calculates the entropy value through the envelope of the signal, it can better describe the dynamic characteristics of the signal when dealing with non-stationary signals or signals with large fluctuations. In addition, the envelope entropy can capture the long-term trend of the signal, unlike permutation entropy or information entropy that overly rely on local details. For some signals with strong periodicity and large fluctuations, the envelope entropy has particular advantages. And the minimum envelope entropy can effectively remove the noise in the signal and simplify the signal structure, making the main information of the signal more prominent. Therefore, in this embodiment, the envelope entropy is selected as the fitness of the TTAO algorithm.

[0075] Specifically, the triangular topology aggregation optimizer (TTAO) is a new mathematics-based meta-heuristic algorithm used to solve continuous optimization and engineering application problems. The core of this algorithm is based on the similar triangular topology in mathematics. In the early stage, it conducts extensive exploration by randomly generating several vertices as candidate solutions. As the iteration progresses, these candidate solutions will be gradually optimized to help the algorithm avoid local optima. The steps of using TTAO for parameter optimization include:

[0076] S11. Randomly generate 6 initial populations within the value range of the parameter group. Each initial population contains multiple initial individuals. Calculate the fitness value of each individual in the initial population, obtain the initial best fitness value and the initial optimal solution, and set the number of iterations to 30 times.

[0077] Specifically, the way to generate the initial individual is: X0i = LB + (UB - LB) × rand(0,1), where LB represents the lower bound of the parameter set; UB represents the upper bound of the parameter set; rand(0,1) represents a random number between [0, 1]. In this step, a set of initial solutions is generated, representing multiple possible parameter settings of the model in the search space.

[0078] Iteratively execute the following steps to obtain the optimal modal decomposition number and the optimal filter length:

[0079] S12. Take each individual in the current population as the first individual, generate the second individual and the third individual based on the first individual, update the current population, calculate the fitness values of the second individual and the third individual, and compare them with the current best fitness value. If the current best fitness value is not the minimum fitness value, then take the minimum fitness value as the best fitness value and the corresponding individual as the optimal solution; otherwise, do not perform any operation.

[0080] The method for generating the second individual and the third individual includes:

[0081] X 2i = X 1i + l × f(θ),

[0082]

[0083] where X 2i represents the second individual corresponding to the i-th first individual; X 3i represents the third individual; X 1i represents the i-th first individual; l represents the size of the triangular topological unit, and t represents the current iteration number, T represents the total number of iterations; f(θ) and both represent direction vectors.

[0084] Specifically, the calculation form of the above direction vector is:

[0085]

[0086]

[0087] where and are both random numbers between [0, π].

[0088] S13. Generate candidate solutions based on the individuals in the current population, update the current population, and calculate the candidate fitness value of the candidate solution. If the candidate fitness value is the minimum fitness value, then update the best fitness value to the candidate fitness value and update the optimal solution to the candidate solution; otherwise, do not perform any operation.

[0089] The method for generating candidate solutions is:

[0090] X 4i = r1 × X 1i + X2 × X 2i + r3 × X 3i8 ,

[0091] where X 4i represents a candidate solution; r1, r3, and r3 all represent random numbers between [0, 1]; X 2i represents the second individual corresponding to the i-th first individual; X 3i represents the third individual corresponding to the i-th first individual; X 1i represents the i-th first individual.

[0092] Through the above aggregation operation, the TTAO algorithm generates new candidate solutions, further enhancing the diversity and robustness of the solution set.

[0093] S14. Randomly select an individual in the current population as a random individual, linearly cross the random individual and the optimal solution to generate a crossed individual, update the current population, and calculate the crossing fitness value of the crossed individual. If the crossing fitness value is the minimum fitness value, update the best fitness value to the crossing fitness value and update the optimal solution to the crossed individual; otherwise, compare it with the second-best fitness value. If it is less than the second-best fitness value, update the second-best fitness value to the crossing fitness value and update the second-optimal solution to the crossed individual. If it is greater than or equal to the second-best fitness value, no operation is performed.

[0094] In this stage, the newly generated individuals enhance the diversity of the population through linear combination. Specifically, the method for generating new individuals is as follows:

[0095] X n2i = X bi + α(X bi - X sbi ),

[0096] where X n2i represents the new individual; X bi represents the solution; X sbi represents the second-optimal solution; α represents the aggregation range size, and T represents the total number of iterations, and t represents the t-th iteration.

[0097] S15. Generate a new individual based on the optimal solution and the second-optimal solution and calculate the fitness value. If the fitness value is the minimum fitness value, update the best fitness value to the fitness value and the solution to the new individual; otherwise, no operation is performed.

[0098] S16. Determine whether the iteration end condition is satisfied. If so, end the iteration; otherwise, continue the next iteration.

[0099] Through steps S11 to S16, the TTAO algorithm effectively utilizes the triangular topology for optimization, gradually approaching the global optimal solution, that is, the required optimal modal decomposition number and filter length. The solution is as Figure 7 shown. It can be seen from the figure that the result converges after 19 iterations. The corresponding filter length L is 40, and the decomposition modal number k is 4. This means that when the filter length is 40, the signal is optimally decomposed into 4 modal components, which can minimize modal aliasing to the greatest extent and is beneficial for subsequent signal reconstruction.

[0100] The introduction of the TTAO algorithm significantly improves the performance of FMD. Specifically, the TTAO algorithm optimizes the selection of FMD parameters, making the entire decomposition process more efficient. Secondly, through its unique triangular topology, the TTAO greatly reduces the occurrence of modal aliasing and enhances the accuracy of decomposition. By iteratively updating the population individuals, the TTAO also significantly improves the operation convergence speed.

[0101] S2. Use the FMD algorithm to process the water pipe detection signal based on the optimal modal decomposition number and the optimal filter length, obtain multiple IMF components, and calculate the correlation kurtosis value of each IMF component.

[0102] S21. Construct a modal decomposition constraint function for the water pipe detection signal based on the optimal modal decomposition number and the optimal filter length. Specifically, the modal decomposition constraint function includes a correlation kurtosis calculation formula and a modal-filter relationship formula, and its expression is:

[0103]

[0104] Among them, CK M (u k ) represents the correlation kurtosis of the k-th mode u k , and the maximum value of k is the optimal modal decomposition number; f k (l) represents the filter coefficient corresponding to the k-th mode, and the filter length is l. The maximum value of l is the optimal filter length; n represents the length of the water pipe detection signal, and its total length is N; m represents the displacement order, and its total order is M; T s represents the sampling period.

[0105] S22. Transform the modal decomposition constraint function using iterative eigenvalue decomposition to obtain a generalized eigenfunction.

[0106] S221. Convert the modal-filter relationship formula into a first matrix, and its expression is: u k =Xf x , u k represents the k-th mode, and its expression is: f xdenotes the filter coefficients corresponding to the k-th mode, and its expression is: X represents the signal construction matrix, and its expression is: X = N represents the total length of the water pipe detection signal, and L represents the optimal filter length.

[0107] S222. Convert the relevant kurtosis calculation formula into a second matrix, and its expression is: denotes the conjugate transpose of the k-th mode, W M denotes the weighted correlation matrix, which is determined by the sampling period and the displacement order.

[0108] S223. Obtain the intermediate function based on the first matrix and the second matrix, and its expression is:

[0109]

[0110] where denotes the conjugate transpose of the filter coefficients corresponding to the k-th mode; X (H) denotes the conjugate transpose of the signal construction matrix; W M denotes the weighted correlation matrix; f k denotes the filter coefficients corresponding to the k-th mode; R XWX denotes the weighted correlation matrix; R XX denotes the correlation matrix.

[0111] S224. Maximize the filter coefficients in the intermediate function to obtain the generalized eigenfunction, and its expression is:

[0112] R XWX f k = R XX f k λ,

[0113] where λ represents the maximum eigenvalue.

[0114] S23. Perform iterative solution on the generalized eigenfunction to obtain the filter coefficients of the mode.

[0115] S24. Obtain the IMF component corresponding to the mode based on the filter coefficients.

[0116] The finally determined mode will contain specific components in the original signal. FMD only selects the mode with the largest CK value. In order to eliminate mode mixing or redundant modes, first lock the mode with the largest correlation coefficient, because the higher the correlation coefficient, the more identical components the two modes contain. At the same time, in order to retain the mode with more fault information, among the two modes with the largest correlation coefficient, abandon the mode with the smaller CK. The time-domain diagram of the obtained IMF component is as shown in Figure 8As shown in the figure, in the figure, IMF1 contains the clearest mallet signal. After calculation, the Pearson correlation coefficient (PCC) between IMF1 and the original signal is 0.8130, while the PCCs of the other three components are 0.0023, 0.0082, and 0.0133 respectively. By performing a fast Fourier transform (FFT) on each time-domain component, the Figure 9 frequency-domain diagram shown can be obtained. It can be seen from the figure that the modal components gradually increase from frequency band 1 to frequency band 4, there is no modal aliasing, and the separation is very clear.

[0117] S3. Eliminate modal mixing or redundant modes based on the correlation kurtosis value to obtain a set of characteristic modes.

[0118] S4. Calculate the Pearson correlation coefficient of the elements in the set of characteristic modes, screen the effective IMF components based on the Pearson correlation coefficient, and sum the effective IMF components to obtain the denoised water pipe detection signal. Taking the steel pipe knocking signal and the pipeline leakage signal as examples, the results shown in Figure 10 and Figure 11 are obtained. From Figure 10 and Figure 11 it can be seen that after the treatment of the present invention, for the steel pipe knocking signal, after the knocking ends, the phase change caused by noise has a large attenuation; for the leakage signal, the similarity with the noise signal is also significantly reduced after denoising, which makes it more beneficial to extract the feature vector that is significantly different from the noise and easier to give an early warning through pattern recognition.

[0119] The above is the introduction of the method embodiment. The present invention also provides a DAS-based water pipe detection signal denoising system, which is used to implement the above method. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0120] In addition, the system provided by the present invention includes a central processing unit (CPU), which can execute various appropriate actions and processes according to the computer program instructions stored in the read-only memory (ROM) or the computer program instructions loaded from the storage unit into the random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0121] Multiple components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, speakers, etc.; a storage unit, such as a disk, an optical disc, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0122] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be executed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).

[0123] The functions described above herein can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0124] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed as an independent software package partially on the machine and partially on a remote machine, or executed entirely on a remote machine or server.

[0125] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0126] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for denoising water pipe detection signals based on DAS, characterized in that, The method described above includes: Taking the number of modal decompositions and the filter length as a parameter group, and using the parameter group as an individual in the population of the TTAO algorithm. Using the envelope entropy of the water pipe detection signal as the fitness, optimize and solve it to obtain the optimal number of modal decompositions and the optimal filter length; Based on the optimal number of modal decompositions and the optimal filter length, use the FMD algorithm to process the water pipe detection signal to obtain multiple IMF components, and calculate the correlation kurtosis value of each IMF component; Based on the correlation kurtosis value, eliminate modal mixing or redundant modes to obtain a set of characteristic modes; Calculate the Pearson correlation coefficient of the elements in the set of characteristic modes, screen out the effective IMF components based on the Pearson correlation coefficient, and sum the effective IMF components to obtain the denoised water pipe detection signal.

2. The method for denoising a water pipe detection signal based on DAS according to claim 1, wherein The method for obtaining the optimal number of modal decompositions and the optimal filter length includes: Randomly generate an initial population within the value range of the parameter group. The initial population contains multiple initial individuals. Calculate the fitness value of each individual in the initial population, and obtain the initial best fitness value and the initial optimal solution; Iteratively execute the following steps to obtain the optimal number of modal decompositions and the optimal filter length: Take each individual in the current population as the first individual respectively. Generate the second individual and the third individual based on the first individual, update the current population, and calculate the fitness values of the second individual and the third individual, and compare them with the current best fitness value. If the current best fitness value is not the minimum fitness value, then take the minimum fitness value as the best fitness value, and the corresponding individual as the optimal solution; otherwise, do not perform any operation; Generate candidate solutions based on the individuals in the current population, update the current population, and calculate the candidate fitness values of the candidate solutions. If the candidate fitness value is the minimum fitness value, then update the best fitness value to the candidate fitness value, and update the optimal solution to the candidate solution; otherwise, do not perform any operation; Randomly select an individual in the current population as a random individual, linearly cross the random individual and the optimal solution to generate a crossed individual, update the current population, and calculate the crossed fitness value of the crossed individual. If the crossed fitness value is the minimum fitness value, then update the best fitness value to the crossed fitness value, and update the optimal solution to the crossed individual; otherwise, compare it with the second-best fitness value. If it is less than the second-best fitness value, then update the second-best fitness value to the crossed fitness value, and update the second-optimal solution to the crossed individual. If it is greater than or equal to the second-best fitness value, then do not perform any operation; Generate new individuals based on the optimal solution and the second-optimal solution and calculate the fitness value. If the fitness value is the minimum fitness value, then update the best fitness value to the fitness value, and the solution to the new individual; otherwise, do not perform any operation; Determine whether the iteration end condition is satisfied. If so, end the iteration; otherwise, continue the next iteration.

3. A method for denoising water pipe detection signals based on DAS according to claim 2, characterized in that, The method for generating the initial individual is: X 0i = LB + (UB - LB) × rand(0,1), Wherein, LB represents the lower limit of the value range of the parameter group; UB represents the upper limit of the value range of the parameter group; rand(0,1) represents a random number between [0, 1].

4. A DAS-based water pipe detection signal denoising method according to claim 2, characterized in that, The method for generating the second individual and the third individual includes: X 2i = X 1i + l × f(θ), Among them, X 2i represents the second entity corresponding to the i-th first entity; X 3i represents the third entity; X 1i represents the i-th first entity; l represents the size of the triangular topological unit, and t represents the current iteration number, T represents the total number of iterations; f(θ) and both represent the direction vector.

5. A method for denoising a water pipe detection signal based on DAS according to claim 2, characterized in that, The method for generating the candidate solution includes: X 4i = r1 × X 1i + r2 × X 2i + r3 × X 3i , Among them, X 4i represents a candidate solution; r1, r2, and r3 all represent random numbers between [0, 1]; X 2i represents the second individual corresponding to the i-th first individual; X 3i represents the third individual corresponding to the i-th first individual; X 1i represents the i-th first individual.

6. A method for denoising a water pipe detection signal based on DAS according to claim 2, characterized in that, The method for generating the new individual is: X n2i = X bi + α(X bi - X sbi ) Among them, X n2i represents a new individual; X bi represents a solution; X sbi represents a sub-optimal solution; α represents the size of the aggregation range, and T represents the total number of iterations, and t represents the t-th iteration.

7. A DAS-based water pipe detection signal denoising method according to claim 1, characterized in that The method for obtaining the multiple IMF components includes: Constructing a modal decomposition constraint function for the water pipe detection signal based on the optimal modal decomposition number and the optimal filter length; Converting the modal decomposition constraint function by iterative eigenvalue decomposition to obtain a generalized eigenfunction; Performing iterative solution on the generalized eigenfunction to obtain the filter coefficients of the mode; Obtaining the IMF components corresponding to the mode based on the filter coefficients.

8. A DAS-based water pipe detection signal denoising method according to claim 7, characterized in that The modal decomposition constraint function includes a correlation kurtosis calculation formula and a mode-filter relationship formula, and its expression is: Among them, CK M (u k ) represents the kurtosis of the k-th mode u k , and the maximum value of k is the optimal mode decomposition number; f k (l) represents the filter coefficient corresponding to the k-th mode, and the length of this filter is l, and the maximum value of l is the optimal filter length; n represents the length of the water pipe detection signal, and its total length is N; m represents the displacement order, and its total order is M; T s represents the sampling period.

9. A method for denoising water pipe detection signals based on DAS according to claim 8, characterized in that The method for obtaining the generalized eigenfunction includes: Convert the modal-filter relationship to a first matrix, and its expression is: u k = Xf x , u k represents the k-th mode; f x represents the filter coefficient corresponding to the k-th mode; X represents the signal construction matrix, and its expression is: N represents the total length of the water pipe detection signal, and L represents the optimal filter length; Convert the related kurtosis calculation formula into a second matrix, and its expression is: denotes the conjugate transpose of the k-th mode, W M denotes the weighted correlation matrix, which is determined by the sampling period and the displacement order; Obtaining an intermediate function based on the first matrix and the second matrix, and its expression is: Among them, represents the conjugate transpose of the filter coefficients corresponding to the k-th mode; X (H) represents the conjugate transpose of the signal construction matrix; W M represents the weighted correlation matrix; f k represents the filter coefficients corresponding to the k-th mode; R XWX represents the weighted correlation matrix; R XX represents the correlation matrix; Maximizing the filter coefficients in the intermediate function to obtain the generalized eigenfunction, and its expression is: R XWX f k = R XX f k λ, where λ represents the maximum eigenvalue.

10. A water pipe detection signal denoising system based on DAS, characterized in that, The system is used to implement the method according to any one of claims 1 to 9.

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