Double-source magnetic field comprehensive detection and analysis method and system based on buried metal pipeline

Abstract

The invention provides a double-source magnetic field comprehensive detection and analysis method and system based on a buried metal pipeline, and the method comprises the following steps: applying composite magnetic field excitation to a target section of the pipeline, and synchronously collecting mixed magnetic response signals through a magnetic sensor array disposed along the axial direction of the pipeline. The mixed signal is separated into a slowly varying component representing quasi-static background stress and a burst component representing a pipeline stress event, and the burst component is extracted. And carrying out dispersion elimination inverse operation processing on the extracted burst component, and accurately determining the position information of the pipeline stress event on the pipeline through joint optimization solution by combining the time difference of arrival and the energy attenuation of the original waveform characteristics on different sensors. And inputting energy and position information of a pipeline stress event into the model, and continuously calculating and updating an accumulated damage factor of a specific position of the pipeline. According to the invention, the stress event of the buried metal pipeline can be accurately identified and positioned.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of magnetic field detection, and particularly relates to a double-source magnetic field comprehensive detection and analysis method and system based on a buried metal pipeline. BACKGROUND

[0002] As the blood vessels of modern industry and urban infrastructure, buried metal pipelines bear the task of transporting key substances such as energy and water resources. However, these pipelines will inevitably be affected by soil corrosion, geological subsidence, external impact and internal fluid pressure fluctuation and other factors during long-term service, leading to material fatigue, crack initiation and propagation, and even catastrophic leakage or burst accidents. Traditional pipeline detection technologies, such as visual inspection, ultrasonic detection and ray detection, often need to be excavated or shut down, which is time-consuming, labor-intensive and costly, and it is difficult to achieve continuous and real-time monitoring of a large range and long distance pipeline. Moreover, due to the complexity of the buried environment and the material properties of the pipeline itself, the existing detection methods can usually only detect obvious defects that already exist in the pipeline, and it is difficult to capture various stresses that the pipeline bears under internal or external forces, which seriously affects the safety and reliability of the pipeline. SUMMARY

[0003] The application provides a double-source magnetic field comprehensive detection and analysis method and system based on a buried metal pipeline to solve the above technical problems.

[0004] In a first aspect, the application provides a double-source magnetic field comprehensive detection and analysis method based on a buried metal pipeline, which comprises the following steps: A composite magnetic field superimposed by a low-frequency strong magnetic field and a high-frequency weak magnetic field is applied to a target detection section of the buried metal pipeline for excitation, and a mixed magnetic response signal in response to the composite magnetic field is collected through a magnetic sensor array arranged along the axial direction of the buried metal pipeline; A blind source separation algorithm based on independent component analysis is used to separate the mixed magnetic response signal into a slowly varying component representing quasi-static stress and a burst component representing transient stress, and the burst component is extracted; The burst component is processed by dispersion de-inversion operation based on a preset stress wave-magnetic coupling dispersion model, and the original waveform characteristics of the pipeline stress event before propagation distortion at the initial position are reconstructed; The arrival time difference and energy attenuation amount of the original stress wave corresponding to the original waveform characteristics on different sensors in the magnetic sensor array are combined, and the position information of the pipeline stress event on the buried metal pipeline is determined by joint optimization solution; The cumulative damage degree of the target detection section of the buried metal pipeline is calculated according to the energy and position information of the pipeline stress event positioned each time.

[0005] Optionally, the method further comprises the following steps of: coaxially packaging the first excitation coil for generating the low-frequency strong magnetic field and the second excitation coil for generating the high-frequency weak magnetic field, and deploying the coaxially packaged first and second excitation coils at the target detection section of the buried metal pipeline; applying a direct current through the first excitation coil to establish a circumferential background magnetization field in the wall of the buried metal pipeline; applying a high-frequency alternating current through the second excitation coil to superimpose a high-frequency detection magnetic field on the circumferential background magnetization field to form a composite magnetic field in the wall of the buried metal pipeline; sensing the magnetic flux density variation in response to the composite magnetic field by using the magnetic sensor array linearly and equidistantly deployed along the axial direction of the buried metal pipeline; converting the magnetic flux density variation sensed by the magnetic sensor array into a plurality of mixed magnetic response signals by using a multi-channel synchronous data acquisition device.

[0006] Optionally, the method further comprises the following steps of: segmenting the acquired mixed magnetic response signals, and constructing an observation signal matrix; setting a maximum non-Gaussianity as an optimization objective, and performing iterative calculation on the observation signal matrix by using a fast fixed point algorithm to obtain a demixing matrix; linearly transforming the observation signal matrix by using the demixing matrix, and outputting a plurality of independent signal components; calculating statistical characteristic parameters of the plurality of signal components, respectively, and identifying the slowly varying component and the burst component according to the statistical characteristic parameters, and extracting the signal component identified as the burst component.

[0007] Optionally, the method of calculating statistical characteristic parameters of the plurality of signal components, respectively, and identifying the slowly varying component and the burst component according to the statistical characteristic parameters, and extracting the signal component identified as the burst component comprises the following steps: calculating a kurtosis value and a sparsity value of each signal component, respectively; for any signal component, if the kurtosis value is greater than a preset first kurtosis threshold and the sparsity value is greater than a preset first sparsity threshold, the signal component is identified as the burst component, and the burst component is extracted; If the kurtosis value is less than a preset second kurtosis threshold and the sparsity value is less than a preset second sparsity threshold, the signal component is identified as a slowly varying component, the first kurtosis threshold is greater than the second kurtosis threshold, and the first sparsity threshold is greater than the second sparsity threshold.

[0008] Optionally, the dispersion deconvolution processing of the burst component based on the preset stress wave-magnetic coupling dispersion model to reconstruct the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion includes the following steps: Transforming the burst component from the time domain to the frequency domain to obtain the initial frequency spectrum of the burst component; Pre-establishing a pipeline stress wave-magnetic coupling dispersion model of the buried metal pipeline, and obtaining a nonlinear mapping relationship between stress wave frequency and phase velocity through the stress wave-magnetic coupling dispersion model; According to the nonlinear mapping relationship, performing frequency-dependent phase correction on each frequency component in the initial frequency spectrum to obtain a corrected frequency spectrum; Converting the corrected frequency spectrum back to the time domain through inverse Fourier transform to obtain a compressed pulse signal, and taking the waveform of the pulse signal as the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion.

[0009] Optionally, the pre-establishment of the pipeline stress wave-magnetic coupling dispersion model specifically includes the following steps: Based on the material properties and geometric size parameters of the buried metal pipeline, a three-dimensional finite element model of the buried metal pipeline is established; A broadband impact load is applied at a predetermined position of the three-dimensional finite element model to excite multiple different modes of test stress waves; Through transient dynamics simulation, the propagation velocities of different frequency components of the test stress waves during propagation along the pipeline axis of the buried metal pipeline are calculated; The propagation velocity data of different frequency components are fitted to establish a nonlinear mapping relationship between stress wave frequency and phase velocity, and the nonlinear mapping relationship is solidified as the stress wave-magnetic coupling dispersion model of the buried metal pipeline.

[0010] Optionally, the position information of the pipeline stress event on the buried metal pipeline is determined by joint optimization based on the arrival time difference and energy attenuation amount of the original stress wave corresponding to the original waveform characteristics at different sensors in the magnetic sensor array, which includes the following steps: The pulse peak arrival time and pulse peak amplitude of the original stress wave corresponding to the original waveform characteristics are extracted from each magnetic sensor channel in the magnetic sensor array; The arrival time difference between any two magnetic sensors is calculated according to the pulse peak arrival time; The energy attenuation amount of each magnetic sensor relative to the reference sensor is calculated according to the pulse peak amplitude. Constructing a joint optimization objective function containing a time difference of arrival error term and an energy attenuation amount error term, and using a nonlinear least square algorithm to iteratively optimize the joint optimization objective function, and determining a solution that minimizes the objective function as the action position of the pipeline stress event.

[0011] Optionally, the step of calculating the cumulative damage degree of the target detection section of the buried metal pipeline according to the energy and position information of each positioning pipeline stress event comprises the following steps: Discretize the three-dimensional finite element model of the buried metal pipeline into a plurality of damage calculation units along the pipeline axial direction; According to the energy and position information of the pipeline stress event, calculate the equivalent stress amplitude caused by the corresponding pipeline stress event to each damage calculation unit; Based on the material S-N curve of the buried metal pipeline, find the allowable cycle number corresponding to the equivalent stress amplitude; Take the reciprocal of the allowable cycle number as the damage increment caused by the current pipeline stress event to the corresponding damage calculation unit; Add the damage increment to the cumulative damage factor of the corresponding damage calculation unit to complete the cumulative damage degree accumulation calculation of the target detection section of the buried metal pipeline.

[0012] In a second aspect, the present application further provides a double-source magnetic field comprehensive detection analysis system based on a buried metal pipeline, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the double-source magnetic field comprehensive detection analysis method based on a buried metal pipeline as described in the first aspect when executing the computer program.

[0013] In a third aspect, the present application further provides a computer readable storage medium, which stores instructions, and the instructions make the processor be configured to execute the double-source magnetic field comprehensive detection analysis method based on a buried metal pipeline according to the first aspect when executed by the processor.

[0014] The present application has the following beneficial effects: The application adopts a blind source separation algorithm based on independent component analysis, can intelligently separate complex mixed magnetic response signals into a slowly varying component representing quasi-static background stress and a burst component representing a pipeline stress event, thereby realizing accurate extraction and denoising of key transient events, and greatly improving the signal-to-noise ratio and identification accuracy of the target signal. The extracted burst component is subjected to dispersion deconvolution operation processing, and the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion are reconstructed, avoiding misjudgment caused by signal propagation distortion, thereby realizing high-precision determination of the specific position information of the pipeline stress event on the pipeline, providing a key basis for accurate repair and maintenance. Finally, the cumulative damage factor of the specific position of the pipeline is calculated and updated in real time according to the pipeline stress event, realizing dynamic and quantitative evaluation and prediction of the pipeline fatigue damage, effectively reducing the potential safety risk and operation cost. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a flowchart of a double-source magnetic field comprehensive detection and analysis method based on a buried metal pipeline in one of the embodiments of the application.

[0016] Figure 2 It is a flowchart of applying a magnetic field excitation and collecting a response signal in one of the embodiments of the application.

[0017] Figure 3 It is a flowchart of reconstructing original waveform characteristics in one of the embodiments of the application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be clearly described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, not all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the application.

[0019] The terms "first", "second", and the like in the specification and claims of the application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually a class, not limited to the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents a "or" relationship between the associated objects before and after.

[0020] Figure 1This is a flowchart illustrating a dual-source magnetic field integrated detection and analysis method for buried metal pipelines in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps. For example Figure 1 As shown, the dual-source magnetic field integrated detection and analysis method for buried metal pipelines disclosed in this invention specifically includes the following steps: S101. A composite magnetic field consisting of a low-frequency strong magnetic field and a high-frequency weak magnetic field is applied to the target detection section of the buried metal pipeline for excitation, and a magnetic sensor array deployed along the axial direction of the buried metal pipeline is used to collect the mixed magnetic response signal in response to the composite magnetic field.

[0021] The process involves inducing magnetic response signals within the pipeline through external magnetic field excitation, signals that can be captured by sensors and contain rich information about the pipeline's stress state. Specifically, a first excitation coil for generating a low-frequency, strong magnetic field and a second excitation coil for generating a high-frequency, weak magnetic field are coaxially mounted and deployed in the target detection section. First, a DC or ultra-low-frequency current is applied through the first excitation coil to establish a stable circumferential background magnetization field within the pipeline wall; this magnetic field characterizes the quasi-static stress state of the pipeline. Then, a high-frequency alternating current is applied through the second excitation coil to superimpose a high-frequency detection magnetic field within the pipeline wall, forming a composite magnetic field. This high-frequency magnetic field is more sensitive to transient stress events. Subsequently, a magnetic sensor array, linearly deployed at equal intervals along the axial direction of the buried metal pipeline, senses changes in magnetic flux density in response to the composite magnetic field. Finally, a multi-channel synchronous data acquisition device converts the simulated magnetic flux density changes sensed by the magnetic sensor array into multi-channel digitized hybrid magnetic response signals.

[0022] S102. A blind source separation algorithm based on independent component analysis is used to separate the hybrid magnetic response signal into a slowly varying component representing quasi-static stress and a burst component representing transient stress, and the burst component is extracted.

[0023] The mixed magnetic response signals collected are segmented and an observation signal matrix is constructed. A fast fixed point algorithm is used to iteratively calculate the observation signal matrix to obtain a demixing matrix. The observation signal matrix is linearly transformed using the demixing matrix to output a plurality of independent signal components. In order to identify the physical meaning of the components, the kurtosis value and the sparsity value of each independent signal component are calculated. A first kurtosis threshold and a first sparsity threshold are set to identify the burst component, and the signal component whose kurtosis value is greater than the first kurtosis threshold and whose sparsity value is greater than the first sparsity threshold is determined as the burst component. The signal component identified as the burst component is extracted from the plurality of independent signal components, so that the key information representing the pipeline stress event is stripped from the complex mixed signal.

[0024] S103. Based on the preset stress wave-magnetic coupling dispersion model, the burst component is subjected to dispersion de-inverse operation processing, and the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion are reconstructed.

[0025] The step of pre-establishing the pipeline stress wave-magnetic coupling dispersion model includes: establishing a three-dimensional finite element model based on the material properties and geometric size parameters of the buried metal pipeline; applying a broadband impact load at a predetermined position of the model to excite multi-mode stress waves; through transient dynamics simulation, calculating and extracting the propagation speed of different frequency components of the stress wave during the axial propagation along the pipeline; fitting the speed data to establish a nonlinear mapping relationship between the stress wave frequency and the phase velocity, and solidifying it as the pipeline stress wave-magnetic coupling dispersion model. In actual processing, the extracted burst component is first transformed from the time domain to the frequency domain to obtain its initial frequency spectrum. Then the preset dispersion model is loaded to obtain the nonlinear mapping relationship between the stress wave frequency and the phase velocity. According to the mapping relationship, each frequency component in the initial frequency spectrum is subjected to frequency-dependent phase correction to obtain the original waveform characteristics.

[0026] S104. Combined with the arrival time difference and energy attenuation amount of the original stress wave corresponding to the original waveform characteristics at different sensors in the magnetic sensor array, the position information of the pipeline stress event on the buried metal pipeline is determined by joint optimization.

[0027] Wherein, the pulse peak arrival time and the pulse peak amplitude are accurately extracted from the original waveform features of each sensor channel. According to the pulse peak arrival time, the arrival time difference between any two sensors is calculated. Meanwhile, according to the pulse peak amplitude, the energy attenuation of each sensor relative to the reference sensor is calculated. A joint optimization objective function containing the arrival time difference error term and the energy attenuation error term is constructed. The solution that minimizes the objective function is determined as the accurate action location of the pipeline stress event by using the nonlinear least squares algorithm to iteratively optimize the joint optimization objective function. This step effectively improves the positioning accuracy by fusing time and energy information.

[0028] S105. Calculate the cumulative damage degree of the target detection section of the buried metal pipeline according to the energy and position information of each positioned pipeline stress event.

[0029] Wherein, the three-dimensional finite element model of the buried metal pipeline is discretized into a series of damage calculation units along the axial direction, and each unit represents a specific area of the pipeline. According to the energy of the pipeline stress event and its action location, the equivalent stress amplitude caused by the event to each damage calculation unit is calculated. Based on the S-N curve (stress-cycle number curve) of the buried metal pipeline material, the allowable cycle number corresponding to the equivalent stress amplitude is found. According to the Miner's rule, the reciprocal of the allowable cycle number is taken as the damage increment caused by this event to the corresponding damage calculation unit. Finally, this damage increment is added to the cumulative damage factor of the corresponding damage calculation unit, and the quantitative cumulative damage provides a scientific basis for the remaining life assessment and maintenance decision of the pipeline.

[0030] In one embodiment, the target detection section of the buried metal pipeline is excited by applying a composite magnetic field superimposed by a low-frequency strong magnetic field and a high-frequency weak magnetic field, and a mixed magnetic response signal is collected by a magnetic sensor array deployed along the axial direction of the buried metal pipeline in response to the composite magnetic field, including the following steps: The first excitation coil for generating a low-frequency strong magnetic field and the second excitation coil for generating a high-frequency weak magnetic field are coaxially sleeved and deployed in the target detection section of the buried metal pipeline; A direct current is applied through the first excitation coil to establish a circumferential background magnetization field in the wall of the buried metal pipeline; On the basis of the circumferential background magnetization field, a high-frequency alternating current is applied through the second excitation coil to superimpose a high-frequency detection magnetic field in the wall to form a composite magnetic field; The magnetic flux density variation in response to the composite magnetic field is sensed by a magnetic sensor array linearly deployed at equal intervals along the axial direction of the buried metal pipeline; The magnetic flux density variation sensed by the magnetic sensor array is converted into a multi-channel mixed magnetic response signal by a multi-channel synchronous data acquisition device.

[0031] In this embodiment, the first excitation coil for generating a low-frequency strong magnetic field and the second excitation coil for generating a high-frequency weak magnetic field are coaxially sleeved and deployed in the target detection section. The purpose is to simultaneously establish magnetic fields of different frequencies in the wall of the buried metal pipeline. Coaxial sleeving of the two coils ensures that the generated magnetic field can be coupled to the circumferential and axial directions of the pipeline in the best way, forming a uniform and effective excitation area. A direct current is applied through the first excitation coil, thereby utilizing the ferromagnetic properties of the metal pipeline and forming a stable magnetic field baseline inside the pipeline through external excitation. The principle is that when a direct current or ultra-low frequency current passes through the first excitation coil, a magnetic field is generated around the coil. Since the pipeline is a ferromagnetic material, this magnetic field can penetrate the pipeline wall and induce magnetization in the circumferential direction of the pipeline, forming a circumferential background magnetization field. This background magnetization field is very sensitive to the quasi-static stress of the pipeline material (such as residual stress and stress change caused by long-term load), because stress can change the magnetic permeability and domain structure of the material through magnetostriction effect, thereby affecting the distribution of the magnetization field. Establishing a background magnetization field can provide a reference for subsequent detection of high-frequency transient stress events, enabling differentiation of magnetic responses from different sources in complex environments.

[0032] Next, in the presence of the established circumferential background magnetization field , a high-frequency alternating current is applied through the second excitation coil, generating a high-frequency detection magnetic field . This high-frequency detection magnetic field superimposes the background magnetic field to form a composite magnetic field . The high-frequency magnetic field has higher sensitivity to the small and rapid changes in magnetic permeability caused by transient stress waves (such as stress waves caused by crack initiation, propagation, or external impact) in the pipeline material. By superimposing a high-frequency weak magnetic field, the rapid changes and pipeline stress events indicating potential damage to the pipeline can be effectively detected without interfering with the background magnetization field's characterization of quasi-static stress. When stress changes occur inside the pipeline, whether it is quasi-static stress or pipeline stress events, local changes in the magnetic permeability of the pipeline material will be caused by the magnetostriction effect, thereby causing corresponding changes in the magnetic flux density in the composite magnetic field. Multiple magnetic sensors deployed linearly at equal intervals along the axial direction of the pipeline can simultaneously sense and measure these local changes in magnetic flux density. This array deployment not only provides magnetic response information at different positions along the pipeline, but also accurately determines the location of transient stress events using the distance between sensors and the time difference in signal arrival, greatly improving the spatial resolution and positioning ability of detection.

[0033] Since the magnetic flux density changes sensed by the magnetic sensors are usually outputted in the form of analog voltage signals, these analog signals must be converted into digital signals by an analog-to-digital converter. Therefore, through a multi-channel synchronous data acquisition device, data can be acquired from all sensors in the array at the same time, and the sampling of all channels is precisely synchronized by a common clock, so that the outputs of all sensors at any sampling moment can be recorded at the same time and form a digitized mixed magnetic response signal.

[0034] In one embodiment, a blind source separation algorithm based on independent component analysis is used to separate the mixed magnetic response signal into a slowly varying component representing quasi-static background stress and a burst component representing pipeline stress events, and the burst component is extracted by the following steps: The acquired multi-channel mixed magnetic response signal is segmented and an observation signal matrix is constructed; The maximum non-Gaussianity is set as the optimization objective, and the observation signal matrix is iteratively calculated by using the fast fixed-point algorithm to obtain a demixing matrix; The observation signal matrix is linearly transformed by using the demixing matrix to output a plurality of independent signal components; The statistical characteristic parameters of the plurality of signal components are calculated respectively, and the slowly varying component and the burst component are identified according to the statistical characteristic parameters, and the signal component identified as the burst component is extracted.

[0035] In this embodiment, the amount of original continuous multi-channel mixed magnetic response signal data can be very large, and direct processing will increase the computational burden. By data segmentation, a long sequence signal can be divided into several short time window data segments that are easier to manage, which also helps to meet the condition that the independent component analysis algorithm usually assumes that the signal is stationary or quasi-stationary within the analysis window. The observation signal matrix is to organize these segmented multi-channel signals into a standard data structure, where each row can represent the sampling sequence of a sensor channel within a period of time, or each column can represent the sampling value of all sensors at a certain moment. Since independent source signals are usually non-Gaussian distributed, and the linear mixture of multiple independent source signals tends to be Gaussian distributed. Therefore, by maximizing the non-Gaussianity of the separated signals, these independent source signals can be effectively found. The fast fixed-point algorithm in the independent component analysis algorithm can find a projection direction through iterative optimization, so that the non-Gaussianity of the projected signal reaches the maximum. After multiple iterations, the algorithm finally converges and solves a demixing matrix which contains all the information needed to convert the observed mixed signal back to the independent source signal.

[0036] Next, a linear transformation is performed on the observed signal matrix using the demixing matrix, outputting multiple independent signal components. This step applies the demixing rules obtained in the previous step to the original mixed signal, thereby achieving signal separation. The demixing matrix is ​​a linear transformation matrix that can decouple the mixed signals in the observed signal matrix. This is achieved through simple matrix multiplication, i.e. The observed signal matrix is ​​linearly transformed into an independent component matrix. In the formula, This is an independent component matrix, where each row represents an estimated independent signal component. These output signal components are statistically independent, representing different physical processes or information sources; for example, one component might represent background stress, and another a transient event. The statistical characteristic parameters of these independent signal components can then be calculated, and gradually varying components and sudden burst components can be identified based on these parameters. Different types of physical events produce signals with different statistical properties. Pipeline stress events typically manifest as short-duration, high-amplitude pulse signals with sharp waveforms, resulting in high kurtosis and sparsity values. In contrast, signal changes caused by quasi-static background stress are usually smoother, more closely resembling a Gaussian distribution, with lower kurtosis and sparsity values. Therefore, the kurtosis and sparsity values ​​are calculated for each independent signal component. Kurtosis value The calculation formula is: , in As independent signal components, Its mean, Its standard deviation.

[0037] sparsity value The calculation formula is: Based on preset kurtosis and sparsity thresholds, signal components exhibiting both high kurtosis and high sparsity that meet the burst characteristics are identified as burst components, while signal components meeting the gradual variation characteristics are identified as gradually varying components. Since pipeline stress events are a key factor causing pipeline fatigue damage, it is necessary to accurately filter all signals identified as burst components from the set of independent components. These extracted burst components have been freed from background noise and interference from gradually varying stress signals, resulting in a higher signal-to-noise ratio and purer physical information.

[0038] In one embodiment, the statistical characteristic parameters of multiple signal components are calculated respectively, and the slowly varying components and burst components are identified based on the statistical characteristic parameters. The extraction of the signal components identified as burst components includes the following steps: Calculate the kurtosis and sparsity values ​​for each signal component separately; For any signal component, if the kurtosis value is greater than a preset first kurtosis threshold and the sparsity value is greater than a preset first sparsity threshold, the signal component is identified as a burst component, and the burst component is extracted; If the kurtosis value is less than a preset second kurtosis threshold and the sparsity value is less than a preset second sparsity threshold, the signal component is identified as a slowly varying component, the first kurtosis threshold is greater than the second kurtosis threshold, and the first sparsity threshold is greater than the second sparsity threshold.

[0039] In the present embodiment, different types of signals have significant differences in statistical distribution. Signals representing pipeline stress events usually have sharp waveforms and sparse energy distribution, showing higher non-Gaussianity. Signals representing quasi-static background stress may be more gentle, and their statistical distribution is closer to Gaussian distribution. The first kurtosis threshold and the first sparsity threshold are set to identify burst components. These thresholds can be set according to experience, experimental data or domain knowledge, and the appropriate threshold range is determined by analyzing known burst event signals. The second kurtosis threshold and the second sparsity threshold are set to identify slowly varying components. Slowly varying components usually represent the quasi-static background stress or environmental noise of the pipeline. These signals are characterized by gentle changes and lack of sharp pulse characteristics, and their statistical distribution may be closer to Gaussian distribution or have lower sparsity. Therefore, the second set of thresholds will be lower than the first set of thresholds used to identify burst components, to reflect the smooth and non-pulse characteristics of slowly varying component signals.

[0040] For each independent signal component, the calculated kurtosis value is compared with the first kurtosis threshold, and the sparsity value is compared with the first sparsity threshold. Only when the kurtosis value of a signal component is greater than the first kurtosis threshold and its sparsity value is greater than the first sparsity threshold at the same time, the signal component is determined as a burst component representing a pipeline stress event. Signal components with a kurtosis value less than the second kurtosis threshold and a sparsity value less than the second sparsity threshold are determined as slowly varying components. Similarly, for each independent signal component, the calculated kurtosis value is compared with the second kurtosis threshold, and the sparsity value is compared with the second sparsity threshold. Only when the kurtosis value of a signal component is less than the second kurtosis threshold and its sparsity value is less than the second sparsity threshold at the same time, the signal component is determined as a slowly varying component representing a quasi-static background stress.

[0041] In one embodiment, the burst component is subjected to dispersion deconvolution processing based on a preset stress wave-magnetic coupling dispersion model to reconstruct the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion, including the following steps: Transforming the burst component from the time domain to the frequency domain to obtain the initial frequency spectrum of the burst component; A pipeline stress wave-magnetic coupling dispersion model of the buried metal pipeline is pre-established, and a nonlinear mapping relationship between stress wave frequency and phase velocity is obtained through the stress wave-magnetic coupling dispersion model; A frequency-dependent phase correction is performed on each frequency component in the initial frequency spectrum according to the nonlinear mapping relationship, to obtain a corrected frequency spectrum; The corrected frequency spectrum is converted back to the time domain through inverse Fourier transform to obtain a compressed pulse signal, and a waveform of the pulse signal is taken as an original waveform feature of the pipeline stress event at the initial position without propagation distortion.

[0042] In the embodiment, the burst component is transformed from the time domain to the frequency domain. The time domain signal contains superimposed information of all frequency components, but the amplitude and phase relationship of each frequency component is not directly manifested. Through Fourier transform, the time domain signal can be decomposed into a series of sine and cosine waves of different frequencies, so as to obtain a frequency spectrum of the signal, which contains the amplitude and phase information of each frequency component. Specifically, the time domain burst component extracted from the independent component analysis The fast Fourier transform is applied to obtain the corresponding initial frequency spectrum . The frequency spectrum is a complex function, the modulus value of which represents the intensity of the frequency component, and the phase thereof represents the phase of the frequency component relative to the time origin. Then, the pipeline stress wave-magnetic coupling dispersion model is loaded to obtain the nonlinear mapping relationship between the stress wave frequency and the phase velocity. Since different frequencies of stress waves usually propagate at different speeds in a solid medium, in order to accurately eliminate the dispersion effect in signal propagation, the propagation speed of stress waves at different frequencies of the pipeline material must be accurately understood. The pipeline stress wave-magnetic coupling dispersion model is pre-established through finite element simulation and data fitting, and stores the nonlinear mapping relationship between the stress wave frequency f and the corresponding phase velocity . By loading this model, the accurate phase velocity of the frequency stress wave propagating in the pipeline can be found according to any frequency value. If different frequency components propagate at different speeds, the phase relative to the original waveform will be shifted when reaching the sensor, resulting in waveform broadening and distortion. In order to restore the original waveform, a reverse phase shift needs to be applied to each frequency component, so that it can be realigned when reaching the sensor. In specific implementation, for each frequency component f in the initial frequency spectrum , the corresponding phase velocity is obtained by using the loaded dispersion model. Assuming that the propagation distance from the event starting point to the sensor is L, the accumulated phase shift of the frequency component is . In order to correct the shift, the initial frequency spectrum is phase-adjusted, and the calculation formula of the corrected frequency spectrum is , wherein j is the imaginary unit.

[0043] After the phase correction of each frequency component is completed in the frequency domain, the relative phase relationship of these components has been restored to the state at the starting point. These phase-corrected frequency components can be re-synthesized into a time-domain signal by inverse Fourier transform. Since the phases of all frequency components have been correctly aligned, the waveform originally distorted by dispersion will be refocused to form a more sharply focused pulse signal. After the above series of signal processing, especially the dispersion cancellation inverse operation, the compressed pulse signal obtained has maximized the original form of the stress event at the starting point without propagation distortion. This waveform is no longer affected by the dispersion effect of the pipe material and can more accurately reflect the inherent characteristics of the event such as intensity, duration, and energy distribution. Therefore, the shape and parameters of this compressed pulse signal can be directly used as the original waveform characteristics of the pipe stress event.

[0044] In one embodiment, the pre-established pipe stress wave-magnetic coupling dispersion model specifically includes the following steps: A three-dimensional finite element model of the buried metal pipeline is established based on the material properties and geometric size parameters of the buried metal pipeline; A broadband impact load is applied at a predetermined position of the three-dimensional finite element model to excite a plurality of different modes of test stress waves; The propagation speed of different frequency components of the test stress waves during propagation along the pipe axial direction of the buried metal pipeline is calculated by transient dynamics simulation; The propagation speed data of different frequency components are fitted to establish a nonlinear mapping relationship between the stress wave frequency and phase velocity, and the nonlinear mapping relationship is solidified as the stress wave-magnetic coupling dispersion model of the buried metal pipeline.

[0045] In this embodiment, the finite element method can discretize the complex continuous structure into a finite number of simple elements, and then approximate the response of the entire structure by analyzing these elements and integrating the results. In practice, detailed material properties of the buried metal pipeline need to be input, such as elastic modulus, Poisson's ratio and density, which determine the propagation characteristics of stress waves in the material. At the same time, the geometric size parameters of the pipeline, such as pipeline length, outer diameter, wall thickness, etc., are input to accurately define the physical boundaries of the model. A three-dimensional grid model reflecting these physical characteristics is constructed through finite element software, and appropriate element types and grid densities are set to ensure that the model can accurately capture the propagation details of stress waves. Then a broadband impact load is applied at a predetermined position on the three-dimensional finite element model to excite multi-mode test stress waves, thereby generating stress waves in the virtual pipeline that cover a wide frequency range to comprehensively analyze their dispersion characteristics. To study the propagation speed of stress waves of different frequencies, an excitation source containing these frequencies is needed. The impact load usually has the characteristics of energy concentration and short duration, which is characterized by broadband in the frequency domain, and can excite various stress wave modes in the pipeline, such as longitudinal waves, torsional waves and bending waves.

[0046] Next, the propagation speed of different frequency components of the stress wave during the axial propagation of the pipeline is calculated and extracted through transient dynamics simulation, which can simulate the response of the structure to dynamic loads in the time domain and record the displacement, velocity or stress of any point in the pipeline over time. By setting virtual monitoring points at different positions along the axis of the pipeline, the time series signals of the stress wave passing through these points can be recorded. Then, the signals are processed, such as converting the time domain signals to the frequency domain using Fourier transform, and then determining the propagation speed of the frequency component by calculating the phase difference or arrival time difference of the same frequency component between different monitoring points. Although transient dynamics simulation provides a large number of discrete data points, in order to correct the dispersion of any frequency in practical applications, a continuous function describing the entire frequency range is needed. Therefore, curve fitting techniques are used to establish a nonlinear mapping relationship between the frequency of the stress wave and its phase velocity. Specifically, multiple propagation speed data points can be used as input, and a suitable nonlinear function model (such as a polynomial function or an exponential function) can be fitted by least squares method or other optimization algorithms to obtain a best fitting function that can accurately describe the dispersion characteristics of the stress wave in the pipeline, i.e. the difference in propagation speed of different frequency components. Finally, the fitted function expression, function parameters or lookup table, etc. can be saved as an independent software module or database file, which is the stress wave-magnetic coupling dispersion model. The magnetic coupling here means that the dispersion model serves the processing of the magnetic response signal caused by the stress wave in the magnetic field detection method.

[0047] In one embodiment, the stress wave-magnetic coupling dispersion model can be dynamically updated before the dispersion deconvolution processing is performed, and the specific steps include: The background vibration signal below the preset amplitude is continuously extracted from the mixed magnetic response signal as a calibration signal; The calibration signals collected by two different magnetic sensors are subjected to cross-correlation interference calculation to obtain the empirical Green function of the calibration wave propagating along the pipeline axis; The actual phase velocity of the calibration wave at different frequencies is analyzed from the empirical Green function; The non-linear mapping relationship in the stress wave-magnetic coupling dispersion model is parameter corrected based on the actual phase velocity, and an updated stress wave-magnetic coupling dispersion model is obtained.

[0048] In this embodiment, the buried pipeline will be subjected to low-amplitude background vibration caused by fluid flow, geological micro-motion, environmental noise and other factors in daily operation. These vibrations will generate weak stress waves in the pipeline material, which will in turn induce low-amplitude magnetic response signals that can be captured by magnetic sensors. By filtering the continuously collected mixed magnetic response signals, the background vibration signals which are irrelevant to transient high-frequency stress events can be effectively separated from the complex signals as calibration signals. By cross-correlating the background noise signals collected by two different sensors, the Green function (i.e. impulse response) of the propagation medium between the two sensors can be approximately obtained. This is equivalent to virtually applying a pulse at one of the sensor positions and observing the response at the other sensor. In specific implementation, for any two sensors a and b in the magnetic sensor array deployed along the pipeline axis, the calibration signals and are calculated, and their cross-correlation function is calculated: , where is the time lag. The empirical Green function contains the time and phase information of the calibration wave propagating between the two sensors. By analyzing the Green function in the frequency domain, the propagation characteristics of different frequency components can be separated. Specifically, the empirical Green function can be subjected to Fourier transform to obtain its frequency domain representation. From the frequency domain representation, the phase difference between sensors a and b for the calibration wave at different frequencies can be extracted. Given that the distance between sensors a and b is , the actual phase velocity of the calibration wave at frequency F can be calculated by the following formula: .

[0049] By comparing the actual phase velocity extracted from the background vibration with the model predicted value, the nonlinear mapping relationship in the dispersion model can be adjusted or refitted. Specifically, an optimization algorithm (such as the least squares method) is used to correct the parameters in the pipe stress wave-magnetic coupling dispersion model that describe the relationship between stress wave frequency and phase velocity, thereby obtaining an updated nonlinear mapping relationship, which is then solidified as an updated dispersion model.

[0050] In one embodiment, the arrival time difference and energy attenuation of the original stress wave corresponding to the original waveform feature on different sensors in the magnetic sensor array are combined to determine the location information of the pipeline stress event on the buried metal pipeline by joint optimization, including the following steps: Extract the pulse peak arrival time and pulse peak amplitude of the original stress wave corresponding to the original waveform feature from each magnetic sensor channel in the magnetic sensor array; Calculate the arrival time difference between any two magnetic sensors according to the pulse peak arrival time; Calculate the energy attenuation of each magnetic sensor relative to the reference sensor according to the pulse peak amplitude; Construct a joint optimization objective function containing arrival time difference error terms and energy attenuation error terms, and use a nonlinear least squares algorithm to iteratively optimize the joint optimization objective function, and determine the solution that minimizes the objective function as the action location of the pipeline stress event.

[0051] In this embodiment, the waveform of the transient high-frequency stress event has been reconstructed into a sharper and more concentrated pulse signal through dispersion deconvolution. This clear pulse signal allows its peak position and peak intensity to be identified with high precision. Specifically, for each sensor channel received original waveform feature, the time point of the pulse peak value and the signal amplitude corresponding to the time point can be determined by a signal processing algorithm (such as finding the maximum value of the signal or by curve fitting). These accurately extracted time and amplitude values directly reflect the time and intensity of the stress wave arriving at each sensor. The time required for the stress wave to propagate from the origin to different sensors is different, and this time difference is closely related to the distance from the event origin to each sensor and the propagation speed of the stress wave. By measuring these time differences, the origin location of the event can be geometrically located. For any two sensors k and l in the magnetic sensor array, the arrival time difference between them is calculated as follows: and ​On the other hand, due to the material damping, geometric diffusion and other factors, the stress wave will gradually attenuate in energy when propagating in the pipeline, which is manifested as the decrease of signal amplitude. The degree of attenuation is related to the propagation distance, the farther the distance, the greater the attenuation, so this amplitude attenuation pattern can be used as another important clue for event positioning. A sensor with the largest signal amplitude or a sensor located at the center of the array is selected as the reference sensor with a pulse peak amplitude of Then, for each sensor in the array with a pulse peak amplitude of , the amplitude ratio of the sensor relative to the reference sensor is calculated as: This amplitude ratio reflects the energy attenuation of the stress wave relative to the reference sensor when propagating from the origin to the sensor.

[0052] The true position of the event should make the difference between the predicted signal arrival time and amplitude attenuation based on the position and the actual observed values minimum, so next all the observed spatiotemporal information is integrated into a mathematical framework to find the best event position by optimization method. Specifically, an objective function can be constructed, which quantifies the sum of these differences. The function consists of two parts: one part is the sum of squared errors between the observed arrival time difference and the predicted arrival time difference based on the assumed event position ; the other part is the sum of squared errors between the observed amplitude ratio and the predicted amplitude ratio. The form of the objective function is:

[0053] where is the predicted arrival time difference, is the predicted amplitude ratio.

[0054] The constructed joint optimization objective function is usually nonlinear, and its minimum value cannot be directly solved by simple algebraic method. The nonlinear least squares algorithm can gradually adjust the estimated value of the event action position through iteration, so that it constantly approaches the global minimum value of the objective function. The algorithm starts from an initial guess position, and in each iteration, according to the gradient information of the objective function to , calculates an update direction and step length to update the value of . This process continues until the value of the objective function converges to the minimum, or the change of is less than the preset threshold. The value of that finally minimizes the objective function is determined as the precise action position of the pipeline stress event.

[0055] In one embodiment, the cumulative damage level of the target detection section of the buried metal pipeline is calculated based on the energy and location information of each located pipeline stress event, comprising the following steps: discretizing the three-dimensional finite element model of the buried metal pipeline into a plurality of damage calculation units along the pipeline axial direction; calculating the equivalent stress amplitude of each damage calculation unit caused by the corresponding pipeline stress event according to the energy and location information of the pipeline stress event; finding the allowable cycle number corresponding to the equivalent stress amplitude based on the S-N curve of the material of the buried metal pipeline; taking the reciprocal of the allowable cycle number as the damage increment of the corresponding damage calculation unit caused by the current pipeline stress event; accumulating the damage increment into the cumulative damage factor of the corresponding damage calculation unit to complete the cumulative damage level calculation of the target detection section of the buried metal pipeline.

[0056] In this embodiment, the continuous pipeline structure is converted into discrete analysis objects for localized damage assessment. The principle is that the fatigue damage of the pipeline is usually localized, and in order to accurately track and predict the occurrence and development of damage, the entire pipeline needs to be divided into smaller, manageable areas. Specifically, the three-dimensional finite element model of the pipeline can be divided along its axial direction to form a series of adjacent damage calculation units with the same or different lengths. Each unit represents a specific area of the pipeline, and the material properties and geometric characteristics inside are considered to be uniform. Pipeline stress events (such as internal and external pressure, temperature changes, stress changes caused by physical forces) will generate stress waves, and these stress waves will cause transient stress responses of the pipeline material as they propagate in the pipeline. Since the energy of the stress wave will decrease as the propagation distance increases, the stress amplitude caused by the event to different location damage calculation units is different. For each located pipeline stress event, the energy is , and the action location is . For each damage calculation unit on the pipeline , the center position is , and the equivalent stress amplitude caused by the event to is calculated by a pre-set stress propagation and attenuation model. The stress propagation and attenuation model can be represented as:

[0057] where is the energy-to-stress conversion coefficient, is the stress attenuation index, is the energy of the pipeline stress event, is the center position of the damage calculation unit, is the action position of the pipeline stress event.

[0058] Then the allowable cycle number corresponding to the equivalent stress amplitude is found based on the S-N curve of the buried metal pipeline material, the S-N curve (stress-cycle number curve) is a key characteristic curve describing the fatigue performance of the material, which reflects the cycle number that the material can withstand under different stress amplitudes. For metal materials, the higher the stress amplitude, the shorter the fatigue life (allowable cycle number). The equivalent stress amplitude of each damage calculation unit calculated , the allowable cycle number corresponding to the stress amplitude can be found by querying or calculating the S-N curve , the allowable cycle number represents the cycle number required for the material to fail under the stress amplitude. Based on the Miner rule, the damage caused by each loading cycle is independent and can be linearly accumulated. For a single transient stress event, it can be regarded as a loading cycle. If the material can withstand cycles at a certain stress amplitude before failing, the damage proportion caused by a single cycle is . Therefore, for each damage calculation unit, the damage increment caused by this event to the unit is calculated according to the allowable cycle number found according to its equivalent stress amplitude .

[0059] The fatigue damage of the pipeline is a continuous accumulation process, and each pipeline stress event will cause a certain damage to the pipeline. By accumulating the damage increment caused by each event to the corresponding damage calculation unit , the damage degree of each position of the pipeline can be tracked in real time. The update formula of the cumulative damage factor is , when reaches a certain critical value, it is considered that the unit may fail due to fatigue.

[0060] The application also discloses a double-source magnetic field comprehensive detection and analysis system based on a buried metal pipeline, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the double-source magnetic field comprehensive detection and analysis method based on a buried metal pipeline as described in any one of the above embodiments when executing the computer program.

[0061] Among them, the processor can adopt a central processing unit (CPU), of course, according to the actual use, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), ready-to-program gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. can also be used, and the general-purpose processor can adopt a microprocessor or any conventional processor, etc. The present application does not make any limitation thereto.

[0062] The memory can be an internal storage unit of the computer device, for example, a hard disk or a memory of the computer device, or an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash card (FC) equipped on the computer device, or a combination of the internal storage unit and the external storage device of the computer device.

[0063] The application further discloses a computer readable storage medium, which stores instructions, and the instructions enable a processor to be configured to perform the buried metal pipeline-based dual-source magnetic field comprehensive detection and analysis method described in any of the embodiments when executed by the processor.

[0064] The computer program can be stored in a machine readable medium, and the computer program includes computer program code, which can be in a form of source code, object code, an executable file or some intermediate form, etc., and the machine readable medium includes any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code, and it should be noted that the machine readable medium includes but is not limited to the above-mentioned elements.

[0065] The computer readable storage medium stores the buried metal pipeline-based dual-source magnetic field comprehensive detection and analysis method in the computer readable storage medium, and is loaded and executed on the processor to facilitate storage and application of the method.

[0066] Those skilled in the art should understand that the above discussion of any of the embodiments is only exemplary and is not intended to limit the protection scope of the application to these examples; the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of one or more embodiments of the application as described above, which are not provided in details for the sake of brevity.

[0067] The one or more embodiments of the application are intended to cover all such alternatives, modifications and variations as fall within the broad scope of the application. Therefore, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principle of the one or more embodiments of the application should be included in the protection scope of the application.

Claims

1. A dual-source magnetic field integrated detection analysis method based on buried metal pipeline, characterized in that, The method comprises the following steps: A composite magnetic field formed by superimposing a low-frequency strong magnetic field and a high-frequency weak magnetic field is applied to a target detection section of the buried metal pipeline to excite the target detection section, and a mixed magnetic response signal responsive to the composite magnetic field is collected by a magnetic sensor array arranged along an axial direction of the buried metal pipeline; A blind source separation algorithm based on independent component analysis is used to separate the mixed magnetic response signal into a slowly-varying component representing quasi-static stress and a burst component representing a transient stress event, and the burst component is extracted; The burst component is subjected to dispersion deconvolution processing based on a preset stress wave-magnetic coupling dispersion model, and an original waveform feature of the stress event in the initial position without propagation distortion is reconstructed; The position information of the stress event on the buried metal pipeline is determined by jointly optimizing the arrival time difference and energy attenuation amount of the original stress wave corresponding to the original waveform feature at different sensors in the magnetic sensor array; The cumulative damage degree of the target detection section of the buried metal pipeline is calculated according to the energy and position information of the stress event positioned each time.

2. The buried metal pipeline-based dual-source magnetic field comprehensive detection analysis method according to claim 1, characterized in that, The method of applying the composite magnetic field formed by superimposing the low-frequency strong magnetic field and the high-frequency weak magnetic field to the target detection section of the buried metal pipeline to excite the target detection section, and collecting the mixed magnetic response signal responsive to the composite magnetic field by the magnetic sensor array arranged along the axial direction of the buried metal pipeline comprises the following steps: The first excitation coil for generating the low-frequency strong magnetic field and the second excitation coil for generating the high-frequency weak magnetic field are coaxially sleeved and arranged in the target detection section of the buried metal pipeline; A direct current is applied to the first excitation coil to establish a circumferential background magnetization field in the wall of the buried metal pipeline; Based on the circumferential background magnetization field, a high-frequency alternating current is applied to the second excitation coil to superimpose a high-frequency detection magnetic field on the wall to form the composite magnetic field; The magnetic flux density variation responsive to the composite magnetic field is sensed by the magnetic sensor array arranged linearly along the axial direction of the buried metal pipeline at equal intervals; The magnetic flux density variation sensed by the magnetic sensor array is converted into a plurality of mixed magnetic response signals by a multi-channel synchronous data acquisition device.

3. The buried metal pipeline-based dual-source magnetic field comprehensive detection analysis method according to claim 2, characterized in that, The method of separating the mixed magnetic response signal into the slowly-varying component representing the quasi-static background stress and the burst component representing the stress event of the pipeline by the blind source separation algorithm based on the independent component analysis and extracting the burst component comprises the following steps: The collected mixed magnetic response signals are segmented and an observation signal matrix is constructed; A fast fixed point algorithm is used to iteratively calculate the observation signal matrix to obtain a demixing matrix, with maximization of non-Gaussianity as the optimization objective; The observation signal matrix is linearly transformed by using the demixing matrix to output a plurality of independent signal components; Statistical characteristic parameters of the plurality of signal components are calculated, and the slowly-varying component and the burst component are identified according to the statistical characteristic parameters, and the signal component identified as the burst component is extracted.

4. The buried metal pipeline-based dual-source magnetic field comprehensive detection analysis method according to claim 3, characterized in that, The method of calculating the statistical characteristic parameters of the plurality of signal components, identifying the slowly-varying component and the burst component according to the statistical characteristic parameters, and extracting the signal component identified as the burst component comprises the following steps: The kurtosis value and the sparsity value of each signal component are calculated. For any signal component, if the kurtosis value is greater than a preset first kurtosis threshold and the sparsity value is greater than a preset first sparsity threshold, the signal component is identified as a burst component, and the burst component is extracted; If the kurtosis value is less than a preset second kurtosis threshold and the sparsity value is less than a preset second sparsity threshold, the signal component is identified as a slowly varying component, the first kurtosis threshold is greater than the second kurtosis threshold, and the first sparsity threshold is greater than the second sparsity threshold.

5. The buried metal pipeline-based dual-source magnetic field comprehensive detection analysis method according to claim 1, characterized in that, The dispersion deconvolution operation is performed on the burst component based on the preset stress wave-magnetic coupling dispersion model to reconstruct the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion, including the following steps: Transform the burst component from the time domain to the frequency domain to obtain the initial frequency spectrum of the burst component; A pipeline stress wave-magnetic coupling dispersion model of the buried metal pipeline is established in advance, and a nonlinear mapping relationship between stress wave frequency and phase velocity is obtained through the stress wave-magnetic coupling dispersion model; According to the nonlinear mapping relationship, each frequency component in the initial frequency spectrum is subjected to frequency-dependent phase correction to obtain a corrected frequency spectrum; After the corrected frequency spectrum is converted back to the time domain through inverse Fourier transform, a compressed pulse signal is obtained, and the waveform of the pulse signal is taken as the original waveform characteristics of the pipeline stress event at the initial position without propagation distortion.

6. The buried metal pipeline-based dual-source magnetic field comprehensive detection analysis method according to claim 5, characterized in that, The pipeline stress wave-magnetic coupling dispersion model is established in advance and includes the following steps: A three-dimensional finite element model of the buried metal pipeline is established based on the material properties and geometric size parameters of the buried metal pipeline; A broadband impact load is applied at a predetermined position of the three-dimensional finite element model to excite multiple different modes of test stress waves; The propagation velocities of different frequency components of the test stress waves during propagation along the pipeline axis of the buried metal pipeline are calculated through transient dynamics simulation; The propagation velocity data of different frequency components are fitted to establish a nonlinear mapping relationship between stress wave frequency and phase velocity, and the nonlinear mapping relationship is solidified as the stress wave-magnetic coupling dispersion model of the buried metal pipeline.

7. The buried metal pipeline-based dual-source magnetic field comprehensive detection and analysis method according to claim 1, characterized in that, The arrival time difference and energy attenuation amount of the original stress wave corresponding to the original waveform characteristics at different sensors in the magnetic sensor array are combined to determine the position information of the pipeline stress event on the buried metal pipeline through joint optimization, including the following steps: The pulse peak arrival time and pulse peak amplitude of the original stress wave corresponding to the original waveform characteristics are extracted from each magnetic sensor channel in the magnetic sensor array; The arrival time difference between any two magnetic sensors is calculated based on the pulse peak arrival time; The energy attenuation amount of each magnetic sensor relative to the reference sensor is calculated based on the pulse peak amplitude; A joint optimization objective function including the arrival time difference error term and the energy attenuation amount error term is constructed, and a nonlinear least squares algorithm is used to iteratively optimize the joint optimization objective function, and the solution that minimizes the objective function is determined as the action position of the pipeline stress event.

8. The buried metal pipeline-based dual-source magnetic field comprehensive detection and analysis method according to claim 6, characterized in that, The cumulative damage degree of the target detection section of the buried metal pipeline is calculated based on the energy and position information of the pipeline stress event at each positioning, including the following steps: The three-dimensional finite element model of the buried metal pipeline is discretized into multiple damage calculation units along the pipeline axis; calculating an equivalent stress amplitude of each damage calculation unit caused by the corresponding pipeline stress event according to the energy and position information of the pipeline stress event; finding a permissible cycle number corresponding to the equivalent stress amplitude based on a material S-N curve of the buried metal pipeline; taking the reciprocal of the permissible cycle number as a damage increment of the corresponding damage calculation unit caused by the pipeline stress event; adding the damage increment to a cumulative damage factor of the corresponding damage calculation unit to complete the cumulative damage degree accumulation calculation of the target detection section of the buried metal pipeline. 9.A buried metal pipeline based dual-source magnetic field comprehensive detection analysis system, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the double-source magnetic field comprehensive detection analysis method for buried metal pipelines according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium having stored thereon instructions, the computer-readable storage medium comprising: The instructions cause the processor to be configured to implement the double-source magnetic field comprehensive detection analysis method for buried metal pipelines according to any one of claims 1 to 8 when being executed by the processor.

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