Refuse landfill leakage detection method and system considering inhomogeneous medium stress wave propagation

Abstract

The invention provides a refuse landfill leakage detection method and system considering inhomogeneous medium stress wave propagation, and the method comprises the steps: building an inhomogeneous medium stress wave propagation model, and decomposing an actual wave velocity into a reference wave velocity and a variable quantity; estimating and updating the wave velocity in real time by adopting a self-adaptive algorithm; and finally, accurately positioning the position of the leakage source through a weighted optimization method. The invention also provides a system for realizing the method. The system comprises a sensor array, a signal acquisition and preprocessing module, a self-adaptive wave velocity estimation module, a leakage source positioning module and a result display and alarm module. According to the method, the non-uniformity and the time-varying characteristic of the medium are considered, the self-adaptive algorithm and the weighted optimization strategy are combined, the leakage point positioning precision is effectively improved, and the method has important practical value.

Description

Technical Field

[0001] The invention relates to the technical field of environmental monitoring, and in particular to a leakage detection method and system for a landfill. Background Art

[0002] As an important facility for urban solid waste treatment, leakage detection of landfills is directly related to groundwater environmental safety and public health. In actual projects, leakage detection faces unique technical challenges, which mainly stems from the complexity of landfill media. The landfill contains a multi-layer anti-seepage system. Due to the filling process and natural sedimentation, a complex spatial distribution is formed between various media. At the same time, the biochemical degradation and compaction during the landfill process make the physical properties of media such as soil and garbage show significant non-uniformity. In the prior art, commonly used leakage detection methods include resistance method, acoustic method, etc. However, these methods have the following problems in practical applications: 1. Medium inhomogeneity: The medium such as landfill soil has complex inhomogeneity, which leads to significant changes in the propagation velocity of stress waves in space; 2. Time-varying characteristics: The medium characteristics will change with environmental conditions (such as temperature, humidity, etc.), making it difficult for traditional fixed parameter models to accurately describe the stress wave propagation characteristics; 3. Positioning accuracy: Due to the influence of the above factors, existing methods often have the problem of insufficient positioning accuracy in practical applications. Summary of the invention

[0003] The present invention aims at the key technical difficulties in landfill leakage detection and proposes a landfill leakage detection method that takes into account the propagation of stress waves in non-uniform media. In view of the problem of insufficient positioning accuracy caused by the non-uniformity and time-varying characteristics of landfill media, the present invention proposes innovative solutions from three levels: stress wave propagation model, adaptive algorithm and system implementation.

[0004] The technical solution of the present invention is: a landfill leakage detection method considering the propagation of stress waves in non-uniform media, a plurality of stress wave sensors are arranged around the landfill to form a distributed sensor array, a stress wave sensor position vector is established, the propagation speed of the stress wave is collected, and the obtained data is processed, and the steps are as follows: (1) A wave velocity model is established, and the wave velocity at a spatial position is expressed as the superposition of a reference wave velocity and a wave velocity deviation, wherein the reference wave velocity is calculated by calibrating the wave velocity values ​​of multiple reference points, and the wave velocity deviation is expressed by expanding the spatial basis function. Meanwhile, the time-varying characteristics of the medium are considered, and a time-varying term is introduced into the wave velocity model. The time-varying term includes an initial disturbance value, an attenuation coefficient, and a random disturbance term. A complete wave velocity model for non-uniform time-varying media is established. The model equation is as follows: , , , ,in, For time, For the spatial position The time-varying wave velocity at is the reference wave speed, It is the spatial location The wave velocity deviation, is the time-dependent wave velocity variation term, For the The calibrated wave velocity value of the reference point, is the number of calibration points, For the The coefficients of the spatial basis functions, For the The spatial basis functions, is the total number of spatial basis functions, is the initial disturbance value, is the attenuation coefficient, is the random disturbance term, is a natural constant of mathematics; (2) Based on the above wave velocity model, the stress wave travel time equation is established. The theoretical travel time is obtained by calculating the Euclidean distance from the leakage source to each stress wave sensor and dividing it by the wave velocity. The Euclidean distance is calculated by the three-dimensional coordinates. Then, the wave velocity estimation error is defined as the square sum of the difference between the measured travel time and the theoretical travel time. The wave velocity is iteratively updated using the gradient descent method. , which expresses the spatial position The non-uniform attenuation characteristics of the wave velocity at For the The time step is the spatial position The time-varying wave velocity update at , For the The time step estimation The spatial basis function coefficients, For the The time-varying wave velocity change estimated by the time step, , It is obtained through particle filtering; (3) In the process of locating the leakage source, a weighted objective function is constructed. , ,in, is the objective function with weights, is the total number of stress wave sensors, is the weight of each stress wave sensor, For the The measured travel time of a stress wave sensor, is the Euclidean distance from the leakage source to the stress wave sensor, It is The position vector of the stress wave sensor, is the three-dimensional coordinate of the leakage source, , is the theoretical stress wave propagation time, is the standard deviation parameter, which is used to control the weight decay speed. The gradient descent method is used to update the leakage source position. ,in, is the iteration step length, For the The position estimate of the iteration, For the The position estimate of the iteration, is the gradient of the objective function with respect to the position, and gradient descent is achieved by calculating the gradient of the objective function with respect to the position.

[0005] Preferably, the wave velocity model is solved by a particle filter method, each particle is predicted and updated through a state transfer equation, the particle weight is calculated and updated based on the measured data, and the relationship equation between the observed value and the state quantity is designed: ,in, For the Time step The stress wave sensor measurement value corresponds to The likelihood probability of a particle is Indicates Time step The state vector of a particle, is twice pi, Represents a natural constant An exponential function with base ; The stress wave travel time equation is solved by numerical integration method.

[0006] Furthermore, in order to avoid particle degradation, the number of effective particles is calculated. When the number of effective particles is less than a preset threshold, resampling is performed. A new particle set is generated by calculating the cumulative weight and uniformly sampling within the interval. The system state estimation is calculated based on the current particle set, including the leakage source location estimation, medium parameter estimation and time-varying term estimation. The iteration is stopped when the position estimation change is less than the threshold or the maximum number of iterations is reached.

[0007] Preferably, the spatial basis function adopts a Gaussian radial basis function, which is expressed as: , which expresses the spatial position The non-uniform attenuation characteristics of the wave velocity at For the The center coordinates of the basis functions are distributed in a uniform grid in the landfill area with a grid spacing of ; is the characteristic length parameter; Indicates spatial location arrive Euclidean distance of; number of spatial basis functions and characteristic length The value of is determined based on the actual size of the landfill and the spatial distribution characteristics of the medium heterogeneity.

[0008] Preferably, the prior distribution of each parameter is set in the initialization stage, including setting the initial distribution of the leakage source position to a uniform distribution on the effective area of ​​the landfill, setting the initial distribution of the medium parameter to a normal distribution with a mean of zero vector, the covariance matrix to a constant multiple of the unit matrix, and the initial distribution of the time-varying term to a normal distribution with a mean of zero; The initial covariance matrix of the medium parameters is ,in , for Identity matrix.

[0009] A leakage detection system for implementing a landfill leakage detection method considering the propagation of stress waves in non-uniform media comprises a plurality of stress wave sensors arranged around the landfill, a signal acquisition and preprocessing module, an adaptive wave velocity estimation module, a leakage source positioning module, and a result display and alarm module, wherein the stress wave sensors are arranged in a rectangular manner around the landfill area, the signal acquisition and preprocessing module collects the stress wave velocity in real time, and performs digital filtering, threshold detection, arrival time extraction and wave velocity estimation on the collected original signal; the adaptive wave velocity estimation module estimates and dynamically updates the wave velocity by calculating the difference between the theoretical propagation time and the measured time according to the preprocessed signal sent by the signal acquisition and preprocessing module, so as to adapt to the change of medium characteristics; the leakage source positioning module constructs an objective function considering the reliability of the stress wave sensor by using the updated wave velocity value, and accurately estimates the leakage source position by using the gradient descent algorithm until the preset convergence condition is met; the result display and alarm module displays the positioning result in real time on the monitoring interface in a graphical manner, judges whether to trigger an alarm according to the preset value, and saves the detection data in a database for subsequent analysis.

[0010] Preferably, the signal preprocessing uses a low-pass filter to eliminate noise.

[0011] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it is used to implement a landfill leakage detection method that takes into account the propagation of stress waves in non-uniform media and obtain the location of the leakage source.

[0012] Beneficial effects of the present invention: In terms of stress wave propagation model, the present invention establishes a new type of stress wave propagation model. This model breaks through the limitations of the traditional fixed parameter model and creatively decomposes the actual wave velocity into two parts: the reference wave velocity and the variation, wherein the variation is further subdivided into a spatial variation term and a temporal variation term. The spatial variation term is used to describe the spatial distribution characteristics of the wave velocity caused by the inhomogeneity of the medium, and the temporal variation term reflects the dynamic changes in the wave velocity caused by changes in environmental conditions. This model structure can fully reflect the influence of medium characteristics on stress wave propagation, and provides a theoretical basis for subsequent adaptive algorithms.

[0013] In terms of adaptive algorithms, the present invention has developed a complete set of adaptive estimation methods. The method first constructs an error function for wave velocity estimation by comparing the difference between theoretical travel time and measured travel time in real time. Then, the wave velocity is iteratively updated using the gradient descent principle, and the wave velocity estimate is continuously optimized through multiple iterations. This adaptive mechanism enables the model to dynamically track changes in medium properties and maintain a high estimation accuracy. In order to further improve the positioning accuracy, the present invention also adopts an innovative weighted optimization strategy for leakage source positioning. This strategy constructs an objective function that takes into account the reliability of stress wave sensors and introduces an adaptive weight mechanism, so that the system can automatically adjust the weights of different stress wave sensors in the positioning calculation. When the measured value of a stress wave sensor deviates significantly from the theoretical predicted value, the system will automatically reduce its weight, thereby significantly improving the robustness of the system to abnormal measurement values.

[0014] In terms of system implementation, the present invention designs a complete leakage detection system. The distributed stress wave sensor array adopts an optimized spatial layout to ensure comprehensive coverage of the landfill area. Signal acquisition is responsible for high-precision digital acquisition of stress wave signals and preliminary noise elimination. Signal processing performs in-depth processing on the collected signals, including digital filtering, feature extraction, and arrival time calculation. Leakage location calculation includes wave velocity adaptive estimation and leakage source location calculation. The display output module displays the detection results in real time and has data storage and analysis functions.

[0015] The method of the present invention not only proposes an innovative solution in theory, but also shows significant technical advantages in practical applications. By establishing a non-uniform time-varying medium model, combined with an adaptive estimation algorithm and a weighted optimization strategy, the influence of medium characteristics can be effectively overcome and the leakage point location accuracy can be significantly improved. At the same time, the complete system implementation scheme provided by the present invention provides a reliable guarantee for the engineering application of this method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are only used to illustrate the preferred embodiments and are not to be considered as limiting the present invention. In the accompanying drawings: Figure 1It is the overall framework diagram of the system of the present invention; Figure 2 It is a schematic diagram of the arrangement of stress wave sensors; Figure 3 It is a flow chart of adaptive processing for inhomogeneous media; DETAILED DESCRIPTION

[0017] In the landfill leakage detection project, it is necessary to first establish a stress wave propagation model for inhomogeneous media. Considering the complexity of landfill media, the stress wave propagation model for inhomogeneous media is The wave speed at It can be expressed as the superposition of the reference wave speed and the wave speed deviation: ,in For the spatial position The actual wave speed at is the base wave speed (average wave speed in the medium), is the spatial location The wave velocity deviation at is the three-dimensional space position vector . The calculation formula is: ,in: For the The calibrated wave velocity value of the reference point, is the number of calibration points.

[0018] Expression: ,in For the The coefficients of the spatial basis functions, For the The spatial basis functions, is the total number of spatial basis functions.

[0019] Considering the time-varying characteristics of landfill medium properties that change with environmental conditions (such as temperature, humidity, etc.), the non-uniform time-varying medium wave velocity model is introduced: ,in For time, For the spatial position The time-varying wave velocity at is the time-dependent wave velocity variation term. This model can effectively describe the impact of the heterogeneity and time-varying characteristics of landfill media on stress wave propagation.

[0020] Model: , where is the initial disturbance value, is the attenuation coefficient, is the random disturbance term, It is a natural mathematical constant.

[0021] Based on the above wave velocity model, the stress wave travel time equation is established. A stress wave sensor, the stress wave is transmitted from the leakage source to the Propagation to the stress wave sensor location The theoretical travel time can be expressed as: ,in, is the theoretical stress wave propagation time, It is The position vector of the stress wave sensor, is the three-dimensional coordinate of the leakage source, , is the Euclidean distance from the leakage source to the stress wave sensor.

[0022] ,in, For the The coordinates of the stress wave sensors, is the three-dimensional coordinate of the leakage source.

[0023] In order to improve the positioning accuracy, the present invention adopts an adaptive wave velocity estimation method. First, the wave velocity estimation error is defined as: ,in is the total error, For the The measured travel time of a stress wave sensor, is the total number of stress wave sensors.

[0024] , , is the measurement noise, with mean 0 and variance The normal distribution of .

[0025] The wave velocity estimation error is based on the definition , the wave velocity parameters are iteratively updated using the gradient descent method. In each iteration, the calculation For The coefficients of the spatial basis functions The time-dependent wave velocity variation The partial derivative of : , , where For the A stress wave sensor measures the travel time. For the The theoretical travel time of a stress wave sensor is: Leakage source to The propagation path of a stress wave sensor, For the A stress wave sensor measures the travel time. For the The theoretical travel time of a stress wave sensor is: Leakage source to The propagation path of a stress wave sensor. is the path integral differential element. Update the parameters according to the gradient direction: , where Indicates time The corresponding leakage source location estimation trajectory, For the The iteration The spatial basis function coefficients, For the The iteration The spatial basis function coefficients, is the spatial parameter learning rate, Indicates at location and time The spatial gradient at For the The time-varying function after iterations is: For the The time-varying function after iterations is: is the time-varying parameter learning rate, and It is sequence The physical time corresponding to the iteration. is less than the set threshold or reaches the maximum number of iterations. , to achieve adaptive optimization of the wave velocity model.

[0026] for ,in, Contained in the state vector of the particle filter, the update of the wave speed is achieved through the state estimation of the particle filter: ,in, For the The time step is the spatial position The time-varying wave velocity update at , For the The time step estimation The spatial basis function coefficients, For the The time-varying wave velocity changes estimated by the time step are obtained through particle filtering.

[0027] In the process of locating the leakage source, considering the reliability differences of the measured values ​​of each stress wave sensor, a weighted objective function is constructed: ,in, , , is the objective function with weights, Represents the weight of each stress wave sensor, which is calculated by the following formula: ,in is the standard deviation parameter, which is used to control the weight decay rate. exp is an exponential function with the natural constant e as the base.

[0028] Constructing a weighted objective function The core purpose of the proposed method is to reduce the interference of abnormal stress wave sensor data on the positioning results through dynamic weight allocation, thereby improving the robustness of the system. The design basis is: when the stress wave sensor is measured Time with theory When the deviation is large, the weight of each stress wave sensor decays exponentially with the square of the error, thereby suppressing the contribution of abnormal stress wave sensors to the objective function. This design directly corresponds to the defect of "existing methods are sensitive to abnormal measurements" mentioned in the background technology. The weight of high-error stress wave sensors is automatically reduced through the weighted least squares framework, so that the optimization process focuses on reliable data. As the evaluation criterion for optimizing the location of the leakage source, its gradient descent solution is obtained through partial derivatives The drive coordinates are iteratively updated to minimize positioning errors.

[0029] This weight calculation method ensures that stress wave sensors with smaller deviations between measured values ​​and theoretically predicted values ​​have greater weights.

[0030] The gradient descent method is used to solve the leakage source location: ,in, is the iteration step length, For the The position estimate of the iteration, For the The position estimate of the iteration, is the gradient of the objective function with respect to the position.

[0031] In order to effectively solve the wave velocity model of non-uniform time-varying media in practical engineering and the stress wave travel time equation for leak source location , the present invention adopts a particle filtering method.

[0032] In the specific implementation process, first measure Initial particles constitute the initial particle set: , where the state vector The three-dimensional coordinates of the leakage source , basis function coefficient vector and time-varying terms Spliced ​​together, defined as: , where the symbol Represents the vertical concatenation of column vectors, and the final state vector The dimension is Explanation of the above definition: This definition is the technical means used in the joint estimation of multiple parameters in particle filtering. The state vector The expression is essentially a vertical concatenation of parameters of different dimensions. The purpose is to integrate all parameters to be optimized into a unified high-dimensional vector so that they can be jointly iterated and updated through a probabilistic framework. Therefore, although the above definition is not an equal-dimensional matrix in a strict mathematical sense, it is used in particle filtering and is within the scope that can be understood and implemented by those skilled in the art.

[0033] The initial weight of each particle is set to be uniformly distributed: ,in, For the The initial weight of each particle, the initial state distribution Based on prior knowledge. In practical applications, the initial distribution of the leakage source location can be limited to the landfill area, and the medium parameters can be taken as the statistical average of historical data. , the particle state vector is predicted and updated through the state transfer equation: , , , For the The position estimate of the iteration, for The basis function coefficient vector of the iteration, for The basis function coefficient vector of the iteration, for The time-dependent wave velocity variation term of the iteration is for The time-dependent wave velocity variation term of the iteration, where the covariance matrix parameters of the disturbance term are set according to actual engineering experience: position disturbance , the initial covariance matrix of the medium parameters is ,in , for Identity matrix. Time-varying noise , the time-varying attenuation coefficient , is a position disturbance, which follows the distribution , is a parameter perturbation, following the distribution , is a time-varying noise, which follows the distribution , in the weight update, the standard deviation parameter It is not only used to control the weight decay speed, but also reflects the confidence level of the measurement noise. In this embodiment, the value is 0.001s, which matches the measurement accuracy of the stress wave sensor. It is set to 1500m / s according to the average characteristics of the landfill medium, which is a statistical result based on a large amount of experimental data.

[0034] Based on the measured data, the weight of each particle is calculated and updated: ,in, Indicates Time step The weight of a particle, is the weight of the particle in the previous time step, is the total number of stress wave sensors. It means that it is proportional to, which means that the right side of the equation needs to be normalized.

[0035] The observation likelihood for a single stress wave sensor is calculated as: ,in, For the Time step The stress wave sensor measurement value corresponds to The likelihood probability of a particle is Indicates Time step The state vector of a particle, is twice pi, Represents a natural constant The role of the observation likelihood calculation is to quantify the total number of subintervals of each particle state. (including leakage source location , basis function coefficients and time-varying terms) and the measured data of stress wave sensors degree of match. The exponential term in the formula corresponds to the observation likelihood The product of indicates that the observation likelihood calculation directly determines the distribution of particle weights.

[0036] In an inhomogeneous medium, the theoretical travel time It needs to be solved by path integral. Assume that the stress wave travels along the leakage source to the Straight path of sensors Propagation, the path is evenly discretized into sub-intervals, ensuring that each segment has a length Based on the trapezoidal rule, the theoretical travel time is calculated as: ,in, is the step size of the path integration , , For the path The wave velocity value at discrete points, For the The wave velocity value at discrete points is expanded by the basis function model Calculated.

[0037] Particle weight normalization: According to the theory Compared with the measured travel time The error in calculating the particle weight (i.e. The weights of all particles are then normalized: ,in, for The normalized weight of a particle, Indicates Time step The denominator represents the weight of the particle. The normalization operation ensures that the sum of the weights is 1, providing a probability distribution basis for the subsequent resampling steps.

[0038] In order to avoid particle degradation, resampling is required. Calculate the number of effective particles: ,in, represents the effective number of particles; Number the particles from 1 to ; Indicates Time step The square value of the normalized weight of the particle. Less than the threshold value (in this embodiment, it is set to ), the system resampling is performed, a new particle set is generated and the weights are reset. In the resampling process of the particle filter, when the number of effective particles is Less than threshold When , a new particle set is generated by using the system resampling method.

[0039] Specifically, the cumulative weight is first calculated: , and then uniformly sample in the interval [0,1] Points, ,in It obeys the uniform distribution on the interval [0,1]. By comparison and cumulative weight Determines the index of the new particle.

[0040] Based on the current particle set, calculate the system state estimate, including the location estimate of the leakage source: ,in, Indicates The estimated leakage source location at the time step; Indicates Time step The position state of a particle.

[0041] Medium parameter estimation: ,in, Indicates The medium parameter vector estimated at the time step; Indicates Time step The medium parameter state vector of each particle.

[0042] Time-varying estimation: ,in, Indicates The value of the time-varying term estimated at the time step; Indicates Time step The time-varying state value of a particle.

[0043] The algorithm's iterative termination condition is set to the position estimation change threshold is 0.01m and the maximum number of iterations , the iteration stops when any of the conditions is met.

[0044] In actual engineering applications, the method of the present invention has been verified in a landfill. The landfill uses 8 stress wave sensors arranged around the landfill area, such as Figure 2 As shown in the figure, the spatial layout of stress wave sensors in the landfill area is: 3 on the upper boundary (S1-S3), 3 on the lower boundary (S4-S6), and 1 on each of the left and right boundaries (S7 and S8). The sampling frequency of the stress wave sensor is set to 1000Hz, and a low-pass filter with a cutoff frequency of 200Hz is used for signal preprocessing.

[0045] In order to construct the spatial basis function of the non-uniform wave velocity model, the Gaussian radial basis function is used: ,in, For the The center coordinates of the basis functions ( , is the total number of basis functions), Indicates spatial location arrive The Euclidean distance of is the coverage radius of the basis function (default = 0.5 m). According to the preset grid point distribution (such as uniform grid or adaptive clustering), the stress wave sensor position ( ) are independent of each other. By adjusting the basis function coefficients , which can characterize the spatial inhomogeneity of the wave velocity field.

[0046] The medium parameters adopt normal distribution a priori: ,in, is the basis function index, represents the medium parameter vector at the initial moment; represents the mean vector, with dimension ; Represents the covariance matrix, with dimension . Specifically, represents a zero-dimensional vector, The dimensional identity matrix is ​​multiplied by 0.1.

[0047] The time-varying term prior also uses a normal distribution: ,in, Represents the initial disturbance value; the first parameter 0 represents the mean and 0.01 represents the variance.

[0048] Corresponding to the above method, the present invention proposes a landfill non-uniform medium leakage detection system, including a plurality of stress wave sensors arranged around the landfill, a signal acquisition and preprocessing module, an adaptive wave velocity estimation module, a leakage source positioning module, and a result display and alarm module, wherein the stress wave sensors are arranged in a rectangular manner around the landfill area, the signal acquisition and preprocessing module collects the stress wave velocity in real time, and performs digital filtering, threshold detection, arrival time extraction and wave velocity estimation on the collected original signal; the adaptive wave velocity estimation module estimates and dynamically updates the wave velocity by calculating the difference between the theoretical propagation time and the measured time according to the preprocessed signal sent by the signal acquisition and preprocessing module, so as to adapt to the change of medium characteristics; the leakage source positioning module uses the updated wave velocity value to construct an objective function considering the reliability of the stress wave sensor, and accurately estimates the leakage source position by the gradient descent algorithm until the preset convergence condition is met; the result display and alarm module displays the positioning result in real time on the monitoring interface in a graphical manner, and determines whether to trigger an alarm according to the preset value, and saves the detection data in a database for subsequent analysis. Signal preprocessing uses a low-pass filter to eliminate noise.

[0049] In order to facilitate the promotion and use, a computer readable storage medium is introduced, which stores a computer program. When the computer program is executed by a processor, it is used to implement the above-mentioned landfill non-uniform medium leakage detection method and obtain the leakage source location. The readable storage medium can be a read-only memory (ROM), or a programmable ROM or flash memory, etc. In the current technology, any memory that has a storage function and can install or call the stored program can be used in the method and system of the present invention.

Claims

1. A landfill leakage detection method considering the propagation of stress waves in non-uniform media, wherein a plurality of stress wave sensors are arranged around the landfill to form a distributed stress wave sensor array, a stress wave sensor position vector is established, the propagation velocity of the stress wave is collected, and the obtained data is processed, wherein: (1) A wave velocity model is established, and the wave velocity at a spatial position is expressed as the superposition of a reference wave velocity and a wave velocity deviation, wherein the reference wave velocity is calculated by calibrating the wave velocity values ​​of multiple reference points, and the wave velocity deviation is expressed by expanding the spatial basis function. Meanwhile, the time-varying characteristics of the medium are considered, and a time-varying term is introduced into the wave velocity model. The time-varying term includes an initial disturbance value, an attenuation coefficient, and a random disturbance term. A complete wave velocity model for non-uniform time-varying media is established. The model equation is as follows: , , , ,in, For time, For the spatial position The time-varying wave velocity at is the reference wave speed, It is the spatial location The wave velocity deviation, is the time-dependent wave velocity variation term, For the The calibrated wave velocity value of the reference point, is the number of calibration points, For the The coefficients of the spatial basis functions, For the The spatial basis functions, is the total number of spatial basis functions, is the initial disturbance value, is the attenuation coefficient, is the random disturbance term, is a natural constant of mathematics; (2) Based on the above wave velocity model, the stress wave travel time equation is established. The theoretical travel time is obtained by calculating the Euclidean distance from the leakage source to each stress wave sensor and dividing it by the wave velocity. The Euclidean distance is calculated by the three-dimensional coordinates. Then, the wave velocity estimation error is defined as the square sum of the difference between the measured travel time and the theoretical travel time. The wave velocity is iteratively updated using the gradient descent method. ,in, For the The time step is the spatial position The time-varying wave velocity update at , For the The time step estimation The spatial basis function coefficients, For the The time-varying wave velocity change estimated by the time step, , It is obtained through particle filtering; (3) In the process of locating the leakage source, a weighted objective function is constructed. , ,in, is the objective function with weights, is the total number of stress wave sensors, is the weight of each stress wave sensor, For the The measured travel time of a stress wave sensor, is the Euclidean distance from the leakage source to the stress wave sensor, It is The position vector of the stress wave sensor, is the three-dimensional coordinate of the leakage source, , is the theoretical stress wave propagation time, is the standard deviation parameter, which is used to control the weight decay speed. The gradient descent method is used to update the leakage source position. ,in, is the iteration step length, For the The position estimate of the iteration, For the The position estimate of the iteration, is the gradient of the objective function with respect to the position, and gradient descent is achieved by calculating the gradient of the objective function with respect to the position.

2. The landfill leakage detection method considering the propagation of stress waves in non-uniform media according to claim 1 is characterized in that: The particle filter method is used to solve the wave velocity model, and each particle is predicted and updated through the state transfer equation. The particle weight is calculated and updated based on the measured data, and the relationship equation between the observation value and the state quantity is designed: ,in, For the Time step The stress wave sensor measurement value corresponds to The likelihood probability of a particle is Indicates Time step The state vector of a particle, is twice pi, Represents a natural constant An exponential function with base ; The stress wave travel time equation is solved by numerical integration method.

3. The landfill leakage detection method considering the propagation of stress waves in non-uniform media according to claim 1 is characterized in that: To avoid particle degradation, the number of valid particles is calculated. When the number of valid particles is less than the preset threshold, resampling is performed. A new particle set is generated by calculating the cumulative weight and uniformly sampling within the interval. The system state estimation is calculated based on the current particle set, including the leakage source location estimation, medium parameter estimation and time-varying term estimation. The iteration is stopped when the position estimation change is less than the threshold or the maximum number of iterations is reached.

4. The landfill leakage detection method considering the propagation of stress waves in non-uniform media according to any one of claims 1 to 3, characterized in that: The spatial basis function adopts Gaussian radial basis function, and its expression is: , which expresses the spatial position The non-uniform attenuation characteristics of the wave velocity at For the The center coordinates of the basis functions are distributed in a uniform grid in the landfill area with a grid spacing of ; is the characteristic length parameter; Indicates spatial location arrive Euclidean distance of; number of spatial basis functions and characteristic length The value of is determined based on the actual size of the landfill and the spatial distribution characteristics of the medium heterogeneity.

5. The landfill leakage detection method considering the propagation of stress waves in non-uniform media according to claim 4 is characterized in that: In the initialization stage, the prior distribution of each parameter is set, including setting the initial distribution of the leakage source location to a uniform distribution on the effective area of ​​the landfill, setting the initial distribution of the medium parameter to a normal distribution with a mean of zero vector, and setting the covariance matrix to a constant multiple of the unit matrix. The initial distribution of the time-varying term is also set to a normal distribution with a mean of zero. The initial covariance matrix of the medium parameters is ,in , for Identity matrix.

6. A leakage detection system for implementing the method of claim 5, characterized in that: The invention comprises a plurality of stress wave sensors arranged around the landfill, a signal acquisition and preprocessing module, an adaptive wave velocity estimation module, a leakage source positioning module, and a result display and alarm module, wherein the stress wave sensors are arranged in a rectangular manner around the landfill area, the signal acquisition and preprocessing module collects the stress wave velocity in real time, and performs digital filtering, threshold detection, arrival time extraction and wave velocity estimation on the collected original signal; the adaptive wave velocity estimation module estimates and dynamically updates the wave velocity by calculating the difference between the theoretical propagation time and the measured time according to the preprocessed signal sent by the signal acquisition and preprocessing module, so as to adapt to the change of the medium characteristics; the leakage source positioning module constructs an objective function considering the reliability of the stress wave sensor by using the updated wave velocity value, and accurately estimates the leakage source position by using the gradient descent algorithm until the preset convergence condition is met; the result display and alarm module displays the positioning result in real time on the monitoring interface in a graphical manner, judges whether to trigger an alarm according to the preset value, and saves the detection data in a database for subsequent analysis.

7. The leakage detection system according to claim 6, characterized in that: Signal preprocessing uses a low-pass filter to eliminate noise.

8. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, it is used to implement the method of one of claims 1, 2, 3, and 5 to obtain the location of the leakage source.

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