Pipeline leakage positioning method and device based on time delay estimation and electronic equipment

Abstract

The invention relates to a pipeline leakage positioning technology, and discloses a time delay estimation-based pipeline leakage positioning method, which comprises the following steps of: acquiring acoustic signal acquisition data of a leakage pipeline, and constructing a pipeline leakage point positioning model; performing signal processing on the sound signal acquisition data by using a minimum mean square algorithm to obtain an output signal; obtaining a pre-constructed expected signal, calculating an error signal by using the expected signal and the output signal, and iteratively updating a filter weight vector in a least mean square algorithm according to the error signal to obtain an optimal filter weight vector; and optimizing the pipeline leakage point positioning model according to the optimal filter weight vector to obtain a target pipeline leakage point positioning model, performing time delay calculation on the acoustic signal acquisition data by using the target pipeline leakage positioning model to obtain signal time delay, and positioning a pipeline leakage point according to the signal time delay. The invention further provides a pipeline leakage positioning device, electronic equipment and a storage medium. According to the invention, the accuracy of pipeline leakage positioning can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline leakage locating, and in particular to a pipeline leakage locating method, device and electronic equipment based on time delay estimation. Background Art

[0002] Pipeline leak location refers to the use of technical means to detect and determine the location of pipeline leaks. The location of the leak is usually calculated by using the sensor position and the sound collected by the sensor. The acoustic method is one of the common technologies for pipeline leak location. When a pipeline leaks, water escaping from the leak will generate sound waves, which propagate in the pipeline and are captured by the sensor. Acoustic signal time delay estimation (TDE) is a method for locating leaks based on the time difference of signal propagation. The traditional TDE method determines the leak location by calculating the arrival time difference of sound waves between different sensors. However, these traditional methods may be affected by noise and signal attenuation in complex environments. In recent years, artificial neural networks and deep learning technologies have been widely used in leak location. However, these technologies need to be trained by processing a large amount of historical data. However, in the case of insufficient data, the performance of intelligent learning algorithms is low, resulting in low accuracy in locating pipeline leaks. Summary of the invention

[0003] The present invention provides a pipeline leakage locating method, device and electronic equipment based on time delay estimation, which can improve the accuracy of pipeline leakage locating.

[0004] To achieve the above object, the present invention provides a pipeline leakage location method based on time delay estimation, comprising:

[0005] Acquire the acoustic signal data of the leaking pipeline and build a pipeline leakage point location model;

[0006] Using a least mean square algorithm to process the acoustic signal acquisition data to obtain an output signal;

[0007] Acquire a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update a filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector;

[0008] The pipeline leakage point location model is optimized according to the optimal filter weight vector to obtain a target pipeline leakage point location model, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and the pipeline leakage point is located according to the signal time delay.

[0009] Optionally, the constructing of the pipeline leakage point location model includes:

[0010] Obtain the acoustic signal collection data of the sensor nodes and the installation distance between the sensor nodes;

[0011] The pipeline leakage point positioning model is constructed according to the signal propagation speed in the acoustic signal acquisition data and the installation distance.

[0012] Optionally, the using a least mean square algorithm to perform signal processing on the acoustic signal acquisition data to obtain an output signal includes:

[0013] Processing the acoustic signal acquisition data in segments to obtain segmented acoustic signal acquisition data;

[0014] The output signal is calculated based on the segmented acoustic signal acquisition data using the filter weight vector of the least mean square algorithm.

[0015] Optionally, iteratively updating the filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector includes:

[0016] Using an exponential hyperbolic cosine function as a cost function of the least mean square algorithm, and updating the cost function based on a variable scale factor and a variable step size to obtain an adaptive cost function;

[0017] According to the adaptive cost function and the gradient descent method, the filter weight vector is updated using the error signal, and the peak weight vector of the filter weight vector is extracted to obtain the optimal filter weight vector.

[0018] Optionally, the using an exponential hyperbolic cosine function as a cost function of the least mean square algorithm includes:

[0019] The following formula is used as the cost function ξ(n) of the least mean square algorithm:

[0020] ξ(n)=1-E{γexp{-|cosh[λe(n)]| p}}

[0021] Where λ is the scaling factor, λ>0, γ is the normalization constant, p represents the shape parameter, p>0, e(n) is the error signal, and E is {γexp{-|cosh[λe(n)]| p}}, n is the processing sequence number of the acoustic signal acquisition data, and cosh[·] is the hyperbolic cosine function.

[0022] Optionally, updating the cost function based on a variable scale factor and a variable step size comprises:

[0023] Constructing an adjustment function of the proportional factor in the cost function, and adjusting a scaling parameter in the adjustment function according to an error signal to obtain the variable proportional factor;

[0024] Calculating the variable step size based on the absolute sum of errors of the power and instantaneous exponential hyperbolic cosine function of the acoustic signal acquisition data;

[0025] The cost function is updated in combination with the variable scaling factor and the variable step size.

[0026] Optionally, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay Δt, using the following formula:

[0027]

[0028] Where argmax[·] represents the parameter corresponding to the maximum value of the function in [·], Sinc(·) represents sinc interpolation, M is the order of the filter, w(i) is the i-th filter weight vector of the filter, and τ is the time interval for collecting data of the acoustic signal collected by the sensor node.

[0029] In order to solve the above problems, the present invention also provides a pipeline leakage locating device based on time delay estimation, the device comprising:

[0030] A pipeline leakage point location model building module is used to obtain the acoustic signal acquisition data of the leaking pipeline and build a pipeline leakage point location model;

[0031] A signal processing module, used to perform signal processing on the acoustic signal acquisition data using a least mean square algorithm to obtain an output signal;

[0032] The pipeline leakage point positioning calculation module is used to obtain a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update the filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector; optimize the pipeline leakage point positioning model according to the optimal filter weight vector to obtain a target pipeline leakage point positioning model, use the target pipeline leakage positioning model to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and locate the pipeline leakage point according to the signal time delay.

[0033] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0034] at least one processor; and,

[0035] a memory communicatively connected to the at least one processor; wherein,

[0036] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned pipeline leakage location method based on time delay estimation.

[0037] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned pipeline leakage location method based on time delay estimation.

[0038] The present invention can transform the pipeline leakage point positioning calculation problem into a time delay calculation problem by constructing a pipeline leakage point positioning model, thereby simplifying the calculation process of the pipeline leakage point. In addition, the filter weight vector in the least mean square algorithm is iteratively updated according to the error signal, thereby achieving robustness in processing the collected acoustic signal. In addition, the pipeline leakage point positioning model is optimized according to the optimal filter weight vector, thereby ensuring the calculation accuracy of the signal time delay calculated by the pipeline leakage point positioning model, thereby improving the accuracy of pipeline leakage point positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a flow chart of a pipeline leakage location method based on time delay estimation provided by an embodiment of the present invention;

[0040] Figure 2 A pipeline leakage point positioning model diagram of a pipeline leakage positioning method based on time delay estimation provided by an embodiment of the present invention;

[0041] Figure 3 A functional module diagram of a pipeline leakage locating device based on time delay estimation provided by one embodiment of the present invention;

[0042] Figure 4 A schematic diagram of the structure of an electronic device for implementing a pipeline leakage location method based on time delay estimation provided in an embodiment of the present invention.

[0043] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0045] The embodiment of the present application provides a pipeline leakage location method based on delay estimation. The execution subject of the pipeline leakage location method based on delay estimation includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the pipeline leakage location method based on delay estimation can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0046] Reference Figure 1 FIG. 1 is a flow chart of a pipeline leakage location method based on time delay estimation provided by an embodiment of the present invention. In this embodiment, the pipeline leakage location method based on time delay estimation includes:

[0047] S1. Acquire the acoustic signal data of the leaking pipeline and build a pipeline leakage point location model.

[0048] In the embodiment of the present invention, the acoustic signal collection data refers to the sound signal collected by the sensor node installed on the pipeline.

[0049] Exemplarily, the acoustic signal acquisition data of the leaking pipeline in the embodiment of the present invention can be obtained as follows:

[0050] s 1 (t) = s 0 (t)+n 1 (t)

[0051] s 2 (t) = s 0 (t)+n 2 (t)

[0052] Among them, s 0 (t) is the sound signal of the leakage source; s 1 (t) represents the acoustic signal received by sensor 1, s 2 (t) represents the acoustic signal received by sensor 2; n 1 (t) and n 2 (t) represents the noise signal generated by the environmental impact and the acquisition system.

[0053] As an embodiment of the present invention, the step of constructing a pipeline leakage point location model includes:

[0054] Obtain the acoustic signal collection data of the sensor nodes and the installation distance between the sensor nodes;

[0055] The pipeline leakage point positioning model is constructed according to the signal propagation speed in the acoustic signal acquisition data and the installation distance.

[0056] In the embodiment of the present invention, the pipeline leakage point positioning model refers to a positioning model constructed for calculating the leakage point on the leaking pipeline.

[0057] In the embodiment of the present invention, the pipeline leakage point location model can use the following formula to locate the leakage point:

[0058]

[0059] Among them, d 1 is the distance from the first sensor node to the pipeline leakage point, D is the distance between the two sensor nodes, c is the propagation speed of sound waves, and Δt is the time delay between the signal acquisition data collected by the two sensor nodes.

[0060] S2. Processing the acoustic signal acquisition data using a least mean square algorithm to obtain an output signal.

[0061] In the embodiment of the present invention, the least mean square algorithm refers to an adaptive filtering algorithm, which updates the weights of the adaptive filter by minimizing the energy of the error signal.

[0062] As an embodiment of the present invention, the method of performing signal processing on the acoustic signal acquisition data using a least mean square algorithm to obtain an output signal includes:

[0063] Processing the acoustic signal acquisition data in segments to obtain segmented acoustic signal acquisition data;

[0064] The output signal is calculated based on the segmented acoustic signal acquisition data using the filter weight vector of the least mean square algorithm.

[0065] Exemplarily, the acoustic signal acquisition data is processed in sections, and the following formula may be used for the section processing:

[0066] x(n)=[u(n),u(n-1),…,u(n-M+1)] T

[0067] Where M is the filter order, u(n) is the acoustic signal acquisition data, x(n) is the tap vector of the acoustic signal acquisition data, [·] T Represents a transpose operation.

[0068] Exemplarily, the filter weight vector W(n) can be expressed as:

[0069] W(n)=[w 1 (n),w 2 (n),…,w M (n)] T

[0070] The signal output data y(n) is obtained by the dot product of the tap vector and the filter weight vector W(n):

[0071]

[0072] Among them, n(n-i+1) is the segmented sound signal acquisition data of the i-th filter, w i (n) is the weight vector of the i-th filter in the weight vector of the M-order filter.

[0073] S3. Obtain a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update the filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector.

[0074] In the embodiment of the present invention, the pre-constructed desired signal refers to a target signal obtained after removing interference factors in the acoustic signal, wherein the interference signal includes but is not limited to a noise signal.

[0075] In the embodiment of the present invention, the error signal e(n) is calculated using the expected signal and the output signal, and the following formula may be used for calculation:

[0076] e(n)=d(n)-y(n); then e(n)=d(n)-W T (n)x(n)

[0077] Among them, d(n) is the desired signal and y(n) is the output signal.

[0078] As an embodiment of the present invention, the iterative updating of the filter weight vector in the least mean square algorithm according to the error signal to obtain the optimal filter weight vector includes:

[0079] Using an exponential hyperbolic cosine function as a cost function of the least mean square algorithm, and updating the cost function based on a variable scale factor and a variable step size to obtain an adaptive cost function;

[0080] According to the adaptive cost function and the gradient descent method, the filter weight vector is updated using the error signal, and the peak weight vector of the filter weight vector is extracted to obtain the optimal filter weight vector.

[0081] In the embodiment of the present invention, the exponential hyperbolic cosine function refers to a function that combines the characteristics of an exponential function and a hyperbolic cosine function, and can be used as a cost function in an adaptive algorithm.

[0082] In the embodiment of the present invention, the variable scaling factor refers to an adjustment factor constructed to adapt to different noise environments and thereby improve the accuracy of the cost function in the adaptive algorithm, and the variable step size refers to a step size adaptive adjustment factor introduced to further improve the delay estimation performance in a variable noise environment.

[0083] As an embodiment of the present invention, the use of the exponential hyperbolic cosine function as the cost function of the least mean square algorithm includes:

[0084] The following formula is used as the cost function ξ(n) of the least mean square algorithm:

[0085] ξ(n)=1-E{γexp{-|cosh[λe(n)]| p}}

[0086] Where λ is the scaling factor, λ>0, γ is the normalization constant, p represents the shape parameter, p>0, e(n) is the error signal, and E is {γexp{-|cosh[λe(n)]| p}}, n is the processing sequence number of the acoustic signal acquisition data, and cosh[·] is the hyperbolic cosine function.

[0087] Further, updating the cost function based on a variable scale factor and a variable step size includes:

[0088] Constructing an adjustment function of the proportional factor in the cost function, and adjusting a scaling parameter in the adjustment function according to an error signal to obtain the variable proportional factor;

[0089] Calculating the variable step size based on the absolute sum of errors of the power and instantaneous exponential hyperbolic cosine function of the acoustic signal acquisition data;

[0090] The cost function is updated in combination with the variable scaling factor and the variable step size.

[0091] Exemplarily, the adjustment function λ(n) for constructing the proportional factor in the cost function is as follows:

[0092] λ(n)=λ 0 e -|e(n)|

[0093] Where e(n) is the error signal, 0<λ 0 ≤1 is a static scaling parameter.

[0094] Exemplarily, the variable step size μ(n) is calculated based on the absolute sum of the error of the power and instantaneous exponential hyperbolic cosine function of the acoustic signal acquisition data, and the following implementation steps can be used for calculation:

[0095]

[0096] in, It is the cumulative sum of the absolute values ​​of the errors under the exponential hyperbolic cosine function when iterated to the nth time. is the reciprocal of the sum of the absolute values ​​of the errors, x H (n) represents the transpose of the input vector x(n), x H (n)x(n) is the square norm of the input vector x(n), β is the first adjustment parameter, and ζ is the second adjustment parameter.

[0097] In the embodiment of the present invention, the The reciprocal of the accumulated sum of the absolute values ​​of the errors is taken because as the number of iterations increases, the filter tends to converge and the step size should gradually decrease.

[0098] In the embodiment of the present invention, the It is to normalize the filter weight adjustment amount. The principle is: for larger inputs, the gradient noise will be amplified, so it is necessary to use the square norm of the input vector for normalization.

[0099]

[0100] Furthermore, the limit of the variable step size is:

[0101]

[0102] In the embodiment of the present invention, after the cost function is updated by a variable scaling factor, the updated cost function obtained is as follows:

[0103] w(n+1)=w(n)+γλ(n)μpx(n)exp{-|cosh[λ(n)e(n)]| p}

[0104] sign{cosh[λ(n)e(n)]}|cosh[λ(n)e(n)]| p-1 sinh[λ(n)e(n)]

[0105] Among them, w(n+1) is the updated weight, and w(n) is the weight value of the nth iteration.

[0106] In the embodiment of the present invention, the cost function is updated by combining the variable scale factor and the variable step size to obtain an adaptive cost function as follows:

[0107] w(n+1)=w(n)+γλ(n)μ(n)px(n)exp{-|cosh[λ(n)e(n)]| p}

[0108] sign{cosh[λ(n)e(n)]}|cosh[λ(n)e(n)]| p-1 sinh[λ(n)e(n)]

[0109] S4. Optimizing the pipeline leakage point location model according to the optimal filter weight vector to obtain a target pipeline leakage point location model, using the target pipeline leakage location model to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and locating the pipeline leakage point according to the signal time delay.

[0110] As an embodiment of the present invention, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain the signal time delay Δt, using the following formula:

[0111]

[0112] Where argmax[·] represents the parameter corresponding to the maximum value of the function in [·], Sinc(·) represents sinc interpolation, M is the order of the filter, w(i) is the i-th filter weight vector of the filter, and τ is the time interval for collecting data of the acoustic signal collected by the sensor node.

[0113] The embodiment of the present invention can transform the pipeline leakage point positioning calculation problem into a time delay calculation problem by constructing a pipeline leakage point positioning model, thereby simplifying the calculation process of the pipeline leakage point. In addition, the filter weight vector in the least mean square algorithm is iteratively updated according to the error signal, so as to achieve robustness when processing the collected acoustic signal. In addition, the pipeline leakage point positioning model is optimized according to the optimal filter weight vector, so as to ensure the calculation accuracy of the signal time delay calculated by the pipeline leakage point positioning model, thereby improving the accuracy of pipeline leakage point positioning.

[0114] like Figure 2 As shown, it is a pipeline leakage point positioning model diagram of a pipeline leakage positioning method based on time delay estimation provided by an embodiment of the present invention.

[0115] like Figure 3 , is a functional module diagram of a pipeline leakage locating device based on time delay estimation provided by an embodiment of the present invention.

[0116] The pipeline leakage locating device 100 based on time delay estimation of the present invention can be installed in an electronic device. According to the implemented functions, the pipeline leakage locating device 100 based on time delay estimation can include a pipeline leakage point locating model building module 101, a signal processing module 102 and a pipeline leakage point locating calculation module 103.

[0117] The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and is stored in a memory of the electronic device.

[0118] In this embodiment, the functions of each module / unit are as follows:

[0119] The pipeline leakage point location model building module 101 is used to obtain the acoustic signal acquisition data of the leaking pipeline and build a pipeline leakage point location model.

[0120] In the embodiment of the present invention, the acoustic signal collection data refers to the sound signal collected by the sensor node installed on the pipeline.

[0121] Exemplarily, the acoustic signal acquisition data of the leaking pipeline in the embodiment of the present invention can be obtained as follows:

[0122] s 1 (t) = s 0 (t)+n 1 (t)

[0123] s 2 (t) = s 0 (t)+n 2 (t)

[0124] Among them, s 0 (t) is the sound signal of the leakage source; s 1 (t) represents the acoustic signal received by sensor 1, s 2 (t) represents the acoustic signal received by sensor 2; n 1 (t) and n 2 (t) represents the noise signal generated by the environmental impact and the acquisition system.

[0125] As an embodiment of the present invention, the step of constructing a pipeline leakage point location model includes:

[0126] Obtain the acoustic signal collection data of the sensor nodes and the installation distance between the sensor nodes;

[0127] The pipeline leakage point positioning model is constructed according to the signal propagation speed in the acoustic signal acquisition data and the installation distance.

[0128] In the embodiment of the present invention, the pipeline leakage point positioning model refers to a positioning model constructed for calculating the leakage point on the leaking pipeline.

[0129] In the embodiment of the present invention, the pipeline leakage point location model can use the following formula to locate the leakage point:

[0130]

[0131] Among them, d 1 is the distance from the first sensor node to the pipeline leakage point, D is the distance between the two sensor nodes, c is the propagation speed of sound waves, and Δt is the time delay between the signal acquisition data collected by the two sensor nodes.

[0132] The signal processing module 102 is used to perform signal processing on the acoustic signal acquisition data using a least mean square algorithm to obtain an output signal.

[0133] In the embodiment of the present invention, the least mean square algorithm refers to an adaptive filtering algorithm, which updates the weights of the adaptive filter by minimizing the energy of the error signal.

[0134] As an embodiment of the present invention, the method of performing signal processing on the acoustic signal acquisition data using a least mean square algorithm to obtain an output signal includes:

[0135] Processing the acoustic signal acquisition data in segments to obtain segmented acoustic signal acquisition data;

[0136] The output signal is calculated based on the segmented acoustic signal acquisition data using the filter weight vector of the least mean square algorithm.

[0137] Exemplarily, the acoustic signal acquisition data is processed in sections, and the following formula may be used for the section processing:

[0138] x(n)=[u(n),y(n-1),…,u(n-M+1)] T

[0139] Where M is the filter order, u(n) is the acoustic signal acquisition data, x(n) is the tap vector of the acoustic signal acquisition data, [·] T Represents a transpose operation.

[0140] Exemplarily, the filter weight vector W(n) can be expressed as:

[0141] W(n)=[w 1 (n),w 2 (n),…,w M (n)] T

[0142] The signal output data y(n) is obtained by the dot product of the tap vector and the filter weight vector W(n):

[0143]

[0144] Among them, u(n-i+1) is the segmented sound signal acquisition data of the i-th filter, w i (n) is the weight vector of the i-th filter in the weight vector of the M-order filter.

[0145] The pipeline leakage point positioning calculation module 103 is used to obtain a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update the filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector; optimize the pipeline leakage point positioning model according to the optimal filter weight vector to obtain a target pipeline leakage point positioning model, use the target pipeline leakage positioning model to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and locate the pipeline leakage point according to the signal time delay.

[0146] In the embodiment of the present invention, the pre-constructed desired signal refers to a target signal obtained after removing interference factors in the acoustic signal, wherein the interference signal includes but is not limited to a noise signal.

[0147] In the embodiment of the present invention, the error signal e(n) is calculated using the expected signal and the output signal, and the following formula may be used for calculation:

[0148] e(n)=d(n)-y(n); then e(n)=d(n)-W T (n)x(n)

[0149] Among them, d(n) is the desired signal and y(n) is the output signal.

[0150] As an embodiment of the present invention, the iterative updating of the filter weight vector in the least mean square algorithm according to the error signal to obtain the optimal filter weight vector includes:

[0151] Using an exponential hyperbolic cosine function as a cost function of the least mean square algorithm, and updating the cost function based on a variable scale factor and a variable step size to obtain an adaptive cost function;

[0152] According to the adaptive cost function and the gradient descent method, the filter weight vector is updated using the error signal, and the peak weight vector of the filter weight vector is extracted to obtain the optimal filter weight vector.

[0153] In the embodiment of the present invention, the exponential hyperbolic cosine function refers to a function that combines the characteristics of an exponential function and a hyperbolic cosine function, and can be used as a cost function in an adaptive algorithm.

[0154] In an embodiment of the present invention, the variable scaling factor refers to an adjustment factor constructed to adapt to different noise environments and thereby improve the accuracy of the cost function in the adaptive algorithm, and the variable step size refers to a step size adaptive adjustment factor introduced to further improve the delay estimation performance in a variable noise environment.

[0155] As an embodiment of the present invention, the use of the exponential hyperbolic cosine function as the cost function of the least mean square algorithm includes:

[0156] The following formula is used as the cost function ξ(n) of the least mean square algorithm:

[0157] ξ(n)=1-E{γexp{-|cosh[λe(n)]| p}}

[0158] Where λ is the scaling factor, λ>0, γ is the normalization constant, p represents the shape parameter, p>0, e(n) is the error signal, and E is {γexp{-|cosh[λe(n)]| p}}, n is the processing sequence number of the acoustic signal acquisition data, and cosh[·] is the hyperbolic cosine function.

[0159] Further, updating the cost function based on a variable scale factor and a variable step size includes:

[0160] Constructing an adjustment function of the proportional factor in the cost function, and adjusting a scaling parameter in the adjustment function according to an error signal to obtain the variable proportional factor;

[0161] Calculating the variable step size based on the absolute sum of errors of the power and instantaneous exponential hyperbolic cosine function of the acoustic signal acquisition data;

[0162] The cost function is updated in combination with the variable scaling factor and the variable step size.

[0163] Exemplarily, the adjustment function λ(n) for constructing the proportional factor in the cost function is as follows:

[0164] λ(n)=λ 0 e -|e(n)|

[0165] Where e(n) is the error signal, 0<λ 0 ≤1 is a static scaling parameter.

[0166] Exemplarily, the variable step size μ(n) is calculated based on the absolute sum of the error of the power and instantaneous exponential hyperbolic cosine function of the acoustic signal acquisition data, and the following implementation steps can be used for calculation:

[0167]

[0168]

[0169] in, It is the cumulative sum of the absolute values ​​of the errors under the exponential hyperbolic cosine function when iterated to the nth time. is the reciprocal of the sum of the absolute values ​​of the errors, x H (n) represents the transpose of the input vector x(n), x H (n)x(n) is the square norm of the input vector x(n), β is the first adjustment parameter, and ζ is the second adjustment parameter.

[0170] In the embodiment of the present invention, the The reciprocal of the accumulated sum of the absolute values ​​of the errors is taken because as the number of iterations increases, the filter tends to converge and the step size should gradually decrease.

[0171] In the embodiment of the present invention, the It is to normalize the filter weight adjustment amount. The principle is: for larger inputs, the gradient noise will be amplified, so it is necessary to use the square norm of the input vector for normalization.

[0172]

[0173] Furthermore, the limit of the variable step size is:

[0174]

[0175] In the embodiment of the present invention, after the cost function is updated by a variable scaling factor, the updated cost function obtained is as follows:

[0176] w(n+1)=w(n)+γλ(n)μpx(n)exp{-|cosh[λ(n)e(n)]| p}

[0177] sign{cosh[λ(n)e(n)]}|cosh[λ(n)e(n)]| p-1 sinh[λ(n)e(n)]

[0178] Among them, w(n+1) is the updated weight, and w(n) is the weight value of the nth iteration.

[0179] In the embodiment of the present invention, the cost function is updated by combining the variable scale factor and the variable step size to obtain an adaptive cost function as follows:

[0180] w(n+1)=w(n)+γλ(n)μ(n)px(n)exp{-|cosh[λ(n)e(n)]| p}

[0181] sign{cosh[λ(n)e(n)]}|cosh[λ(n)e(n)]| p-1 sinh[λ(n)e(n)]

[0182] The embodiment of the present invention optimizes the pipeline leakage point location model according to the optimal filter weight vector to obtain a target pipeline leakage point location model, uses the target pipeline leakage location model to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and locates the pipeline leakage point according to the signal time delay.

[0183] As an embodiment of the present invention, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain the signal time delay Δt, using the following formula:

[0184]

[0185] Where argmax[·] represents the parameter corresponding to the maximum value of the function in [·], Sinc(·) represents sinc interpolation, M is the order of the filter, w(i) is the i-th filter weight vector of the filter, and τ is the time interval for collecting data of the acoustic signal collected by the sensor node.

[0186] Reference Figure 4 , which is a schematic diagram of the structure of an electronic device for implementing a pipeline leakage location method based on time delay estimation provided by an embodiment of the present invention.

[0187] The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a pipeline leakage location method program based on time delay estimation.

[0188] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a pipeline leakage location method program based on delay estimation, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0189] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device, such as a code of a pipeline leakage location method program based on time delay estimation, but also be used to temporarily store data that has been output or is to be output.

[0190] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0191] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0192] Figure 4 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0193] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0194] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0195] The pipeline leakage location method program based on time delay estimation stored in the memory 11 of the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0196] Acquire the acoustic signal data of the leaking pipeline and build a pipeline leakage point location model;

[0197] Using a least mean square algorithm to process the acoustic signal acquisition data to obtain an output signal;

[0198] Acquire a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update a filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector;

[0199] The pipeline leakage point location model is optimized according to the optimal filter weight vector to obtain a target pipeline leakage point location model, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and the pipeline leakage point is located according to the signal time delay.

[0200] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0201] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0202] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0203] Acquire the acoustic signal data of the leaking pipeline and build a pipeline leakage point location model;

[0204] Using a least mean square algorithm to process the acoustic signal acquisition data to obtain an output signal;

[0205] Acquire a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update a filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector;

[0206] The pipeline leakage point location model is optimized according to the optimal filter weight vector to obtain a target pipeline leakage point location model, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and the pipeline leakage point is located according to the signal time delay.

[0207] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0208] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0209] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0210] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0211] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0212] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0213] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0214] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A pipeline leakage location method based on time delay estimation, characterized in that: The method comprises: Acquire the acoustic signal data of the leaking pipeline and build a pipeline leakage point location model; Using a least mean square algorithm to process the acoustic signal acquisition data to obtain an output signal; Acquire a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update a filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector; The pipeline leakage point location model is optimized according to the optimal filter weight vector to obtain a target pipeline leakage point location model, the target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and the pipeline leakage point is located according to the signal time delay.

2. The pipeline leakage location method based on time delay estimation according to claim 1, characterized in that: The step of constructing a pipeline leakage point location model comprises: Obtain the acoustic signal collection data of the sensor nodes and the installation distance between the sensor nodes; The pipeline leakage point positioning model is constructed according to the signal propagation speed in the acoustic signal acquisition data and the installation distance.

3. The pipeline leakage location method based on time delay estimation according to claim 1, characterized in that: The method of performing signal processing on the acoustic signal acquisition data using the least mean square algorithm to obtain an output signal includes: Processing the acoustic signal acquisition data in segments to obtain segmented acoustic signal acquisition data; The output signal is calculated based on the segmented acoustic signal acquisition data using the filter weight vector of the least mean square algorithm.

4. The pipeline leakage location method based on time delay estimation according to any one of claims 1 to 3, characterized in that: The iterative updating of the filter weight vector in the least mean square algorithm according to the error signal to obtain the optimal filter weight vector includes: Using an exponential hyperbolic cosine function as a cost function of the least mean square algorithm, and updating the cost function based on a variable scale factor and a variable step size to obtain an adaptive cost function; According to the adaptive cost function and the gradient descent method, the filter weight vector is updated using the error signal, and the peak weight vector of the filter weight vector is extracted to obtain the optimal filter weight vector.

5. The pipeline leakage location method based on time delay estimation according to claim 4, characterized in that: The method of using the exponential hyperbolic cosine function as the cost function of the least mean square algorithm includes: The following formula is used as the cost function ξ(n) of the least mean square algorithm: ξ(n)=1-E{γexp{-|cosh[λe(n)]| p }} Where λ is the scaling factor, λ>0, γ is the normalization constant, p represents the shape parameter, p>0, e(n) is the error signal, and E is {γexp{-|cosh[λe(n)]| p }}, n is the processing sequence number of the acoustic signal acquisition data, and cosh[·] is the hyperbolic cosine function.

6. The pipeline leakage location method based on time delay estimation according to claim 4, characterized in that: The updating of the cost function based on the variable scale factor and the variable step size comprises: Constructing an adjustment function of the proportional factor in the cost function, and adjusting a scaling parameter in the adjustment function according to an error signal to obtain the variable proportional factor; Calculating the variable step size based on the absolute sum of errors of the power and instantaneous exponential hyperbolic cosine function of the acoustic signal acquisition data; The cost function is updated in combination with the variable scaling factor and the variable step size.

7. The pipeline leakage location method based on time delay estimation according to any one of claims 1-3, 5, and 6, characterized in that: The target pipeline leakage location model is used to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay Δt: Where argmax[·] represents the parameter corresponding to the maximum value of the function in [·], Sinc(·) represents sinc interpolation, M is the order of the filter, w(i) is the i-th filter weight vector of the filter, and τ is the time interval for collecting data of the acoustic signal collected by the sensor node.

8. A pipeline leakage locating device based on time delay estimation, characterized in that: The device can implement the pipeline leakage location method based on time delay estimation as claimed in any one of claims 1 to 7, and the device includes: A pipeline leakage point location model building module is used to obtain the acoustic signal acquisition data of the leaking pipeline and build a pipeline leakage point location model; A signal processing module, used to perform signal processing on the acoustic signal acquisition data using a least mean square algorithm to obtain an output signal; The pipeline leakage point positioning calculation module is used to obtain a pre-constructed expected signal, calculate an error signal using the expected signal and the output signal, and iteratively update the filter weight vector in the least mean square algorithm according to the error signal to obtain an optimal filter weight vector; optimize the pipeline leakage point positioning model according to the optimal filter weight vector to obtain a target pipeline leakage point positioning model, use the target pipeline leakage positioning model to perform time delay calculation on the acoustic signal acquisition data to obtain a signal time delay, and locate the pipeline leakage point according to the signal time delay.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the pipeline leakage location method based on time delay estimation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the pipeline leakage location method based on time delay estimation as claimed in any one of claims 1 to 7 is implemented.

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