A water supply network leakage precise positioning method and device fusing wave number spectrum noise reduction and phase analysis

Abstract

The application discloses a kind of fusion wave number spectrum noise reduction and phase analysis for water supply network leak precise positioning method, comprising: first sensing array and second sensing array are arranged along the length direction of pipeline interval, to collect the vibration data of water supply network;Extract the feature in wave number-frequency spectrum, to obtain the phase information corresponding to vibration data;According to the installation data of phase information and first sensing array and second sensing array and pipeline, leak location is positioned.The application also provides a kind of water supply network leak precise positioning device.The method provided by the application can provide an efficient solution for the engineering promotion of reducing pipe network leakage rate.

Description

Technical Field

[0001] This invention belongs to the field of municipal water supply technology, and in particular relates to a method and device for accurate location of leaks in water supply networks that integrates wavenumber spectrum noise reduction and phase analysis. Background Technology

[0002] Leaks in water supply networks not only waste precious water resources but can also trigger a chain of disasters such as drinking water pollution and ground subsidence, directly threatening urban public safety and the ecological environment. Traditional acoustic leak detection technology faces three major technical bottlenecks: strong environmental noise interference, difficulty in accurately estimating leakage wave velocity, and severe attenuation of high-frequency signals. This often results in location errors reaching several meters, making it difficult to meet the precision requirements of modern pipeline maintenance.

[0003] Current mainstream positioning technologies employ a combination of sensor arrays at both ends of the pipeline and correlation analysis. However, their core challenge lies in the fact that the propagation speed of underground pipeline leakage noise is affected by multiple factors, including pipe material, pipe diameter, and burial environment, making real-time and accurate measurement difficult. Furthermore, the wave velocity of leakage noise is controlled by the dominant mode, which cannot be determined a priori in complex piping systems. In current engineering practice, correlation analysis is forced to rely on empirical wave velocities of fluid wave or shell wave modes for model estimation. This simplistic "one-size-fits-all" approach is a fundamental flaw limiting positioning accuracy. Even more critically, leakage noise typically propagates less than 50 meters in non-metallic pipes (such as PVC, PE, and concrete pipes), resulting in extremely rapid signal attenuation. This renders traditional dual-sensor correlation analysis methods completely ineffective in the context of scrap metal pipes, creating a significant technical blind spot.

[0004] Excessive deviation in the location of leakage will lead to a significant increase in total costs, including repeated excavation, road restoration, and traffic diversion.

[0005] Patent document CN120724633A discloses an online leakage location method for pipeline networks based on data fusion and intelligent optimization algorithms. The method determines whether leakage has occurred in the raw water pipeline network through water balance analysis; Kalman filtering is performed on the collected flow data to remove noise; a hydraulic model of the raw water pipeline network is constructed, and the leakage location is solved using an improved gray wolf optimization algorithm; two leakage location schemes are run simultaneously, and the validity of the location results is determined by comparing the consistency of the location results.

[0006] Patent document CN121322862A discloses a water supply network leakage location system and method based on acoustic pressure coordination. The system includes a pressure sensing layer, a collaborative control unit, an acoustic sensing layer, a hydroacoustic collaborative location solver, and a result output module. The pressure sensing layer monitors the network pressure in real time through sparsely deployed pressure sensors to identify abnormal areas. The collaborative control unit dynamically activates the minimum set of acoustic sensors. The hydroacoustic collaborative location solver uses a hydraulic model to correct acoustic parameters and accurately locates leaks in areas of abnormal pressure through acoustic time difference inversion. The result output module pushes the location results, leakage amount, and maintenance plan to the operation and maintenance platform. The system achieves leakage through pressure warning, precise acoustic measurement, and joint inversion. Summary of the Invention

[0007] The purpose of this invention is to provide a method and device for accurate location of leaks in water supply networks that integrates wavenumber spectrum noise reduction and phase analysis. This method can overcome the technical bottleneck of not being able to detect or accurately locate leaks in buried pipelines, and provides an efficient solution that can be engineered and promoted to reduce the leakage rate of pipeline networks.

[0008] To achieve the first objective of this invention, the following technical solution is provided: a method for accurately locating leaks in water supply networks by integrating wavenumber spectrum denoising and phase analysis, comprising the following steps: The first and second sensor arrays are arranged at intervals along the length of the pipeline to collect vibration data of the water supply network. Two-dimensional Fourier transform and wavenumber spectrum filtering are performed on the input vibration data to reduce noise and obtain the corresponding wavenumber-frequency spectrum. Features are extracted from the wavenumber-frequency spectrum to obtain the phase information corresponding to the vibration data; The location of the leak is determined based on the phase information, the installation data of the first and second sensor arrays and the pipeline.

[0009] This invention is based on wavenumber-frequency domain mode separation technology using array sampling, which transforms the problem of identifying leakage sound signal features under strong background noise into the problem of identifying sparse spatial wavenumber spectrum, thus greatly improving noise resistance.

[0010] Specifically, the installation data for the first and second sensor arrays includes phase information and spatial distance between the sensor arrays.

[0011] Specifically, the pipeline installation data includes the pipeline cover height.

[0012] Specifically, both the first and second sensor arrays are based on the Nyquist sampling theorem, and the sampling frequencies in the time and spatial dimensions are set to be more than twice the maximum frequency of the sampled signal.

[0013] Specifically, the sampling duration in the time dimension needs to be greater than 5 seconds, and the sampling interval in the spatial dimension usually needs to be less than 0.5 meters.

[0014] Specifically, the expression for the two-dimensional Fourier transform is as follows: ; in, yes Fourier transforms in the x and t directions, with x pointing towards the pipe axis, The axial wave number of the pipeline is represented by i, where i represents the imaginary unit and f represents the frequency.

[0015] Specifically, the wavenumber spectrum filtering noise reduction is achieved through a pre-constructed tilt filter, which is designed based on preset upper and lower limits of wave velocity, frequency, and wavenumber.

[0016] Specifically, the tilt filter determines the center parameters based on the soil type and pipe type in the water supply network scenario, the empirical wave velocity range of the leakage sound signal in the 20–2000 Hz range along the surface axis, and the theoretical wavenumber-frequency slope range corresponding to each mode.

[0017] Specifically, the process for locating the leakage point is as follows: When the sensing array is located in the far field of the leak point, the phase difference between the samples taken by the sensing array on both sides of the leak point satisfies the following equations: ; The upper and lower limits of n(f) are determined by the sensor spacing L and the axial wave velocity c: ; Where λ is the calculated frequency f m1 The corresponding wavelength; Construct a set of values ​​for n(f) based on its range, and then calculate the value of n(fm1) based on this set. l The solution set W(f) of 1 m1 ); According to different frequencies l The repeated elements of the solution set W(fm) of 1 can be selected and the distance can be verified multiple times. l 1; When the sensing array is located near the leak point, the axial wave velocity... c x,t The wavenumber spectrum of the sensor array under near-field sampling conditions can be obtained by performing Fourier transforms in the x and t directions. Based on the wavenumber spectrum of the sensor array, the phase difference between the samples taken by the sensor array on both sides of the leak point satisfies: ; The quadratic function has two roots, both of which satisfy l1∈[0,L]. Due to symmetry, the distance between the two sensing arrays and the leak point is initially estimated based on the phase difference of the solutions of the two roots at different frequencies, and the solutions of the two roots and the two distances are assigned.

[0018] To achieve the second objective of this invention, the following technical solution is provided: a water supply network leak precise location device, used to perform the steps of the above-mentioned water supply network leak precise location method by fusing wavenumber spectrum noise reduction and phase analysis.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: By fusing wavenumber spectrum denoising and phase analysis with a surface sensor array, high-confidence coordinate inversion of the leakage source is achieved, providing core technical support for subsequent "one-time excavation and precise repair". Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the water supply network leakage accurate location method that combines wavenumber spectrum denoising and phase analysis provided in this embodiment; Figure 2 This is a schematic diagram of the tilt filter provided in this embodiment; Figure 3 This is a reference diagram for leakage location calculation provided in this embodiment; Figure 4 This is a schematic diagram of the water supply network leak precise location device provided in this embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] like Figure 1 The image shows the precise location method for water supply network leaks by fusing wavenumber spectrum denoising and phase analysis provided in this embodiment. The specific steps are as follows: Sampling method: Two sets of vibration sensor arrays are arranged at equal intervals on the ground surface along the axial direction above the water supply pipe on both sides of the suspected leak point.

[0023] Sampling strategy: Consider sampling strategies under different soil conditions (sand, clay, etc.) and pipeline conditions (PE pipe, steel pipe, cast iron pipe, etc.); Based on the Nyquist sampling theorem, the temporal and spatial sampling frequencies of each sensing array should be higher than twice the maximum frequency of the sampled signal, and the array aperture should be controlled to ensure that the calculated wavenumber-frequency spectrum has sufficient resolution.

[0024] Spatiotemporal signal processing: Two-dimensional Fourier transforms of the vibration signals synchronously acquired by an array of equally spaced vibration sensors are performed in both the time domain (sampling time > 5 seconds) and the spatial domain (sampling interval < 0.5 meters) to convert the time-domain signal into the corresponding wavenumber-frequency spectrum, the expression of which is as follows: ; in, yes Fourier transforms in the x and t directions, with x pointing towards the pipe axis, The axial wave number of the pipeline is represented by i, where i represents the imaginary unit and f represents the frequency.

[0025] Wavenumber spectrum filtering and noise reduction: For the matrix after two-dimensional Fourier transform, tilt filters can be constructed according to the upper and lower limits of wave velocity, frequency and wavenumber of the signal to be extracted, so as to achieve specific mode extraction.

[0026] Location process: Based on the noise-reduced leakage vibration signal, its phase information is extracted, and the precise coordinates of the leak point are calculated based on the phase information between the two sets of sensor arrays, the spatial distance, and the soil cover height of the pipeline.

[0027] More specifically, because the leakage signal propagates in different modes within the pipe and soil medium, the sensor sampling results are a mixture of various modes and a mixed signal containing a large amount of background noise.

[0028] When a signal propagates in a specific mode, it exhibits a strong coupling relationship between wavenumber and frequency in the wavenumber-frequency spectrum. Based on this characteristic, a two-dimensional Fourier transform is used to convert the sensor array signal to the wavenumber-frequency domain. Then, a tilt filter is applied to the sensor array based on the wavenumber-frequency domain characteristics to extract the specific mode signal. A single-mode signal has a single propagation speed under non-dispersive conditions, and the leakage point can be located using phase analysis methods.

[0029] Input the soil type (sand, clay, etc.) and pipe type (PE, steel, cast iron, etc.) at the site, and look up the table to output the empirical wave velocity range of the leakage sound signal along the ground axis within the range of 20–2000 Hz. At the same time, the theoretical wavenumber-frequency slope range corresponding to each mode is given, which is used as the center parameter of the subsequent tilt filter to generate the a priori wave velocity table.

[0030] According to the Nyquist criterion, the sampling frequency should be ≥ 4 kHz; the maximum observation wavenumber and maximum observation frequency for spatial sampling must meet the following requirements: In the formula, k max For the maximum observed wavenumber, f max denoted as the maximum observation frequency, c as the axial propagation speed of the leaking sound wave on the ground surface, and d as the sensor sampling interval.

[0031] Perform a two-dimensional discrete Fourier transform on the M×N dimensional array signal (M is the number of sensors, N is the number of sampling points) to obtain the wavenumber-frequency; In the formula, express The (th) of the matrix p,q ) elements, Represents the imaginary unit. P and Q These represent the number of discrete Fourier transforms in space and time, respectively, in this embodiment. .

[0032] For the matrix after two-dimensional Fourier transform Tilt filters can be constructed based on the upper and lower limits of the wave velocity of the signal to be extracted, such as... Figure 2 As shown, its Figure 2 In v 1 and v 2 represent two different wave speeds.

[0033] The signal extracted by the tilt filter will have a specific wave velocity component, containing only the selected specific mode and a small amount of background noise.

[0034] .

[0035] The signal extracted by the tilt filter will have a specific wave velocity component, containing only the selected specific modes (after noise reduction processing). Furthermore, because the upper and lower limits of the signal's wave velocity are determined during the construction of the tilt filter, the modal wave velocities explicitly displayed on the wavenumber-frequency spectrum are... c This will become a priori known condition.

[0036] The leakage signals after noise reduction from the sensors at both ends of the sensor array are extracted for location calculation, such as... Figure 3 As shown, in Figure 3 middle, L The distance between the two sensors. l 1 and l2 represents the axial upward spacing between the sensor and the leak point. l 1 'and l 2' represents the straight-line distance between the leak point and the sensor array. c 0 represents the free field wavenumber of the medium. h For the depth of the pipeline, k 0、 k , k r These are the free field wavenumber, the axial component of the wavenumber, and the radial component of the wavenumber, respectively.

[0037] (1) When the sensor array is located in the far field of the leak point (axial distance from the leak point > 3m): For sensor array sampling points that are further horizontally away from the leak point, the angle between the direction of leakage signal propagation and the horizontal direction is smaller, and the influence of the burial depth of the leak point can gradually be ignored. Its propagation mode is close to the plane wave propagation form that propagates with the free field wave number of the medium.

[0038] Under far-field conditions, the phase difference between the samples taken by the sensor arrays on both sides of the leak point satisfies the following equations: The upper and lower limits of n(f) are determined by the sensor spacing L and the axial wave velocity c: Where λ is the calculated frequency. f m1 The corresponding wavelength. Based on the range of values ​​for the variable n(f), its value set can be constructed, based on n( f m1 The set of values ​​for ) can be used to calculate l The solution set W ( ) f m1 Furthermore, according to different frequencies l The solution set W ( ) f m Duplicate elements can be selected and multiple distance validations can be performed. l 1.

[0039] (2) When the sensor array is located near the leak point (axial distance from the leak point ≤ 3m): For near-field sampling sensor arrays, the angle between the sensor array arrangement direction and the leakage signal propagation direction is a function related to the burial depth of the leakage point and the sampling position of the sensor array, resulting in wave velocity divergence. Regarding the axial wave velocity... c x,t Taking Fourier transforms in the x and t directions, we can obtain the expression for the wavenumber spectrum of the sensor array under near-field sampling conditions: ; In the formula, δ(·) is the Dirac function. Due to the nonlinear transformation of the phase, the results in the boxed part of the formula show a significant difference from the Fourier transform results with linear phase transformation.

[0040] Therefore, the leak location model under near-field conditions needs to be modified.

[0041] The phase difference between the samples taken by the sensor arrays on both sides of the leak point satisfies: ; In the formula l 1' and l 2' is the straight-line distance between the leak point and the sensor array, and c0 is the free field wavenumber of the medium. According to the Pythagorean theorem and l 1+ l The relationship 2=L can be simplified to: The right-hand side of the equation, denoted by g(f), can be further simplified: After expanding and rearranging terms to simplify, we get: The quadratic function has two roots, and both roots satisfy... l 1∈[0,L], due to symmetry, the two roots are respectively l 1 and l The solution for 2 requires a preliminary estimate of the distance between the two sensing arrays and the leak point based on the phase difference at different frequencies, and a reasonable allocation of the solution to the two distances.

[0042] This embodiment also provides a device for accurately locating leaks in water supply networks, such as... Figure 4 The steps described above are for performing the method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis.

[0043] exist Figure 4 Sensor array 1 and sensor array 2 are arranged on both sides of the pipeline. By collecting vibration data from the sensor arrays on both sides, the data is processed in a remote system to determine the location of the final leak point.

[0044] Furthermore, the terms "upper," "lower," "inner," "outer," "front," and "rear" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0045] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included in the scope of the claims of the present invention.

[0046] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for precise location of leaks in water supply networks that integrates wavenumber spectrum denoising and phase analysis, characterized in that, Includes the following steps: The first and second sensor arrays are arranged at intervals along the length of the pipeline to collect vibration data of the water supply network. Two-dimensional Fourier transform and wavenumber spectrum filtering are performed on the input vibration data to reduce noise and obtain the corresponding wavenumber-frequency spectrum. Features are extracted from the wavenumber-frequency spectrum to obtain the phase information corresponding to the vibration data; The location of the leak is determined based on the phase information, the installation data of the first and second sensor arrays and the pipeline.

2. The method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis according to claim 1, characterized in that, Both the first and second sensor arrays are based on the Nyquist sampling theorem, which stipulates that the sampling frequencies in the time and space dimensions should be higher than twice the maximum frequency of the sampled signal.

3. The method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis according to claim 2, characterized in that, The sampling duration in the time dimension needs to be greater than 5 seconds, while the sampling interval in the spatial dimension usually needs to be less than 0.5 meters.

4. The method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis according to claim 1, characterized in that, The expression for the two-dimensional Fourier transform is as follows: ； in, yes Fourier transforms in the x and t directions, with x pointing towards the pipe axis, The axial wave number of the pipeline is represented by i, where i represents the imaginary unit and f represents the frequency.

5. The method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis according to claim 1, characterized in that, The wavenumber spectrum filtering noise reduction is achieved through a pre-constructed tilt filter, which is designed based on preset upper and lower limits of wave velocity, frequency, and wavenumber.

6. The method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis according to claim 5, characterized in that, The tilt filter determines the center parameters based on the soil type and pipe type in the water supply network scenario, the empirical wave velocity range of the leakage sound signal in the 20–2000 Hz range along the surface axis, and the theoretical wavenumber-frequency slope range corresponding to each mode.

7. The method for accurate location of leaks in water supply networks by fusing wavenumber spectrum denoising and phase analysis according to claim 1, characterized in that, The process for locating the leakage point is as follows: When the sensing array is located in the far field of the leak point, the phase difference between the samples taken by the sensing array on both sides of the leak point satisfies the following equations: ； The upper and lower limits of n(f) are determined by the sensor spacing L and the axial wave velocity c: ； Where λ is the wavelength corresponding to the calculated frequency fm1; Construct a set of values ​​for n(f) based on its range, and then calculate the value of n(fm1) based on this set. l The solution set W(fm1) of 1; According to different frequencies l The repeated elements of the solution set W(fm) of 1 can be selected and the distance can be verified multiple times. l 1; When the sensing array is located near the leak point, the axial wave velocity... c x,t The wavenumber spectrum of the sensor array under near-field sampling conditions can be obtained by performing Fourier transforms in the x and t directions. Based on the wavenumber spectrum of the sensor array, the phase difference between the samples taken by the sensor array on both sides of the leak point satisfies: ； The quadratic function has two roots, both of which satisfy l1∈[0,L]. Due to symmetry, the distance between the two sensing arrays and the leak point is initially estimated based on the phase difference of the solutions of the two roots at different frequencies, and the solutions of the two roots and the two distances are assigned.

8. A precise leak location device for water supply network, characterized in that, The steps are for performing the method for accurately locating leaks in a water supply network by fusing wavenumber spectrum denoising and phase analysis as described in any one of claims 1 to 7.

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