A method, device and system for estimating the direction of water supply network leakage noise

Abstract

The embodiment of the specification discloses a kind of water supply network leakage noise azimuth estimation method, device and system.The method includes the sound signal acquisition of positioning base array acquisition, the element spacing of positioning base array is less than half wavelength.The method includes based on the channel signal of each element in sound signal acquisition, using power spectral density extraction each channel signal in sound signal main frequency band.The method includes based on the main frequency band of all channel signals in sound signal, determine the adaptive main frequency band of sound signal.In addition, the method includes based on sound signal and its adaptive main frequency band, using beam forming algorithm, estimate the azimuth of leakage noise.In this way, the embodiment of the specification can use small aperture array instead of listening leakage rod to collect pipe network sound signal, filter out the frequency component with high signal-to-noise ratio in adaptive main frequency band from sound signal, and then realize high-resolution azimuth estimation by beam forming algorithm.

Description

TECHNICAL FIELD

[0001] One or more embodiments of the present specification relate to the technical field of orientation estimation of water supply pipeline leakage noise, in particular to a method, device and system for orientation estimation of water supply pipeline leakage noise. BACKGROUND

[0002] Urban water supply pipelines are the infrastructure that sustains the normal operation of the city, and their safety and reliability are directly related to the life of residents, industrial production and social public safety. However, with the development of urbanization, factors such as pipeline aging, poor pipe material, construction quality problems and external environmental erosion have led to increasingly serious pipeline leakage problems. Generally, traditional leakage detection methods usually rely on workers holding sound listening rods to detect one by one, that is, workers manually patrol along the pipeline and identify whether there is leakage by listening to the leakage noise. This manual detection method is inefficient and not easy to identify the leakage point, especially in harsh environments, it is difficult to identify, and even if it can be identified, the identification error rate is high. SUMMARY

[0003] One or more embodiments of the present specification describe a method, device and system for orientation estimation of water supply pipeline leakage noise.

[0004] In a first aspect of the present specification, a method for orientation estimation of water supply pipeline leakage noise is provided. The method includes obtaining an acoustic signal collected by a positioning array, the element spacing of the positioning array being less than half the wavelength. The method includes extracting the main frequency band of each channel signal in the acoustic signal based on the channel signal collected by each element in the acoustic signal using the power spectral density. The method includes determining the adaptive main frequency band of the acoustic signal based on the main frequency band of all channel signals in the acoustic signal. In addition, the method includes estimating the orientation of the leakage noise based on the acoustic signal and its adaptive main frequency band using a beamforming algorithm.

[0005] In a second aspect of the present specification, a device for orientation estimation of water supply pipeline leakage noise is provided. The device includes a collection module configured to obtain an acoustic signal collected by a positioning array, the element spacing of the positioning array being less than half the wavelength. The device includes an extraction module configured to extract the main frequency band of each channel signal in the acoustic signal based on the channel signal collected by each element in the acoustic signal using the power spectral density. The device includes a determination module configured to determine the adaptive main frequency band of the acoustic signal based on the main frequency band of all channel signals in the acoustic signal. In addition, the device includes an estimation module configured to estimate the orientation of the leakage noise based on the acoustic signal and its main frequency band using a beamforming algorithm.

[0006] In a third aspect of the present specification, a system for orientation estimation of leakage noise of a water supply network is provided. The system comprises a positioning array configured to collect acoustic signals of the water supply network, the positioning array comprising at least three array elements. In addition, the system comprises a computing device configured to perform the method of the first aspect.

[0007] In a fourth aspect of the present specification, an electronic device is provided. The electronic device comprises a processor and a memory, the processor being connected to the memory. The memory is configured to store executable program code. The processor is configured to execute a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described above.

[0008] In a fifth aspect of the present specification, a computer readable storage medium is provided, the computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the method described above.

[0009] It should be understood that the content described in the summary section is not intended to limit or define key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present specification, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present specification, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0011] Figure 1 A schematic diagram showing an example environment in which a plurality of embodiments of the present specification can be implemented is shown;

[0012] Figure 2 A flow chart of a method for orientation estimation of leakage noise of a water supply network according to some embodiments of the present specification is shown;

[0013] Figure 3 A principle block diagram of a process for orientation estimation of leakage noise of a water supply network according to some embodiments of the present specification is shown;

[0014] Figure 4a A distribution image of power spectral density of acoustic signals under a simulation test of a water supply network at different frequencies according to some embodiments of the present specification is shown;

[0015] Figure 4b A distribution image of cumulative energy of acoustic signals under a simulation test of a water supply network at different frequencies according to some embodiments of the present specification is shown;

[0016] Figure 5A beam pattern of acoustic signals under water supply network simulation test of some embodiments of the present specification is shown;

[0017] Figure 6 An example block diagram of a leakage noise azimuth estimation device of some embodiments of the present specification is shown;

[0018] Figure 7 A structural schematic diagram of an electronic device of some embodiments of the present specification is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present specification will be described clearly and completely below in combination with the drawings in the embodiments of the present specification.

[0020] The terms "first", "second", "third", etc. in the specification and claims of the present specification and the above drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units that are not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0021] As described above, the azimuth estimation of water supply network leakage noise is usually detected by manually holding an instrument such as a leak rod and listening. The pipeline leakage noise is a wideband random signal, and its energy and frequency change irregularly within a certain range. In addition, it is difficult to identify due to complex environmental noise. The accuracy of listening detection is related to the experience of the operator, and the detection result often has a large error rate.

[0022] In some related technologies, the probability of the possible leakage area of the pipe network is estimated by using the Bayesian algorithm, so as to detect and locate the leakage. At the same time, the clustering analysis method is used according to the pressure change of the pipe network to classify it, realize virtual partitioning, and reduce the detection range. This technology mainly predicts the leakage of the entire pipe network, pre-locates the area range where the leakage may occur before the leakage occurs, so as to prevent the leakage from occurring in advance, and cannot locate the azimuth of the point where the leakage has occurred.

[0023] Therefore, this specification proposes a scheme for estimating the azimuth of leakage noise in water supply networks. In this embodiment, automated azimuth estimation is performed using acoustic signals acquired by a positioning array. Power spectral density (PSD) is used to determine the main distribution range of accumulated energy in the acoustic signals acquired from the water supply network, including the main distribution range of accumulated energy in the channel signals acquired by each array element (i.e., the acoustic signals acquired by the sensors where the array elements are located) (i.e., the dominant frequency band referred to herein). Based on the dominant frequency bands of all channel signals, an adaptive dominant frequency band for the acoustic signals is determined. Based on this adaptive dominant frequency band, the complex components of the frequency points where leakage noise occurs are extracted from the acoustic signals, and then the azimuth angle of the leakage noise is estimated relatively accurately using a beamforming algorithm.

[0024] In this way, acoustic signals are collected using positioning arrays placed on the ground or attached to the outer wall of the pipeline, eliminating the reliance on human experience and operational inconvenience of existing azimuth estimation methods. Furthermore, by using the dominant frequency band to filter out frequency components with high signal-to-noise ratios from the acoustic signal, the interference of environmental noise bands on the azimuth estimation is reduced. The dominant frequency bands of the various channels in the acoustic signal are fused to obtain an adaptive dominant frequency band, which helps reduce the impact of noise and interference. This allows for high-resolution azimuth estimation through beamforming algorithms. The automated azimuth estimation method described in this specification is simple to operate and easy to promote. When leakage is detected in the pipeline network, it is easy to effectively estimate the location of the leakage, facilitating intervention by operators and improving leakage handling efficiency.

[0025] Figure 1 Example environment 100 in which several embodiments of this specification may be implemented is shown. For example... Figure 1 As shown, environment 100 includes a water supply network (using water pipe 106 as an example) located below ground 104. In this embodiment, a positioning array 108 is installed on the outer wall of the water pipe 106 (e.g., magnetically attached to the outer wall, or mechanically mounted using fasteners). Alternatively, the positioning array 108 can be placed on the ground for collecting acoustic signals. This positioning array 108 is a small-aperture array composed of several acoustic sensors arranged with an element spacing less than half a wavelength, i.e., less than 0.6 m. In some implementations, the element spacing of this positioning array is approximately no more than 0.2 m. In one example, three acoustic sensors are arranged in a triangular array (as shown in the figure). Assuming a sound velocity of 1200 m / s and a leakage noise center frequency of 1000 Hz, the element spacing of the positioning array is approximately 0.1~0.2 m. Figure 1As shown, the environment 100 also includes a computing device 102, including desktop computers, laptops, tablets, servers, and other devices with computing capabilities. The positioning array 108 sends the detected and collected acoustic signals to the computing device 102, and the computing device 102 estimates the location 116 of the leakage noise based on the acoustic signals 110.

[0026] like Figure 1 As shown, the computing device 102 determines the dominant frequency band of the leakage noise energy distribution from the acoustic signal 110, including dominant frequency bands 112-1, 112-2, ..., 112-N determined based on the channel signals acquired by each element in the positioning array 108. In some examples, the power spectral density of the accumulated energy is calculated based on the power spectral density of the channel signal, and the main distribution range of the accumulated energy, i.e., the main frequency range, is determined based on the power spectral density of the accumulated energy, and this frequency range is determined as the dominant frequency band of the channel signal. Then, multi-channel fusion is performed on dominant frequency band 1, dominant frequency band 2, ..., dominant frequency band N to obtain an adaptive dominant frequency band 114. Signal data of each frequency within the adaptive dominant frequency band is extracted from the acoustic signal 110, and the azimuth 116 of the leakage noise is estimated using a beamforming algorithm, i.e., the azimuth angle corresponding to the peak value is determined as the azimuth angle of the leakage noise.

[0027] It should be understood that the architecture and functionality in example environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure. Embodiments of this disclosure can also be applied to other environments with different structures and / or functionalities.

[0028] Figure 2 A flowchart of a method 200 for estimating the location of leakage noise in a water supply network, according to some embodiments of this specification, is shown. This method 200 can, for example, be derived from... Figure 1 The computing device 102 in the middle performs the operation. For example... Figure 2 As shown in block 202, method 200 can acquire acoustic signals collected by a positioning array, the element spacing of which is less than half a wavelength. In one example, noise at a water supply network is acquired in real time using a small-aperture triangular array. This array is easy to install on the outer wall of the pipe or to be carried and placed on the ground.

[0029] In block 204, method 200 can extract the dominant frequency band of each channel signal in the acoustic signal based on the channel signal acquired by each array element, using power spectral density. In some implementations, the power spectral density of the channel signal is determined based on the channel signal, thus allowing the distribution of random noise frequency energy to be measured. Next, the power spectral density is normalized, and the cumulative energy distribution is calculated, i.e., the cumulative power spectral density is determined. Then, based on the cumulative power spectral density and preset upper and lower energy limits, the dominant frequency band of the channel signal is determined. In this way, the dominant frequency bands of multiple channel signals can be obtained.

[0030] In block 206, method 200 can determine an adaptive dominant frequency band for the acoustic signal based on the dominant frequency bands of all channel signals in the acoustic signal. In some implementations, the minimum lower limit value among the dominant frequency bands of all channel signals is determined as the lower limit value of the adaptive dominant frequency band, and the maximum upper limit value among the dominant frequency bands of all channel signals is determined as the upper limit value of the adaptive dominant frequency band. The adaptive dominant frequency band of the acoustic signal is determined based on the lower limit value and the upper limit value of the adaptive dominant frequency band.

[0031] In block 208, method 200 can estimate the azimuth of leakage noise based on the acoustic signal and its adaptive dominant frequency band using a beamforming algorithm. In some implementations, target signal data located within the adaptive dominant frequency band is extracted from the acoustic signal. Based on this target signal data, a spatial covariance matrix is ​​constructed. Based on the spatial covariance matrix, a noise subspace is extracted, and a spatial spectrum function is constructed based on the noise subspace and the angular scanning range. Based on the spatial spectrum function, the angle corresponding to the peak point is estimated as the azimuth angle of the leakage noise.

[0032] Using the above method, the frequency range of leakage noise can be captured by power spectral density, and the range can reflect the stability of leakage noise in the time and frequency domain. It can more accurately reflect the characteristics of pipeline leakage noise. Furthermore, based on the frequency range of the main energy distribution, which incorporates the characteristics of multi-channel signals, the frequency components with high signal-to-noise ratio can be screened from the acoustic signal, and then high-resolution azimuth estimation can be achieved through beamforming algorithms.

[0033] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0034] Figure 3 A block diagram illustrating the principle of the location estimation process for water supply network leakage noise according to some embodiments of this specification is shown. Figure 3 As shown, the acoustic signal in box 302 is acquired by a positioning array. In one example, it is assumed that the positioning array is a triangular array, and its spatial coordinate configuration is as follows:

[0035] Acoustic sensor 1: Acoustic sensor 2: Acoustic sensor 3: ,in For the spacing between array elements, = 0.1m. The triangular array is an equilateral triangle array, which has good azimuth resolution capability.

[0036] The array position matrix is represented as:

[0037] The acoustic signal sampling frequency of the positioning base array is The signal of each sensor channel is normalized:

[0038]

[0039] wherein, is the original signal of the mth sensor, i.e., the acoustic signal of block 301, is the normalized signal, i.e., the acoustic signal obtained in block 302.

[0040] The acoustic signal in block 302 is subjected to Discrete Fourier Transform (DFT) to obtain .

[0041] The acoustic signal is subjected to Discrete Fourier Transform to obtain:

[0042] In block 304, the power spectral density of the acoustic signal is obtained based on

[0043] . In some embodiments, the power spectral density is calculated using a periodogram method:

[0044]

[0045] wherein, represents the power of the signal at the kth frequency point, represents the absolute value of the complex amplitude of the signal at the kth frequency point, and N represents the total sampling point number of the signal, represents the real part of , and represents the imaginary part of .

[0046] In some embodiments, the power spectral density is estimated using a Welch method:

[0047]

[0048] ​​​​Wherein, the Hamming window function window = hamming (256); the overlap rate overlap = 50%; the FFT point number nfft = 1024; For the acoustic signal, the acoustic signal of the mth sensor. The core idea of the Welch method is to divide the long signal into several shorter equal length segments (usually overlapping), calculate the periodogram for each segment, and then average the periodograms to obtain the estimate of the power spectrum. This method can effectively reduce the estimation variance caused by the randomness of the signal and noise.

[0049] Then, the power spectrum density obtained by the block 304 is normalized:

[0050]

[0051] Wherein, the sum of the power spectrum densities of all frequency points is represented by M, and M is the total number of frequency points, The normalized power spectrum density is represented by generally between 0 and 1.

[0052] In block 306, the cumulative distribution of energy is calculated according to the normalized power spectrum density, and the following is obtained:

[0053]

[0054] Wherein, the cumulative power spectrum density is calculated based on the normalized power spectrum density, and the normalized cumulative energy distribution (I.e. the cumulative power spectrum density) is monotonically increasing, and the sum of the cumulative normalized power spectrum density is equal to 1.

[0055] Then, based on the cumulative power spectrum density obtained by the block 306, the main frequency band 308-1 is determined. From the signal frequency, the frequency range of the leakage noise shows certain regularity under different conditions. Generally, the frequency is high when the pipe diameter is small, the flow rate is fast, the water pressure is large, and the distance from the leak is close. The frequency range of the leakage noise is usually greater than 1200Hz; the frequency is low when the pipe diameter is large, the flow rate is slow, the water pressure is small, and the distance from the leak is very far. The frequency range of the leakage noise is usually less than 600Hz. According to the prior information of the leakage noise, the frequency range of most pipeline leakage noises is 0-4000Hz. Based on the time-frequency characteristics of the leakage noise, the energy threshold range Such as [0.05, 0.95], [0.1, 0.9]. Then the main frequency band Is defined as:

[0056]

[0057]

[0058] ​​The upper and lower limits of the main frequency band can be determined from the cumulative power spectral density according to the upper and lower limits of the energy threshold range. Based on the foregoing process, the sound signals collected by each sensor are processed, and the main frequency band 308-1 is extracted from the respective collected sound signals.

[0059] The main frequency bands 308-1 extracted from the three sensor channels are fused to obtain an adaptive frequency band 308-2:

[0060]

[0061] The frequency scanning range is: with a step size of 10 Hz, is the lower limit of the main frequency band of the acoustic sensor 1, 2, and 3, respectively, is the upper limit of the main frequency band of the acoustic sensor 1, 2, and 3, respectively.

[0062] Next, the sound signal x m (n) in block 302 is subjected to a Fast Fourier Transform (FFT) to obtain X(k), i.e., the sound signal is converted from a time-domain signal to a frequency-domain signal using the Fourier transform. Based on the adaptive main frequency band determined in block 308-2, the complex components of each frequency point located within the adaptive main frequency band are extracted from the sound signal converted to the frequency domain to form a target signal data, i.e., a frequency-domain data matrix 312. For example, each frequency point is taken from the upper limit and the lower limit of the adaptive main frequency band in a frequency interval manner, including , , , , The complex components of the above-mentioned frequency points are used to form the frequency-domain data matrix.

[0063] To improve the estimation accuracy, N sampling points are divided into K=10 segments, each with a length of L=floor(N / K). For a frequency , the frequency-domain signal of the kth segment is:

[0064] where is the frequency index, is the sampling frequency.

[0065] The spatial covariance matrix of each frequency is calculated using the multi-ping technology (see block 314):

[0066]

[0067] where K is the total number of segments and k is the kth segment.​

[0068] To ensure numerical stability, diagonal loading is performed:

[0069] Diagonal loading is to add a small positive number on the main diagonal of the covariance matrix to prevent the matrix from being ill-conditioned. When the covariance matrix is close to singular, the smallest eigenvalue can be close to zero, leading to numerical instability when inverting the matrix. Wherein is the regularization parameter, and I is the identity matrix.

[0070] Next, the spatial covariance matrix of block 314 is eigen-decomposed to obtain the noise subspace 316:

[0071]

[0072]

[0073] The eigenvalues are arranged in descending order:

[0074] Assuming the number of signal sources is 1, the noise subspace is:

[0075] Wherein, is the diagonal matrix of eigenvalues; is the diagonalization operator of linear algebra; the eigenvectors are arranged in descending order of eigenvalues, and divided into signal subspace and noise subspace, and the second and third eigenvectors are taken as the noise subspace.

[0076] Based on the angle scanning range of block 318, the steering vector 320 of each direction is calculated. The time delay of the mth array element at the azimuth angle θ is:

[0077]

[0078] Wherein is the coordinate of the mth array element, such as the coordinate of the acoustic sensor 1 is , c is the speed of sound, for example, c = 1200 m / s.

[0079] The steering vector is defined as: In the formula, is the signal frequency in , θ is the azimuth angle, is the time delay of the signal arriving at the mth sensor relative to the reference sensor, and j is the imaginary unit.

[0080] The spatial spectrum function 322 is constructed by the orthogonality of the steering vector and the noise subspace:

[0081]

[0082] Calculate the spatial spectral power at each frequency point based on the spatial spectral function. .

[0083] Next, the spatial spectral power at all frequency points is normalized and accumulated to obtain the average cumulative spatial spectral density. (See box 324):

[0084]

[0085] in, freq_bins is extracted based on the adaptive main frequency band. For frequency intervals, such as 10Hz, This represents the number of frequency points.

[0086] After that, Perform a scan with an angular resolution of 1° and estimate the azimuth angle: The maximum value (i.e., peak value) located was determined as the azimuth of the leakage noise, 326. This frequency domain processing method fully utilizes the frequency characteristics of the signal, achieving high-resolution azimuth estimation.

[0087] Figure 4a and Figure 4b Images from water supply network simulation tests of some embodiments of this specification are shown to illustrate the process of determining the main frequency band of the channel signal in the acoustic signal of the water supply network in the embodiments of this specification. Figure 4a The diagram illustrates the power spectral density distribution of channel signals at different frequencies under water supply network simulation tests according to some embodiments of this specification. Distribution image 400A shows the power spectral density distribution in the range of 0~4000Hz. According to the method of the embodiments of this specification, the main distribution range of accumulated energy is determined based on the power spectral density, thereby determining the dominant frequency band of the channel signal. Figure 4b The diagram illustrates the distribution of accumulated energy of channel signals at different frequencies under water supply network simulation tests of some embodiments of this specification. Distribution image 400B shows the accumulated energy in the range of 0~4000Hz. Based on the set energy threshold range [L,H], such as [0.1,0.9], the range [520,840]Hz between the two dashed lines in the diagram can be determined as the main frequency band of the channel signal.

[0088] The method described in this specification utilizes power spectral density to determine the distribution range of the main energy of leakage noise from each channel of the acoustic signal, i.e., the dominant frequency band of the channel signal. Multi-channel fusion is performed on the dominant frequency bands extracted from multiple sensor channels to obtain an adaptive dominant frequency band. This facilitates beamforming algorithms in estimating the complex components at each frequency point within the dominant frequency band range, enabling high-resolution estimation of the location of leakage noise.

[0089] Figure 5 A beam pattern of the acoustic signal under the water supply network simulation test of some embodiments of the present specification is shown. The beam pattern 500 shows the distribution of the average cumulative spatial spectral density at each angle within a 360-degree range, and the average cumulative spatial spectral density reaches a peak at about 60 degrees, and the angle corresponding to the peak is determined as the azimuth angle of the leakage noise. That is, the leakage noise is located at the 60-degree azimuth of the detection angle range of the positioning array. Knowing the azimuth of the leakage noise, the azimuth intersects with the pipeline burial path, so that the leakage position can be located, and the pipeline repair or replacement pipeline treatment can be carried out.

[0090] Figure 6 An example block diagram of the azimuth estimation device 600 of the leakage noise of the water supply network of some embodiments of the present specification is shown. As shown, the device 600 includes an acquisition module 602 configured to acquire an acoustic signal collected by a positioning array, and the element spacing of the positioning array is less than half the wavelength. The device 600 further includes an extraction module 604 configured to extract a main frequency band of each channel signal in the acoustic signal based on the channel signal collected by each element in the acoustic signal using the power spectral density. The device further includes a determination module 606 configured to determine an adaptive main frequency band of the acoustic signal based on the main frequency band of all channel signals in the acoustic signal. In addition, the device 600 further includes an estimation module 608 configured to estimate the azimuth of the leakage noise based on the acoustic signal and its adaptive main frequency band using a beamforming algorithm. Figure 6

[0091] In some embodiments, the extraction module 604 includes a first determination unit configured to determine the power spectral density of the channel signal based on the channel signal. The extraction module 604 includes a second determination unit configured to determine the cumulative power spectral density based on the normalized power spectral density. In addition, the extraction module 604 further includes a third determination unit configured to determine the main frequency band of the channel signal based on the cumulative power spectral density and the preset upper and lower energy limit values.

[0092] In some embodiments, the determination module 606 includes a fourth determination unit configured to determine the minimum lower limit value of the main frequency band of all channel signals as the lower limit value of the adaptive main frequency band, and determine the maximum upper limit value of the main frequency band of all channel signals as the upper limit value of the adaptive main frequency band. The determination module 606 further includes a fifth determination unit configured to determine the adaptive main frequency band based on the lower limit value of the adaptive main frequency band and the upper limit value of the adaptive main frequency band.

[0093] ​In some implementations, the estimation module 608 includes a first extraction unit configured to extract target signal data located within an adaptive dominant frequency band from the acoustic signal. In one example, the first extraction unit uses a Fourier transform to convert the acoustic signal from a time-domain signal to a frequency-domain signal. Based on the adaptive dominant frequency band, it extracts complex components at each frequency point within the frequency-domain acoustic signal to constitute the target signal data. The estimation module 608 includes a first construction unit configured to construct a spatial covariance matrix based on the target signal data. In one example, the spatial covariance matrix is ​​constructed using a multi-shot technique based on the target signal data. The estimation module 608 includes a second extraction unit configured to extract a noise subspace based on the spatial covariance matrix. The estimation module 608 includes a second construction unit configured to construct a spatial spectrum function based on the noise subspace and the angular scanning range. Furthermore, the estimation module 608 also includes an estimation unit configured to estimate the angle corresponding to the peak point as the azimuth angle of the leakage noise based on the spatial spectrum function. In one example, the spatial spectral power at each frequency point is calculated based on the spatial spectral function. The average cumulative spatial spectral density is determined based on the normalized spatial spectral power. Based on the average cumulative spatial spectral density, peak points are identified, and the angles corresponding to these peak points are estimated as the azimuth angles of the leakage noise.

[0094] In this way, the apparatus of the embodiments of this specification can preprocess the acoustic signal using power spectral density and filter out the complex components of each frequency point in the adaptive main frequency band. In this way, the covariance matrix can estimate the azimuth of leakage noise based on the frequency components with high signal-to-noise ratio. The peak finally found by the beamforming algorithm is obtained by normalizing, accumulating and averaging the spatial spectral power of all frequency points in the adaptive main frequency band, thus realizing high-resolution azimuth estimation.

[0095] Figure 7 A block diagram of an electronic device 700 that can implement various embodiments of the present disclosure is shown. (See diagram for reference.) Figure 7 As shown, device 700 includes a processor 701, which can perform various appropriate actions and processes based on computer program instructions loaded into random access memory (RAM) 703 according to computer program instructions stored in read-only memory (ROM) 702. RAM 703 may also store various programs and data required for the operation of device 700. The processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0096] The various processes and processes described above, such as the method 200, can be performed by the processor 701. For example, in some embodiments, the method 200 can be implemented as a software program tangibly embodied in a machine-readable medium. In some embodiments, some or all of the software program can be loaded and / or installed onto the device 700 via the ROM 702. When the software program is loaded onto the RAM 703 and executed by the processor 701, one or more acts of the method 200 described above can be performed.

[0097] The embodiments of the present specification also provide a computer-readable storage medium, which stores instructions, when the instructions are executed on a computer or a processor, cause the computer or the processor to perform one or more steps of the above-mentioned method embodiments. The constituent modules of the above-mentioned electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in the computer-readable storage medium.

[0098] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted by the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. integrated with one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital versatile disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0099] The above-described embodiments are merely exemplary diagnostic description rather than limitation on the scope of the present specification, and various modifications and improvements made by those of ordinary skill in the art to the technical solutions of the present specification without departing from the design spirit of the present specification shall fall within the protection scope of the claims of the present specification.

Claims

1. A method for estimating the location of leakage noise in a water supply network, characterized in that, include: Acquire acoustic signals collected by a positioning array, wherein the element spacing of the positioning array is less than half a wavelength; Based on the channel signals collected by each array element in the acoustic signal, the main frequency band of each channel signal in the acoustic signal is extracted using the power spectral density; Determining the adaptive main frequency band of the acoustic signal based on the main frequency bands of all channel signals includes: determining the minimum lower limit value of the main frequency bands of all channel signals as the lower limit value of the adaptive main frequency band, and determining the maximum upper limit value of the main frequency bands of all channel signals as the upper limit value of the adaptive main frequency band; and determining the adaptive main frequency band based on the lower limit value and the upper limit value of the adaptive main frequency band; and Based on the acoustic signal and its adaptive main frequency band, the location of leakage noise is estimated using a beamforming algorithm.

2. The method according to claim 1, characterized in that, Based on the channel signals acquired by each array element in the acoustic signal, the main frequency band of each channel signal in the acoustic signal is extracted using power spectral density, including: Based on each channel signal of the acoustic signal, the power spectral density of the channel signal is determined; Based on the normalized power spectral density, the cumulative power spectral density is determined; and The main frequency band of the channel signal is determined based on the cumulative power spectral density and the preset upper and lower energy limits.

3. The method according to claim 1, characterized in that, Based on the acoustic signal and its adaptive dominant frequency band, the location of the leakage noise is estimated using a beamforming algorithm, including: Based on the adaptive main frequency band, target signal data located within the adaptive main frequency band is extracted from the acoustic signal; Based on the target signal data, construct a spatial covariance matrix; Based on the spatial covariance matrix, the noise subspace is extracted; Based on the noise subspace and angular scanning range, a spatial spectrum function is constructed; and Based on the spatial spectrum function, the angle corresponding to the peak point is estimated as the azimuth angle of the leakage noise.

4. The method according to claim 3, characterized in that, Extracting target signal data located within the adaptive main frequency band from the acoustic signal based on the adaptive main frequency band includes: The acoustic signal is converted from a time-domain signal to a frequency-domain signal using Fourier transform; and Based on the adaptive main frequency band, complex components at each frequency point within the adaptive main frequency band are extracted from the acoustic signal converted into a frequency domain signal to form the target signal data.

5. The method according to claim 3, characterized in that, Based on the aforementioned spatial spectrum function, estimating the angle corresponding to the peak point as the location of the leakage noise includes: Based on the aforementioned spatial spectrum function, the spatial spectral power at each frequency point is calculated; Based on the normalized spatial spectral power, the average cumulative spatial spectral density is determined; and Based on the average cumulative spatial spectral density, the peak point is determined and the angle corresponding to the peak point is estimated as the azimuth angle of the leakage noise.

6. A device for estimating the location of leakage noise in a water supply network, characterized in that, include: The acquisition module is configured to acquire acoustic signals collected by the positioning array, wherein the element spacing of the positioning array is less than half a wavelength. The extraction module is configured to extract the main frequency band of each channel signal in the acoustic signal based on the channel signal collected by each array element in the acoustic signal using power spectral density; The determining module is configured to determine an adaptive main frequency band of the acoustic signal based on the main frequency bands of all channel signals in the acoustic signal, including: determining the minimum lower limit value among the main frequency bands of all channel signals as the lower limit value of the adaptive main frequency band, and determining the maximum upper limit value among the main frequency bands of all channel signals as the upper limit value of the adaptive main frequency band; and determining the adaptive main frequency band based on the lower limit value and the upper limit value of the adaptive main frequency band; and The estimation module is configured to estimate the location of leakage noise based on the acoustic signal and its adaptive main frequency band using a beamforming algorithm.

7. The apparatus according to claim 6, characterized in that, The extraction module includes: The first determining unit is configured to determine the power spectral density of the channel signal based on each channel signal of the acoustic signal; The second determining unit is configured to determine the cumulative power spectral density based on the normalized power spectral density; and The third determining unit is configured to determine the main frequency band of the channel signal based on the cumulative power spectral density and preset upper and lower energy limits.

8. The apparatus according to claim 6, characterized in that, The estimation module includes: The first extraction unit is configured to extract target signal data located within the adaptive main frequency band from the acoustic signal based on the adaptive main frequency band. The first construction unit is configured to construct a spatial covariance matrix based on the target signal data; The second extraction unit is configured to extract the noise subspace based on the spatial covariance matrix; The second construction unit is configured to construct a spatial spectrum function based on the noise subspace and the angular scan range; and The estimation unit is configured to estimate the angle corresponding to the peak point as the azimuth angle of the leakage noise based on the spatial spectrum function.

9. A system for estimating the location of leakage noise in a water supply network, characterized in that, include: A positioning array is configured to collect acoustic signals from a water supply network, and the positioning array consists of at least three array elements. as well as A computing device configured to perform the method according to any one of claims 1 to 5.

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