Discharge sound source positioning method based on frequency correction

Abstract

The invention relates to a discharge sound source positioning method and device based on frequency correction, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of computers. The method comprises the following steps: acquiring discharge sound signals of a to-be-positioned discharge sound source received by a plurality of microphones, and performing unilateral Fourier filtering processing on the discharge sound signals to obtain filtered signals; constructing an observation model containing a frequency error term according to the filtered signal; determining joint probability distribution information based on the observation model and a noise model, a signal model and a frequency error model which are constructed in advance; using an expectation maximization model to carry out iterative calculation to obtain a sound source signal mean value in the joint probability distribution information; determining the signal energy amplitude of each point on the space grid according to the sound source signal mean value obtained by the latest iterative calculation; and determining the discharge sound source position of the discharge sound source to be positioned according to the signal energy amplitude. By adopting the method, the discharge sound source positioning efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular, to a method, device, computer device, computer-readable storage medium, and computer program product for discharging sound source localization based on frequency correction. Background Art

[0002] With the development of the safety operation monitoring technology of power systems and industrial equipment, the acoustic detection method has gradually attracted wide attention due to its non-contact and non-invasive characteristics. In the condition monitoring of various high-voltage electrical equipment, the discharge detection technology has become an important means to evaluate the insulation status of the equipment and warn of potential faults. Therefore, how to efficiently localize the discharging sound source has become an important research direction.

[0003] Traditional technologies usually perform discharging sound source localization by manually selecting feature frequency bands or adjusting detection parameters; however, performing discharging sound source localization in this way requires a lot of manual processing time, resulting in low efficiency of discharging sound source localization. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for discharging sound source localization based on frequency correction that can improve the efficiency of discharging sound source localization.

[0005] In a first aspect, the present application provides a method for discharging sound source localization based on frequency correction. The method includes:

[0006] Obtain the discharging sound signals of the discharging sound source to be localized received by multiple microphones, and perform single-sided Fourier filtering processing on the discharging sound signals to obtain filtered signals;

[0007] According to the filtered signals, construct an observation model including a frequency error term;

[0008] Based on the observation model, as well as the pre-constructed noise model, signal model, and frequency error model, determine the joint probability distribution information;

[0009] Use the expectation maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information;

[0010] According to the mean value of the sound source signal obtained by the latest iterative calculation, determine the signal energy amplitude of each point on the spatial grid;

[0011] According to the signal energy amplitude, determine the discharging sound source position of the discharging sound source to be localized.

[0012] In one embodiment, the performing single-sided Fourier filtering processing on the discharging sound signals to obtain filtered signals includes:

[0013] Construct a signal sampling point data matrix according to the discharge sound signal;

[0014] Perform single-sided Fourier filtering on the real signal in the signal sampling point data matrix to obtain the filtered signal.

[0015] In one embodiment, the constructing an observation model including a frequency error term according to the filtered signal includes:

[0016] Construct a steering matrix according to the filtered signal;

[0017] Fuse the steering matrix and the frequency error term to obtain the observation model.

[0018] In one embodiment, the determining the joint probability distribution information based on the observation model, and a pre-constructed noise model, signal model, and frequency error model includes:

[0019] Construct the noise model, the signal model, and the frequency error model;

[0020] Determine the basic joint probability distribution information according to the observation model, the noise model, the signal model, and the frequency error model;

[0021] Take the logarithm of the basic joint probability distribution information to obtain the joint probability distribution information.

[0022] In one embodiment, the iteratively calculating the mean value of the sound source signal in the joint probability distribution information by using the expectation maximization model includes:

[0023] Iteratively calculate the covariance of the sound source signal in the joint probability distribution information by using the expectation maximization model;

[0024] Iteratively calculate the mean value of the sound source signal based on the covariance of the sound source signal by using the expectation maximization model.

[0025] In one embodiment, the determining the discharge sound source position of the to-be-located discharge sound source according to the signal energy amplitude includes:

[0026] Select target signal energy amplitudes greater than a preset signal energy amplitude from the signal energy amplitudes;

[0027] Take the region corresponding to the target signal energy amplitude as the discharge sound source position.

[0028] In a second aspect, the present application also provides a discharge sound source localization device based on frequency correction. The device includes:

[0029] A signal acquisition module, configured to acquire discharge sound signals of a discharge sound source to be located received by a plurality of microphones, and perform single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals;

[0030] A model construction module, configured to construct an observation model including a frequency error term according to the filtered signals;

[0031] An information determination module, configured to determine joint probability distribution information based on the observation model, and a pre-constructed noise model, signal model, and frequency error model;

[0032] A mean calculation module, configured to iteratively calculate the mean value of the sound source signal in the joint probability distribution information by using an expectation maximization model;

[0033] An amplitude determination module, configured to determine the signal energy amplitude of each point on a spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation;

[0034] A position determination module, configured to determine the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude.

[0035] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Acquire discharge sound signals of a discharge sound source to be located received by a plurality of microphones, and perform single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals;

[0037] Construct an observation model including a frequency error term according to the filtered signals;

[0038] Determine joint probability distribution information based on the observation model, and a pre-constructed noise model, signal model, and frequency error model;

[0039] Iteratively calculate the mean value of the sound source signal in the joint probability distribution information by using an expectation maximization model;

[0040] Determine the signal energy amplitude of each point on a spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation;

[0041] Determine the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain the discharge sound signals of the discharge sound source to be located received by multiple microphones, and perform single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals;

[0044] According to the filtered signals, construct an observation model including a frequency error term;

[0045] Based on the observation model, as well as the pre-constructed noise model, signal model and frequency error model, determine the joint probability distribution information;

[0046] Use the expectation maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information;

[0047] According to the mean value of the sound source signal obtained from the latest iterative calculation, determine the signal energy amplitude at each point on the spatial grid;

[0048] According to the signal energy amplitude, determine the discharge sound source position of the discharge sound source to be located.

[0049] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0050] Obtain the discharge sound signals of the discharge sound source to be located received by multiple microphones, and perform single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals;

[0051] According to the filtered signals, construct an observation model including a frequency error term;

[0052] Based on the observation model, as well as the pre-constructed noise model, signal model and frequency error model, determine the joint probability distribution information;

[0053] Use the expectation maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information;

[0054] According to the mean value of the sound source signal obtained from the latest iterative calculation, determine the signal energy amplitude at each point on the spatial grid;

[0055] According to the signal energy amplitude, determine the discharge sound source position of the discharge sound source to be located.

[0056] The above-mentioned discharge sound source localization method, device, computer equipment, computer-readable storage medium and computer program product based on frequency correction obtain discharge sound signals of the discharge sound source to be located received by multiple microphones, and perform single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals; construct an observation model including a frequency error term according to the filtered signals; determine joint probability distribution information based on the observation model, as well as a pre-constructed noise model, signal model and frequency error model; use the expectation maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information; determine the signal energy amplitude of each point on the spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation; and determine the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude. This solution effectively solves the positioning accuracy problem caused by inaccurate frequency estimation in discharge sound source localization by introducing a frequency error term into the steering matrix and embedding it into the observation model. This method first performs single-sided Fourier filtering processing on the discharge sound signals received by the microphones to construct an observation model including a frequency error term; further, based on this observation model, combined with the noise model, signal model and frequency error model, a complete joint probability distribution information is established, enabling the system to simultaneously estimate signal parameters and frequency errors; the mean value of the sound source signal is iteratively calculated through the expectation maximization algorithm, and this joint estimation method avoids the serious dependence of the traditional method on the frequency setting of the steering matrix; finally, the signal energy amplitude of each point on the spatial grid is calculated using the mean value of the sound source signal to accurately locate the discharge sound source, improving the efficiency and accuracy of discharge sound source localization. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0058] Figure 1 It is a schematic flowchart of a discharge sound source localization method based on frequency correction in an embodiment;

[0059] Figure 2 It is a schematic flowchart of the steps of filtering processing in an embodiment;

[0060] Figure 3 It is a schematic flowchart of a discharge sound source localization method based on frequency correction in another embodiment;

[0061] Figure 4 It is a structural block diagram of a discharge sound source localization device based on frequency correction in an embodiment;

[0062] Figure 5 It is the internal structure diagram of a computer device in an embodiment. Specific implementation manners

[0063] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant regulations.

[0065] In an exemplary embodiment, as Figure 1 shown, a method for discharging sound source localization based on frequency correction is provided. In this embodiment, the method is exemplified by being applied to a terminal; it can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but not limited to various personal computers, laptop computers, smart phones, tablet computers, etc.; the server can be an independent physical server, can also be a server cluster or distributed system composed of multiple physical servers, and can also be a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0066] Step S101: Obtain the discharging sound signals of the discharging sound source to be localized received by multiple microphones, and perform single-sided Fourier filtering processing on the discharging sound signals to obtain the filtered signals.

[0067] Step S102: Construct an observation model including a frequency error term according to the filtered signals.

[0068] Step S103: Determine the joint probability distribution information based on the observation model, as well as the pre-constructed noise model, signal model and frequency error model.

[0069] Step S104: Use the expectation maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information.

[0070] Step S105: Determine the signal energy amplitude of each point on the spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation.

[0071] Step S106: Determine the discharging sound source position of the discharging sound source to be localized according to the signal energy amplitude.

[0072] Among them, the microphone can be an acoustic sensor for receiving the discharge sound signal. For example, the microphone can be a device arranged in a specific array for collecting the sound signals emitted by the discharge source in space.

[0073] Among them, the discharge sound signal can be an acoustic signal generated by the discharge source and received by the microphone. For example, the discharge sound signal can be the time-domain signal collected by the microphone when the sound wave generated by the electrical equipment discharge during the partial discharge detection.

[0074] Among them, the single-sided Fourier filtering process can be a process of performing Fourier transform on the real signal and only retaining the positive frequency part. For example, the single-sided Fourier filtering process can be to transform the real signal received by the microphone and take the positive frequency part to obtain a complex signal, whose center frequency and bandwidth can be determined according to specific circumstances.

[0075] Among them, the filtered signal can be the signal obtained after the single-sided Fourier filtering process. For example, the filtered signal can be the complex signal obtained after performing the single-sided Fourier filtering operation on the real signal received by the microphone.

[0076] Among them, the frequency error term can be the error of the frequency estimation in the steering matrix. For example, the frequency error term can be the difference (Δf) between the estimated frequency and the actual frequency introduced in the first-order Taylor expansion of the steering matrix, which is used to correct the frequency error in the calculation.

[0077] Among them, the observation model can be a mathematical model describing the relationship between the signals received by the microphone array and the sound source signals. For example, the observation model can be a mathematical model expressed as y = Φx + E, where y is the signal received by the microphone, Φ is the steering matrix containing the frequency error, x is the sound source signal in space, and E is the noise signal received by the microphone.

[0078] Among them, the noise model can be a model describing the statistical characteristics of the system noise. For example, the noise model can be a probability model assuming that the noise precision α follows the Gamma distribution.

[0079] Among them, the signal model can be a model describing the statistical characteristics of the sound source signal. For example, the signal model can be a probability model assuming that the signal follows a zero-mean complex Gaussian distribution.

[0080] Among them, the frequency error model can be a model describing the statistical characteristics of the frequency error. For example, the frequency error model can be a probability model assuming that the frequency error Δf follows the Gamma distribution.

[0081] Among them, the joint probability distribution information can be the probability distribution representation that unifies the observation model, the noise model, the signal model, and the frequency error model.

[0082] Among them, the expectation maximization model can be an iterative algorithm for estimating the parameters of a probability model. For example, the expectation maximization model can be the Expectation-Maximization (EM) algorithm, and parameters such as the mean of the sound source signal, the covariance of the sound source signal, and the frequency error term are obtained through iterative calculation.

[0083] Among them, the mean of the sound source signal can be the expected value of the sound source signal estimated by the expectation maximization algorithm. For example, the mean of the sound source signal can be the expected value μ of the sound source signal x obtained by iterative calculation of the EM algorithm.

[0084] Among them, the covariance of the sound source signal can be the second-order statistic of the sound source signal estimated by the expectation maximization algorithm. For example, the covariance of the sound source signal can be the covariance matrix Σ of the sound source signal x obtained by iterative calculation of the EM algorithm.

[0085] Among them, the signal energy amplitude can be a parameter used to characterize the energy of the sound source at each point in space. For example, the signal energy amplitude can represent the energy magnitude at the assumed sound source location.

[0086] Among them, the spatial grid can be a set of points obtained by discretely dividing the spatial region where the sound source may exist. For example, the spatial grid can divide the three-dimensional space or two-dimensional plane into multiple points according to certain rules, and is used to calculate the signal energy amplitude at each point to determine the sound source location.

[0087] Among them, the discharge sound source location can refer to the spatial location where the discharge event occurs. For example, the discharge sound source location can be determined by analyzing the signal energy amplitude to find the spatial coordinate points corresponding to the region with significantly larger energy.

[0088] Optionally, after the terminal obtains the discharge sound signals of the sound source to be located received by multiple microphones, it performs unilateral Fourier filtering on the discharge sound signals, that is, performs unilateral Fourier filtering operation on the real number signals received by the microphones, and takes the positive frequency part to obtain complex signals. The center frequency and bandwidth are determined according to the actual situation. Subsequently, the terminal constructs an observation model containing a frequency error term based on the filtered signals. This observation model is expressed as y = Φx + E, where y is the signal received by the microphone, Φ is the steering matrix containing frequency errors, x is the sound source signal in space, and E is the noise signal received by the microphone. The terminal determines the joint probability distribution information based on the observation model, as well as the pre-constructed noise model, signal model, and frequency error model. Among them, the noise model assumes that the noise precision α follows a Gamma distribution; the signal model assumes that the signal follows a zero-mean complex Gaussian distribution; the frequency error model assumes that the frequency error Δf follows a Gamma distribution. Based on the above models, the terminal uses the Expectation-Maximization (EM) model to iteratively calculate the mean μ of the sound source signal, the covariance Σ of the sound source signal, and the frequency error term Δf in the joint probability distribution information until the preset convergence condition is met. When the terminal completes the iterative calculation, it calculates the signal energy amplitude P at each point on the spatial grid according to the mean of the sound source signal. Finally, the terminal determines the area with a significantly larger signal energy amplitude as the position of the discharge sound source according to the signal energy amplitude.

[0089] In the above method for discharging sound source localization based on frequency correction, the discharging sound signals of the discharging sound source to be localized received by multiple microphones are acquired, and the discharging sound signals are processed by unilateral Fourier filtering to obtain filtered signals; an observation model including a frequency error term is constructed according to the filtered signals; based on the observation model, as well as the pre-constructed noise model, signal model and frequency error model, the joint probability distribution information is determined; the expectation maximization model is used to iteratively calculate the mean value of the sound source signal in the joint probability distribution information; according to the mean value of the sound source signal obtained by the latest iterative calculation, the signal energy amplitude of each point on the spatial grid is determined; according to the signal energy amplitude, the discharging sound source position of the discharging sound source to be localized is determined. This solution effectively solves the problem of positioning accuracy caused by inaccurate frequency estimation in discharging sound source localization by introducing a frequency error term into the steering matrix and embedding it into the observation model. This method first performs unilateral Fourier filtering on the discharging sound signals received by the microphones to construct an observation model including a frequency error term; further based on this observation model, combined with the noise model, signal model and frequency error model, a complete joint probability distribution information is established, enabling the system to simultaneously estimate signal parameters and frequency errors; the mean value of the sound source signal is iteratively calculated by the expectation maximization algorithm, and this joint estimation method avoids the serious dependence of the traditional method on the frequency setting of the steering matrix; finally, the signal energy amplitude of each point on the spatial grid is calculated using the mean value of the sound source signal to accurately locate the discharging sound source, improving the efficiency and accuracy of discharging sound source localization.

[0090] In an exemplary embodiment, with reference to Figure 2 , the discharging sound signals are processed by unilateral Fourier filtering to obtain filtered signals, which specifically includes the following contents:

[0091] Step S201: A signal sampling point data matrix is constructed according to the discharging sound signals;

[0092] Step S202: The real number signals in the signal sampling point data matrix are processed by unilateral Fourier filtering to obtain filtered signals.

[0093] Among them, the signal sampling point data matrix may be a data structure constructed according to the discharging sound signals received by multiple microphones.

[0094] Among them, the real number signals may be the unprocessed time-domain sound signals received by the microphone array. For example, the real number signals may be the discharging sound signals y collected by multiple microphones in the time domain, and these signals are real values.

[0095] Optionally, after the terminal obtains the discharge sound signals of the sound sources to be located received by multiple microphones, it constructs a signal sampling point data matrix based on the discharge sound signals. The signal sampling point data matrix organizes and arranges the signal values received by each microphone at each sampling moment. Specifically, the terminal forms a column vector with the data of the l-th sampling point received by M microphones. By arranging and combining the signals at multiple sampling moments, the terminal constructs a complete signal sampling point data matrix y. Subsequently, the terminal performs single-sided Fourier filtering on the real signals in the signal sampling point data matrix, that is, performs single-sided Fourier transform on the real signal y received by the microphone array, and takes the positive frequency part to obtain a complex signal Y, thereby completing the frequency domain conversion of the discharge sound signal and obtaining the filtered signal.

[0096] The technical solution provided in this embodiment effectively converts the time domain signal into a frequency domain signal by constructing a signal sampling point data matrix from the discharge sound signals received by multiple microphones and performing single-sided Fourier filtering on the real signals in the matrix; this structured signal organization method is conducive to accurately representing the spatial sound field information received by the microphone array; and the single-sided Fourier filtering is conducive to focusing on the main frequency components of the discharge sound signal. By selecting appropriate center frequencies and bandwidths, irrelevant frequency components and background noise are filtered out, and the effective spectrum of the signal is retained; this processing method provides an accurate frequency domain information basis for constructing an observation model containing frequency error terms subsequently, thereby facilitating the improvement of the accuracy and efficiency of discharge sound source localization.

[0097] In an exemplary embodiment, an observation model containing a frequency error term is constructed according to the filtered signal, which specifically includes the following content: a steering matrix is constructed according to the filtered signal; and the steering matrix and the frequency error term are fused to obtain the observation model.

[0098] Among them, the steering matrix can be a matrix describing the space-frequency response characteristics of the sound source signal propagating to the microphone array. For example, the steering matrix can be a matrix denoted as A.

[0099] Among them, the frequency error term can refer to the error in frequency estimation in the steering matrix. For example, the frequency error term can refer to Δf, which represents the difference between the actual frequency and the center frequency f c and satisfies a probability distribution.

[0100] Among them, the fusion process of the steering matrix and the frequency error term can be an operation process of introducing the frequency error into the calculation of the steering matrix. For example, the fusion process of the steering matrix and the frequency error term can be to perform a first-order Taylor expansion on the steering matrix A at the center frequency f c to obtain a steering matrix containing the frequency error term.

[0101] Optionally, the terminal constructs a steering matrix based on the filtered signal. The terminal takes the center of the microphone array as the origin, and determines the set of grid points within the spatial search area and the set of position coordinates of the microphone array. For each microphone position and each grid point position, the terminal calculates the acoustic propagation characteristics of the sound wave propagating from the grid point to the microphone position, and constructs a steering vector. The terminal combines all the steering vectors into a complete steering matrix. Then, the terminal performs a fusion process on the steering matrix and the frequency error term, and introduces the frequency error term Δf into the steering matrix. Specifically, the terminal performs a first-order Taylor expansion on the steering matrix A at the center frequency f c to obtain a steering matrix containing the frequency error term, which serves as an observation model containing the frequency error term.

[0102] The technical solution provided in this embodiment constructs a steering matrix based on the filtered signal, and this matrix describes the spatial-frequency response characteristics of the sound source signal propagating from the spatial position to the microphone array; then, by fusing the frequency error term with the steering matrix, the observation model can more accurately reflect the frequency error existing in the actual environment; this fusion process is realized by first-order Taylor expansion, and the frequency error term Δf is introduced into the steering matrix calculation, thereby improving the accuracy of the discharge sound source localization.

[0103] In an exemplary embodiment, based on the observation model, as well as the pre-constructed noise model, signal model, and frequency error model, the joint probability distribution information is determined, which specifically includes the following contents: constructing the noise model, signal model, and frequency error model; determining the basic joint probability distribution information according to the observation model, noise model, signal model, and frequency error model; and performing a logarithm processing on the basic joint probability distribution information to obtain the joint probability distribution information.

[0104] Among them, the basic joint probability distribution information can be information describing the joint probability relationship between the observed signal, the sound source signal, the noise signal, and the frequency error. For example, the basic joint probability distribution information can be expressed as the joint probability distribution between the sound source signal, the model parameters, and the observed signal, where the set of model parameters includes the noise precision parameter, the signal distribution parameter, and the frequency error parameter.

[0105] Optionally, the terminal constructs a noise model, a signal model, and a frequency error model. When constructing the noise model, the terminal assumes that the noise signal received by the microphone array follows a Gaussian distribution with a precision of α, and the precision parameter α follows a gamma distribution with parameters a and b close to 0. When constructing the signal model, the terminal assumes that the sound source signal in space follows a complex Gaussian distribution with a parameter of ζ, where ζ is a vector containing the grid point precision parameters, and the precision parameter vector ζ follows a specific prior distribution, which helps to achieve a sparse representation of the sound source. When constructing the frequency error model, the terminal assumes that the frequency error term follows a gamma distribution with a parameter of ρ, where ρ is a constant. Then, the terminal determines the basic joint probability distribution information according to the observation model, the noise model, the signal model, and the frequency error model, and this joint probability distribution represents the probability relationship between the observed signal, the sound source signal, and the set of model parameters. Finally, the terminal takes the logarithm of the basic joint probability distribution information to obtain the joint probability distribution information, converting the product of probabilities into the sum of logarithmic values.

[0106] The technical solution provided in this embodiment is beneficial to comprehensively and systematically describe various uncertainties in the discharge sound source localization problem by constructing a noise model, a signal model, and a frequency error model and fusing these models into the form of a joint probability distribution based on the observation model; specifically, the noise model captures the statistical characteristics of the noise received by the microphone, the signal model introduces the prior knowledge of the spatial sparsity of the sound source signal, and the frequency error model effectively considers the deviation between the actual frequency and the theoretical frequency; by taking the logarithm of the basic joint probability distribution information and converting the product of probabilities into the form of the sum of logarithms, it not only simplifies the complexity of subsequent mathematical derivations but also avoids the problem of numerical underflow that may be caused by the multiplication of small values, ensuring the stability of the calculation; thus, it is beneficial to improve the accuracy of discharge sound source localization in an actual environment with frequency errors.

[0107] In an exemplary embodiment, the mean value of the sound source signal in the joint probability distribution information is iteratively calculated using the expectation-maximization model, which specifically includes the following: using the expectation-maximization model, iteratively calculating the covariance of the sound source signal in the joint probability distribution information; using the expectation-maximization model, based on the covariance of the sound source signal, iteratively calculating the mean value of the sound source signal.

[0108] Optionally, the terminal uses the expectation maximization model to iteratively calculate the covariance of the sound source signal by taking the derivative of each item of the joint probability distribution information and setting it to zero. In each iteration process, the terminal calculates the precision matrix of the sound source signal, which is composed of the product term of the noise precision parameter and the steering matrix and the signal precision diagonal matrix. Then the terminal takes the inverse of this precision matrix to obtain the covariance of the sound source signal. Next, the terminal uses the expectation maximization model to iteratively calculate the mean of the sound source signal based on the already calculated covariance of the sound source signal. In the calculation process, the terminal multiplies the observed signal received by the microphone array with the steering matrix, then weights it with the noise precision parameter, and finally multiplies it with the covariance of the sound source signal to obtain the mean of the sound source signal, which reflects the intensity estimation of the sound source signal at each position in space. Finally, the terminal uses the expectation maximization model to iteratively calculate the frequency error term based on the already calculated covariance of the sound source signal and the mean of the sound source signal. The terminal sets the partial derivative of the joint probability distribution with respect to the frequency error term to zero, establishes an expression for the frequency error term and solves it to obtain the updated value of the frequency error term.

[0109] For example, in practical applications, the terminal can perform the iterative calculation process according to the following steps: The terminal first initializes the model parameters, including setting the frequency error term to zero, setting the noise precision parameter to a small positive value, and setting the sound source signal precision parameter to a set of uniform small values. Then, the terminal starts to perform iterative calculations. In the first iteration, the terminal constructs the precision matrix of the sound source signal according to the initial parameters. This matrix contains the product term of the steering matrix with zero frequency and the noise precision and the signal precision diagonal matrix. Through matrix inversion operation, the terminal obtains the initial covariance matrix of the sound source signal. Next, based on this covariance matrix, the terminal calculates the mean of the sound source signal. Specifically, it multiplies the observed signal with the conjugate transpose of the steering matrix, and then multiplies it with the noise precision and the covariance of the sound source signal. Finally, the terminal updates the frequency error term using the iterative method. By calculating the derivative of the steering matrix with respect to the frequency and combining the mean and covariance of the sound source signal, a nonlinear equation about the frequency error is constructed and solved. The terminal repeats the above three steps until the parameter change is less than the preset threshold or the maximum number of iterations is reached, and finally obtains the converged estimated values of the mean of the sound source signal, the covariance of the sound source signal, and the frequency error term. The peak position of the mean of the sound source signal indicates the spatial position of the discharge sound source.

[0110] The technical solution provided in this embodiment is conducive to forming an efficient solution structure with dependency transmission by designing a specific iterative calculation order, that is, first calculating the covariance of the sound source signal, then calculating the mean value of the sound source signal based on the covariance, and finally calculating the frequency error term based on both. This specific order conforms to the principle of Bayesian inference, enabling each step of the calculation to make full use of the results of the previous step, reducing the computational complexity of the solution, increasing the convergence speed of the algorithm, and effectively suppressing the interference of noise and frequency errors. Therefore, it is conducive to accurately locating the discharge sound source position in a complex noise environment with frequency errors.

[0111] In an exemplary embodiment, the discharge sound source position of the discharge sound source to be located is determined according to the signal energy amplitude, and the specific content is as follows: From the signal energy amplitudes, the target signal energy amplitudes greater than the preset signal energy amplitude are selected; the region corresponding to the target signal energy amplitude is used as the discharge sound source position.

[0112] Among them, the preset signal energy amplitude can be a threshold standard for screening effective sound source positions. For example, the preset signal energy amplitude can be a preset threshold used to distinguish significantly larger signal energy amplitudes from ordinary noise or interference signals. Only the signal energy amplitudes exceeding this threshold are considered as signals from real discharge sound sources.

[0113] Among them, the target signal energy amplitude can be the signal energy amplitude selected from all signal energy amplitudes that is greater than the preset signal energy amplitude. For example, the target signal energy amplitude can be the signal energy amplitude greater than the preset threshold, and the positions corresponding to these signal energy amplitudes are predicted to be the positions where the actual discharge sound source is located.

[0114] Among them, the region corresponding to the target signal energy amplitude can be the position where the spatial grid points with higher signal energy amplitudes are located. For example, the region corresponding to the target signal energy amplitude can be the region composed of those grid points with significantly larger signal energy amplitudes in the grids divided in space.

[0115] Optionally, the terminal selects target signal energy amplitudes greater than a preset signal energy amplitude from the calculated signal energy amplitude distribution of the entire spatial grid. During the selection process, the terminal can normalize the signal energy amplitudes of all grid points so that the maximum value is 1, and then set an appropriate threshold, such as 0.7 or 0.8, to screen out the grid points with signal energy amplitudes greater than this threshold. The signal energy amplitudes of these selected grid points are the target signal energy amplitudes, which usually indicate that the energy of the sound source signal is significantly concentrated at these positions. Subsequently, the terminal uses the region corresponding to the target signal energy amplitude as the discharge sound source position. When determining this region, the terminal can combine adjacent grid points with target signal energy amplitudes to form a continuous region, and calculate the geometric center or energy-weighted center of this region as the coordinate of the finally determined discharge sound source position.

[0116] The technical solution provided in this embodiment, by setting a preset signal energy amplitude as the screening threshold, selects target signal energy amplitudes that are significantly higher than the background from all signal energy amplitudes in the spatial grid, which is beneficial to effectively filter out the interference of environmental noise and low-energy stray signals and improve the signal-to-noise ratio of discharge sound source localization; this threshold-based selection mechanism can adaptively cope with different signal intensities and noise environments to ensure that only the high-energy regions that truly represent the discharge sound source are identified; at the same time, by taking the region corresponding to the target signal energy amplitude that meets the conditions as the discharge sound source position as a whole, rather than only taking a single maximum value point, it is beneficial to overcome the position deviation caused by spatial discretization sampling and frequency error and improve the accuracy of the localization result.

[0117] The following uses an application example to illustrate the discharge sound source localization method based on frequency correction provided in this application. This application example takes the application of this method to a terminal as an example.

[0118] This application example relates to a sparse Bayesian inference discharge sound source localization method based on frequency correction, belonging to the field of partial discharge detection; this method includes introducing an estimated frequency error using a second-order Taylor expansion to obtain an error estimation model of the microphone array signal; then assuming that the frequency error model follows a gamma distribution according to the microphones; finally, embedding this distribution into the sparse Bayesian inference algorithm to achieve the DOA (Direction of Arrival) estimation after frequency correction of the collected discharge sound signal. Compared with traditional algorithms, this method considers the frequency estimation error of each microphone, avoids the serious dependence on the frequency setting of the steering matrix during DOA estimation, and ensures the application effect of the algorithm in the actual environment.

[0119] The specific method is as follows:

[0120] S1. The computer acquires the discharge sound signals of the to-be-located discharge sound sources received by multiple microphones, and constructs a signal sampling point data matrix based on the discharge sound signals. The data of the l-th sampling point received by each microphone:

[0121] y .,l =[y 1,l ,y 2,l ,y 3,l ,...,y M,l T (1)

[0122] where (·) .,l represents the l-th column, (·) T represents the transpose, y is the signal received by all microphones, and y is the signal received by a single microphone;

[0123] S1.1. Perform a single-sided Fourier filtering operation on the real signal y received by the microphone, and take the positive frequency part to obtain the complex signal Y. The center frequency and bandwidth are determined according to the situation.

[0124] S2. According to the multi-snapshot microphone array signal reception model (also known as the observation model) assumed by the following formula, establish the frequency error model of the microphone array:

[0125] y=Ax+E(2)

[0126] x is the sound source signal in space, E is the noise signal received by the microphone, and A is the steering matrix;

[0127] S2.1. Assume the steering matrix with the center of the microphone array as the origin:

[0128] A=[a(Ω1,J1),...,a(Ω1,J S );...;a(Ω M ,J1),...,a(Ω M ,J S )]∈C M×S (3)

[0129] C M×S represents a complex matrix with M rows and S columns, where M represents the number of microphones, S represents the number of sound sources, Ω represents the set of microphone coordinates, J represents the set of angles of the positions where potential sound sources are located, and a(Ω,J) represents the steering vector. For example, the steering vector of the m-th microphone and the s-th sound source:

[0130]

[0131] where exp(·) represents taking the exponential, π is the circumference ratio, f is the sound source frequency, θ s and ​are respectively the elevation angle and azimuth angle of the position of the sound source s relative to the center of the microphone, u m and v m are respectively the abscissa and ordinate of the microphone m, and c is the speed of sound;

[0132] S2.2. According to the model shown in Equation (4), perform a first-order Taylor expansion of the steering matrix A at the center frequency fc to obtain the microphone frequency error model shown in the following equation:

[0133] Φ = A c + ΔfB c (5)

[0134] The above equation represents the steering vector at the frequency fc, is the first-order partial derivative of the steering matrix A, and Δf is the error in frequency estimation in the steering matrix.

[0135] S2.3. According to the frequency error model in (6), update the multi-snapshot observation model of the microphone array assumed in (2) to:

[0136] y = Φx + E(6)

[0137] S3. Assume that the noise model of model (7) is:

[0138]

[0139] p(E|α) represents the probability that E occurs under the premise of α, L is the number of signal sampling points, ∏(·) is the product, e .,l is the l-th column vector in E, CN(e .,l |0, α -1 I M ) represents that e .,l obeys a Gaussian distribution with a mean of 0 and a covariance of α -1 I M The Gaussian distribution, I M is the identity matrix with a dimension size of M, and the noise precision α satisfies the gamma distribution G(α|a, b):

[0140]

[0141] where a and b are constants close to 0, and Γ(a) is the gamma coefficient of a.

[0142] S4. Combine the noise model and the multi-snapshot model of the microphone array in (7) to obtain the likelihood function:

[0143]

[0144] where is the square of the F norm.

[0145] S5. Assume the signal model is as follows:

[0146]

[0147] In the above formula, Λ = diag(ζ) = diag(ζ1,..., ζ n ,..., ζ N ), ζ is an intermediate parameter, and assume ζ satisfies the following distribution:

[0148]

[0149] N is the number of grids divided in space, that is, the number of assumed potential sound sources.

[0150] S6. Assume the frequency error Δf model satisfies the following distribution:

[0151] p(Δf|γ) = G(Δf|1, γ) = γexp(-γΔf) (12)

[0152] In the formula, γ is a constant, then:

[0153] S7. Combining the observation model assumed in S2, the noise model assumed in S3, the likelihood function obtained in S4, the signal model assumed in S5, and the frequency error model assumed in S6, let Using the maximum a posteriori method, we can obtain:

[0154]

[0155] Since p(y) is completely independent of all distribution parameters, then:

[0156]

[0157] The joint probability distribution p(y, x, Θ), its decomposition expression is:

[0158] p(y, x, Θ) = p(y|x, Δf, α)p(x|ζ)p(ζ)p(α)p(Δf) (15)

[0159] Then taking the logarithm of the above formula, we can obtain the logarithmic probability distribution:

[0160]

[0161] In the above formula, ln(·) is to take the logarithm.

[0162] S8. Through the Expectation-Maximization (EM) algorithm, based on L, the mean μ and covariance Σ of x, ζ, α, and the expression of Δf can be iteratively estimated as follows:

[0163] μ = αΣΦH y

[0164] Σ = (αΦ H Φ + Λ) -1

[0165]

[0166] where κ n = 1 - [ζ n -1 Σ n,n ,

[0167] S9. Use a computer to iteratively calculate the mean μ, covariance Σ, ζ, α, and Δf;

[0168] S9.1. Initialize the mean μ, covariance Σ, ζ, α, and Δf;

[0169] S9.2. Input Y, and calculate A c , B c , C c according to the coordinates Ω of the microphone and the set J of potential sound sources under actual conditions;

[0170] S9.3. Calculate the variance Σ, mean μ, ζ, α, and Δf in sequence according to the expressions obtained by the EM algorithm;

[0171] S9.4. End the iterative operation when the convergence condition is met, and the convergence condition is set according to the actual situation.

[0172] S10. After calculating each parameter, the signal energy amplitude corresponding to each potential sound source point can be obtained according to the following formula:

[0173]

[0174] In the above formula, ||·||2 represents taking the 2-norm, P represents the energy magnitude at the assumed sound source, and the region with significantly larger values is set as the position of the discharge sound source.

[0175] Among them, S2 - S6 are necessary model assumptions and their inferences; S7 - S8 are the expressions for deriving and solving each parameter; S9 is that the computer calculates the mean μ of x based on the filtered microphone array signal Y, microphone position, spatial domain information A, and the expressions of each parameter; S10 uses the mean μ to characterize the energy distribution in the spatial domain, and regards the places with high energy amplitude as the discharge sound source points; the expressions calculated by the EM algorithm iteration are all interdependent and need to be solved iteratively, and there is no closed solution.

[0176] For example, refer to Figure 3 ​, the computer (terminal) collects the discharge sound signals received by multiple microphones in real time; performs single-sided Fourier filtering on the discharge sound signals to obtain the filtered signals; among them, according to the second-order Taylor expansion of the steering matrix at the frequency, a steering vector with frequency error correction is designed, the design of the system model includes a noise model, a signal model and a frequency error model, the system observation model is obtained, the likelihood function is obtained through Bayes' theorem, and the expressions of each parameter are calculated by EM; the parameters are iteratively calculated and updated according to the expressions, and it is judged whether the convergence condition is satisfied. If it is satisfied, the next step is carried out, otherwise the iterative calculation continues; the energy amplitudes of each point on the spatial grid are obtained, and the position with large energy is regarded as the sound source position.

[0177] The technical solution provided by this application example effectively solves the problem of positioning accuracy caused by inaccurate frequency estimation in discharge sound source positioning by introducing a frequency error term into the steering matrix and embedding it into the observation model. This method first performs single-sided Fourier filtering on the discharge sound signals received by the microphones to construct an observation model containing a frequency error term; further, based on this observation model, combined with the noise model, the signal model and the frequency error model, a complete joint probability distribution information is established, enabling the system to simultaneously estimate the signal parameters and the frequency error; the mean value of the sound source signal is iteratively calculated by the expectation maximization algorithm. This joint estimation method avoids the serious dependence of the traditional method on the frequency setting of the steering matrix; finally, the signal energy amplitudes of each point on the spatial grid are calculated using the mean value of the sound source signal to accurately locate the discharge sound source, improving the efficiency and accuracy of discharge sound source positioning.

[0178] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0179] Based on the same inventive concept, an embodiment of the present application further provides a discharge sound source localization device based on frequency correction for implementing the above-mentioned discharge sound source localization method based on frequency correction. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the discharge sound source localization device based on frequency correction provided below can refer to the limitations on the discharge sound source localization method based on frequency correction in the above text, and will not be elaborated here.

[0180] In an exemplary embodiment, as Figure 4 shown, a discharge sound source localization device based on frequency correction is provided. The discharge sound source localization device 400 may include:

[0181] A signal acquisition module 401, configured to acquire the discharge sound signals of the discharge sound source to be located received by a plurality of microphones, and perform unilateral Fourier filtering processing on the discharge sound signals to obtain filtered signals;

[0182] A model construction module 402, configured to construct an observation model including a frequency error term according to the filtered signals;

[0183] An information determination module 403, configured to determine joint probability distribution information based on the observation model, as well as a pre-constructed noise model, signal model, and frequency error model;

[0184] A mean calculation module 404, configured to iteratively calculate the mean value of the sound source signal in the joint probability distribution information by using the expectation maximization model;

[0185] An amplitude determination module 405, configured to determine the signal energy amplitude of each point on the spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation;

[0186] A position determination module 406, configured to determine the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude.

[0187] In an exemplary embodiment, the signal acquisition module 401 is further configured to construct a signal sampling point data matrix according to the discharge sound signals; and perform unilateral Fourier filtering processing on the real number signals in the signal sampling point data matrix to obtain filtered signals.

[0188] In an exemplary embodiment, the model construction module 402 is further configured to construct a steering matrix according to the filtered signals; and perform fusion processing on the steering matrix and the frequency error term to obtain an observation model.

[0189] In an exemplary embodiment, the information determination module 403 is further configured to construct a noise model, a signal model, and a frequency error model; determine basic joint probability distribution information according to the observation model, the noise model, the signal model, and the frequency error model; and perform a logarithm operation on the basic joint probability distribution information to obtain joint probability distribution information.

[0190] In an exemplary embodiment, the mean calculation module 404 is further configured to iteratively calculate the covariance of the sound source signal in the joint probability distribution information by using the expectation maximization model; and iteratively calculate the mean value of the sound source signal based on the covariance of the sound source signal by using the expectation maximization model.

[0191] In an exemplary embodiment, the position determination module 406 is further configured to select a target signal energy amplitude greater than a preset signal energy amplitude from the signal energy amplitudes; and use the region corresponding to the target signal energy amplitude as the discharge sound source position.

[0192] Each module in the above discharge sound source positioning device based on frequency correction can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0193] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 5As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for discharging sound source localization based on frequency correction. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0194] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0195] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0196] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0197] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0198] Those of ordinary skill in the art can understand that all or part of the processes in the above-described method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the method embodiments as described above. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0199] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0200] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for discharging sound source localization based on frequency correction, characterized in that, The method includes: Obtaining discharge sound signals of a discharge sound source to be located received by multiple microphones, and performing single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals; Constructing an observation model including a frequency error term according to the filtered signals; Determining joint probability distribution information based on the observation model, as well as a pre-constructed noise model, signal model, and frequency error model; Using the expectation-maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information; Determining the signal energy amplitude of each point on the spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation; Determining the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude.

2. The method according to claim 1, characterized in that, The performing single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals includes: Constructing a signal sampling point data matrix according to the discharge sound signals; Performing single-sided Fourier filtering processing on the real number signals in the signal sampling point data matrix to obtain the filtered signals.

3. The method according to claim 1, wherein The constructing an observation model including a frequency error term according to the filtered signals includes: Constructing a steering matrix according to the filtered signals; Performing fusion processing on the steering matrix and the frequency error term to obtain the observation model.

4. The method according to claim 1, wherein The determining joint probability distribution information based on the observation model, as well as a pre-constructed noise model, signal model, and frequency error model includes: Constructing the noise model, the signal model, and the frequency error model; Determining basic joint probability distribution information according to the observation model, the noise model, the signal model, and the frequency error model; Performing a logarithm processing on the basic joint probability distribution information to obtain the joint probability distribution information.

5. The method according to claim 1, wherein The using the expectation-maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information includes: Using the expectation-maximization model to iteratively calculate the covariance of the sound source signal in the joint probability distribution information; Using the expectation-maximization model to iteratively calculate the mean value of the sound source signal based on the covariance of the sound source signal.

6. The method according to any one of claims 1 to 5, characterized in that The determining the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude includes: Selecting target signal energy amplitudes greater than a preset signal energy amplitude from the signal energy amplitudes; Taking the region corresponding to the target signal energy amplitude as the discharge sound source position.

7. A discharge sound source localization device based on frequency correction, characterized in that, The device includes: A signal acquisition module, configured to obtain discharge sound signals of a discharge sound source to be located received by multiple microphones, and perform single-sided Fourier filtering processing on the discharge sound signals to obtain filtered signals; A model construction module, configured to construct an observation model including a frequency error term according to the filtered signals; An information determination module, configured to determine joint probability distribution information based on the observation model, as well as a pre-constructed noise model, signal model, and frequency error model; A mean value calculation module, configured to use the expectation-maximization model to iteratively calculate the mean value of the sound source signal in the joint probability distribution information; An amplitude determination module, configured to determine the signal energy amplitude of each point on the spatial grid according to the mean value of the sound source signal obtained by the latest iterative calculation; A position determination module, configured to determine the discharge sound source position of the discharge sound source to be located according to the signal energy amplitude.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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