A power line audible noise extraction method and system

Abstract

The application discloses a power transmission line audible noise extraction method and system, which comprises the following steps: obtaining a power transmission line sound signal; performing variational mode decomposition on the obtained power transmission line sound signal to obtain a plurality of mode components; wherein, the optimal result obtained by the variational mode decomposition is taken as a target, a multiverse optimization algorithm is used to determine the optimal values of the mode decomposition layer number and the penalty factor in the variational mode decomposition, and the power transmission line sound signal is subjected to the variational mode decomposition through the optimal values of the mode decomposition layer number and the penalty factor; audible noise signals are extracted from the plurality of mode components; and the audible noise signals are reconstructed to obtain power transmission line audible noise. The accurate extraction of the power transmission line audible noise is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of audible noise extraction of power transmission lines, and in particular to an audible noise extraction method and system of power transmission lines. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] In order to better study the distribution law of audible noise of ultra-high voltage power transmission lines, it is necessary to extract the audible noise of power transmission lines on site, but the background noise needs to be filtered out to better analyze the characteristics of the audible noise of power transmission lines. At present, the filtering of noise mainly develops based on wavelet denoising theory, and various improved schemes are proposed on this basis, such as empirical mode decomposition, ensemble empirical mode decomposition, complementary ensemble empirical mode decomposition, etc., and the method of permutation entropy is used to select useless signals after decomposition. The existing method for extracting effective sound signals has the following disadvantages:

[0004] (1) The existing method for extracting effective sound signals has a great impact on the sound pressure signal source. For example, the empirical mode decomposition method is prone to modal aliasing, and the ensemble empirical mode decomposition may have some impact on the source signal due to the addition of white noise.

[0005] (2) Some experts have proposed that the method of variational mode decomposition can better process the signal source. When the parameters in the variational mode decomposition algorithm are different, the accuracy of the effective sound signal obtained is different. The existing algorithm does not optimize the parameters in the variational mode decomposition algorithm, so it cannot guarantee to obtain the optimal effective sound signal.

[0006] (3) Although the method of permutation entropy can measure the complexity of nonlinear non-stationary signals, in complex environments, the audible noise of power transmission lines is affected by multiple background sound sources, and single-scale permutation entropy is difficult to comprehensively extract the weak background noise in the audible noise. The accuracy of the sound noise obtained is limited. SUMMARY

[0007] In order to solve the above problems, the present application proposes an audible noise extraction method and system of power transmission lines, which uses a multiverse optimization algorithm to determine the optimal values of the mode decomposition layer number and the penalty factor in variational mode decomposition, and performs variational mode decomposition on the sound signal of the power transmission line through the optimal values of the mode decomposition layer number and the penalty factor, thereby realizing accurate extraction of the audible noise of the power transmission line.

[0008] To achieve the above purpose, the present application adopts the following technical solutions:

[0009] In a first aspect, an audible noise extraction method of power transmission lines is proposed, comprising:

[0010] acquire a power transmission line sound signal;

[0011] perform variational modal decomposition on the acquired power transmission line sound signal to obtain a plurality of modal components; wherein, aiming at the optimal result obtained by variational modal decomposition, a multi-universe optimization algorithm is used to determine the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition, and the variational modal decomposition is performed on the power transmission line sound signal through the optimal values of the modal decomposition layer number and the penalty factor;

[0012] extract an audible noise signal from the plurality of modal components;

[0013] reconstruct the audible noise signal to obtain a power transmission line audible noise.

[0014] In a second aspect, a power transmission line audible noise extraction system is provided, comprising:

[0015] a sound signal acquisition module configured to acquire a power transmission line sound signal;

[0016] a power transmission line audible noise acquisition module configured to perform variational modal decomposition on the acquired power transmission line sound signal to obtain a plurality of modal components; wherein, aiming at the optimal result obtained by variational modal decomposition, a multi-universe optimization algorithm is used to determine the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition, and the variational modal decomposition is performed on the power transmission line sound signal through the optimal values of the modal decomposition layer number and the penalty factor; an audible noise signal is extracted from the plurality of modal components; and the audible noise signal is reconstructed to obtain a power transmission line audible noise.

[0017] In a third aspect, an electronic device is provided, comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, when the computer instructions are run by the processor, the steps of a power transmission line audible noise extraction method are completed.

[0018] In a fourth aspect, a computer readable storage medium is provided for storing computer instructions, when the computer instructions are executed by a processor, the steps of a power transmission line audible noise extraction method are completed.

[0019] Compared with the prior art, the beneficial effects of the present application are:

[0020] 1、The present application adopts a multi-universe optimization algorithm to determine the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition, and performs variational modal decomposition on the power transmission line sound signal through the optimal values of the modal decomposition layer number and the penalty factor, thereby realizing accurate extraction of the power transmission line audible noise.

[0021] 2、The method of sample entropy is used to extract audible noise signals from the multiple modal components obtained by the variational modal decomposition, can cope with more complex environment, can more sensitively extract tiny background noise, so that the obtained audible noise of the power transmission line is more accurate and complete.

[0022] Advantages of the additional aspects of the application will be in part apparent from the following description, in part will become apparent from the following description, or will be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0023] The drawings accompanying the specification of this application form a part thereof, serve to provide further understanding of the application, and together with the description of the exemplary embodiments of the application, serve to explain the application, and do not constitute improper limitations on the application.

[0024] Figure 1 Flowchart of the method disclosed in Example 1;

[0025] Figure 2 Conceptual model of the multiverse algorithm disclosed in Example 1;

[0026] Figure 3 Flowchart of the multiverse algorithm disclosed in Example 1. DETAILED DESCRIPTION

[0027] The application will be further described below in conjunction with the drawings and examples.

[0028] It should be noted that the following detailed description is exemplary, and is intended to provide further explanation of the application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs.

[0029] Example 1

[0030] In this example, a method for extracting audible noise of a power transmission line is disclosed, as shown in Figure 1 , comprising:

[0031] Obtaining a power transmission line sound signal;

[0032] Performing variational modal decomposition on the obtained power transmission line sound signal to obtain multiple modal components; wherein, with the optimal result obtained by variational modal decomposition as the target, the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition are determined by using a multiverse optimization algorithm, and the power transmission line sound signal is subjected to variational modal decomposition through the optimal values of the modal decomposition layer number and the penalty factor;

[0033] Extracting audible noise signals from the multiple modal components;

[0034] Reconstructing the audible noise signals to obtain audible noise of the power transmission line.

[0035] In specific implementation, there is background noise in the acquired power line sound signal, and in the embodiment, audible noise of the power line is extracted from the power line sound signal through variational mode decomposition (VMD), and in order to ensure the accuracy and integrity of the extracted audible noise, the penalty factor a and the value of K in VMD are optimized through a multi-universe optimization algorithm (MVO) to determine the optimal values of the penalty factor a and the value of K. The collected power line sound signal is subjected to variational mode decomposition using the optimal values of the penalty factor a and the value of K, the noise in the power line sound signal is determined through sample entropy, and finally the noise component is removed to reconstruct the remaining modal component to obtain the audible noise signal of the power line.

[0036] Variational mode decomposition (VMD) is an adaptive non-recursive modal decomposition model, which can decompose the original signal into a group of modal components with a certain center frequency and bandwidth, and the sum of the estimated bandwidths of each modal is minimum, and the constraint condition is that the sum of all modes is equal to the original signal. With the help of HHT (Hilbert-Huang transform) transform, frequency mixing and other methods, the variational model is searched iteratively.

[0037] The constraint variational model of variational mode decomposition is as follows:

[0038]

[0039] In the formula, k is the number of modal decomposition layers; u k (t) is the kth modal component; ω k is the center frequency of the kth modal component; f(t) is the original power line sound signal, δ(t) is the unit impulse function; * is convolution; dt is the derivative of the function; s.t. is the constraint condition. In order to solve formula (1), the Lagrange operator needs to be introduced, and the variational model solving can be converted into an unconstrained variational model, and each modal component is obtained through the calculation of k value. The expression of the unconstrained variational model is as follows:

[0040]

[0041] In the formula, a is the penalty factor; λ(t) is the Lagrange multiplier operator. k is the number of modal decomposition layers; u k (t) is the kth modal component; ω k is the center frequency of the kth modal component; f(t) is the original power line sound signal, δ(t) is the unit impulse function; * is convolution; dt is the derivative of the function, and the unconstrained variational model is solved through the alternating direction multiplier method (ADMM) to obtain each modal component, and the specific steps are as follows:

[0042] S1: initialization At this time, the iteration number is 0;

[0043] S2: update {uk}, {ωk}, λ according to formula (3), formula (4), formula (5);

[0044]

[0045]

[0046]

[0047] In the formula, ^ represents Fourier transform, is the kth modal component in the n+1th iteration, is the original signal function with the center frequency ω, is the kth modal component in the n+1th iteration with the center frequency ω, is the Lagrange multiplier operator in the nth iteration, α is the penalty factor, ω is the center frequency, and β represents the step size.

[0048] S3: repeatedly iterate S2 until the function satisfies the following conditions:

[0049]

[0050] In the formula, represents the kth modal component in the kth iteration, and ε is a threshold value. In the variational modal decomposition, the value of k and the penalty factor α are particularly important, so in this embodiment, the multi-universe optimization algorithm is used to determine the optimal values of the modal decomposition layer number k and the penalty factor α in the variational modal decomposition, and the optimal values of the modal decomposition layer number and the penalty factor are used to perform variational modal decomposition on the power line sound signal.

[0051] The multi-universe optimization algorithm (MVO) establishes a mathematical model based on the three main concepts of the multi-universe theory: white holes, black holes, and wormholes. It defines the candidate solutions of the modal decomposition layer number k and the penalty factor α as universes, and the fitness of the candidate solutions as the expansion rate of the universes. In the iteration process, each candidate solution is a black hole, and the universes with good fitness become white holes according to the roulette wheel principle. The black holes and white holes exchange matter, and some black holes can pass through the wormhole to link to the vicinity of the optimal universe (searching around the group optimal value). The conceptual model of the multi-universe algorithm is shown in Figure 2 .

[0052] Assuming that there is a universe matrix in the solution space, the formula is

[0053]

[0054] In the formula: n is the number of variables; m is the number of universes, i.e., the number of candidate solutions.

[0055]

[0056] In the formula: is the jth variable of the ith universe; U i is the ith universe, NI(U i ) is the standard inflation rate of the ith universe; r1 is a random number between 0 and 1; is the jth variable of the kth universe selected according to the spiral mechanism.

[0057] MVO has two important coefficients: wormhole existence probability (WEP) and travelling distance rate (TDR). According to the two coefficients, the universe update formula is established as:

[0058]

[0059] In the formula: X j is the jth variable of the current optimal universe; U i is the ith universe, NI(U i ) is the standard inflation rate of the ith universe; lb j and ub j respectively refer to the lower and upper limits of x, r2 and r3 are random numbers between 0 and 1.

[0060] The specific formula of TDR and WEP is:

[0061] WEP = WEP min + l x (WEP max - WEP min / L) (10)

[0062]

[0063] In the formula: WEP min , WEP max are the minimum and maximum probabilities of wormhole existence respectively; l is the current iteration number; L is the maximum iteration number; p is the speed detection speed coefficient, the higher the value of p, the faster the local detection speed and the shorter the time.

[0064] From the universe update formula, it can be seen that TDR determines the step size of the update of the universe elements. However, as can be seen from formula (11), due to the fixed value of p, TDR will be forced to decrease with the increase of the iteration number, which cannot reflect the advantages and disadvantages of the universe elements and is prone to local convergence. Therefore, the travelling distance rate formula in the multi-universe optimization algorithm is improved in this embodiment to obtain the improved MVO, wherein the improved travelling distance rate formula is:

[0065]

[0066] In the formula, NImIn is the minimum fitness value, the minimum fitness value obtained in the whole population search; Nmean is the average fitness value, the sum of all fitness values of the whole solution is divided by the population size to obtain; when NImIn is close to Nmean, the improved calculation formula is not much different from the original one; when NImIn is less than Nmean, the search step is increased to avoid falling into a local optimum.

[0067] In this embodiment, the modal components obtained by the variational modal decomposition are evaluated according to the fitness, and the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition are determined by using the multi-universe optimization algorithm, wherein the fitness is represented by the signal-to-noise ratio of the modal components obtained by the variational modal decomposition.

[0068] The fitness function plays a crucial role in the optimization algorithm. In this embodiment, in order to improve the noise suppression capability and realize accurate extraction of the characteristic line spectrum, the signal-to-noise ratio of the denoised signal is selected as the fitness function. The goal of this fitness function is to maximize the output signal-to-noise ratio, thereby suppressing noise to the greatest extent. The specific form of the fitness function is shown in the following formula (13). In the iteration process of the MVO algorithm, the optimal solution finally obtained is the individual with the best fitness during the entire search process, and the corresponding coefficients are also obtained.

[0069]

[0070] In the formula, f is the fitness value, N is the length of the sound signal, s(t) is the acquired power line sound signal, S'(t) is the reconstructed power line audible noise signal, and is the ratio of the energy of the power line sound signal to the energy of the reconstructed power line audible noise.

[0071] In this embodiment, each universe in the multi-universe optimization algorithm is taken as a candidate solution of the modal decomposition layer number and the penalty factor, and the universe is iteratively optimized with the minimum fitness as the target.

[0072] In each iteration process, the universe in the iteration process is substituted into the variational modal decomposition, the acquired power line sound signal is subjected to the variational modal decomposition, the modal components are obtained, the audible noise signal is extracted from the multiple modal components, the audible noise signal is reconstructed, the reconstructed power line audible noise is obtained, and the fitness value is calculated according to the reconstructed power line audible noise and the acquired power line sound signal.

[0073] When the difference between the fitness values obtained by two iterations is less than a set threshold, the iteration is stopped.

[0074] The MVO algorithm flowchart is shown in Figure 3

[0075] The steps of determining the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition by using the multiverse optimization algorithm are as follows:

[0076] S01: Determine the problem objective: First, the objective function to be optimized needs to be determined, i.e., the parameters to be optimized and the optimization goal. In this problem, the parameters to be optimized are the penalty factor α and the K value in VMD, and the optimization goal is to make the VMD decomposition result optimal.

[0077] S02: Determine the parameters of the multiverse optimization algorithm: The multiverse optimization algorithm has many parameters that need to be set, such as the number of universes, the dimension of the universe, the number of iterations, the contraction speed, etc. The values of these parameters are determined according to the actual situation.

[0078] S03: Initialize the universe: Randomly initialize multiple universes, each of which represents a candidate solution for α and K.

[0079] S04: Calculate the fitness function: input each universe as a parameter into VMD for decomposition, obtain the decomposed modal components, extract the audible noise signal from the multiple modal components, reconstruct the audible noise signal, and obtain the reconstructed audible noise of the transmission line; calculate the fitness value of the fitness function according to the reconstructed audible noise of the transmission line and the obtained transmission line sound signal. In this problem, the fitness value can be represented by the signal-to-noise ratio (SNR) of the decomposed signal, i.e., the larger the fitness value, the smaller the noise of the decomposed signal, and the better the decomposition result.

[0080] S05: Update the universe: according to the fitness value, update the position and speed of the universe, so that the universe moves in the direction of higher fitness.

[0081] S06: Shrink the universe: if the fitness value does not improve within a certain number of iterations, the range of the universe needs to be shrunk in order to better search for the optimal solution.

[0082] S07: Continue iteration: repeat steps S04-S06 until a predetermined number of iterations is reached or a convergence condition is met, where the convergence condition is that the difference between the fitness values obtained by adjacent iterations is less than a set threshold value. When the convergence condition is met, it means that the fitness value has reached the maximum.

[0083] S08: Output the result: output the universe with the maximum fitness value in the iteration process as the final parameter solution, i.e., obtain the optimal values of the modal decomposition layer number and the penalty factor.

[0084] ​In order to filter out the noise existing in the sound signal and better retain the effective information of the sound noise, the embodiment utilizes sample entropy to judge each modal component obtained by VMD decomposition.

[0085] Sample entropy is an entropy measurement method for measuring signal complexity and information amount, and can be used to evaluate the complexity and importance of each modal component obtained by VMD decomposition. The formula principle is as follows:

[0086] Let the signal sequence be x(t), the sample window length be N, and the sample entropy be SampEn(m, r, N), wherein m represents the order of matching degree, and r represents the tolerance of template matching. The calculation steps of sample entropy are as follows:

[0087] S11: Divide the signal sequence into a plurality of sample windows with a length of N to obtain N-ω+1 windows, wherein ω represents the length of the template window.

[0088] S12: For each sample window i, a template with a length of m+1 is constructed, and the number of occurrences of the template in the sample window C i (m) is calculated.

[0089] S13: For each sample window i, the matching degree with other sample windows j≠i is calculated, and if the distance between the two windows is less than the tolerance r, it is considered that they are matched successfully, and the matching number A i,j (m, r) is obtained.

[0090] S14: For each window i, the sample entropy SampEn(m, r, N) is calculated, and the formula is:

[0091]

[0092] Wherein, U m,r (x i ,x j ) represents the matching number between sample windows i and j, that is, U m,r (x i ,x j )=A i,j (m, r)-A i,j+1 (m, r), wherein A i,j (m, r) represents the number of successful matches of the template with a length of m+1 in sample windows i and j, the matching distance is less than or equal to the tolerance r, and ω represents the window size.

[0093] The value of SampEn is related to the values of m and r, and in the embodiment, m=2 and r=0.2SD are selected by referring to the data, and SD is the standard deviation of the original data.

[0094] In summary, by calculating the sample entropy, the complexity and information amount of the signal sequence can be evaluated, so as to evaluate each modal component obtained by VMD decomposition. Specifically, each modal component can be regarded as a signal sequence, and the sample entropy of the signal sequence is calculated to obtain the sample entropy value of the signal sequence, which represents the complexity and information amount of the signal sequence. According to the size of the sample entropy value, the importance and contribution of each modal component can be evaluated, so as to establish a judgment standard and select appropriate modal components for subsequent processing and feature extraction.

[0095] After calculating the sample entropy of each modal component in this embodiment, the modal component with a sample entropy greater than or equal to a set value is selected as an audible noise component, the modal component with a sample entropy less than the set value is deleted, the audible noise signal is reconstructed, and the audible noise of the power transmission line is obtained.

[0096] The power transmission line audible noise extraction method disclosed in this embodiment uses the multi-universe optimization algorithm to optimize the initial parameter K value and the penalty coefficient a of the variational modal decomposition, determines the optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition, and performs variational modal decomposition on the power transmission line sound signal through the optimal values of the modal decomposition layer number and the penalty factor, thereby realizing accurate extraction of the audible noise of the power transmission line. In addition, the sample entropy method is used to extract the audible noise signal from the multiple modal components obtained by the variational modal decomposition, which can cope with more complex environments and extract small background noise more sensitively, so that the audible noise of the power transmission line obtained is more accurate and complete.

[0097] Embodiment 2

[0098] In this embodiment, a power transmission line audible noise extraction system is disclosed, comprising:

[0099] The sound signal acquisition module is configured to acquire the power transmission line sound signal.

[0100] The power transmission line audible noise acquisition module is configured to perform variational modal decomposition on the acquired power transmission line sound signal to obtain multiple modal components. The optimal values of the modal decomposition layer number and the penalty factor in the variational modal decomposition are determined by using the multi-universe optimization algorithm, and the power transmission line sound signal is subjected to variational modal decomposition through the optimal values of the modal decomposition layer number and the penalty factor. The audible noise signal is extracted from the multiple modal components. The audible noise signal is reconstructed to obtain the audible noise of the power transmission line.

[0101] Embodiment 3

[0102] In this embodiment, an electronic device is disclosed, comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, the steps of the power line audible noise extraction method disclosed in embodiment 1 are completed.

[0103] Embodiment 4

[0104] In this embodiment, a computer readable storage medium is disclosed, for storing computer instructions, when the computer instructions are executed by a processor, the steps of the power line audible noise extraction method disclosed in embodiment 1 are completed.

[0105] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application rather than limit them, although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered within the protection scope of the claims of the present application.

Claims

1. A method for extracting audible noise from transmission lines, characterized in that, include: Acquire sound signals from power transmission lines; Variational mode decomposition (VMD) is performed on the acquired transmission line sound signal to obtain multiple mode components. With the goal of optimizing the VMD results, the multiverse optimization algorithm is used to determine the optimal values ​​of the number of mode decomposition levels and the penalty factor in the VMD. The transmission line sound signal is then subjected to VMD decomposition using these optimal values. The journey distance rate formula in the multiverse optimization algorithm is improved, and the improved journey distance rate formula is as follows: TDR = In the formula: WEP max This represents the highest probability of a wormhole existing. This is the minimum fitness value; Here, p represents the average fitness value, and p is the velocity detection velocity coefficient. l L is the current iteration number, L is the maximum iteration number, and TDR is the journey distance rate. Extracting audible noise signals from multiple modal components; The audible noise signal is reconstructed to obtain the audible noise of the transmission line.

2. The method for extracting audible noise from transmission lines as described in claim 1, characterized in that, The modal components obtained by variational mode decomposition are evaluated by fitness. With the goal of minimizing fitness, the optimal values ​​of the number of mode decomposition layers and the penalty factor in variational mode decomposition are determined by a multiverse optimization algorithm. Fitness is represented by the signal-to-noise ratio of the modal components obtained by variational mode decomposition.

3. The method for extracting audible noise from transmission lines as described in claim 2, characterized in that, Each universe in the multiverse optimization algorithm is treated as a candidate solution with a modality decomposition level and a penalty factor. The universe is iteratively optimized with the goal of minimizing fitness.

4. The method for extracting audible noise from transmission lines as described in claim 2, characterized in that, In each iteration, the universe in that iteration is substituted into variational mode decomposition to perform variational mode decomposition on the acquired transmission line sound signal to obtain mode components. Aural noise signals are extracted from multiple mode components and reconstructed to obtain reconstructed transmission line audible noise. Based on the reconstructed transmission line audible noise and the acquired transmission line sound signal, the fitness value is calculated.

5. The method for extracting audible noise from transmission lines as described in claim 2, characterized in that, The iteration stops when the difference between the fitness values ​​obtained from two iterations is less than a set threshold.

6. The method for extracting audible noise from transmission lines as described in claim 1, characterized in that, Calculate the sample entropy of each modal component, and select modal components whose sample entropy is greater than or equal to a set value as audible noise components.

7. A system for extracting audible noise from power transmission lines, characterized in that, include: A sound signal acquisition module is used to acquire sound signals from power transmission lines. The audible noise acquisition module for transmission lines is used to perform variational mode decomposition (VMD) on the acquired transmission line sound signal to obtain multiple mode components. The objective is to optimize the VMD results. A multiverse optimization algorithm is used to determine the optimal values ​​of the number of mode decomposition levels and the penalty factor in the VMD. The optimal values ​​of the number of mode decomposition levels and the penalty factor are then used to perform VMD on the transmission line sound signal. The journey distance rate formula in the multiverse optimization algorithm is improved; the improved journey distance rate formula is as follows: TDR = In the formula: WEP max This represents the highest probability of a wormhole existing. This is the minimum fitness value; Here, p represents the average fitness value, and p is the velocity detection velocity coefficient. l L is the current iteration number, L is the maximum iteration number, and TDR is the journey distance rate. The audible noise signal is extracted from multiple modal components; the audible noise signal is reconstructed to obtain the audible noise of the transmission line.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, complete the steps of the method for extracting audible noise from a transmission line as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the steps of the method for extracting audible noise from a transmission line as described in any one of claims 1-6.

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