Method and system for monitoring noise of charge post, and readable medium

Abstract

To provide a method and system for monitoring the noise of a charge post, and a readable medium.SOLUTION: A method for monitoring noise of a charge post including use, for a sound noise monitoring system, of a 3D ball array consisting of a plurality of microphones, comprises the steps of: obtaining respectively detected noise data including sound pressure signals measured by the plurality of microphones; obtaining an initial noise signal; obtaining a noise signal having a target analysis frequency; uniformly classifying a scanning area into some discrete scanning grid points; obtaining a vector constituted of beam formation energy estimation values at all scanning grid points; creating an energy transfer model; and calculating a high resolution sound source energy distribution of a charge post to be measured in the energy transfer model.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present application relates to the technical field of noise measurement, and in particular to a charging post noise monitoring method, a noise monitoring system and a readable medium. [Background technology]

[0002] Current charging stations have different technical levels. In order to meet the demands of the fast rhythm of modern urban life, people are trying to speed up the charging speed of electric vehicles as much as possible. Therefore, DC charging stations have broad development prospects. However, along with this, problems with the design of DC charging stations are gradually emerging.

[0003] At present, many DC charging posts have their own noise processing problems, and at the same time, due to improper placement in public places, they cause inconvenience to residents. According to the measurement results of related experiments, when a DC fast charging post is in operation, the noise in the neighborhood is about 70dB, and the noise in the residential neighborhood is about 55dB, which sounds like the sound of a large vacuum cleaner used in supermarkets. In many situations, when electric vehicles are charging, people are generally in the car or near the vehicle, so the noise generated by the DC charging post during operation is likely to disturb people in the car or near the vehicle. Therefore, the measurement and positioning of the noise surround situation are of important reference value for the design and placement of fast charging posts. The noise of a DC fast charging post is mainly generated by the cooling fan system and the transformer.

[0004] Currently, the most common measurement methods are the continuous scanning method and the discrete point measurement method, but both have their own shortcomings. The continuous scanning method places very strict requirements on the control of the measurement device during measurement, and the discrete point measurement method collects a small number of data samples, so the accuracy of the results cannot be guaranteed. A common method is to use a sound level meter to carry out rational inspection of the equipment, but this method can only roughly inspect high-frequency noise, and it is difficult to effectively monitor low-frequency noise, and it is quite difficult to effectively orient and position the noise formation. Most of the noise generated by DC fast charging posts is low-frequency, which is why there are almost no effective measurement methods for DC fast charging posts. Summary of the Invention [Problem to be solved by the invention]

[0005] Based on this, in order to solve the problem that conventional measurement methods can only roughly test high-frequency noise, can hardly effectively monitor low-frequency noise, and cannot effectively orient and position the noise formation, it is an issue to provide a noise monitoring method, a noise monitoring system, and a readable medium for charging posts. [Means for solving the problem]

[0006] The present invention includes a 3D ball array of microphones for use in a noise monitoring system, In an open state of the charging post to be measured, noise detection data including sound pressure signals measured by a plurality of microphones from several measurement positions surrounding the charging post to be measured in a circumferential direction are acquired; obtaining an initial noise signal by performing a noise reduction process on the obtained noise detection data; obtaining a noise signal of a target analysis frequency by filtering the initial noise signal; selecting a scan area on the sound source plane and uniformly dividing the scan area into a number of discrete scan grid points; obtaining a vector of beamforming energy estimates for every scan grid point according to the noise signal of the target frequency; creating an energy transfer model with a vector comprised of beamforming energy estimates for at least every scan grid point and an energy transfer matrix; A method for monitoring noise at a charging post is provided, comprising the step of calculating a high-resolution sound source energy distribution of the measured charging post in the energy transfer model by a Bayesian estimation method.

[0007] The present application further relates to A 3D ball array is used to collect noise data of the charging post under test at the measurement location; The present invention provides a noise monitoring system including an information processing device for connecting a signal to the 3D ball array, the information processing device including a memory and a processing device coupled together, the memory being used for storing program data and the processing device being used for executing the program data to realize the charging post noise monitoring method described in the above content.

[0008] The present application further provides a computer readable medium having a computer program stored thereon, the computer program being executed by a processing device to realize the charging post noise monitoring method described in the above content. Effect of the Invention

[0009] This application relates to a noise monitoring method, a noise monitoring system and a movable medium for a charging station, in which the noise monitoring method for a charging station uses a 3D ball array to perform asynchronous measurement and collect noise detection data from several measurement positions. At the same time, through noise removal and filtering processing for the noise detection data, a noise signal of a target analysis frequency that can represent the noise characteristics of the charging station being measured is obtained, and a vector composed of the beamforming energy estimate value of every scanning grid point is obtained, which can quickly realize the inverse analysis of the visualized sound source, improve the spatial resolution of the sound source positioning, and its robustness and intuition are good. In addition, by using the Bayesian estimation method to explain the variables in the sound field in the perspective of statistical optimization, the distribution of the sound source energy at the scanning grid points is calculated, and the fault location in the charging station being measured is accurately located, so that the resolution of the imaging result is high, the algorithm has strong anti-noise ability and strong self-adaptation ability. Therefore, the beamforming sound source location method based on Bayesian estimation can quickly locate the fault of the DC fast charging post by performing fast orientation to the low-frequency noise generated from the charging post under test. [Brief description of the drawings]

[0010] [Figure 1] 1 is a schematic flow diagram of a charging post noise monitoring method provided by an embodiment of the present application. [Diagram 2] 1 is a schematic diagram illustrating the spatial distribution of measurement positions relative to a measured charging post during a charging post noise monitoring method provided by an embodiment of the present application; [Diagram 3] 1 is a schematic diagram illustrating a division of scanning grid points in a charging post noise monitoring method provided by an embodiment of the present application; [Figure 4] A spatial distribution map of the noise signal and average noise collected when the 3D ball array was located at the measurement point directly in front of the charging post being measured. [Diagram 5]This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 30° clockwise directly in front of the charging post being measured. [Figure 6] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 60° clockwise directly in front of the charging post being measured. [Figure 7] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 90° clockwise directly in front of the charging post being measured. [Figure 8] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 120° clockwise directly in front of the charging post being measured. [Figure 9] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 150° clockwise directly in front of the charging post being measured. [Figure 10] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 180° clockwise directly in front of the charging post being measured. [Figure 11] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 210° clockwise directly in front of the charging post being measured. [Figure 12] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 240° clockwise directly in front of the charging post being measured. [Figure 13] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 270° clockwise directly in front of the charging post being measured. [Figure 14] This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 300° clockwise directly in front of the charging post being measured. [Figure 15]This is a spatial distribution map of the noise signal and average noise collected when the 3D ball array was positioned at a measurement point 330° clockwise directly in front of the charging post being measured. [Figure 16] 1 is a spatial distribution of noise made up of all measurement locations provided by an embodiment of the present application. [Figure 17] 1 is a schematic diagram of noise grade classification of the spatial distribution of noise composed of all measurement positions provided by an embodiment of the present application; [Figure 18] FIG. 2 is a sound source energy distribution diagram based on beamforming in a charging post noise monitoring method provided by an embodiment of the present application. [Figure 19] FIG. 2 is a sound source energy distribution map based on Bayesian estimation and beamforming in a charging post noise monitoring method provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] In order to more clearly describe the objectives, technical means and advantages of the present application, the present application will be described in more detail below in conjunction with the drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application, and do not limit the present application.

[0012] This application provides a method for noise monitoring of a charging post, including a 3D ball array of multiple microphones used in the noise monitoring system, in which the 3D ball array is an existing technology, and the specific details thereof will not be described again.

[0013] As shown in FIG. 1, in an embodiment of the present application, the charging station noise monitoring method includes the following steps S100 to S700.

[0014] S100 is a step of acquiring noise detection data from several measurement positions around the charging post to be measured in a circumferential direction of the charging post to be measured in an open state of the charging post to be measured,

[0015] Specifically, the noise detection data is a sound pressure signal measured by a microphone in a 3D ball array.

[0016] In step S200, an initial noise signal is obtained by performing a noise removal process on the acquired noise detection data. Specifically, depending on the placement environment of the charging post to be measured, there will be disturbances such as background noise (other DC fast charging posts, passersby, vehicle sounds, shops, etc.) and environmental disturbances (temperature, air density). Therefore, in this application, the acquired noise detection data is subjected to noise removal processing to eliminate the influence of the background environment and improve the accuracy of subsequent noise positioning results.

[0017] S300 is a step of obtaining a noise signal of a target analysis frequency by filtering an initial noise signal, Specifically, the target analysis frequency is a frequency segment, or a specific frequency value, or a plurality of specific frequency values, that can represent the noise characteristics of the charging post to be measured.

[0018] More specifically, since the noise generated by a DC fast charging post is mostly low frequency noise, the target frequency can be set to a low frequency, and the noise of the charging post to be measured can be effectively detected.

[0019] A specific filtering method is to pass the initial noise signal through a Bessel circuit to eliminate high frequency noise and obtain low frequency noise.

[0020] S400 is a step of selecting a scanning area on a sound source plane and uniformly dividing the scanning area into a number of discrete scanning grid points; Specifically, the sound source plane is the measurement plane directly in front of the 3D ball array, and the scanning area is the scanning detection area of ​​the 3D ball array that is centered on the charging post to be measured, includes the outer surface of the charging post to be measured directly in front of the 3D ball array, and can be located at each measurement position in the circumferential direction.

[0021] For example, in the embodiment described below, as shown in Figure 3, the black dots are discrete scanning grid points, which are uniformly distributed in the scanning area. Through the division of the scanning grid points, the noise output of the charging post under test is regarded as the combined output of multiple discrete point sound sources.

[0022] S500 is a step of obtaining a vector consisting of beamforming energy estimates for every scan grid point due to a noise signal of a target frequency.

[0023] S600 is the step of creating an energy transfer model with a vector comprised of at least the beamforming energy estimates for every scan grid point and an energy transfer matrix.

[0024] S700 is a step of calculating a high-resolution sound source energy distribution of the measured charging post in the energy transfer model by a Bayesian estimation method.

[0025] In this embodiment, noise detection data is collected at several measurement positions through asynchronous measurement using a 3D ball array. At the same time, noise detection data is subjected to noise removal and filtering processes to obtain a target frequency noise signal that can represent the characteristics of the charging post being measured, and a vector composed of beamforming energy estimates of scanning grid points is obtained from this, which can quickly realize the inverse analysis of the visualized sound source, improve the spatial resolution of the sound source positioning, and its robustness and intuition are good. In addition, the Bayesian estimation method is used to explain the variables in the sound field in the perspective of statistical optimization to calculate the distribution of the sound source energy of the scanning grid points, and the fault location in the charging post being measured is accurately located, so that the resolution of the imaging result is high, the algorithm has strong anti-noise ability, and strong self-adaptation ability. Therefore, the beamforming sound source positioning method based on the Bayesian estimation method can quickly locate the fault of the DC fast charging post by quickly performing orientation to the low-frequency noise generated by the charging post being measured.

[0026] By using the above method, the probability of various accidents caused by the fault can be greatly reduced by detecting and dealing with the fault in the early stage. When abnormal sounds or sounds at abnormal frequency segments occur during the operation of the DC fast charging station, feedback on the problem can be provided in time, which can play a role in preventing accidents from occurring and also enable the structural design of the charging station to be adjusted in a more directional manner.

[0027] In an embodiment of the present application, S100 includes the following S110 to S120.

[0028] S110 is a step of setting a plurality of measurement positions at preset angular intervals with the center of the contour of the charging post to be measured as an origin and a preset distance as a radius; Specifically, the outer surface contour of the charging post equipment to be measured is drawn within a plane, and the center of the contour is set as the origin of the movement trajectory of the 3D ball array.

[0029] S120 is a step of presetting a measurement height and performing at least one noise detection at each measurement position along the predefined movement direction, Specifically, as shown in Figure 2, while the distance from the origin does not change, it moves clockwise (or counterclockwise) at a rotation angle of 30°, goes around the charging post to be measured, and collects noise detection data at all measurement positions. The dark-colored ball array in Figure 2 is the current measurement position of the 3D ball array, and the light-colored ball array diagrams show other measurement positions.

[0030] Of course, another measurement method of the present application is to move the vehicle clockwise at a rotation angle of 30° while keeping the distance from the outer surface of the charging post being measured constant, and to obtain noise detection data from all measurement positions by going around the charging post being measured.

[0031] The preset measurement height is the height of the cooling fan system or transformer of the charging post to be measured, or is half the height of the charging post to be measured.

[0032] In this embodiment, the noise generated by the cooling fan system and transformer of the charging post to be measured is taken as the main noise source of the charging post, and therefore it is taken as the main monitoring point.

[0033] In an embodiment of the present application, the step S200 includes the following steps S210 to S230.

[0034] In step S210, the noise detection data is subjected to a short-time Fourier transform to obtain an initial spectrum and an initial phase.

[0035] S220 is a step of transmitting the initial spectrum to a noise prediction model, running the noise prediction model, and obtaining a background noise spectrum output from the noise prediction model, Specifically, the noise prediction model performs convergence training when the charging post to be measured is not open. As a specific modeling method, the pure signal and the noise signal are sampled at a certain frequency to form multiple new pure signals and noise signals of the same length. The pure signal and the noise signal are randomly shrunk and expanded and then mixed to obtain various mixed audios and ensure the diversity of the mixed audio. Various mixed audio signals are subjected to Fourier transform to finally obtain the input spectrum and train the noise prediction model.

[0036] In step S230, the background noise spectrum is subtracted from the initial spectrum, and the resulting signal undergoes Fourier transformation to obtain the initial noise signal after noise removal processing.

[0037] In this embodiment, the acquired noise detection data undergoes a noise removal process to eliminate the influence of the background environment, thereby improving the accuracy of the subsequent noise positioning result.

[0038] In one embodiment of the present application, in step S500, a vector composed of beamforming energy estimates for every scanning grid point is calculated using the following formula:

[0039]

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[0044] In practical calculations, the main diagonal elements of the cross power spectrum matrix C can be set to zero to eliminate the effect of noise.

[0045] In this embodiment, the 3D ball array is used to quickly realize the inverse analysis of the visualized sound source by the asynchronously measured beamforming method, which is robust and intuitive. At each measurement position, the scanning grid point corresponding to the maximum noise direction for the measured charging post can be obtained. Furthermore, the maximum noise position of all measurement positions around the measured charging post can be obtained, and the asynchronous measurement method can achieve the purpose of complementation and further improve the vertical resolution.

[0046] Although the above beamforming method can quickly realize the visualization sound source inverse analysis, the imaging result is not clear, especially for low frequency, as shown in FIG. 18.

[0047] In an embodiment of the present application, the S600 includes the following S610 to S620. S610 is a step of creating an energy transfer matrix shown in Formula 2 (Equation 2),

[0048]

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[0050] In an embodiment of the present application, the step S700 includes the following steps S710 to S730.

[0051] S710 is the variational prior distribution q1(x|γ x ),q2(γ x ) and q3(γ ε ), the joint distribution p(x,γ x ,γ ε |y) ∝ p(θ,y), Among them, q1(·), q2(·), q3(·) and p(·) are all probability density functions, and ∝ indicates a positive correlation.

[0052] S720 uses mean field theory to estimate the variational and prior parameters x (i.e., the high-resolution source energy distribution), γ x and γ ε In the step of integrating into the variable θ, q(θ)=q1(x)q2(γ x )q3(γ ε ) is satisfied. Among them, γ x is the covariance matrix of x, and γ ε is the covariance matrix of ε, which can be kept constant. The minimized KL (Kullback-Leibler) dispersion rule is calculated as follows:

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[0058] In this embodiment, the Bayesian estimation method can be used to explain the variables in the sound field from the perspective of statistical optimization, and by obtaining a solution for the high-resolution sound source energy distribution x, the Bayesian estimation method can be combined with beamforming to perform sound source positioning, and fast orientation can be performed for the low-frequency noise generated by the DC fast charging station. As shown in Figure 19, the imaging result has high resolution, the algorithm has strong anti-noise ability, and strong self-adaptation ability.

[0059] In an embodiment of the present application, after S700, the charging station noise monitoring method further includes the following S810 to S820.

[0060] In step S810, an average noise value at the measurement position of the charging post to be measured is calculated based on the noise detection data.

[0061] S820 is a step of creating a noise radiation map consisting of all measurement positions based on the azimuth angles of all measurement positions and the noise values ​​corresponding to each measurement position.

[0062] For example, in one embodiment described below, Figures 3 to 15 show the spatial distribution of noise signals and average noise collected from individual measurement locations, at a stage where the average noise values ​​are matched to corresponding measurement locations in a spatial distribution map of maximum temperature.

[0063] Figure 16 shows the spatial distribution of noise made up of all measurement locations, including the average noise value for all measurement locations.

[0064] Figure 17 shows the noise level classification of the spatial distribution of noise made up of all measurement positions. During the experiment, only the charging head on the left side of the charging post being measured was in operation. As can be seen from Figure 17, the areas with the loudest noise were the exhaust ports on both sides of the charging post, and the noise in the direction of the left exhaust port had already exceeded the Class 4 noise standard, resulting in noise pollution.

[0065] In this embodiment, by creating a noise radiation map, the noise surround situation in the vicinity of the charging post being measured can be intuitively shown, which is of great reference value for manufacturers' designs and the placement locations of DC charging posts.

[0066] In one embodiment of the present application, the S100 can collect multiple sets of noise detection data at each measurement location. The above-mentioned S810 includes the following S811 to S815. S811 is a step of selecting one measurement position. S812 is a step of determining whether the difference between the maximum value and the minimum value of the plurality of sets of noise detection data at the measurement positions exceeds 5 dB. S813 is a step in which, when the difference between the maximum and minimum values ​​of the multiple sets of noise detection data at the measurement position exceeds 5 dB, an average value is calculated for the multiple sets of noise detection data using the energy averaging method, and this average value is used as the average noise value at the measurement position.

[0067] Specifically, when calculating the average value using the energy averaging method, 10 sets of noise detection data are collected at the same time and time interval, and the specific calculation method can refer to the ISO near-field test method, and the specific description will not be repeated.

[0068] S814 is a step in which, when the difference between the maximum value and the minimum value among the multiple sets of noise detection data at the measurement position is less than 5 dB, an arithmetic average value is calculated for the multiple sets of noise detection data to determine the average noise value at the measurement position.

[0069] S815 is a step of returning to the step of selecting one measurement position until all measurement positions have been selected.

[0070] The present application further provides a noise monitoring system.

[0071] In one embodiment of the present application, the noise monitoring system includes a 3D ball array and an information processing device.

[0072] Specifically, the 3D ball array includes multiple microphones to collect sound pressure signals. More specifically, the 3D ball array uses a high-performance 64-bit ball array to maximize the accuracy of noise detection, thereby highly simulating the noise disturbance felt by people in a real environment.

[0073] An information processing device is connected to the 3D ball array, and is further connected to a memory and a processing device, the memory being coupled to the processing device, the memory being used for storing program data, and the processing device being used for executing the program data to realize the charging station noise monitoring method described in the above.

[0074] The present application further provides a computer-readable medium having a computer program stored thereon, which, when executed by a processing device, realizes the charging post noise monitoring method described in the above content.

[0075] The technical features described in the above embodiments can be arbitrarily combined. The steps of each method do not limit the implementation procedure, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described one by one, but it should be understood that any combination of these technical features is within the scope described in this specification as long as they do not conflict with each other.

[0076] The above examples only describe some embodiments of the present application, and the descriptions are relatively specific and detailed, but they should not be understood as limiting the scope of the claims of the present application. Those skilled in the art can make many modifications and modifications without departing from the idea of ​​the present application, which of course fall within the scope of protection of the present application.

Claims

1. A 3D ball array of microphones for use in a noise monitoring system, In an open state of the charging post to be measured, noise detection data including sound pressure signals measured by a plurality of microphones from several measurement positions surrounding the charging post to be measured in a circumferential direction are acquired; obtaining an initial noise signal by performing a noise reduction process on the obtained noise detection data; obtaining a noise signal of a target analysis frequency by filtering the initial noise signal; selecting a scan area on the sound source plane and uniformly dividing the scan area into a number of discrete scan grid points; obtaining a vector of beamforming energy estimates for every scan grid point according to the noise signal of the target frequency; creating an energy transfer model with a vector comprised of beamforming energy estimates for at least every scan grid point and an energy transfer matrix; Calculating a high-resolution sound source energy distribution of the measured charging post in the energy transfer model by a Bayesian estimation method; A method for monitoring noise at a charging post.

2. The step of acquiring noise detection data from several measurement positions around the charging post to be measured in a circumferential direction in an open state of the charging post to be measured includes: A step of setting a plurality of measurement positions at preset angular intervals with the center of the contour line of the charging post to be measured as an origin and a preset distance as a radius; presetting a measurement height and performing at least one noise detection at each measurement location along the predefined direction of movement; The method for monitoring noise at a charging post according to claim 1 .

3. The step of obtaining an initial noise signal by performing a noise removal process on the obtained noise detection data includes: obtaining an initial spectrum and an initial phase by performing a short-time Fourier transform on the noise detection data; Sending the initial spectrum to a noise prediction model, running the noise prediction model to obtain a background noise spectrum output from the noise prediction model, and performing intensive training on the noise prediction model in an unopened state of the charging post to be measured; and The step of subtracting the background noise spectrum from the initial spectrum and obtaining the initial noise signal after noise removal processing through Fourier transformation is included. The method for monitoring noise at a charging post according to claim 1 .

4. In the step of obtaining a vector consisting of beamforming energy estimates for every scanning grid point by the noise signal of the target analysis frequency, the vector consisting of beamforming energy estimates for every scanning grid point is [0010] Among them, y ｎ is the beamforming result of the nth scanning grid point on the target analysis frequency f, N is the total number of scanning grid points, and T is the transpose of the vector. y in the above number 1 ｎ Formula for (f): [0025] Among them, a ｎ is the steering vector from every microphone in the 3D ball array to the nth scanning grid point, * denotes the conjugate transpose, n denotes the order of the scanning grid point, and ||·|| ２ Is L ２ , C is the cross power spectrum matrix, a in the above number 2 ｎ Formula for (f): [0030] where M is the total number of microphones in the 3D ball array, m is the order of the microphones in the 3D ball array, and T denotes the transpose of the vector. a in the above number 3 ｍ，ｎ Formula for calculation: [0045] where j is a multiple unit, and r ｍ，ｎ is the distance between the mth microphone and the nth scanning grid point, c is the speed of sound in air, and f is the target analysis frequency. The formula for calculating C(f) in the above formula 2 is: [0050] Among them, p ｍ (f) shows the f frequency component obtained after noise removal and filtering of the sound pressure signal measured by the mth microphone in the 3D ball array, E[·] shows the expected value, and T shows the transpose of the vector. The method for monitoring noise at a charging post according to claim 1 .

5. generating an energy transfer model using a vector comprised of beamforming energy estimates for at least every scan grid point and an energy transfer matrix, The energy transfer matrix shown in Equation 2: [006] Among them, h ｎ１，ｎ２ is an element in H, The energy transfer model is created as follows: [0070] where y is a vector consisting of beamforming energy estimates for every scan grid point, and ε = (ε １ , ε ２ , ..., ε ｎ ) Ｔ is the model error, T is the vector transpose, and h ｎ１，ｎ２ is an element in the energy transfer matrix H, and n １ = 1 ,...,N、n ２ =1 , . . . , N,a ｎ１ is the nth １ are steering vectors from the scanning grid points to the 3D ball array, and a ｎ２ is the nth ２ are steering vectors from the scanning grid points to the 3D ball array, where * indicates the conjugate transpose. The charging post noise monitoring method according to claim 4 .

6. The step of solving for a high-resolution energy distribution of the measured charging post in the energy transfer model based on the Bayesian estimation method includes: Variation / prior distribution q １ (x | γ ｘ ), q ２ (γ ｘ ) and q ３ (γ ε ), the joint distribution p(x,γ ｘ , γ ε |y) ∝ p(θ, y); and Among them, q １ (・), q ２ (・), q ３ (·) and p(·) are both probability density functions, ∝ indicates positive correlation, By the mean field theory, the variational and prior distribution parameters x and γ ｘ and γ ε Integrate into the variable θ and give q(θ) = q １ (x)q ２ (γ ｘ )q ３ (γ ε ) and among them, γ ｘ is the covariance matrix of x, and γ ε is the covariance matrix of ε, The minimized KL dispersivity criterion is given by Equation 8: [0080] Based on the prior distribution knowledge, we show the variation / prior distribution of x with mean μ, and the variance is a multidimensional Gaussian distribution of Σ. Based on the sparsity and Gaussian mixture properties of the Student t distribution, we use γ ｘ and γ ε The step of expressing the variational and prior distribution of as the multiplicative product of the inverse gamma distribution. [0090] Among them, N(·) indicates normal distribution, According to the KL variance minimization rule, the iteration process of x is as follows: [0010] where l is the lth iteration, Gamma ｘ and γ ε The iterative process is as follows: ##EQU00011## The iterative deadline condition is ##EQU00012## where ρ is a constant, indicating that it is subject to error. The charging post noise monitoring method according to claim 5, characterized in that it comprises the three steps.

7. After obtaining a high-resolution sound source distribution solution of the measured charging post in the energy transfer model based on the Bayesian estimation method, the charging post noise monitoring method further comprises: Calculating a noise level at a measurement position of the charging post to be measured based on the noise detection data; generating a noise radiation map consisting of all the measurement locations based on the azimuth angles of all the measurement locations and the noise values ​​corresponding to each measurement location; The method for monitoring noise at a charging post according to claim 1 .

8. In an open state of the charging post to be measured, noise detection data is acquired from several measurement positions around a circumference of the charging post to be measured, and a plurality of sets of noise detection data are collected from each measurement position; The step of measuring a noise value at a measurement position of the charging post to be measured using the noise detection data includes: selecting a measurement location; determining whether a difference between a maximum value and a minimum value of the plurality of sets of noise detection data at the measurement positions exceeds 5 dB; calculating an average value of the plurality of sets of noise detection data by an energy averaging method when the difference between the maximum value and the minimum value of the plurality of sets of noise detection data at the measurement position exceeds 5 dB, and setting the average value as an average noise value at the measurement position; calculating an arithmetic mean value for the plurality of sets of noise detection data when the difference between the maximum value and the minimum value is less than 5 dB, and setting the arithmetic mean value as an average noise value for the corresponding measurement position; and returning to the step of selecting a measurement location until every measurement location has been selected. The charging post noise monitoring method according to claim 3 .

9. A 3D ball array is used to collect noise data from the charging posts under test; A noise monitoring system comprising a memory and a processing device coupled together, wherein the memory is used to store program data, and the processing device is used to execute the program data to realize the charging post noise monitoring method described in any one of claims 1 to 8, and further comprising an information processing device for connecting a signal to the 3D ball array.

10. A computer readable medium having a computer program stored thereon, the computer program being executed by a processor to implement the charging post noise monitoring method according to any one of claims 1 to 8.

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