Method and apparatus for detecting vibration distribution of an object based on a microphone array

Abstract

The present application relates to the field of voiceprint detection, in particular to the detection and positioning of object surface vibration. The present application is realized by the following technical scheme: a method for detecting object vibration distribution based on a microphone array, S01, hardware arrangement and sound collection; a multi-channel sound collection device is arranged around the component to be detected to collect sound signals of the component to be detected; S03, sound pressure distribution calculation step; based on the sound signals, a plurality of groups of frequency domain signal data are obtained through FFT transformation, and each group of the frequency domain signal data contains phase data and amplitude data of a microphone at different frequency points; S05, vibration distribution map acquisition step; the vibration distribution map atlas is converted into a vibration distribution map atlas based on the sound pressure gradient method. The present application aims to provide a method and device for detecting object vibration distribution based on a microphone array, which can not only identify the vibration of the object to be detected, detect abnormal sound of the voiceprint, but also can be positioned.

Description

Technical Field

[0001] This invention relates to the field of voiceprint detection, specifically to the detection and localization of vibrations on the surface of an object. Background Technology

[0002] Voiceprint detection technology has been increasingly widely used in the inspection of objects and equipment in recent years, such as for non-destructive testing of the health of industrial equipment. Industrial equipment, such as large manufacturing equipment and molding equipment, is widely used in factories, research institutions, and other places, and is an important asset of its owners. During the use of these industrial equipment, it is necessary to monitor their health status daily to prevent them from stopping operation due to malfunctions or causing safety hazards.

[0003] Among various health monitoring methods, voiceprint detection technology has become an important means of fault detection in industrial equipment due to its advantages such as controllable hardware investment costs, reduced labor costs, and wide monitoring range, compared to video surveillance and on-site monitoring by administrators. For example, Chinese patent document CN 112513757 A discloses a system for monitoring industrial equipment, which places audio sensors near the industrial equipment to capture voiceprint signals. The system also includes a computing device to identify the voiceprint signals, marking abnormal sounds as anomalous sounds, and comparing them with faulty anomalous sounds in a database.

[0004] With technological advancements, the requirements for voiceprint detection are becoming increasingly stringent. Current voiceprint detection technologies often qualitatively determine whether an object being detected, such as industrial equipment, exhibits abnormal sounds. However, industrial equipment typically has numerous components and covers a large area, necessitating considerations for localization beyond simply identifying abnormal sounds. This means using voiceprint recognition and analysis to pinpoint the specific component experiencing the malfunction. Summary of the Invention

[0005] The purpose of this invention is to provide a method and device for detecting the vibration distribution of an object based on a microphone array. This method can not only identify the vibration of the object under test and detect abnormal voiceprints, but also locate the sound pressure signal at different spatial locations.

[0006] This invention is achieved through the following technical solution: a method for detecting the vibration distribution of an object based on a microphone array, comprising the following steps: S01, hardware setup and sound acquisition; setting up a multi-channel sound collection device around the component under test to collect the sound signal of the component under test; the sound signal is in time-domain signal format, and the multi-channel sound collection device includes multiple microphones; S03, sound pressure distribution calculation step; based on the sound signal, performing an FFT transformation to obtain multiple sets of frequency domain signal data, each set of frequency domain signal data including the phase data and amplitude data of one microphone at different frequency points; determining the weighting coefficient 'a' for each microphone. m Based on the frequency domain signal data corresponding to each microphone and the corresponding weighting coefficient a m Sound pressure reconstruction is performed to obtain sound pressure distribution data p(r) for each reconstruction point. The sound pressure distribution data of all reconstruction points are summarized into a sound pressure distribution map atlas. The sound pressure distribution maps at different frequencies contain the location information of each reconstruction point and the sound pressure value of the reconstruction point at that frequency. S05, vibration distribution map acquisition step: Based on the sound pressure distribution map atlas, it is converted into a vibration distribution map atlas using the sound pressure gradient method. The vibration distribution maps at different frequencies contain the location information of each reconstruction point and the vibration velocity value of the reconstruction point at that frequency.

[0007] As a preferred embodiment of the present invention, in S03, for the weighting coefficient a m The method for determining it is either the general method or the least squares method.

[0008] As a preferred embodiment of the present invention, in S03, for the weighting coefficient a m The method for determining this is the Green's function method, specifically: ,in, It is the Green's function, r m Let r be the position of the m-th microphone, r be the position of the reconstructed point, j be an imaginary number, and k be the wavenumber; then, the Green's function is applied. To calculate the weighting coefficient 'a' for each microphone m , ,in, The generalized inverse of the Green's function. This is the actual sound pressure level measured by microphone m, collected from the frequency domain signal data of each group; the sound pressure distribution data p(r) at the reconstructed point is calculated as follows: m is the microphone number, and M is the total number of microphones.

[0009] As a preferred embodiment of the present invention, in S01, the microphone array is configured as a spiral array.

[0010] As a preferred embodiment of the present invention, in S03, a filtering step is performed before the FFT transformation to filter out low-frequency noise and high-frequency noise of the sound signal and retain the mid-range sound signal.

[0011] As a preferred embodiment of the present invention, the frequency value of the mid-range sound signal is 10Hz-20KHz.

[0012] As a preferred embodiment of the present invention, step S05 further includes the following sub-steps: S051, converting the sound pressure distribution data p(r) from the frequency domain format to the beam domain format by using FFT transformation; S052, filtering and separating the propagating waves using the Helmholtz equation, and retaining the evanescent waves; S053, using the retained evanescent wave data as input data, and obtaining the vibration velocity distribution map using the sound pressure gradient method.

[0013] An electronic device includes a processor and a memory; the processor is connected to the memory.

[0014] The memory is used to store executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to execute the method.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0016] In summary, the present invention has the following beneficial effects:

[0017] 1. Ordinary sound signals are converted into sound pressure distribution maps. The sound pressure distribution map contains the location information of each reconstruction point and the sound pressure value of the reconstruction point at that frequency. Therefore, it can not only analyze whether there is an abnormality in the component under test, but also find out the location of the abnormality.

[0018] 2. Finally, a two-dimensional vibration distribution map is generated, which intuitively displays the high / low vibration zones.

[0019] 3. The microphone is arranged in a spiral pattern, which is very suitable for detecting surface vibration of objects. The algorithm is not complicated, runs quickly, and does not require contact with the object under test or the setting of a third-party sensor. This method can avoid interfering with the normal operation of the object under test and also avoids sensor wiring costs.

[0020] 4. It adopts a dual noise suppression mechanism, namely bandpass filtering and evanescent wave separation, which improves the signal-to-noise ratio and can work stably even in noisy industrial environments.

[0021] 5. It has high adaptability to the testing environment. It adopts a multi-channel microphone array combined with statistical optimal weights, which can tolerate some microphone failures or obstructions, making it suitable for curved surfaces or obstructed objects. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart illustrating the sub-step of S03 in Example 1;

[0024] Figure 3 This is a flowchart of the sub-step S05 of Example 2. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to the accompanying drawings.

[0026] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0027] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0028] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0029] Example 1, as Figure 1 and Figure 2 As shown, a method for detecting the vibration distribution of an object based on a microphone array is described. Figure 1The diagram shows the flowchart of the method. Step S01 involves hardware setup and sound acquisition. In this embodiment, an industrial drive device (including a motor and gearbox) is used as an example. A multi-channel sound collection device is placed near the component under test. This device consists of multiple microphones arranged in a preset array. The number of microphones and the array configuration can be set according to the specific testing scenario; this document does not impose any restrictions. A higher number of microphones results in more complete vibration signal acquisition, but also increases hardware costs.

[0030] The number of microphones to consider should be determined by four factors: the detection frequency range, the target object size, resolution requirements, and the signal-to-noise ratio (SNR). The detection frequency range refers to the range of frequencies of the vibrational sound signals to be collected; according to the Nyquist law, higher frequency vibrations require more and denser sampling. The target object size refers to the dimensions of the part being measured; larger objects require more microphones to cover the space. Higher resolution requires a greater number of acoustic signal channels, i.e., more microphones. The number of microphones needed varies depending on the SNR environment. For example, in low SNR environments, more microphones are needed to suppress noise.

[0031] However, generally, the number of microphones should not be less than 32; otherwise, it will affect the accuracy of data acquisition and processing in subsequent steps, and insufficient spatial sampling will lead to distortion in the reconstruction of subsequent steps. In this embodiment, 208 channels, i.e., 208 microphones, are used.

[0032] The array geometry of the microphone directly affects the accuracy of sound field reconstruction and noise immunity. One of three types can be chosen: open planar array, spherical array, or nested array. Spherical arrays offer strong omnidirectional sound source localization and resistance to environmental noise, but their complex structure and high cost in practical applications make them suitable. Nested arrays can handle both high and low frequencies and offer high flexibility, but require very complex calibration.

[0033] In this embodiment, an open planar array, such as a network or a spiral, is used. The advantage of this approach is that it is very suitable for detecting surface vibrations of objects, and the algorithm design is not complex, with fast running efficiency.

[0034] like Figure 2 As shown, the process then proceeds to step S03, the sound pressure distribution calculation step.

[0035] The final result of this step is the creation of a sound pressure distribution atlas.

[0036] At the end of step S01, after setting up the number and array of microphones, the signal of the component under test is directly acquired. At this time, the input signal is the original sound signal.

[0037] At this point, the first sub-step of S03, S031, the filtering step, is initiated. This involves performing a bandpass filter on the audio signal to remove interference waves, thus processing the original audio signal into a cleaned-up audio signal.

[0038] This step involves preprocessing the sound signal, which contains numerous interfering sounds, such as low-frequency noise and high-frequency noise. The former often originates from power fluctuations and environmental vibrations, and its presence can distort subsequent steps in this application, such as the FFT transformation, leading to errors in the subsequent sound pressure level calculation. The latter originates from electronic circuit noise, radio interference, or sharp environmental noises, which can mask the true vibration signal of an object. After the FFT transformation, it may be misinterpreted as high-frequency vibration, causing false hotspots in the reconstructed sound pressure level map, ultimately leading to misdiagnosis of the fault.

[0039] In this case, engineers set the frequency range to remove sound signals with values ​​higher than high frequency and lower than low frequency. The high and low frequency values ​​are set by the engineers themselves; in this embodiment, the low frequency value is 10Hz and the high frequency value is 20kHz.

[0040] After the bandpass filtering operation, the second sub-step of S03, S032, the FFT transform step, is performed.

[0041] This step involves using a Fast Fourier Transform (FFT) to obtain the frequency domain signal from the noise-removed audio signal. The noise-removed audio signal is in a time-domain format, representing the waveform of sound recorded by the microphone changing over time. After FFT, this signal becomes a frequency-domain signal. This format is a digital array containing frequency points and their corresponding phase and amplitude data for that microphone at that frequency. There are multiple frequency points, representing discrete frequency values ​​within the set frequency range from the lowest to the highest frequency in the previous step. The phase data represents the initial time difference of the signal at that frequency. The amplitude data represents the signal intensity at that frequency.

[0042] For example, microphone A has an amplitude of 0.8 rad and a phase of 0.2 rad at 50 Hz; an amplitude of 0.5 rad and a phase of 0.5 rad at 100 Hz... In some application scenarios, engineers can treat the frequency domain signal format as a two-dimensional spectrum for visualization purposes.

[0043] Each microphone corresponds to its own set of frequency domain signal data. In this embodiment, after FFT transformation, 208 sets of frequency domain signal data are obtained from 208 microphones.

[0044] Then, proceed to the last sub-step in step S03, S033, the sound pressure calculation step.

[0045] Since there are multiple microphones, such as 208 microphones in this embodiment, S033 first needs to calculate the weight coefficient 'a' for each microphone. m Where m is the microphone number. The weighting coefficients can be determined using various methods available in the art, such as the generalized method or the least squares method. In this embodiment, the Green's function method is used, and the specific formula is as follows.

[0046]

[0047] in, It is the Green's function, i.e., the transfer function, r m Let be the position of the m-th microphone, which is a known quantity. Let r be the position of the reconstructed point, also a known quantity. The final sound pressure distribution map obtained by S03 is a two-dimensional attribute and corresponds to the projection of the object under test onto the monitored two-dimensional surface. That is, the reconstructed point positions on the sound pressure distribution map correspond to the positions of the object under test. For example, if the resolution of the sound pressure distribution map is 640*480, it means there are 307200 reconstructed points. On the monitored two-dimensional surface, the third gear of the reducer is in the lower left corner, and the lower left corner of the sound pressure distribution map corresponds to the third gear portion.

[0048] In the formula above, j is an imaginary number, a constant. k is the wave number, k = 2πf / c, where f is the sound wave frequency, directly obtained from the frequency domain signal after FFT, requiring no calculation, and c is the speed of sound. Therefore, k is also determined here.

[0049] It should be noted that, based on the formula and parameter analysis above, at this point... The calculation can be obtained when the device is turned on, rather than after the audio is captured.

[0050] Subsequently, through the Green's function To calculate the weighting coefficient 'a' for each microphone m The formula is as follows: ,in, This involves the generalized inverse calculation of the Green's function, which can be achieved using existing techniques such as Singular Value Decomposition (SVD) or QR decomposition. This formula calculates the contribution weight 'a' of each microphone to the sound pressure level at the reconstructed point. m .in This is the actual sound pressure level measured by microphone m. This value is obtained after FFT transformation via S032 mentioned above, and can be directly collected here.

[0051] Proceed to the final sub-step of S033, based on the weight coefficient a. m , and the sound pressure level collected by the microphone Sound pressure reconstruction is then performed to obtain sound pressure distribution data p(r). This is a synthesis step, and its calculation formula is as follows:

[0052] This formula synthesizes the final sound pressure level of the reconstructed point, where m is the microphone number and M is the total number of microphones, which is 208 in this embodiment.

[0053] It should be noted that at the end of S032, 208 sets of frequency domain signal data are obtained. However, at the end of S033, the final form of the sound pressure distribution data of all reconstructed points is a sound pressure distribution map atlas, which is not 208 sets, but only 1 set. This is because in the calculation process of S033, a synthesis calculation has already been performed based on the data of each microphone and the corresponding weights, so only one set of sound pressure distribution maps is generated.

[0054] The sound pressure level distribution atlas contains multiple spectrograms, each representing the sound pressure level distribution at a specified frequency, such as the sound pressure level distribution at 50 Hz, 60 Hz, and 70 Hz. Each sound pressure level distribution map includes the location information of each reconstructed point and the sound pressure value at that reconstructed point at that frequency, which also includes phase and amplitude data.

[0055] Besides using the Green's function to calculate the sound pressure distribution map as mentioned above, there are other mathematical models, such as the MVDR model, which can also be used to calculate the sound pressure distribution.

[0056] Entering step S05, the vibration distribution map acquisition step, is essentially the reconstruction of the inverse vibration model. The core function of this step is to present the results of S03 in a more intuitive way—the vibration model—making it easier for staff to observe. The vibration model is implemented as a vibration distribution map atlas, which contains multiple vibration distribution maps at different frequencies, such as vibration distribution maps at 50Hz, 60Hz, and 70Hz sound pressure levels. Each vibration distribution map contains the location information of each reconstructed point and the vibration velocity data of that reconstructed point at that frequency.

[0057] The vibration distribution map is obtained by converting the sound pressure distribution map of S03 using the sound pressure gradient method in the prior art. On the vibration distribution map, engineers can see the vibration velocity data of each reconstructed point. For example, if the vibration velocity data in the lower left corner of the map mentioned above is too high, it is very likely that there is a problem with gear No. 3. It may be loosely installed, resulting in poor meshing with other meshing parts, thus producing more noise.

[0058] The algorithm for the sound pressure gradient method is existing technology and will not be described in detail here.

[0059] In addition, users can select the frequency corresponding to the vibration distribution map to be observed first, based on the actual testing environment. For example, if the motor speed is 600 rpm and the gear ratio of the reduction gear connected to the motor is 25:1, then the frequency corresponding to 2.5 kHz is the frequency to be observed first, as the vibration velocity data at this frequency can be observed more directly and accurately.

[0060] Example 2 differs from Example 1 in that, in step S05, the inverse vibration model reconstruction includes a sub-step: a propagation wave filtering step. For example... Figure 3 As shown.

[0061] Sound waves consist of two types: propagating waves and evanescent waves. Propagating waves are sound waves that can travel long distances, such as environmental noise, and can mask the true vibration signals of an object's surface. Evanescent waves are attenuated waves that exist only near the surface of an object, directly carrying vibration information; they are weak in intensity and have a short propagation distance. Ordinary sound signals contain both types of sound waves, but by filtering out propagating waves, only evanescent waves are retained.

[0062] This is because the signals generated by minute vibrations on an object's surface are very weak and easily drowned out by propagating waves and environmental noise. Evanescent waves are specifically designed for surface vibrations, and filtering them amplifies these weak signals. This step allows the system to detect vibrations that are inaudible to the human ear or conventional equipment, increasing sensitivity by 10 times. For example, in fault diagnosis, it can detect early cracks or loosening of the component under test, preventing equipment damage.

[0063] Specifically, the sound pressure distribution data p(r) obtained at the end of S03 is first converted from the frequency domain format to the beam domain format by using FFT transformation.

[0064] Subsequently, the Helmholtz equation is used for filtering and separation, removing the propagating wave and retaining the evanescent wave. The two parts—using FFT for beam domain format transcribing and using the Helmholtz equation to filter the propagating wave—are existing technologies, and the specific mathematical formulas will not be elaborated here.

[0065] The retained evanescent wave data is used as input data, and the vibration velocity distribution atlas is converted using the sound pressure gradient method. This is the same as in Example 1.

[0066] The present invention also discloses an electronic device that may include: at least one processor, at least one network interface, a memory, and at least one communication bus.

[0067] The communication bus can be used to enable communication between the various components mentioned above.

[0068] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0069] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0070] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0071] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a broadcast upgrade application. The processor can be used to call the application stored in the memory and execute the steps of the highly versatile design method for transition metal chromophores mentioned in the foregoing embodiments.

[0072] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above-described broadcast upgrade method embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0073] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0075] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A method for detecting the vibration distribution of an object based on a microphone array, comprising the following steps: S01, hardware setup and sound acquisition; setting up a multi-channel sound collection device around the component under test to collect the sound signal of the component under test; the sound signal is in time-domain signal format, and the multi-channel sound collection device includes multiple microphones; S03, sound pressure distribution calculation step; based on the sound signal, performing an FFT transformation to obtain multiple sets of frequency domain signal data, each set of frequency domain signal data including the phase data and amplitude data of one microphone at different frequency points; determining the weighting coefficient α for each microphone. m Based on the frequency domain signal data corresponding to each microphone and the corresponding weighting coefficient a m Sound pressure reconstruction is performed to obtain sound pressure distribution data p(r) for each reconstruction point. The sound pressure distribution data of all reconstruction points are summarized into a sound pressure distribution map atlas. The sound pressure distribution maps at different frequencies contain the location information of each reconstruction point and the sound pressure value of the reconstruction point at that frequency. S05, vibration distribution map acquisition step: Based on the sound pressure distribution map atlas, it is converted into a vibration distribution map atlas using the sound pressure gradient method. The vibration distribution maps at different frequencies contain the location information of each reconstruction point and the vibration velocity value of the reconstruction point at that frequency.

2. The method for detecting the vibration distribution of an object based on a microphone array according to claim 1, characterized in that: In S03, for the weighting coefficient a m The method for determining it is either the general method or the least squares method.

3. The method for detecting the vibration distribution of an object based on a microphone array according to claim 1, characterized in that: In S03, for the weighting coefficient a m The method for determining this is the Green's function method, specifically: ,in, It is the Green's function, r m Let r be the position of the m-th microphone, r be the position of the reconstructed point, j be an imaginary number, and k be the wavenumber; then, the Green's function is applied. To calculate the weighting coefficient 'a' for each microphone m , ,in, The generalized inverse of the Green's function. This is the actual sound pressure level measured by microphone m, collected from the frequency domain signal data of each group; the sound pressure distribution data p(r) at the reconstructed point is calculated as follows: m is the microphone number, and M is the total number of microphones.

4. The method for detecting the vibration distribution of an object based on a microphone array according to claim 1, characterized in that: In S01, the microphone array is configured as a spiral array.

5. The method for detecting the vibration distribution of an object based on a microphone array according to claim 1, characterized in that: In S03, a filtering step is performed before the FFT transformation to filter out low-frequency noise and high-frequency interference in the sound signal, while retaining the mid-range sound signal.

6. The method for detecting the vibration distribution of an object based on a microphone array according to claim 5, characterized in that: The frequency of the mid-range sound signal is 10Hz-20kHz.

7. The method for detecting the vibration distribution of an object based on a microphone array according to claim 1, characterized in that: S05 also includes the following sub-steps: S051, converting the sound pressure distribution data p(r) from the frequency domain format to the beam domain format using FFT transformation; S052, filtering and separating using the Helmholtz equation to remove the propagating wave and retain the evanescent wave; S053, using the retained evanescent wave data as input data, and obtaining the vibration distribution map using the sound pressure gradient method.

8. An electronic device, comprising a processor and a memory; the processor being connected to the memory; The memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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