Vibration source frequency positioning method for beat vibration detection of building structure and electronic equipment

Abstract

The invention relates to the field of building structure vibration detection and analysis, and discloses a vibration source frequency positioning method for building structure beat vibration detection and electronic equipment. The method comprises the steps of determining a beat vibration frequency based on a time domain waveform of a vibration signal sample; based on the linear average amplitude spectrum of the vibration signal sample, determining interested spectrum peak frequency points of the left half part of the linear average amplitude spectrum, and determining a series of band-pass frequency bands corresponding to the interested spectrum peak frequency points based on a preset series of bandwidths; performing band-pass filtering processing on the vibration signal sample based on the serialized band-pass frequency bands corresponding to the interested spectrum peak frequency points to obtain band-pass sub-signals; and determining an effective value fluctuation frequency approximation coefficient based on the band-pass sub-signal and the beat vibration frequency, and taking the frequency band with the highest effective value fluctuation frequency approximation coefficient as the frequency band where the beat vibration source frequency is located. By combining the information of the time domain and the frequency, the characteristic value capable of representing the existence possibility of the beat vibration in each narrow-band frequency band is obtained, and then the vibration source frequency is accurately positioned.

Description

Technical Field

[0001] This disclosure relates to the field of building structure vibration detection and analysis, and in particular to a method, apparatus, storage medium, electronic device and computer program product for locating the vibration source frequency of building structure beat vibration detection. Background Technology

[0002] In workshops such as coal preparation plants and stone quarries that use vibrating equipment, the vibrations from the equipment are transmitted to the building structure, including floors and columns, causing structural vibrations. If the vibrations generated by two pieces of equipment cannot be effectively isolated, and their frequencies are relatively close, the building structure will experience beat vibration. On the one hand, this may cause the coupled amplitude to exceed design standards; on the other hand, due to the extremely low fluctuation frequency of beat vibration, it can seriously affect the physical and mental health of operators. Therefore, it is essential to accurately detect whether beat vibration is occurring and to locate the distant frequency of the beat vibration, thereby identifying the distant equipment and providing a basis for taking appropriate measures to reduce and isolate vibration.

[0003] The basic principle of beat vibration is that when signals are superimposed, the peaks of the waves amplify each other, while the peaks and troughs attenuate each other. The amplitude of the coupled signal exhibits a periodic change, and the frequency of this change is the difference between the frequencies of the two source signals. When the frequency difference between the signals is very small, the amplitude of the coupled signal exhibits obvious periodic fluctuations, with the fluctuation frequency being the difference between the frequencies of the two signals.

[0004] Under ideal conditions, the test signal consists only of ideal harmonic signals with stable frequency and amplitude. As long as the test signal is long enough, the spectral resolution of the Fourier transform will be high enough, and the closest spectral peak in the spectrum will be the frequency of the source signal.

[0005] However, in industrial settings, due to voltage and load fluctuations, the excitation frequency of equipment can fluctuate slightly over time. This results in a single source not exhibiting a single spectral peak in the Fourier transform spectrum, but rather side spectral peaks. Under these complex conditions, traditional frequency domain observation methods are insufficient to effectively determine whether a peak adjacent to another source originates from a different source or is a side spectral peak of the same source. This makes it even more difficult to pinpoint the frequency of the beat vibration source. Summary of the Invention

[0006] In view of this, and addressing the deficiencies in the related technologies as described above, the object of the present invention is to provide at least one method, apparatus, storage medium, electronic device, and computer program product for locating the vibration source frequency in building structure beat vibration detection. By combining various information in the time domain and frequency domain, characteristic values ​​that can characterize the possibility of beat vibration in each narrowband frequency band are obtained, thereby accurately locating the vibration source frequency from complex vibration detection signals.

[0007] To address the aforementioned technical problems, at least one embodiment of this application provides a method for locating the vibration source frequency in building structure beat vibration detection, the method comprising: Based on the time-domain waveform of the vibration signal samples, it is determined whether a beat vibration phenomenon exists and the beat vibration frequency is determined; wherein, the vibration signal samples include vibration signals corresponding to multiple data acquisition points; Based on the linear average amplitude spectrum of the vibration signal sample, determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum, and determine the series of bandwidth corresponding to each peak frequency point of interest based on the preset series of bandwidths. Based on the series of bandpass frequency bands corresponding to the peak frequency points of each of the aforementioned spectra of interest, the vibration signal samples are subjected to bandpass filtering to obtain bandpass sub-signals; Based on the bandpass sub-signal and the beat frequency, the effective value fluctuation frequency approximation coefficient is determined, and the frequency band with the highest effective value fluctuation frequency approximation coefficient is taken as the frequency band where the beat vibration source frequency is located.

[0008] At least one embodiment of this application also provides a vibration source frequency localization device for detecting beat vibration in building structures, comprising: The beat frequency determination module is used to determine whether a beat phenomenon exists and to determine the beat frequency based on the time-domain waveform of the vibration signal sample. A serialized bandpass determination module is used to determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum based on the linear average amplitude spectrum of the vibration signal sample, and to determine the serialized bandpass corresponding to each peak frequency point of interest based on a preset serialized bandwidth. The bandpass sub-signal determination module is used to perform bandpass filtering on the vibration signal sample based on the series of bandpass frequency bands corresponding to each of the frequency points of the spectrum of interest peaks to obtain the bandpass sub-signal; The beat vibration source frequency determination module is used to determine the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat vibration frequency, and to take the frequency band with the highest effective value fluctuation frequency approximation coefficient as the frequency band where the beat vibration source frequency is located.

[0009] At least one embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0010] At least one embodiment of this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described above.

[0011] At least one embodiment of this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0012] The method, apparatus, storage medium, electronic device, and computer program product for locating the vibration source frequency of building structure beat vibration detection provided in this application embodiment, compared with the prior art, obtains characteristic values ​​that can characterize the possibility of beat vibration in each narrowband frequency band by combining multiple information in the time domain and frequency domain, thereby accurately locating the vibration source frequency from complex vibration detection signals.

[0013] In some optional embodiments, the frequency point in the left half of the linear average amplitude spectrum where the peak characteristic value is equal to 1 is defined as the spectral peak frequency point.

[0014] In some optional embodiments, determining the peak frequency points of interest in the left half of the linear average amplitude spectrum based on the linear average amplitude spectrum of the vibration signal samples, and determining the series of passbands corresponding to each peak frequency point of interest based on a preset series of bandwidths, includes: The linear average amplitude spectrum of the vibration signal sample is determined by Fourier transform, and the amplitude corresponding to each frequency point in the left half of the linear average amplitude spectrum is determined. Based on the comparison results of the normalized amplitude and preset threshold of each spectral peak frequency point, the set of spectral peak frequency points of interest is determined; For any one of the frequency points of interest in the set of frequency points of interest, the serialized passband corresponding to the current frequency point of interest is determined based on the preset serialized bandwidth.

[0015] In some optional embodiments, determining the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat frequency includes: The short-time effective value sequence of the bandpass sub-signal is calculated using the sliding window method; Perform a Fourier transform on the short-time effective value sequence to obtain the amplitude spectrum of the short-time effective value sequence; Based on the peak characteristic value and amplitude of each frequency point in the amplitude spectrum of the short-time effective value sequence, the first high-spectral peak and the second high-spectral peak are determined, and the approximate centroid peak frequency is determined based on the first high-spectral peak and the second high-spectral peak. Based on the approximate centroid peak frequency and the beat frequency, the approximate coefficient of the effective value fluctuation frequency is determined.

[0016] In some optional embodiments, the formula for determining the approximation coefficient of the effective value fluctuation frequency includes:

[0017] in, This is an approximation coefficient for the effective value fluctuation frequency. For beat frequency, This is the approximate frequency of the centroid spectrum peak.

[0018] In some optional embodiments, the formula for determining the approximate centroid spectral peak frequency includes:

[0019] in, To approximate the peak frequency of the centroid spectrum, The frequency of the first high-frequency peak. The amplitude of the first high spectral peak. The frequency of the second highest spectral peak. This represents the amplitude of the second highest spectral peak. Attached Figure Description

[0020] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0021] Figure 1 A flowchart illustrating a method for locating the vibration source frequency in a building structure beat vibration detection embodiment provided in this disclosure; Figure 2 A time-domain waveform diagram of a vibration signal sample provided in an embodiment of this disclosure; Figure 3 An amplitude spectrum waveform of a vibration signal sample provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram of the result of peak detection in the amplitude spectrum provided in an embodiment of the present disclosure; Figure 5 A schematic diagram of the envelope frequency similarity coefficients of each bandpass sub-signal provided in an embodiment of this disclosure; Figure 6 The diagram shows the waveform and envelope waveform of the reconstructed time-domain signal of the vibration source frequency band of the detected beat vibration phenomenon provided in this embodiment of the disclosure. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been presented in the various embodiments of the present invention to enable the reader to better understand the present invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0023] Example 1: The embodiments of the present invention relate to a method for locating the vibration source frequency in the detection of vibration in building structures.

[0024] The following is a detailed explanation of the implementation details of the geothermal well main water-producing layer determination method in this embodiment. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0025] The vibration source frequency localization method for building structure beat vibration detection in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities.

[0026] like Figure 1 As shown, the method for locating the vibration source frequency in building structure beat vibration detection provided in this embodiment includes the following steps: Step 110: Based on the time-domain waveform of the vibration signal sample, determine whether there is a beat vibration phenomenon and determine the beat vibration frequency.

[0027] The vibration signal sample includes vibration signals corresponding to multiple data acquisition points.

[0028] Specifically, firstly, vibration signal acquisition equipment is used at a sampling frequency. A signal of length N is acquired, and its mean is zeroed out to obtain vibration signal samples. Then, observe the time-domain waveform of the vibration signal sample (i.e., the original signal sample). If the amplitude exhibits periodic changes, it indicates the presence of beat vibration, and the period of amplitude fluctuation is the beat vibration period. The reciprocal of the amplitude fluctuation period is the beat frequency. .

[0029] Step 120: Based on the linear average amplitude spectrum of the vibration signal sample, determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum, and determine the serialized passband corresponding to each peak frequency point of interest based on the preset serialized bandwidth.

[0030] Specifically, based on width The sliding window extracts signal segments from the original signal samples, then performs a Fast Fourier Transform on each signal segment to obtain the amplitude spectrum of each segment, and finally performs a linear average of the amplitude spectra of the signal samples to obtain the linear average amplitude spectrum of the signal samples. The frequencies corresponding to each data point in the left half of the linear average amplitude spectrum are... ,and .

[0031] For the left half of the linear average amplitude spectrum Further processing can be performed using the following function to calculate the peak characteristic value corresponding to each frequency point. :

[0032] if If, then the data point is a spectral peak; if If the value is not a spectral peak, then the data point is not a spectral peak.

[0033] Understandably, the frequency points in the left half of the linear average amplitude spectrum where the peak eigenvalue is equal to 1 can be defined as spectral peak frequencies.

[0034] The preset series of bandwidths include: .

[0035] Step 130: Based on the series of bandpass frequency bands corresponding to the frequency points of each of the spectrum of interest peaks, perform bandpass filtering on the vibration signal samples to obtain bandpass sub-signals.

[0036] Specifically, for each bandpass frequency band Bandpass filtering is performed on the original signal samples to obtain bandpass sub-signals. In this example, the bandpass filtering method used includes setting all parts of the signal's Fourier spectrum outside the passband to zero, and then performing an inverse Fourier transform.

[0037] Step 140: Determine the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat frequency, and take the frequency band with the highest effective value fluctuation frequency approximation coefficient as the frequency band where the beat source frequency is located.

[0038] The vibration source frequency localization method for building structure beat vibration detection provided in this embodiment, compared with the prior art, obtains characteristic values ​​that can characterize the possibility of beat vibration in each narrow band by combining multiple information in the time domain and frequency domain, thereby accurately locating the vibration source frequency from the complex vibration detection signal.

[0039] Example 2: Based on the above embodiments, this embodiment further explains and illustrates the vibration source frequency localization method for building structure beat vibration detection provided in the above embodiments.

[0040] In step 110: Based on the time-domain waveform of the vibration signal sample, determine whether there is a beat vibration phenomenon and determine the beat vibration frequency.

[0041] The vibration signal sample includes vibration signals corresponding to multiple data acquisition points.

[0042] Specifically, firstly, vibration signal acquisition equipment is used at a sampling frequency. A signal of length N is acquired, and its mean is zeroed out to obtain vibration signal samples. Then, observe the time-domain waveform of the vibration signal sample (i.e., the original signal sample). If the amplitude exhibits periodic changes, it indicates the presence of beat vibration, and the period of amplitude fluctuation is the beat vibration period. The reciprocal of the amplitude fluctuation period is the beat frequency. .

[0043] In step 120: Based on the linear average amplitude spectrum of the vibration signal sample, determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum, and determine the serialized passband corresponding to each peak frequency point of interest based on the preset serialized bandwidth.

[0044] In some embodiments, determining the peak frequency points of interest in the left half of the linear average amplitude spectrum based on the linear average amplitude spectrum of the vibration signal samples, and determining the series of bandwidth corresponding to each peak frequency point of interest based on a preset series of bandwidths, includes: The linear average amplitude spectrum of the vibration signal sample is determined by Fourier transform, and the amplitude corresponding to each frequency point in the left half of the linear average amplitude spectrum is determined. Based on the comparison results of the normalized amplitude and preset threshold of each spectral peak frequency point, the set of spectral peak frequency points of interest is determined; For any one of the frequency points of interest in the set of frequency points of interest, the serialized passband corresponding to the current frequency point of interest is determined based on the preset serialized bandwidth.

[0045] Specifically, based on width The sliding window extracts signal segments from the original signal samples, then performs a Fast Fourier Transform on each signal segment to obtain the amplitude spectrum of each segment, and finally performs a linear average of the amplitude spectra of the signal samples to obtain the linear average amplitude spectrum of the signal samples. The frequencies corresponding to each data point in the left half of the linear average amplitude spectrum are... ,and .

[0046] For the left half of the linear average amplitude spectrum Further processing can be performed using the following function to calculate the peak characteristic value corresponding to each frequency point. :

[0047] if If, then the data point is a spectral peak; if If the value is not a spectral peak, then the data point is not a spectral peak.

[0048] In some embodiments, each frequency point in the left half of the linear average amplitude spectrum where the peak eigenvalue is equal to 1 is defined as a spectral peak frequency point.

[0049] The preset series of bandwidths include: .

[0050] First, the amplitude of each spectral peak frequency point is... Normalize the peak value to 0~1 to obtain the normalized amplitude. Then, the normalized amplitude of each spectral peak frequency point is compared with a preset threshold. By comparison, the frequency points of interest with higher normalized amplitudes are obtained, and their corresponding indices are... The preset threshold can be set between 0.05 and 0.15, specifically 0.1, or selected according to actual needs.

[0051] Then, focus on the frequencies of each spectral peak of interest. Set a series of bandwidth A series of bandpass frequency bands were obtained. In this example, the series bandwidth set can be the aforementioned preset series bandwidth.

[0052] In step 130: Based on the series of bandpass frequency bands corresponding to each of the frequency points of interest in the spectrum peak, the vibration signal sample is subjected to bandpass filtering to obtain a bandpass sub-signal.

[0053] Specifically, for each bandpass frequency band Bandpass filtering is performed on the original signal samples to obtain bandpass sub-signals. In this example, the bandpass filtering method used includes setting all parts of the signal's Fourier spectrum outside the passband to zero, and then performing an inverse Fourier transform.

[0054] In step 140: Based on the bandpass sub-signal and the beat frequency, the effective value fluctuation frequency approximation coefficient is determined, and the frequency band with the highest effective value fluctuation frequency approximation coefficient is taken as the frequency band where the beat vibration source frequency is located.

[0055] In some embodiments, determining the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat frequency includes: The short-time effective value sequence of the bandpass sub-signal is calculated using the sliding window method; Perform a Fourier transform on the short-time effective value sequence to obtain the amplitude spectrum of the short-time effective value sequence; Based on the peak characteristic value and amplitude of each frequency point in the amplitude spectrum of the short-time effective value sequence, the first high-spectral peak and the second high-spectral peak are determined, and the approximate centroid peak frequency is determined based on the first high-spectral peak and the second high-spectral peak. Based on the approximate centroid peak frequency and the beat frequency, the approximate coefficient of the effective value fluctuation frequency is determined.

[0056] Specifically, for each bandpass sub-signal in the aforementioned bandpass band, the effective value ripple frequency approximation coefficient is calculated. The specific calculation steps include: a. Calculate the short-time effective value of the bandpass sub-signal using the sliding window method. The specific steps are as follows:

[0057] in, The window width can be set to a value between 1 and 2 times the sampling frequency; in this example, it is set to 1 times the sampling frequency. b. For short-time effective value sequences After performing a mean-zeroing operation, a Fourier transform is performed to obtain the amplitude spectrum; c. Detect the spectral peaks in the amplitude spectrum of the short-time effective value sequence using the aforementioned peak feature calculation method and spectral peak discrimination method; d. Based on the first high spectral peak detected Second highest spectral peak Calculate the peak frequency of the approximate centroid spectrum The specific calculation formula is as follows:

[0058] In the formula, The peak value of the first high spectral peak. The peak value of the second highest spectral peak; e. Calculate the approximation coefficient for the effective value fluctuation frequency. The specific calculation formula is as follows:

[0059] in, This is an approximation coefficient for the effective value fluctuation frequency. For beat frequency, This is the approximate frequency of the centroid spectrum peak.

[0060] Furthermore, the aforementioned steps for calculating the effective value fluctuation frequency approximation coefficient are performed on the bandpass sub-signal of each frequency band to obtain the effective value fluctuation frequency approximation coefficient for each bandpass sub-signal. Approximate coefficient of effective value fluctuation frequency The highest frequency band is the frequency band where the beat vibration source frequency is located.

[0061] The vibration source frequency localization method for building structure beat vibration detection provided in this embodiment, compared with the prior art, obtains characteristic values ​​that can characterize the possibility of beat vibration in each narrow band by combining multiple information in the time domain and frequency domain, thereby accurately locating the vibration source frequency from the complex vibration detection signal.

[0062] Example 3: Based on the above embodiments, this embodiment provides a specific example.

[0063] In this embodiment, the vibration test signal is first subjected to Fourier transform to obtain the self-power spectrum of the beat vibration signal; then, adaptive hard sparsity processing is performed on the signal self-power spectrum to obtain a sparse self-power spectrum with discrete distribution of the vibration source frequency band; then, the sparse self-power spectrum is binarized, and the rising and falling edges of the binarized self-power spectrum are detected to obtain the start and end frequencies and center frequency of each vibration source frequency band; then, a series of bandwidths are set around the center frequency of each vibration source frequency band to reconstruct the bandpass sub-signals; finally, the beat vibration characteristic coefficients of each reconstructed sub-signal are calculated, and the vibration source frequency of the beat vibration phenomenon is determined based on the beat vibration characteristic coefficients. Specifically, the following steps are included: 1) Use a vibration signal acquisition device to acquire a signal of length N, and perform a mean-zeroing operation to obtain the original signal sample. .like Figure 2 As shown, the amplitude of the original signal exhibits obvious periodic fluctuations, with significant beat vibration.

[0064] 2) Observe the time-domain waveform of the original signal sample. Taking the peak of the amplitude change as the starting point, count 10 amplitude change cycles, which takes approximately 100 seconds. This allows us to estimate the beat period, i.e., the amplitude fluctuation period. The beat frequency can be obtained as approximately Perform a Fourier transform on the original signal sample to obtain the amplitude spectrum of the signal sample. .like Figure 3 As shown, there are several sets of very similar spectral peaks, making it difficult to determine the exact peaks. Figure 2 The beat vibration phenomenon in the image is caused by the spectra of which two vibration sources?

[0065] 3) With the aforementioned beat cycle The window width is twice the value of the beat cycle. One-quarter of the step size is used as the sliding step size. Multiple Fourier transforms are performed on the original signal sample using a sliding window to obtain multiple amplitude spectra. Then, the amplitude spectra are linearly averaged to obtain the linear average amplitude spectrum of the signal sample. ,like Figure 4 As shown by the thin gray solid line in the image.

[0066] 4) The left half of the amplitude spectrum Further processing involves calculating the peak eigenvalues ​​at each frequency point using the following function. : , if If, then the data point is a spectral peak; if If the value is not a spectral peak, then the data point is not a spectral peak.

[0067] 5) Normalize the peak values ​​of each spectral peak to 0-1, set the threshold to 0.1, identify spectral peaks with normalized peak values ​​greater than the threshold, and extract the frequency of each spectral peak. The recognition results are as follows Figure 4 The specific spectral peak frequencies are represented by the black vertical dashed lines in the middle, as shown below. Figure 5 The vertical axis is indicated by the middle axis.

[0068] 6) Focusing on the frequencies of each spectral peak Set a series of bandwidth A series of bandpass frequency bands were obtained. This example sets the series bandwidth to .

[0069] 7) For each bandpass frequency band Bandpass filtering is performed on the original signal samples to obtain bandpass sub-signals. The bandpass filtering method used in this example sets all parts of the signal's Fourier spectrum outside the passband to zero, and then performs an inverse Fourier transform.

[0070] 8) Calculate the approximate coefficients of the effective value fluctuation frequency of each bandpass sub-signal according to the following steps. : a. Calculate the short-time effective value of the bandpass sub-signal using the sliding window method. Specifically: , in, The window width is preferably between 1 and 2 times the sampling frequency; in this example, it is set to 1 times the sampling frequency. b. For short-time effective value sequences After performing a mean-zeroing operation, a Fourier transform is performed to obtain the amplitude spectrum; c. Use the peak feature calculation method and spectral peak discrimination method described in step 4) to detect the spectral peaks in the amplitude spectrum of the short-time effective value sequence; d. Based on the first high spectral peak detected Second highest spectral peak Calculate the peak frequency of the approximate centroid spectrum The details are as follows: , In the formula The peak value of the first high spectral peak. The peak value of the second highest spectral peak; e. Calculate the approximation coefficient for the effective value fluctuation frequency. The details are as follows: .

[0071] Approximate coefficients of the effective value fluctuation frequency of bandpass sub-signals in each frequency band The calculation results are as follows Figure 6 As shown, the approximate coefficient of the effective value fluctuation frequency of the bandpass sub-signal with a center frequency of 13.97Hz and a bandwidth of 0.2Hz can be observed. The highest value indicates that the frequency band [13.97~14.17] is the frequency band where the beat vibration source is located.

[0072] The time-domain waveform of the [13.97~14.17] frequency band is as follows: Figure 6 As shown by the thin gray line, the effective value waveform curve is as follows: Figure 6 As shown by the thick black solid line. (Comparison) Figure 6 and Figure 2 It can be seen that the amplitude variation law of the bandpass sub-signal at the source frequency determined by the method provided by the present invention is consistent with that of the original signal sample, proving the effectiveness of the method provided by the present invention.

[0073] Example 4: Another embodiment of this application relates to a vibration source frequency location device for detecting beat vibration in building structures.

[0074] The following is a detailed description of the implementation details of the vibration source frequency location device for building structure beat vibration detection in this embodiment. The following content is only for ease of understanding and is not necessary for implementing this solution. The vibration source frequency location device for building structure beat vibration detection provided in this embodiment includes: The beat frequency determination module is used to determine whether a beat phenomenon exists and to determine the beat frequency based on the time-domain waveform of the vibration signal sample. A serialized bandpass determination module is used to determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum based on the linear average amplitude spectrum of the vibration signal sample, and to determine the serialized bandpass corresponding to each peak frequency point of interest based on a preset serialized bandpass. The bandpass sub-signal determination module is used to perform bandpass filtering on the vibration signal sample based on the series of bandpass frequency bands corresponding to each of the frequency points of the spectrum of interest peaks to obtain the bandpass sub-signal; The beat vibration source frequency determination module is used to determine the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat vibration frequency, and to take the frequency band with the highest effective value fluctuation frequency approximation coefficient as the frequency band where the beat vibration source frequency is located.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of each module in the vibration source frequency positioning device for this building structure beat vibration detection can be referred to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0076] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0077] Example 5: Another embodiment of this application relates to an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the vibration source frequency localization method for building structure beat vibration detection in the above embodiments.

[0078] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0079] The processor manages the bus and handles general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory, on the other hand, is used to store data used by the processor during operation.

[0080] Example 6: Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0081] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] In some embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in the above embodiments.

[0083] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for locating the vibration source frequency in the detection of building structure beat vibration, characterized in that, include: Based on the time-domain waveform of the vibration signal sample, determine whether beat vibration exists and determine the beat vibration frequency; Based on the linear average amplitude spectrum of the vibration signal sample, determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum, and determine the series of bandwidth corresponding to each peak frequency point of interest based on the preset series of bandwidths. Based on the series of bandpass frequency bands corresponding to the peak frequency points of each of the aforementioned spectra of interest, the vibration signal samples are subjected to bandpass filtering to obtain bandpass sub-signals; Based on the bandpass sub-signal and the beat frequency, the effective value fluctuation frequency approximation coefficient is determined, and the frequency band with the highest effective value fluctuation frequency approximation coefficient is taken as the frequency band where the beat vibration source frequency is located.

2. The method according to claim 1, characterized in that, The frequency point in the left half of the linear average amplitude spectrum where the peak characteristic value is equal to 1 is defined as the spectral peak frequency point.

3. The method according to claim 1, characterized in that, The process of determining the peak frequency points of interest in the left half of the linear average amplitude spectrum based on the vibration signal samples, and determining the series of passbands corresponding to each peak frequency point of interest based on a preset series of bandwidths, includes: The linear average amplitude spectrum of the vibration signal sample is determined by Fourier transform, and the amplitude corresponding to each frequency point in the left half of the linear average amplitude spectrum is determined. Based on the comparison results of the normalized amplitude and preset threshold of each spectral peak frequency point, the set of spectral peak frequency points of interest is determined; For any one of the frequency points of interest in the set of frequency points of interest, the serialized passband corresponding to the current frequency point of interest is determined based on the preset serialized bandwidth.

4. The method according to claim 1, characterized in that, The determination of the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat frequency includes: The short-time effective value sequence of the bandpass sub-signal is calculated using the sliding window method; Perform a Fourier transform on the short-time effective value sequence to obtain the amplitude spectrum of the short-time effective value sequence; Based on the peak characteristic value and amplitude of each frequency point in the amplitude spectrum of the short-time effective value sequence, the first high-spectral peak and the second high-spectral peak are determined, and the approximate centroid peak frequency is determined based on the first high-spectral peak and the second high-spectral peak. Based on the approximate centroid peak frequency and the beat frequency, the approximate coefficient of the effective value fluctuation frequency is determined.

5. The method according to claim 4, characterized in that, The formula for calculating the approximate coefficient of the effective value fluctuation frequency includes: in, This is an approximation coefficient for the effective value fluctuation frequency. For beat frequency, This is the approximate frequency of the centroid spectrum peak.

6. The method according to claim 1, characterized in that, The formula for determining the approximate centroid peak frequency includes: in, To approximate the peak frequency of the centroid spectrum, The frequency of the first high-frequency peak. The amplitude of the first high spectral peak. The frequency of the second highest spectral peak. This represents the amplitude of the second highest spectral peak.

7. A vibration source frequency location device for detecting vibration in building structures, characterized in that, include: The beat frequency determination module is used to determine whether a beat phenomenon exists and to determine the beat frequency based on the time-domain waveform of the vibration signal sample. A serialized bandpass determination module is used to determine the peak frequency points of the spectrum of interest in the left half of the linear average amplitude spectrum based on the linear average amplitude spectrum of the vibration signal sample, and to determine the serialized bandpass corresponding to each peak frequency point of interest based on a preset serialized bandwidth. The bandpass sub-signal determination module is used to perform bandpass filtering on the vibration signal sample based on the series of bandpass frequency bands corresponding to each peak frequency point of the spectrum of interest to obtain the bandpass sub-signal; The beat vibration source frequency determination module is used to determine the effective value fluctuation frequency approximation coefficient based on the bandpass sub-signal and the beat vibration frequency, and to take the frequency band with the highest effective value fluctuation frequency approximation coefficient as the frequency band where the beat vibration source frequency is located.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the steps of the method as described in any one of claims 1 to 6.

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

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

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