Array frequency meter and frequency measurement method

Abstract

This application relates to the field of frequency measurement technology, specifically to an array-type frequency meter and a frequency measurement method. The method includes: acquiring triaxial acceleration data of engineering structure locations; extracting high-frequency and low-frequency fluctuation components; obtaining the high-frequency disturbance characteristic values ​​at each sampling time in the high-frequency disturbance component, and the low-frequency noise fluctuation amount at each sampling time in the low-frequency fluctuation component, thereby obtaining the coupling influence degree; utilizing the magnitude relationship between the coupling influence degree and its nonlinear fitting value, and the difference in the change of the coupling influence degree between adjacent sampling times, obtaining the multi-interference significance at each sampling time, thereby obtaining a dynamic smoothing factor; filtering the acceleration time-series data using the exponentially weighted moving average method; and measuring the vibration frequency and vibration amplitude of the engineering structure locations through frequency domain transformation. This application can improve the reliability of vibration frequency identification and the accuracy of vibration amplitude measurement at engineering structure locations.

Description

Technical Field

[0001] This application relates to the field of frequency measurement technology, specifically to an array-type frequency meter and a frequency measurement method. Background Technology

[0002] Currently, array-type frequency meters sense vibration changes at points in engineering structures by varying triaxial acceleration. During data processing, they utilize Fast Fourier Transform (FFT) to measure the vibration frequency and amplitude at these points, which can be used to analyze structural damage and safety hazards at these points.

[0003] However, intelligent sensing systems are susceptible to power supply interference and electromagnetic interference during the acquisition of triaxial acceleration. Existing technologies can use the Exponentially Weighted Moving Average (EWMA) method to filter triaxial acceleration signals in real time. However, the smoothing factor of the traditional EWMA method is fixed and cannot be adaptively adjusted according to the interference characteristics of triaxial acceleration. This results in a low signal-to-noise ratio of the filtered triaxial acceleration signal, affecting the reliability of vibration frequency identification and the accuracy of vibration amplitude measurement at subsequent engineering structural points. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an array-type frequency meter and a frequency measurement method, the specific technical solution of which is as follows: This application provides an array-based frequency measurement method, including the following steps: Measure the vibration frequency and amplitude at points on the engineering structure and obtain triaxial acceleration data at those points; Modal decomposition is performed on the acceleration time series data of each axis to extract high-frequency and low-frequency fluctuation components. Based on the local variation and fluctuation of the high-frequency disturbance component in the time series data, the high-frequency disturbance feature value of each sampling time in the high-frequency disturbance component is obtained. The low-frequency noise fluctuation amount of each sampling time in the low-frequency disturbance component is obtained by using the data deviation of the low-frequency fluctuation component. Thus, the coupling influence degree corresponding to each sampling time in the acceleration time series data of each axis is obtained. By utilizing the relationship between the coupling influence degree and its nonlinear fitting value, as well as the difference in the change of coupling influence degree between adjacent sampling times, the significance of multiple interferences at each sampling time is obtained. Then, the smoothing factor of the exponential weighted moving average method is adjusted to obtain the dynamic smoothing factor. The exponentially weighted moving average method is used to filter the acceleration time series data of each axis based on a dynamic smoothing factor. Then, the vibration frequency and vibration amplitude of the points on the engineering structure are measured through frequency domain transformation.

[0005] Preferably, the extraction process of the high-frequency and low-frequency fluctuation components specifically includes: Modal decomposition is performed on the acceleration time series data of each axis. The first and last modal components after decomposition are used as the high-frequency disturbance component and low-frequency fluctuation component of the acceleration time series data of the corresponding axis, respectively.

[0006] Preferably, the high-frequency disturbance feature value of each element in the high-frequency disturbance component is specifically determined by: constructing a sliding window centered on each sampling time in the high-frequency disturbance component, calculating the mean of the absolute values ​​of the first-order differences of all data within the sliding window at each sampling time and the degree of dispersion of all the absolute values ​​of the first-order differences, and using the sum of the mean and the degree of dispersion as the high-frequency disturbance feature value corresponding to each sampling time in the high-frequency disturbance component.

[0007] Preferably, the low-frequency noise fluctuation amount at each sampling time in the low-frequency fluctuation component is specifically determined by: calculating the absolute difference between the data corresponding to each sampling time in the low-frequency fluctuation component and the mean of the low-frequency fluctuation component, and using this difference as the low-frequency noise fluctuation amount at each sampling time in the low-frequency fluctuation component.

[0008] Preferably, the formula for calculating the coupling influence degree corresponding to each sampling time is: ; The coupling effect degree is the value at the i-th sampling time in the acceleration time series data. Let be the high-frequency disturbance characteristic value corresponding to the i-th sampling time in the high-frequency disturbance component. Let be the amount of low-frequency noise fluctuation corresponding to the i-th sampling time in the low-frequency fluctuation component. This is a preset constant.

[0009] Preferably, the significance of the multiple interferences is specifically as follows: In the formula, Let represent the significance of multiple disturbances at the i-th sampling time. Let be the nonlinear fitted value of the coupling influence at the i-th sampling time. Let be the number of adjacent sampling points at the i-th sampling time. To avoid a pre-defined constant with a denominator of zero, Let g be the coupling influence degree between the i-th sampling time and the g-th adjacent sampling time.

[0010] Preferably, for the acceleration time series data of each axis, the coupling influence degree corresponding to all sampling times in the acceleration time series data is nonlinearly fitted to obtain the nonlinear fitting value of the coupling influence degree at each sampling time.

[0011] Preferably, the dynamic smoothing factor is: In the formula, Let be the dynamic smoothing factor corresponding to the i-th sampling time. For normalization function, Let represent the significance of multiple disturbances at the i-th sampling time. To preset the limit smoothing factor, This is the preset smoothing error.

[0012] Preferably, a fast Fourier transform is performed on the filtered acceleration data of each axis after filtering to obtain the vibration frequency and vibration amplitude of each axis, which are used as the vibration frequency and vibration amplitude of the engineering structure points.

[0013] This application also provides an array-type frequency meter, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described array-type frequency measurement methods.

[0014] As can be seen from the above, the array-type frequency meter and frequency measurement method provided in this application have at least the following beneficial effects: Considering that intelligent sensing systems are susceptible to the coupling effects of power supply interference and electromagnetic interference during the acquisition of triaxial acceleration, which can affect the reliability of vibration frequency identification and the accuracy of vibration amplitude measurement at subsequent engineering structural points, this application analyzes the local high-frequency disturbance characteristics and low-frequency noise fluctuations caused by power supply interference and electromagnetic interference. By combining these characteristics, the coupling effects of power supply interference and electromagnetic interference on triaxial acceleration signals can be measured more accurately, and the interference characteristics of triaxial acceleration signals can be more clearly demonstrated. This information is then used to adaptively adjust the smoothing factor of the subsequent EWMA exponentially weighted moving average method. Furthermore, this application measures and analyzes the significance of power supply interference and electromagnetic interference on acceleration time series data by considering the difference between the coupling influence degree and its nonlinear fitting value, and combining the adjacent changes of the coupling influence degree, so as to accurately and adaptively adjust the smoothing factor of the EWMA exponentially weighted moving average method in the future. This application adaptively adjusts the smoothing factor of the EWMA exponentially weighted moving average method based on the significance of multiple interferences. This makes the adjusted smoothing factor more effective in suppressing power supply interference and electromagnetic interference on triaxial acceleration, thereby improving the signal-to-noise ratio of the filtered triaxial acceleration signal and avoiding affecting the reliability of vibration frequency identification and the accuracy of vibration amplitude measurement of subsequent engineering structural points. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of an array-type frequency measurement method provided in this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an array-type frequency meter and frequency measurement method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of an array-type frequency meter and frequency measurement method provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of an array-type frequency measurement method according to an embodiment of this application, including the following steps: Step 1: Obtain triaxial acceleration data of the points on the engineering structure.

[0021] To reliably identify the vibration frequency of points in an engineering structure and improve the accuracy of vibration amplitude measurement, this application uses an array-type frequency meter to identify and measure the vibration frequency and amplitude of points in the engineering structure. The array-type frequency meter includes multiple intelligent sensor units connected end-to-end in sequence. Each intelligent sensor unit can independently complete triaxial acceleration data acquisition, data processing, and data transmission. The data processing unit can use an STM32L431CCT6 data processing chip. Each intelligent sensor unit includes a stainless steel tube body, with adjacent stainless steel tube bodies connected to each other via flexible bending joints. All stainless steel tube bodies are equipped with triaxial MEMS accelerometers. The stainless steel tube body also includes a head section and multiple extension tubes. The head section houses a data processing unit, a power conversion unit, and a communication unit. All triaxial MEMS accelerometers are connected sequentially via power cables and a CAN bus (Controller Area Network) routed within the stainless steel tube body.

[0022] Therefore, in this embodiment, an array-type frequency meter with an intelligent sensing system is used to identify and measure the vibration frequency and amplitude of the engineering structure points. Specifically, the triaxial MEMS accelerometer in the intelligent sensing system collects triaxial acceleration data of the engineering structure points, including linear acceleration in the X, Y, and Z axes. The engineering structure points can be the walls of the engineering body. The sampling rate of the triaxial acceleration data is 1 kHz, the preset frame time is 320 ms, and the preset frame shift time is 80 ms. Thus, the acceleration time series data of each axis can be obtained. The acceleration time series data is processed and analyzed to accurately obtain the vibration frequency and amplitude of the engineering structure points.

[0023] Step 2: Obtain high-frequency disturbance characteristic values ​​and low-frequency noise fluctuations based on acceleration time series data, and use the high-frequency disturbance characteristic values ​​and low-frequency noise fluctuations to obtain the coupling influence degree.

[0024] Because the triaxial MEMS accelerometer in the intelligent sensing system is susceptible to power supply interference and electromagnetic interference during the triaxial acceleration acquisition process, the existing technology can use the EWMA exponentially weighted moving average method to perform real-time filtering of the triaxial acceleration signal. In order to improve the signal-to-noise ratio of the filtered triaxial acceleration signal, the smoothing factor of the EWMA exponentially weighted moving average method needs to be adaptively adjusted according to the interference characteristics of the triaxial acceleration, so as to avoid affecting the reliability of vibration frequency identification and vibration amplitude measurement of subsequent engineering structural points.

[0025] In general, triaxial MEMS accelerometers in intelligent sensing systems are subject to coupling effects of power supply interference and electromagnetic interference during the acquisition of triaxial acceleration data. This results in low-frequency periodic noise fluctuations and local high-frequency disturbances in the acceleration time-series data. Therefore, to analyze the interference characteristics of each sampling point in the acceleration time-series data, the acceleration time-series data of each axis is used as input to the Empirical Mode Decomposition (EMD) algorithm. The EMD algorithm is used to obtain the first and last mode components in the acceleration time-series data decomposition process. The first and last mode components are then normalized, either by maximum value normalization or range normalization. The results of the normalization are recorded as high-frequency disturbance components and low-frequency fluctuation components, respectively, reflecting the changes in high-frequency and low-frequency components of the acceleration time-series data.

[0026] Since the Empirical Mode Decomposition (EMD) algorithm decomposes the data in order from high frequency to low frequency, the first mode component represents the high frequency component and is used to characterize the local high frequency mutation characteristics of the acceleration time series data, while the last mode component represents the low frequency component and is used to characterize the low frequency periodic noise fluctuations of the acceleration time series data.

[0027] Therefore, in order to analyze the local high-frequency disturbances in acceleration time series data, a sliding window is constructed with each sampling time of the high-frequency disturbance component as the center. In this embodiment, a sliding window of size 1×21 is set. If there are missing values ​​in the sliding window, the missing values ​​are filled by the mean. The mean of the absolute values ​​of the first-order differences of all data in the sliding window at each sampling time and the dispersion of all the absolute values ​​of the first-order differences are calculated. The sum of the mean and the dispersion is used as the high-frequency disturbance feature value of each sampling time in the high-frequency disturbance component. The dispersion can be the variance or the standard deviation. Preferably, in this embodiment, the variance is used as the dispersion of all the absolute values ​​of the first-order differences in the sliding window.

[0028] It is understandable that the high-frequency disturbance eigenvalues ​​reflect the local high-frequency disturbance characteristics of each element in the high-frequency disturbance component. Due to the influence of local high-frequency disturbances, the variation of elements within the sliding window will be large and the dispersion of the variation will be large. Therefore, the formula uses the fusion feature of the mean of the absolute value of the first-order difference and the degree of dispersion to measure the high-frequency disturbance eigenvalues.

[0029] Meanwhile, in order to analyze the low-frequency periodic noise fluctuations in acceleration time series data, the mean of the low-frequency fluctuation component is calculated, and the absolute difference between the data corresponding to each sampling time and its mean is calculated. This difference is denoted as the low-frequency noise fluctuation amount corresponding to each sampling time in the low-frequency fluctuation component, reflecting the fluctuation level of low-frequency periodic noise in acceleration time series data.

[0030] Based on the above analysis, for acceleration time series data of any axis, the coupling influence degree corresponding to the i-th sampling time in the acceleration time series data is calculated by combining the high-frequency disturbance characteristic value and the low-frequency noise fluctuation. : ; Let be the high-frequency disturbance characteristic value corresponding to the i-th sampling time in the high-frequency disturbance component. Let be the amount of low-frequency noise fluctuation corresponding to the i-th sampling time in the low-frequency fluctuation component. This is a preset constant used to avoid the product being 0, and its value range is [0.01, 0.05]. In this embodiment, the value is 0.02.

[0031] Understandably, based on existing feature measurement methods, this embodiment uses a product approach to fuse two feature values ​​with noise characteristics. In order to avoid the problem that the calculation principle fails due to the product being 0 in the formula, this embodiment adds a preset constant to each part of the product factor, thereby measuring the coupling effect of power supply interference and electromagnetic interference on acceleration time series data.

[0032] Among them, the coupling effect degree reflects the coupling effect of power supply interference and electromagnetic interference on acceleration time series data. The greater the coupling effect degree, the more likely the sampled data at that sampling moment is affected by the coupling effect of power supply interference and electromagnetic interference. At this time, it is easier to generate sudden noise, which affects the accuracy of acceleration time series data. Therefore, the EWMA exponential weighted moving average method needs to set a larger smoothing factor to ensure that the acceleration time series data after filtering is smoother.

[0033] Step 3: Utilize the relationship between the coupling influence degree and its nonlinear fitting value, as well as the difference in the change of coupling influence degree between adjacent sampling times, to obtain the significance of multiple interferences at each sampling time. Then, adjust the smoothing factor of the exponentially weighted moving average method to obtain the dynamic smoothing factor.

[0034] To further explore the disturbance characteristics of acceleration time series data for each axis, the coupling influence degree of all sampling times in the acceleration time series data for each axis is used as the input of nonlinear least squares method. The specific model for nonlinear fitting is a cubic polynomial function model. The nonlinear least squares method is used to obtain the nonlinear fitting value of the coupling influence degree at each sampling time, reflecting the predicted change of the coupling influence degree at different sampling times during the process of time change.

[0035] Generally, if the coupling influence is significantly higher than its nonlinear fitting value, and the difference in the coupling influence between the sampling time and its adjacent sampling time is greater, it indicates that the acceleration time series data is more significantly affected by the coupling influence of power supply interference and electromagnetic interference, and can more accurately reflect the interference characteristics of the acceleration time series data of each axis.

[0036] Therefore, for the acceleration time series data of each axis, the adjacent previous sampling point and the adjacent next sampling point of each sampling point in the acceleration time series data are respectively used as the adjacent sampling points of each sampling point in the acceleration time series data. That is, the number of adjacent sampling points of each sampling point may be 1 or 2. For example, the first sampling point has only the adjacent next sampling point, and the last sampling point has only the adjacent previous sampling point.

[0037] Based on the above analysis, for the acceleration time series data of each axis, the significance of multiple disturbances corresponding to each sampling time in the acceleration time series data is calculated. In this embodiment, the specific steps are as follows: In the formula, Let represent the significance of multiple disturbances at the i-th sampling time. Let be the nonlinear fitted value of the coupling influence at the i-th sampling time. Let be the number of adjacent sampling points at the i-th sampling time. A preset constant is used to avoid the denominator being zero. This constant ranges from 0.001 to 0.01, and in this embodiment, it is set to 0.005. Let g be the coupling influence degree between the i-th sampling time and the g-th adjacent sampling time.

[0038] Understandably, based on existing feature measurement methods, this embodiment uses a fractional approach to measure the relationship between the coupling influence degree and its nonlinear fitting value. If the coupling influence degree is significantly higher than its nonlinear fitting value, the calculation result of the fraction will be significantly greater than 1. At the same time, this embodiment uses the absolute difference calculation method to measure the difference in change between adjacent sampling points in order to accurately measure the significance of multiple interferences.

[0039] Among them, the significance of multiple interferences reflects the degree of influence of power supply interference and electromagnetic interference on acceleration time series data. The greater the significance of multiple interferences, the higher the degree of influence of power supply interference and electromagnetic interference on acceleration time series data. In this case, the smoothing factor of the EWMA exponential weighted moving average method should be increased to make the filtered acceleration time series data smoother, thereby reducing the influence of power supply interference and electromagnetic interference on acceleration time series data.

[0040] Therefore, based on the significance of multiple disturbances, the smoothing factor of the EWMA exponentially weighted moving average method is adaptively adjusted, and the dynamic smoothing factor at each sampling time in the acceleration time series data is calculated: In the formula, Let be the dynamic smoothing factor corresponding to the i-th sampling time. The normalization function can be maximum normalization. The preset limit smoothing factor is used to prevent the adjusted smoothing factor from being too large. In this embodiment, the value is set to 0.8. The preset smoothing error is used to prevent the adjusted smoothing factor from being 0. In this embodiment, the value is 0.1.

[0041] Therefore, by adaptively adjusting the smoothing factor of the EWMA exponentially weighted moving average method through multiple interference significance, the adjusted smoothing factor can more effectively suppress power supply interference and electromagnetic interference on triaxial acceleration, thereby improving the signal-to-noise ratio of the filtered triaxial acceleration signal and avoiding affecting the reliability of vibration frequency identification and the accuracy of vibration amplitude measurement of subsequent engineering structural points.

[0042] Step 4: Filter the acceleration time series data of each axis using the exponentially weighted moving average method based on the dynamic smoothing factor, and then measure the vibration frequency and vibration amplitude of the points on the engineering structure through frequency domain transformation.

[0043] To more accurately identify and measure the vibration frequency and amplitude at points on the engineering structure, the exponentially weighted moving average (EWMA) method is used to perform real-time filtering on the triaxial acceleration signal. The acceleration time series data of each axis is used as the input of the EWMA method, and the dynamic smoothing factor corresponding to each sampling time in the acceleration time series data is used as the smoothing factor of the EWMA method. The EWMA method is used to filter the acceleration time series data of each axis, and the filtered acceleration data of each axis is obtained after filtering to improve the signal-to-noise ratio of the triaxial acceleration signal after filtering.

[0044] Among them, the dynamic smoothing factor is subject to the preset limit smoothing factor. and preset smoothing error The strict amplitude limiting control ensures that the linear characteristics of the original vibration frequency energy distribution are not lost while suppressing local transient spike pulses, thus meeting the preconditions of the FFT (Fast Fourier Transform).

[0045] Furthermore, the filtered acceleration data of each axis after filtering is used as the input of the Fast Fourier Transform (FFT). The vibration frequency and vibration amplitude of each axis are obtained through the FFT, thereby realizing the measurement of the vibration frequency and vibration amplitude of the points on the engineering structure. This can be used to analyze structural damage and safety hazards at the points on the engineering structure.

[0046] Based on the same inventive concept as the above method, this application also provides an array-type frequency meter, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described array-type frequency measurement methods.

[0047] The array-type frequency meter provided in this application adopts an integrated intelligent sensing system, including a three-axis MEMS accelerometer, a data processing unit, a power conversion unit, and a communication unit. The array-type frequency meter includes multiple intelligent sensor units connected end-to-end in sequence. Each intelligent sensor unit can independently complete the acquisition, processing, and transmission of three-axis acceleration data. The data processing unit can use an STM32L431CCT6 data processing chip.

[0048] The intelligent sensor unit includes a stainless steel tube body, with adjacent stainless steel tube bodies connected to each other via flexible bending joints. All stainless steel tube bodies are equipped with triaxial MEMS (Micro-Electro-Mechanical System) accelerometer sensors. The stainless steel tube body also includes a head section and multiple extension tubes. The head section contains a data processing unit, a power conversion unit, and a communication unit. All triaxial MEMS accelerometer sensors are connected sequentially via power cables and a CAN bus (Controller Area Network) laid inside the stainless steel tube bodies.

[0049] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0050] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0051] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. An array-type frequency measurement method, characterized in that, Includes the following steps: Obtain triaxial acceleration data at points on the engineering structure; Modal decomposition is performed on the acceleration time series data of each axis to extract high-frequency and low-frequency fluctuation components. Based on the local variation and fluctuation of the high-frequency disturbance component in the time series data, the high-frequency disturbance feature value of each sampling time in the high-frequency disturbance component is obtained. The low-frequency noise fluctuation amount of each sampling time in the low-frequency disturbance component is obtained by using the data deviation of the low-frequency fluctuation component. Thus, the coupling influence degree corresponding to each sampling time in the acceleration time series data of each axis is obtained. By utilizing the relationship between the coupling influence degree and its nonlinear fitting value, as well as the difference in the change of coupling influence degree between adjacent sampling times, the significance of multiple interferences at each sampling time is obtained. Then, the smoothing factor of the exponential weighted moving average method is adjusted to obtain the dynamic smoothing factor. The exponentially weighted moving average method is used to filter the acceleration time series data of each axis based on a dynamic smoothing factor. Then, the vibration frequency and vibration amplitude of the points on the engineering structure are measured through frequency domain transformation.

2. The array-type frequency measurement method as described in claim 1, characterized in that, The extraction process of the high-frequency and low-frequency fluctuation components is as follows: Modal decomposition is performed on the acceleration time series data of each axis. The first and last modal components after decomposition are used as the high-frequency disturbance component and low-frequency fluctuation component of the acceleration time series data of the corresponding axis, respectively.

3. The array-type frequency measurement method as described in claim 1, characterized in that, The high-frequency disturbance characteristic value of each element in the high-frequency disturbance component is specifically determined as follows: a sliding window is constructed with each sampling time in the high-frequency disturbance component as the center, and the mean of the absolute value of the first difference of all data in the sliding window at each sampling time and the degree of dispersion of all the absolute values ​​of the first difference are calculated. The sum of the mean and the degree of dispersion is used as the high-frequency disturbance characteristic value corresponding to each sampling time in the high-frequency disturbance component.

4. The array-type frequency measurement method as described in claim 1, characterized in that, The low-frequency noise fluctuation amount at each sampling time in the low-frequency fluctuation component is specifically calculated as follows: the absolute difference between the data corresponding to each sampling time in the low-frequency fluctuation component and the mean of the low-frequency fluctuation component is used as the low-frequency noise fluctuation amount corresponding to each sampling time in the low-frequency fluctuation component.

5. The array-type frequency measurement method as described in claim 1, characterized in that, The formula for calculating the coupling influence at each sampling time is as follows: ; The coupling effect degree is the value at the i-th sampling time in the acceleration time series data. Let be the high-frequency disturbance characteristic value corresponding to the i-th sampling time in the high-frequency disturbance component. Let be the amount of low-frequency noise fluctuation corresponding to the i-th sampling time in the low-frequency fluctuation component. This is a preset constant.

6. The array-type frequency measurement method as described in claim 5, characterized in that, The significance of the multiple interferences is specifically as follows: In the formula, Let represent the significance of multiple disturbances at the i-th sampling time. Let be the nonlinear fitted value of the coupling influence at the i-th sampling time. Let be the number of adjacent sampling points at the i-th sampling time. To avoid a pre-defined constant with a denominator of zero, Let g be the coupling influence degree between the i-th sampling time and the g-th adjacent sampling time.

7. The array-type frequency measurement method as described in claim 6, characterized in that, For the acceleration time series data of each axis, a nonlinear fitting is performed on the coupling influence degree corresponding to all sampling times in the acceleration time series data to obtain the nonlinear fitting value of the coupling influence degree at each sampling time.

8. The array-type frequency measurement method as described in claim 1, characterized in that, The dynamic smoothing factor is: In the formula, Let be the dynamic smoothing factor corresponding to the i-th sampling time. For normalization function, Let represent the significance of multiple disturbances at the i-th sampling time. To preset the limit smoothing factor, This is the preset smoothing error.

9. The array-type frequency measurement method as described in claim 1, characterized in that, A fast Fourier transform is performed on the filtered acceleration data of each axis to obtain the vibration frequency and amplitude of each axis, which are used as the vibration frequency and amplitude of the points on the engineering structure.

10. An array-type frequency meter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the array-type frequency measurement method as described in any one of claims 1-9.

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