Vibration source identification method and device based on modal decomposition, terminal and medium

Abstract

The invention provides a vibration source identification method and device based on modal decomposition, a terminal and a medium, and the method comprises the steps: decomposing an original signal through employing a preset modal decomposition algorithm, calculating a correlation coefficient between each decomposed modal component signal and the original signal, comparing the correlation coefficient corresponding to each modal component signal with the calculated average value of all correlation coefficients, and identifying and screening out a plurality of target vibration signals tending to the same frequency based on a comparison result; and carrying out cross-correlation operation on the plurality of target vibration signals by using a preset cross-correlation algorithm, and carrying out vibration source positioning based on the calculated time difference corresponding to the target vibration signals. According to the method, original signals are decomposed through a modal decomposition algorithm, target vibration signals tending to be in the same frequency are recognized and screened out from decomposed modal component signals, then the time difference corresponding to the target vibration signals is calculated based on a cross-correlation algorithm, and recognition and positioning of the vibration signals tending to be in the same frequency are achieved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a vibration source identification method, device, terminal and medium based on modal decomposition. Background Art

[0002] At present, the measurement of vibration signals is of great significance for improving marine safety, resource development efficiency, disaster warning capabilities and structural health monitoring. Vibration signals are usually generated by factors such as tides, ocean currents, sea waves, seismic activities, biological communities and traffic and shipping. They not only contain vibration signals of multiple complex frequencies, but also some vibration signals of the same frequency. However, the existing recognition and positioning technologies for vibration signals mainly include time domain cross-correlation algorithms and frequency domain algorithms. These algorithms can only meet the measurement of vibration signals of different frequencies. The frequency difference range of vibration signals of different frequencies is generally tens of hertz to hundreds of hertz, and it is impossible to identify and locate vibration signals of the same frequency. In addition, due to slightly different local densities, multiple wavefronts from the same vibration source are unlikely to reach the sensor at the same frequency, but are close to the same frequency. The frequency difference range is generally very small, reaching a negligible level. However, the existing recognition and positioning technologies for vibration signals cannot identify and locate such vibration signals that tend to be at the same frequency.

[0003] Therefore, the prior art has defects and needs to be improved and developed. Summary of the invention

[0004] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a vibration source identification method, device, terminal and medium based on modal decomposition are provided, aiming to solve the problem in the prior art that vibration signals approaching the same frequency cannot be identified and located, and can realize the identification and location of vibration signals approaching the same frequency.

[0005] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0006] A vibration source identification method based on modal decomposition, wherein the method comprises:

[0007] Decomposing the acquired original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals, and calculating the correlation coefficient between each of the modal component signals and the original signal;

[0008] Calculating a corresponding average value according to the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficient between each modal component signal and the original signal with the average value to obtain a corresponding comparison result;

[0009] Based on the comparison result, the modal component signal is identified and screened to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold;

[0010] A preset cross-correlation algorithm is used to perform cross-correlation operations on the multiple target vibration signals to calculate the time differences corresponding to the multiple target vibration signals, and the vibration source is located based on the time differences to obtain corresponding positioning results.

[0011] In one implementation, the original signal is a signal containing multi-point vibration information and noise, which is collected by a data acquisition system according to a preset sampling rate and number of sampling points.

[0012] In one implementation, before calculating the correlation coefficient between each of the modal component signals and the original signal, the method further includes:

[0013] Filtering the zero-frequency noise signal in the multiple modal component signals to obtain multiple filtered modal component signals;

[0014] Wherein, the calculating the correlation coefficient between each of the modal component signals and the original signal comprises:

[0015] The correlation coefficient between each of the modal component signals in the filtered multiple modal component signals and the original signal is calculated.

[0016] In one implementation, before using a preset modal decomposition algorithm to decompose the acquired original signal to obtain multiple modal component signals, the method further includes:

[0017] Based on the permutation entropy, the optimal number of modal components when the original signal is decomposed using the preset modal decomposition algorithm is determined; wherein the optimal number of modal components is an integer.

[0018] In one implementation, the method of decomposing the acquired original signal using a preset modal decomposition algorithm to obtain multiple modal component signals includes:

[0019] Utilizing the preset modal decomposition algorithm, and decomposing the acquired original signal according to the optimal number of modal components, a plurality of modal component signals corresponding to the optimal number of modal components are obtained; wherein the preset modal decomposition algorithm is a preset variational modal decomposition algorithm or a preset empirical mode decomposition algorithm.

[0020] In one implementation, the preset frequency threshold is 0.1 Hz.

[0021] In one implementation, the identifying and screening the modal component signals based on the comparison results to obtain a plurality of target vibration signals tending to be of the same frequency includes:

[0022] When the comparison result indicates that the correlation coefficient between the modal component signal and the original signal is not greater than the average value, filtering the modal component signal;

[0023] When the comparison result indicates that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained to obtain a plurality of target vibration signals tending to be of the same frequency.

[0024] The present invention also discloses a vibration source identification device based on modal decomposition, wherein the device comprises:

[0025] A signal decomposition module is used to decompose the acquired original signal using a preset modal decomposition algorithm to obtain multiple modal component signals;

[0026] A correlation coefficient calculation module, used to calculate the correlation coefficient between each of the modal component signals and the original signal;

[0027] An average value calculation module, used for calculating a corresponding average value according to the correlation coefficient between all the modal component signals and the original signal;

[0028] A comparison module, used for comparing the correlation coefficient between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result;

[0029] A signal screening module, used for identifying and screening the modal component signal based on the comparison result to obtain a plurality of target vibration signals tending to be of the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be of the same frequency is not greater than a preset frequency threshold;

[0030] A time difference calculation module, used for performing a cross-correlation operation on the plurality of target vibration signals using a preset cross-correlation algorithm to calculate the time differences corresponding to the plurality of target vibration signals;

[0031] The vibration source positioning module is used to locate the vibration source based on the time difference to obtain a corresponding positioning result.

[0032] The present invention also discloses a terminal, which includes: a memory, a processor, and a vibration source identification program based on modal decomposition stored in the memory and executable on the processor. When the vibration source identification program based on modal decomposition is executed by the processor, the steps of the vibration source identification method based on modal decomposition as described above are implemented.

[0033] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the vibration source identification method based on modal decomposition as described above.

[0034] The present invention provides a vibration source identification method, device, terminal and medium based on modal decomposition. The vibration source identification method based on modal decomposition includes: using a preset modal decomposition algorithm to decompose the acquired original signal to obtain multiple modal component signals, and calculating the correlation coefficient between each modal component signal and the original signal; calculating a corresponding average value according to the correlation coefficient between all the modal component signals and the original signal, and comparing the correlation coefficient between each modal component signal and the original signal with the average value to obtain a corresponding comparison result; based on the comparison result, identifying and screening the modal component signals to obtain multiple target vibration signals tending to the same frequency; wherein the frequency difference between the multiple target vibration signals tending to the same frequency is not greater than a preset frequency threshold; using a preset cross-correlation algorithm to perform cross-correlation operations on the multiple target vibration signals to calculate the time difference corresponding to the multiple target vibration signals, and locating the vibration source based on the time difference to obtain a corresponding positioning result. It can be seen that the present invention decomposes the original signal through a modal decomposition algorithm, and identifies and selects target vibration signals that are close to the same frequency from the decomposed modal component signals, and then calculates the time difference corresponding to the target vibration signal through a cross-correlation algorithm, and locates the vibration source based on the time difference, thereby solving the problem in the prior art that vibration signals that are close to the same frequency cannot be identified and located, and further realizing the identification and positioning of vibration signals that are close to the same frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a preferred embodiment of a vibration source identification method based on modal decomposition in the present invention;

[0036] Figure 2 It is a flow chart of a specific vibration source identification method based on modal decomposition disclosed by the present invention;

[0037] Figure 3 It is a flow chart of a specific vibration source identification method based on modal decomposition disclosed by the present invention;

[0038] Figure 4 It is a structural schematic diagram of a forward transmission distributed sensing system based on a double-end structure disclosed by the present invention;

[0039] Figure 5 It is a frequency domain schematic diagram of an original signal disclosed by the present invention;

[0040] Figure 6It is a frequency domain schematic diagram of a decomposed signal disclosed by the present invention;

[0041] Figure 7 It is a functional principle block diagram of a preferred embodiment of a vibration source identification device based on modal decomposition in the present invention;

[0042] Figure 8 It is a functional principle block diagram of a preferred embodiment of the terminal in the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] See also Figure 1 , Figure 1 Flowchart of the vibration source identification method based on modal decomposition in the present invention. Figure 1 As shown, the vibration source identification method based on modal decomposition according to the embodiment of the present invention includes:

[0045] Step S11: Decompose the acquired original signal using a preset modal decomposition algorithm to obtain a plurality of modal component signals, and calculate the correlation coefficient between each of the modal component signals and the original signal.

[0046] In this embodiment, the acquired original signal is decomposed using a preset modal decomposition algorithm, and then the correlation coefficient between each decomposed modal component signal and the original signal is calculated.

[0047] It should be pointed out that the original signal is a signal containing multiple vibration information and noise collected by the data acquisition system according to the preset sampling rate and number of sampling points. Among them, by setting the sampling rate and the number of sampling points, the frequency resolution of the algorithm can be distinguished by 0.1Hz, so as to distinguish vibration signals tending to the same frequency. For example, the original signal containing multiple vibration information and noise of approximately the same frequency (i.e. tending to the same frequency) from the optical fiber sensor collected by the data acquisition system is obtained, and the original signal is decomposed into multiple modal components with limited bandwidth.

[0048] In this embodiment, when the acquired original signal is decomposed by using the preset modal decomposition algorithm, the number of modal components of the preset modal decomposition algorithm can be optimized based on the permutation entropy, and then the original signal can be decomposed using the modal decomposition algorithm optimized based on the permutation entropy, that is, the optimal number of modal components when the original signal is decomposed by using the preset modal decomposition algorithm is determined based on the permutation entropy; wherein the optimal number of modal components is an integer, and then the preset modal decomposition algorithm is used to decompose the acquired original signal according to the optimal number of modal components to obtain a plurality of modal component signals corresponding to the optimal number of modal components; wherein the preset modal decomposition algorithm can be a preset variational modal decomposition algorithm or a preset empirical mode decomposition algorithm.

[0049] It should be pointed out that the variational mode decomposition (VMD) algorithm is a non-recursive adaptive signal processing method, whose main goal is to decompose the original signal containing multi-point vibration information and noise into an integer number of sub-signals, namely, variational mode components (IMF), and each IMF corresponds to a vibration signal and a noise signal respectively.

[0050] For example, when the original signal consists of k different center frequencies ω k The signal is composed of , then the bandwidth of each modal component signal constituting the original signal in the frequency domain is estimated to be minimum, forming a minimization problem, namely:

[0051]

[0052] Among them, {u k}={u1,...,u k} represents the various modal components contained in the original vibration sensing signal detected by the sensor, {ω k}={ω1,...,ω k} represents the center frequency corresponding to each modal component, represents the partial derivative with respect to t, δ(t) represents the unit impulse function, u k (t) represents the kth modal component at time t, and j represents the complex form.

[0053] The sum of the modal component signals is equal to the original sensor signal as the constraint condition of the above minimization problem, thereby obtaining a minimization problem with constraints, and the constraints are:

[0054]

[0055] Wherein, f represents the original signal containing vibration information and noise, and K represents the number of different frequency signals contained in the original signal.

[0056] In order to solve the constrained minimization problem, the Lagrange multiplier method can be introduced to convert it into an unconstrained minimization problem, that is, a convex optimization problem formed by the augmented Lagrangian function, namely:

[0057]

[0058] Where λ represents the Lagrange multiplier, represents the Lagrangian function, α represents the quadratic penalty factor, f(t) represents the original signal at time t, λ(t) represents the Lagrangian multiplier at time t, and u k (t) represents the kth modal component at time t, and the main function of the Lagrange multiplier is to represent the coefficients of each vector in the linear combination of the gradient in the constraint condition, and the main function of the quadratic penalty factor is to suppress the influence of noise on the signal and prevent distortion.

[0059] Then, the convex optimization problem formed by the augmented Lagrangian function can be optimized and solved by the alternating direction method of multipliers (ADMM). This solution method mainly decomposes the original objective function into various sub-component minimization problems, and then solves the optimal solution of each sub-minimization problem through parallel operation. Finally, the optimal solution in the original objective function is obtained through comparative analysis. Among them, the corresponding variational mode function {u k The update method can be expressed as:

[0060]

[0061] in, represents a one-sided spectrum, represents the spectral residual, represents the original signal in the frequency domain, represents the i-th modal component in the frequency domain, represents the Lagrange multiplier in the frequency domain, and ω represents the center frequency.

[0062] Moreover, the one-sided spectrum is the current signal spectrum residual The Wiener filter output is The bilateral spectrum can be obtained according to the symmetry of the real-valued signal, and then the time domain signal can be obtained by inverse Fourier transform.

[0063] According to the above formula, it can be found that the center frequency corresponding to each submodal component signal mainly exists in the estimated term. Therefore, each submodal component signal can be updated and optimized by the following formula, namely:

[0064]

[0065] in, represents the n+1th update of the kth center frequency ω.

[0066] Similarly, transform it into the frequency domain, that is:

[0067]

[0068] in, represents the kth modal component with center frequency ω in the frequency domain.

[0069] According to the above formula, the setting of the number of IMF components will have a significant impact on the center frequency estimation. An inappropriate number of component settings will cause a deviation between the IMF component and the actual signal, resulting in modal aliasing. Usually, when the set number of decompositions is less than the actual number of decompositions, a single IMF component will contain multiple frequency band components, and when the set number of decompositions is greater than the actual number, a single frequency band will be decomposed into multiple components. Therefore, selecting an appropriate number of decompositions is crucial to the decomposition effect of the VMD algorithm.

[0070] It should also be pointed out that permutation entropy (PE), as a parameter to measure the complexity of one-dimensional time series, can be used to measure the degree of disorder within the signal, that is, the larger the entropy value, the more complex and random the decomposed signal is; the smaller the entropy value, the simpler and more regular the decomposed signal is. Therefore, in order to determine the optimal number of modal components in the VMD algorithm, the permutation entropy value can be used as an effect indicator of the number of decomposed IMFs.

[0071] See also Figure 2 As shown, the specific process of determining the optimal number of modal components when the original signal is decomposed using the preset modal decomposition algorithm based on the permutation entropy can be as follows: the initial number of modal components in the preset modal decomposition algorithm can be set to 2, that is, k=2, and the setting range of the number of modal components in the algorithm can be 2 to 10, that is, k∈[2,10]. When k is 2, that is, when the current number of modal components is 2, the original signal is subjected to VMD decomposition to obtain 2 IMF components, and then the permutation entropy (PE, Permutation Entropy) of the two IMF components is calculated, and then the permutation entropy average of the two IMF components is calculated. When k is 3, the permutation entropy of the three IMF components is calculated, and then the permutation entropy average of the three IMF components is calculated, and so on, until the calculation of k is 10 is completed, and finally the k value corresponding to the minimum permutation entropy average is determined as the optimal number of modal components, that is, the optimal k value.

[0072] Step S12: calculating a corresponding average value according to the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficients between each modal component signal and the original signal with the average value to obtain a corresponding comparison result.

[0073] It can be understood that the IMF components are selected based on the average value comparison method of correlation analysis, that is, the average correlation coefficient value corresponding to all modal component signals is calculated, and then the correlation coefficient corresponding to each modal component signal is compared with the average value to obtain the corresponding comparison result.

[0074] For example, after the original signal is decomposed, k IMF components are obtained, and the correlation coefficients between each IMF component and the original signal are r1, r2, ..., r k , averaging the correlation coefficients of each IMF component yields:

[0075]

[0076] in, represents the average value, r i It represents the correlation coefficient corresponding to the i-th IMF component, and the calculated average value (average correlation coefficient value) is used as the threshold for judging whether the IMF component needs to be filtered.

[0077] Step S13, identifying and screening the modal component signal based on the comparison result to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold.

[0078] It can be understood that when the comparison result shows that the correlation coefficient between the modal component signal and the original signal is not greater than the average value, the modal component signal is filtered, and when the comparison result shows that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained to obtain multiple target vibration signals tending to the same frequency, that is, the modal components with a correlation coefficient less than the average value are filtered out, and the modal components with a correlation coefficient greater than the average value are retained to determine the target vibration signal, that is, after the original signal containing multi-point vibration information and noise is decomposed by the VMD algorithm, it is necessary to identify and screen the decomposed IMF components to filter out the noise signal, retain the signal containing the main approximately same-frequency vibration information, and obtain the target vibration signal. It should be pointed out that when the frequency difference between multiple signals is not greater than the preset frequency threshold, it can be considered that the multiple signals tend to the same frequency, and the preset frequency threshold can be set to 0.1 Hz, that is, when the frequency difference between multiple signals is not greater than 0.1 Hz, it is determined that it is a signal tending to the same frequency.

[0079] Step S14: performing cross-correlation operations on the multiple target vibration signals using a preset cross-correlation algorithm to calculate the time differences corresponding to the multiple target vibration signals, and locating the vibration source based on the time differences to obtain corresponding positioning results.

[0080] In this embodiment, a cross-correlation algorithm is used to perform cross-correlation operations on the identified and screened target vibration signals with similar frequencies to obtain their corresponding time differences, thereby realizing the positioning of multiple vibration sources with similar frequencies.

[0081] It can be seen that in the embodiment of the present invention, the original signal is decomposed by a modal decomposition algorithm, and the target vibration signal that is close to the same frequency is identified and screened out from the decomposed modal component signals, and then the time difference corresponding to the target vibration signal is calculated by a cross-correlation algorithm, and the vibration source is located based on the time difference, thereby solving the problem in the prior art that vibration signals that are close to the same frequency cannot be identified and located, and further realizing the identification and positioning of vibration signals that are close to the same frequency.

[0082] See also Figure 3 As shown, the embodiment of the present invention discloses a specific vibration source identification method based on modal decomposition. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0083] Step S21: Decompose the acquired original signal using a preset modal decomposition algorithm to obtain multiple modal component signals.

[0084] Step S22: filtering the zero-frequency noise signal in the multiple modal component signals to obtain multiple filtered modal component signals.

[0085] In this embodiment, after the acquired original signal is decomposed by a preset modal decomposition algorithm to obtain multiple modal component signals, the zero-frequency noise signal in the multiple modal component signals can be directly filtered to obtain multiple modal component signals after filtering. It should be pointed out that after removing the zero-frequency noise, the change trend of the original signal tends to be horizontal, thereby improving the quality and accuracy of signal processing.

[0086] Step S23: Calculate the correlation coefficient between each of the modal component signals in the filtered multiple modal component signals and the original signal.

[0087] In this embodiment, after the zero-frequency noise signal is removed from the decomposed multiple modal component signals, the correlation coefficient between the remaining modal component signals and the original signal is calculated.

[0088] Step S24, calculating a corresponding average value according to the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficients between each modal component signal and the original signal with the average value to obtain a corresponding comparison result.

[0089] Step S25, identifying and screening the modal component signal based on the comparison result to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold.

[0090] Step S26: perform cross-correlation operations on the multiple target vibration signals using a preset cross-correlation algorithm to calculate the time difference corresponding to the multiple target vibration signals, and locate the vibration source based on the time difference to obtain a corresponding positioning result.

[0091] For the specific contents of the above step S21, and steps S24 to S26, reference may be made to the corresponding contents disclosed in the above embodiments, which will not be elaborated here.

[0092] It can be seen that in the embodiment of the present invention, the original signal is decomposed by a modal decomposition algorithm, and the target vibration signal that is close to the same frequency is identified and screened out from the decomposed modal component signals, and then the time difference corresponding to the target vibration signal is calculated by a cross-correlation algorithm, and the vibration source is located based on the time difference, thereby solving the problem in the prior art that vibration signals that are close to the same frequency cannot be identified and located, and further realizing the identification and positioning of vibration signals that are close to the same frequency.

[0093] The technical solution of this application for vibration source identification and positioning can be applied to the forward transmission distributed sensing system based on the double-end structure. Figure 4 As shown, the phase information (original signal) demodulated by the distributed sensing system is collected by the data acquisition system, and then the phase information is subjected to variational mode decomposition and cross-correlation operations based on the technical solution of vibration source identification and positioning of the present application, thereby realizing the identification and positioning of multi-point vibration signals tending to the same frequency. Among them, the data acquisition system can collect 3×3 structures or other structures.

[0094] For example, to evaluate the performance of the sensing system, a 20-meter sensing fiber section wrapped and fixed around a disc-shaped piezoelectric transducer (PZT) was vibrated. Two PZTs were placed at the 50-kilometer end of the sensing fiber. The length of the sensing fiber was approximately 101.03 kilometers. The two PZTs vibrated continuously based on sinusoidal drive signals of 10 Hz and 10.1 Hz, respectively. The signal transmitted its displacement to the bonding fiber in the form of strain. The phase signal detected by the double-ended structure is shown in Fig. Figure 5As shown, Figure 5 Represents the frequency domain signal of one section in the double-ended structure. Figure 5 It can be found that the detected phase signal contains two vibration signals of 10Hz and 10.1Hz, whose amplitudes are 250v and 80v respectively. Then the VMD algorithm based on permutation entropy optimization is used to decompose them. The decomposition results are as follows: Figure 6 As shown, from the Figure 6 It can be seen that the two vibration signals at 10Hz and 10.1Hz, namely IMF3 and IMF4, are well restored. Finally, the time difference corresponding to the two vibration source frequencies is calculated by the cross-correlation algorithm, so as to realize the vibration source positioning based on the time difference. For details, see the above Figure 2 As shown in the figure, the k IMF components decomposed are selected based on the average value comparison method of correlation analysis, that is, the correlation coefficient between each IMF component and the original signal is calculated, and then the average value of the k correlation coefficients is taken, and the IMF components with correlation coefficients greater than the average value are retained. Then, the cross-correlation algorithm is used to calculate the time difference corresponding to the frequencies of the two vibration sources, so as to realize the multi-point vibration source positioning with approximately the same frequency based on the time difference.

[0095] In one embodiment, if Figure 7 As shown, based on the above-mentioned vibration source identification method based on modal decomposition, the present invention also provides a vibration source identification device based on modal decomposition, including:

[0096] The signal decomposition module 11 is used to decompose the acquired original signal using a preset modal decomposition algorithm to obtain multiple modal component signals;

[0097] A correlation coefficient calculation module 12, used to calculate the correlation coefficient between each of the modal component signals and the original signal;

[0098] An average value calculation module 13, used to calculate a corresponding average value according to the correlation coefficient between all the modal component signals and the original signal;

[0099] A comparison module 14, used for comparing the correlation coefficient between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result;

[0100] A signal screening module 15 is used to identify and screen the modal component signal based on the comparison result to obtain a plurality of target vibration signals tending to be of the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be of the same frequency is not greater than a preset frequency threshold;

[0101] A time difference calculation module 16, configured to perform a cross-correlation operation on the plurality of target vibration signals using a preset cross-correlation algorithm to calculate the time differences corresponding to the plurality of target vibration signals;

[0102] The vibration source positioning module 17 is used to locate the vibration source based on the time difference to obtain a corresponding positioning result.

[0103] Figure 8 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may include:

[0104] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0105] When the processor 502 executes the program, the electrical impedance tomography method based on sparse recovery provided in the above embodiment is implemented.

[0106] Furthermore, the terminal further includes:

[0107] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0108] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0109] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0111] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0112] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0113] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the electrical impedance tomography method based on sparse recovery as described above is implemented.

[0114] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the claims.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can read instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0117] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA, Programmable Gate Array), a field programmable gate array (FPGA, Field-Programmable Gate Array), etc.

[0118] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A vibration source identification method based on modal decomposition, characterized in that: The method comprises: Decomposing the acquired original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals, and calculating the correlation coefficient between each of the modal component signals and the original signal; Calculating a corresponding average value according to the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficient between each modal component signal and the original signal with the average value to obtain a corresponding comparison result; Based on the comparison result, the modal component signal is identified and screened to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold; A preset cross-correlation algorithm is used to perform cross-correlation operations on the multiple target vibration signals to calculate the time differences corresponding to the multiple target vibration signals, and the vibration source is located based on the time differences to obtain corresponding positioning results.

2. The vibration source identification method based on modal decomposition according to claim 1 is characterized in that: The original signal is a signal containing multi-point vibration information and noise, which is collected by a data acquisition system according to a preset sampling rate and number of sampling points.

3. The vibration source identification method based on modal decomposition according to claim 1 is characterized in that: Before calculating the correlation coefficient between each of the modal component signals and the original signal, the method further includes: Filtering the zero-frequency noise signal in the multiple modal component signals to obtain multiple filtered modal component signals; Wherein, the calculating the correlation coefficient between each of the modal component signals and the original signal comprises: The correlation coefficient between each of the modal component signals in the filtered multiple modal component signals and the original signal is calculated.

4. The vibration source identification method based on modal decomposition according to claim 1 is characterized in that: Before decomposing the acquired original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals, the method further includes: Based on the permutation entropy, the optimal number of modal components when the original signal is decomposed using the preset modal decomposition algorithm is determined; wherein the optimal number of modal components is an integer.

5. The vibration source identification method based on modal decomposition according to claim 4 is characterized in that: The method of decomposing the acquired original signal by using a preset modal decomposition algorithm to obtain multiple modal component signals includes: Utilizing the preset modal decomposition algorithm, and decomposing the acquired original signal according to the optimal number of modal components, a plurality of modal component signals corresponding to the optimal number of modal components are obtained; wherein the preset modal decomposition algorithm is a preset variational modal decomposition algorithm or a preset empirical mode decomposition algorithm.

6. The vibration source identification method based on modal decomposition according to claim 1 is characterized in that: The preset frequency threshold is 0.1 Hz.

7. The vibration source identification method based on modal decomposition according to any one of claims 1 to 6, characterized in that: The step of identifying and screening the modal component signals based on the comparison results to obtain a plurality of target vibration signals tending to be of the same frequency includes: When the comparison result indicates that the correlation coefficient between the modal component signal and the original signal is not greater than the average value, filtering the modal component signal; When the comparison result indicates that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained to obtain a plurality of target vibration signals tending to be of the same frequency.

8. A vibration source identification device based on modal decomposition, characterized in that: The device comprises: A signal decomposition module is used to decompose the acquired original signal using a preset modal decomposition algorithm to obtain multiple modal component signals; A correlation coefficient calculation module, used for calculating the correlation coefficient between each of the modal component signals and the original signal; An average value calculation module, used for calculating a corresponding average value according to the correlation coefficient between all the modal component signals and the original signal; A comparison module, used for comparing the correlation coefficient between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result; A signal screening module, used for identifying and screening the modal component signal based on the comparison result to obtain a plurality of target vibration signals tending to be of the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be of the same frequency is not greater than a preset frequency threshold; A time difference calculation module, used for performing a cross-correlation operation on the plurality of target vibration signals using a preset cross-correlation algorithm to calculate the time differences corresponding to the plurality of target vibration signals; The vibration source positioning module is used to locate the vibration source based on the time difference to obtain a corresponding positioning result.

9. A terminal, characterized in that: include: A memory, a processor, and a vibration source identification program based on modal decomposition stored in the memory and executable on the processor, wherein the vibration source identification program based on modal decomposition, when executed by the processor, implements the steps of the vibration source identification method based on modal decomposition as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which can be executed to implement the steps of the vibration source identification method based on modal decomposition as described in any one of claims 1 to 7.

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