A method, device and apparatus for determining the number of decomposed modes in variational mode decomposition
The signal is decomposed by incrementing the decomposition mode number and judging the optimal decomposition mode number based on the sparse index, the problem of difficulty in setting the decomposition mode number in variational mode decomposition is solved, and more effective signal decomposition is achieved.
Patent Information
- Application Number
- CN202210081537.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-01-24
- Publication Date
- 2025-04-08
- Estimated Expiration
- 2042-01-24
AI Technical Summary
The setting of the decomposition mode number K in variational modal decomposition has a great influence. There is currently a lack of methods to accurately predict the decomposition mode number K, which leads to limited decomposition effect.
By selecting the incrementing decomposition mode number k in sequence, performing several decomposition processes on the signal to be processed, the sparse index corresponding to each decomposition process is determined separately. When the sparse index when the decomposition mode number k+1 decreases compared to k, it is determined that k is the optimal decomposition mode number.
By determining the optimal decomposition mode number, signal decomposition can be effectively performed, endpoint effect and modal aliasing can be reduced, and decomposition effect can be improved.
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Figure CN114417935B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of signal processing technology, and in particular to a method, device and equipment for determining the number of decomposed modes in variational mode decomposition. Background Art
[0002] Nonlinear and non-stationary signals are the most common type of signals in engineering applications. When performing signal analysis on complex composite nonlinear and non-stationary signals, the complex nonlinear and non-stationary signals are generally decomposed into multiple simple signals first, and then the decomposed signals are analyzed and processed. The mainstream composite signal decomposition methods include wavelet decomposition, wavelet packet decomposition, and empirical mode decomposition (EMD). Among these methods, wavelet decomposition requires the selection of wavelet basis functions that match the signal in advance, and wavelet packet decomposition requires not only the specification of the wavelet basis but also the setting of the number of decomposition layers. Both methods lack certain adaptability. EMD can adaptively decompose the signal into a series of intrinsic mode components (IMF) through continuous iteration according to the characteristics of the signal, but the entire decomposition process will be affected by the extreme point search method, interpolation method, and the criteria for stopping iteration. The decomposed components will have problems such as endpoint effects and mode aliasing.
[0003] The variational mode decomposition (VMD) method has the advantages of fast convergence speed and strong noise robustness over other methods such as wavelet decomposition and EMD with its rigorous mathematical theory guidance. It can also effectively reduce the problems of endpoint effect, over-envelope and under-envelope in decomposition. Although VMD decomposition is excellent, its decomposition effect is greatly affected by the parameter settings in the decomposition process, including the decomposition mode number K, penalty factor alpha, Lagrange multiplier lanmda, etc. Among these parameters, the setting of the decomposition mode number K is the most important, because under the premise of the correct setting of the decomposition mode number K, as long as other parameters are set within a reasonable range, the signal can generally be effectively decomposed. Therefore, the correct setting of the decomposition mode number K is the biggest bottleneck limiting whether VMD can effectively decompose. However, there is currently no method that can accurately predict the decomposition mode number K. Summary of the invention
[0004] In view of this, embodiments of the present application provide a method, apparatus, and device for determining the number of decomposed modes in variational modal decomposition for signal processing.
[0005] In a first aspect, an embodiment of the present application provides a method for determining the number of decomposed modes in variational mode decomposition, which is used for signal processing, comprising:
[0006] Get the signal to be processed;
[0007] By using a variational mode decomposition method, selecting a decomposition mode number k that increases in sequence to perform several decomposition processes on the signal to be processed, and determining the sparsity index corresponding to each decomposition process of the signal to be processed; wherein the decomposition mode number k is a natural number greater than 1;
[0008] When the sparse index corresponding to the decomposition process when the decomposition mode number is k+1 in the several decomposition processes is lower than the sparse index corresponding to the decomposition process when the decomposition mode number is k, it is determined that k is the optimal decomposition mode number;
[0009] The signal to be processed is processed according to the optimal decomposition mode number.
[0010] Preferably, the respectively determining the sparse index corresponding to each decomposition process of the signal to be processed comprises:
[0011] By using a variational mode decomposition method, the signal is decomposed by a mode number k to obtain k eigenmode components;
[0012] For each of the k eigenmode components, a marginal spectrum is calculated respectively, and the sparsity of each of the k marginal spectra is determined;
[0013] The sparsity index of the signal to be processed at the current k value is determined according to the sparsity of each marginal spectrum.
[0014] Preferably, the formula for calculating the marginal spectrum is:
[0015]
[0016] Where h represents the marginal spectrum, ω represents the frequency, T represents the time, H represents the time-frequency spectrum matrix, t represents the moment in the time-frequency spectrum, and dt represents the integral.
[0017] Preferably, the formula for determining the sparsity of each of the k marginal spectra is:
[0018] E(X 2 ) / (E(X)) 2
[0019] Where E represents the mean processing, and X represents the marginal spectrum signal of the intrinsic mode component requiring sparsity.
[0020] Preferably, determining the sparsity of each marginal spectrum in the k marginal spectra further comprises:
[0021] Calculating a marginal spectrum for each of the k eigenmode components, and determining the sparsity of each of the k marginal spectra;
[0022] Calculating the energy weight factor corresponding to each eigenmode component;
[0023] The energy weight factor corresponding to each eigenmode component is multiplied by the sparsity of the marginal spectrum to obtain a final sparsity.
[0024] In a second aspect, an embodiment of the present application provides a device for determining the number of decomposed modes in variational mode decomposition, which is used for signal processing, including:
[0025] A first acquisition module, used to acquire a signal to be processed;
[0026] The second acquisition module is used to select the decomposition mode number k that increases in sequence through the variational mode decomposition method to perform several decomposition processes on the signal to be processed, and respectively determine the sparse index corresponding to each decomposition process of the signal to be processed; wherein the decomposition mode number k is a natural number greater than 1, and the initial value is 2;
[0027] A judging module, used to judge that when the number of decomposition modes in the decomposition processes is k+1, the sparse index corresponding to the decomposition process is lower than the sparse index corresponding to the decomposition process when the number of decomposition modes is k, and judge k to be the optimal decomposition mode number;
[0028] A signal processing module is used to perform signal processing on the signal to be processed according to the optimal decomposition mode number.
[0029] Preferably, the respectively determining the sparse index corresponding to each decomposition process of the signal to be processed comprises:
[0030] By using a variational mode decomposition method, the signal is decomposed by a mode number k to obtain k eigenmode components;
[0031] For each of the k eigenmode components, a marginal spectrum is calculated respectively, and the sparsity of each of the k marginal spectra is determined;
[0032] The sparsity index of the signal to be processed at the current k value is determined according to the sparsity of each marginal spectrum.
[0033] Preferably, the formula for calculating the marginal spectrum is:
[0034]
[0035] Where h represents the marginal spectrum, ω represents the frequency, T represents the time, H represents the time-frequency spectrum matrix, t represents the moment in the time-frequency spectrum, and dt represents the integral.
[0036] Preferably, the formula for determining the sparsity of each of the k marginal spectra is:
[0037] E(X 2) / (E(X)) 2
[0038] Where E represents the mean processing, and X represents the marginal spectrum signal of the intrinsic mode component requiring sparsity.
[0039] Preferably, determining the sparsity of each marginal spectrum in the k marginal spectra further comprises:
[0040] Calculating a marginal spectrum for each of the k eigenmode components, and determining the sparsity of each of the k marginal spectra;
[0041] Calculating the energy weight factor corresponding to each eigenmode component;
[0042] The energy weight factor corresponding to each eigenmode component is multiplied by the sparsity of the marginal spectrum to obtain a final sparsity.
[0043] In a third aspect, an embodiment of the present application provides a device for determining the number of decomposed modes in variational modal decomposition, which is used for signal processing and includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for determining the number of decomposed modes in variational modal decomposition is executed.
[0044] The present invention proposes a method, device and equipment for determining the number of decomposition modes in variational mode decomposition. The method performs signal decomposition processing on a signal to be processed by selecting decomposition mode numbers that increase in sequence, and then compares the sparsity index obtained by processing the signal according to adjacent decomposition mode numbers to obtain the optimal decomposition mode number. Variational mode decomposition is performed on the signal according to the optimal decomposition mode number, and an effective signal decomposition result can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0046] Figure 1 A schematic diagram of an application scenario of the technical solution provided in the embodiment of the present application;
[0047] Figure 2 A schematic diagram of a method for determining the number of decomposed modes in variational mode decomposition provided in an embodiment of the present application;
[0048] Figure 3 This is a schematic diagram of a first simulation signal provided in an embodiment of the present application;
[0049] Figure 4 Schematic diagram of 6 harmonic signals constituting the first simulation signal;
[0050] Figure 5 It is a schematic diagram of the result obtained by decomposing the simulation signal 1 according to the optimal decomposition mode number;
[0051] Figure 6 A schematic diagram of a second simulation signal provided in an embodiment of the present application;
[0052] Figure 7 is a schematic diagram of three sub-signals constituting the second simulation signal;
[0053] Figure 8 is a schematic diagram of the result obtained by decomposing the simulation signal 2 according to the optimal decomposition mode number;
[0054] Fig. 9 A schematic diagram of a device for determining the number of decomposed modes in variational modal decomposition provided in an embodiment of the present application;
[0055] Fig.10 The present invention provides a schematic diagram of the structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION
[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0057] To facilitate understanding of the embodiments of the present invention, specific embodiments will be further explained below in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present invention.
[0058] Figure 1 Schematic diagram of application scenario of the technical solution provided in the embodiment of this application. Figure 1 As shown, the present method is applied to signal processing. Through the method and apparatus for determining the number of decomposed modes in variational modal decomposition provided in the present application, the number of decomposed modes of variational modal decomposition can be determined. Furthermore, based on the determined number of decomposed modes, variational modal decomposition is used to analyze and process the signal to be processed.
[0059] Figure 2 Schematic diagram of a method for determining the number of decomposed modes in variational mode decomposition provided in an embodiment of the present application. Figure 2 As shown, the method includes:
[0060] Step S201: Obtain a signal to be processed.
[0061] Specifically, the signal to be processed may be composed of a plurality of harmonic signals, may be composed of a linear frequency modulation signal and a segmented signal, or may be composed of a plurality of frequency modulation signals.
[0062] Step S202: by using the variational mode decomposition method, select the decomposition mode number k that increases in sequence to perform several decomposition processes on the signal to be processed, and determine the sparsity index corresponding to each decomposition process of the signal to be processed, where k is a natural number greater than 1.
[0063] Exemplarily, the signal to be processed is decomposed several times, and the decomposition mode number k is 2, 3, 4, ..., respectively, in each decomposition process, and increases in sequence. The sparse index corresponding to the decomposition mode number being 2, 3, 4, ... is determined respectively.
[0064] In a more specific embodiment, respectively determining the sparse index corresponding to each decomposition process of the signal to be processed includes:
[0065] The signal is decomposed into k decomposition modes by a variational mode decomposition method to obtain k eigenmode components.
[0066] Specifically, the decomposition mode number k is taken as 2, and the signal is decomposed by VMD to obtain two eigenmode components.
[0067] The marginal spectrum of each of the k eigenmode components is calculated respectively, and the sparsity of each of the k marginal spectra is determined.
[0068] Specifically, the corresponding marginal spectrum is calculated for each of the two obtained intrinsic mode components, and then the sparsity corresponding to each marginal spectrum is determined respectively through the obtained two marginal spectra.
[0069] Preferably, the marginal spectrum can be calculated according to the following formula:
[0070]
[0071] Where h represents the marginal spectrum, ω represents the frequency, T represents the time, H represents the time-frequency spectrum matrix, t represents the moment in the time-frequency spectrum, and dt represents the integral.
[0072] Preferably, the sparsity can be calculated according to the following formula:
[0073] E(X 2 ) / (E(X)) 2
[0074] Where E represents the mean processing, and X represents the marginal spectrum signal of the intrinsic mode component requiring sparsity.
[0075] The sparsity index of the signal to be processed at the current k value is determined according to the sparsity of each marginal spectrum.
[0076] Specifically, the sparse index of the signal to be processed is determined according to the sparsities corresponding to the two obtained marginal spectra.
[0077] Preferably, the average value of the marginal spectrum sparsities corresponding to k eigenmodal components is taken as the sparsity index of VMD decomposition under the decomposition mode number k.
[0078] In a more specific example, the method further includes: calculating the energy weight factor corresponding to each eigenmode component.
[0079] There are many ways to reflect the energy of a signal. In the present invention, the amplitude is used to reflect the energy of the marginal spectrum signal of the intrinsic mode component. The larger the amplitude, the greater the energy of the marginal spectrum signal; the larger the energy weight factor, the greater the corresponding weight.
[0080] The energy weight factor corresponding to each eigenmode component is multiplied by the sparsity of the marginal spectrum to obtain the final sparsity.
[0081] Optionally, the energy weight factor is defined as the ratio of the maximum amplitude on the marginal spectrum corresponding to the current eigenmode component to the maximum amplitude on the marginal spectrum corresponding to the k eigenmode components. Specifically, it can be expressed by the following formula:
[0082]
[0083] Where max represents the maximum value, bjp represents the marginal spectrum, and i represents the i-th eigenmode component.
[0084] Therefore, the energy weight factor corresponding to each intrinsic mode component is multiplied by the sparsity of the marginal spectrum to obtain the final sparsity, which can be expressed as
[0085]
[0086] Step S203: when the sparse index corresponding to the decomposition process when the decomposition mode number is k+1 in the several decomposition processes is lower than the sparse index corresponding to the decomposition process when the decomposition mode number is k, it is determined that k is the optimal decomposition mode number.
[0087] Specifically, because the decomposition mode numbers k that increase in sequence are selected to decompose the signal to be processed respectively, the sparse indices corresponding to different decomposition mode numbers k can be obtained, and the sparse indices corresponding to adjacent decomposition mode numbers k can be judged. When the sparse index decreases, the previous decomposition mode number is determined to be the optimal decomposition mode number.
[0088] For example, if the sparse index corresponding to the decomposition mode number of 3 is lower than the sparse index corresponding to the decomposition mode number of 2, then the optimal decomposition mode number is determined to be 2.
[0089] Step S204: performing signal processing on the signal to be processed according to the optimal decomposition mode number.
[0090] Specifically, according to the determined optimal mode number 2, the signal is processed by VMD decomposition.
[0091] In order to verify the practicability of the present invention, the present invention carries out decomposition experiments on different simulation signals.
[0092] Figure 3 This is a schematic diagram of the first simulation signal provided in the embodiment of the present application. Figure 4 This is a schematic diagram of the six harmonic signals that make up the first simulation signal. Please refer to Figure 4 , the simulation signal 1 is composed of 6 harmonic signals, the high-frequency components of the 6 harmonic signals are 2500Hz, 2000Hz, 1500Hz, 1000Hz, 800Hz, 500Hz, the corresponding envelope signal frequencies are 100Hz, 50Hz, 80Hz, 100Hz, 100Hz, 100Hz, and the sampling frequency of the signal is 6kHz. The simulation signal 1 is processed according to the method for determining the number of decomposed modes in variational mode decomposition of the present invention. Table 1 shows the sparse index under different values of the number of decomposed modes k.
[0093] Table 1
[0094]
[0095] According to the data in Table 1, it is determined that 6 is the best decomposition mode number. The simulation signal 1 is analyzed and processed by taking 6 as the best decomposition mode number. Figure 5 The figure shows the result of decomposing the simulation signal 1 according to the optimal number of decomposition modes. Figure 4 and Figure 5 It can be seen that the result obtained by decomposing the simulation signal 1 according to the optimal decomposition mode number is basically the same as the original signal that constitutes the simulation signal 1.
[0096] Figure 6 This is a schematic diagram of a second simulation signal provided in an embodiment of the present application. Figure 7 This is a schematic diagram of the three sub-signals that make up the second simulation signal. Please refer to Figure 7, the simulation signal 2 is composed of a linear frequency modulation signal and a segmented signal, the frequency corresponding to the first segment of the segmented signal is 2000Hz, the frequency corresponding to the second segment is 1000Hz, and the sampling frequency of the simulation signal is 6kHz. The simulation signal 2 is processed according to the method for determining the number of decomposed modes in variational mode decomposition of the present invention. Table 2 shows the sparse index under different values of the number of decomposed modes k.
[0097] Table 2
[0098] Decomposition mode number k Sparse Indicators 2 174.278 3 1596.800 4 1180.500
[0099] According to the data in Table 2, it is determined that 3 is the best decomposition mode number. The simulation signal 2 is analyzed and processed by taking 3 as the best decomposition mode number. Figure 8 The figure shows the result of decomposing the simulation signal 2 according to the optimal decomposition mode number. Figure 7 and Figure 8 It can be seen that the result obtained by decomposing the simulation signal 2 according to the optimal decomposition mode number is basically the same as the original signal that constitutes the simulation signal 2.
[0100] pass Figure 4 and Figure 5 The comparison, and Figure 7 and Figure 8 By comparing the above, it can be seen that the method of the present invention can well determine the optimal decomposition mode number, and further decompose the signal by using the determined decomposition mode number to obtain an effective signal decomposition result.
[0101] Based on the method for determining the number of decomposed modes in variational modal decomposition provided in the above embodiment, this embodiment further provides a device for determining the number of decomposed modes in variational modal decomposition. Specifically, Fig. 9 FIG. 4 shows an optional structural block diagram of the device for determining the number of decomposed modes in variational mode decomposition. Fig. 9 As shown, the device comprises:
[0102] The first acquisition module 901 is used to acquire a signal to be processed.
[0103] The second acquisition module 902 is used to select a decomposition mode number k that increases in sequence through a variational mode decomposition method to perform several decomposition processes on the signal to be processed, and determine the sparsity index corresponding to each decomposition process of the signal to be processed; wherein the decomposition mode number k is a natural number greater than 1, and its initial value is 2.
[0104] In some possible embodiments, respectively determining the sparse index corresponding to each decomposition process of the signal to be processed includes:
[0105] By using a variational mode decomposition method, the signal is decomposed by a mode number k to obtain k eigenmode components;
[0106] For each of the k eigenmode components, a marginal spectrum is calculated respectively, and the sparsity of each of the k marginal spectra is determined;
[0107] Preferably, the formula for finding the marginal spectrum is
[0108]
[0109] Where h represents the marginal spectrum, ω represents the frequency, T represents the time, H represents the time-frequency spectrum matrix, t represents the moment in the time-frequency spectrum, and dt represents the integral.
[0110] Preferably, the formula for determining the sparsity of each of the k marginal spectra is:
[0111] E(X 2 ) / (E(X)) 2
[0112] Where E represents the mean processing, and X represents the marginal spectrum signal of the intrinsic mode component requiring sparsity.
[0113] The sparsity index of the signal to be processed at the current k value is determined according to the sparsity of each marginal spectrum.
[0114] In some possible embodiments, determining the sparsity of each marginal spectrum in the k marginal spectra further includes:
[0115] Calculating a marginal spectrum for each of the k eigenmode components, and determining the sparsity of each of the k marginal spectra;
[0116] Calculating the energy weight factor corresponding to each eigenmode component;
[0117] The energy weight factor corresponding to each eigenmode component is multiplied by the sparsity of the marginal spectrum to obtain a final sparsity.
[0118] The judgment module 903 is used to judge that when the number of decomposition modes is k+1, the sparse index corresponding to the decomposition processing is lower than the sparse index corresponding to the decomposition processing when the number of decomposition modes is k, and determine that k is the optimal decomposition mode number.
[0119] The signal processing module 904 is used to perform signal processing on the signal to be processed according to the optimal decomposition mode number.
[0120] Fig.10The schematic diagram of the structure of a computer device provided in the embodiment of this specification is shown, and the computer device may include: a processor 1001, a memory 1002, an input / output interface 1003, a communication interface 1004 and a bus 1005. The processor 1001, the memory 1002, the input / output interface 1003 and the communication interface 1004 are connected to each other in the device through the bus 1005. The computer device can be used to perform the above Figure 2 The method shown.
[0121] The processor 1001 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.
[0122] The memory 1002 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1002 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1002 and are called and executed by the processor 1001.
[0123] The input / output interface 1003 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.
[0124] The communication interface 1004 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).
[0125] The bus 1005 includes a path that transmits information between various components of the device (eg, the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 ).
[0126] It should be noted that, although the above device only shows the processor 1001, the memory 1002, the input / output interface 1003, the communication interface 1004 and the bus 1005, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of this specification, and does not necessarily include all the components shown in the figure.
[0127] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0128] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.
[0129] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for determining the number of decomposed modes in variational mode decomposition for signal processing, characterized in that: The method comprises: Get the signal to be processed; By using the variational mode decomposition method, a decomposition mode number k that increases in sequence is selected to perform several decomposition processes on the signal to be processed, and the sparse index corresponding to each decomposition process of the signal to be processed is determined respectively; wherein the decomposition mode number k is a natural number greater than 1; the respectively determining the sparse index corresponding to each decomposition process of the signal to be processed comprises: by using the variational mode decomposition method, the signal is decomposed by the decomposition mode number k to obtain k intrinsic mode components; for each intrinsic mode component of the k intrinsic mode components, the marginal spectrum is calculated respectively to determine the sparsity of each marginal spectrum of the k marginal spectra; according to the sparsity of each marginal spectrum, the sparse index of the signal to be processed at the current k value is determined; When the sparse index corresponding to the decomposition process when the decomposition mode number is k+1 in the several decomposition processes is lower than the sparse index corresponding to the decomposition process when the decomposition mode number is k, it is determined that k is the optimal decomposition mode number; The signal to be processed is processed according to the optimal decomposition mode number.
2. The method according to claim 1, characterized in that The formula for finding the marginal spectrum is: Where h represents the marginal spectrum, ω represents the frequency, T represents the time, H represents the time-frequency spectrum matrix, t represents the moment in the time-frequency spectrum, and dt represents the integral.
3. The method according to claim 1, characterized in that The formula for determining the sparsity of each of the k marginal spectra is E(X 2 ) / (E(X)) 2 Where E represents the mean processing, and X represents the marginal spectrum signal of the intrinsic mode component requiring sparsity.
4. The method according to claim 1, characterized in that: The determining the sparsity of each marginal spectrum in the k marginal spectra further includes: Calculating a marginal spectrum for each of the k eigenmode components, and determining the sparsity of each of the k marginal spectra; Calculating the energy weight factor corresponding to each eigenmode component; The energy weight factor corresponding to each eigenmode component is multiplied by the sparsity of the marginal spectrum to obtain a final sparsity.
5. A device for determining the number of decomposed modes in variational modal decomposition, for signal processing, characterized in that: The device comprises: A first acquisition module, used to acquire a signal to be processed; The second acquisition module is used to select the decomposition mode number k that increases in sequence through the variational mode decomposition method to perform several decomposition processes on the signal to be processed, and respectively determine the sparse index corresponding to each decomposition process of the signal to be processed; wherein the decomposition mode number k is a natural number greater than 1, and the initial value is 2; the respectively determining the sparse index corresponding to each decomposition process of the signal to be processed comprises: performing decomposition process of the decomposition mode number k on the signal through the variational mode decomposition method to obtain k intrinsic mode components; respectively calculating the marginal spectrum of each intrinsic mode component in the k intrinsic mode components, and determining the sparsity of each marginal spectrum in the k marginal spectra; and determining the sparse index of the signal to be processed at the current k value according to the sparsity of each marginal spectrum; A judging module, used to judge that when the number of decomposition modes in the decomposition processes is k+1, the sparse index corresponding to the decomposition process is lower than the sparse index corresponding to the decomposition process when the number of decomposition modes is k, and judge k to be the optimal decomposition mode number; A signal processing module is used to perform signal processing on the signal to be processed according to the optimal decomposition mode number.
6. The device according to claim 5, characterized in that The formula for finding the marginal spectrum is: Where h represents the marginal spectrum, ω represents the frequency, T represents the time, H represents the time-frequency spectrum matrix, t represents the moment in the time-frequency spectrum, and dt represents the integral.
7. The device according to claim 5, characterized in that The determining the sparsity of each marginal spectrum in the k marginal spectra further includes: Calculating a marginal spectrum for each of the k eigenmode components, and determining the sparsity of each of the k marginal spectra; Calculating the energy weight factor corresponding to each eigenmode component; The energy weight factor corresponding to each eigenmode component is multiplied by the sparsity of the marginal spectrum to obtain a final sparsity.
8. A device for determining the number of decomposed modes in variational modal decomposition, comprising a processor and a memory, wherein: The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method according to any one of claims 1 to 4 is executed.