CN117421561A - Turbulence denoising method and system based on parameter optimization VMD (virtual machine direction detector) combined wavelet - Google Patents
Turbulence denoising method and system based on parameter optimization VMD (virtual machine direction detector) combined wavelet Download PDFInfo
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Abstract
The invention belongs to the technical field of ocean turbulence denoising, and discloses a turbulence denoising method and system based on a parameter optimization VMD (virtual model detector) combined wavelet. The invention acquires the original shearing signal through the matrix profile turbulator, initializes the parameter of NGO algorithm, sets the minimum envelope entropy as the fitness value, and acquires the corresponding minimum envelope entropyαAndKthe value is used as an optimal parameter of a VMD algorithm, the VMD of the optimal parameter is used for carrying out modal decomposition on the original turbulence shear signal, noise components are removed according to correlation coefficients of modal components and the original turbulence shear signal, effective components are reserved, and the primary denoising is completed; and then carrying out wavelet threshold denoising on the effective components to realize secondary denoising, and carrying out signal reconstruction on each denoised component to obtain purer turbulence shear signals. The invention can eliminate turbulent flow shearNoise in the signals interferes, and accuracy of ocean turbulence data is improved.
Description
Technical Field
The invention belongs to the technical field of ocean turbulence denoising, and particularly relates to a turbulence denoising method and system based on a parameter optimization VMD (virtual model detector) combined wavelet.
Background
Ocean turbulence is a complex flow phenomenon in ocean currents, the distribution and evolution of which in the ocean environment is critical to understanding the process of ocean circulation, tides, mixing, etc. Ocean turbulence is a complex fluid motion that is nonlinear. In a marine environment, the direction and flow rate of ocean turbulence varies greatly and is random. Therefore, a more scientific and precise turbulence observation instrument is required to be continuously optimized and designed for ocean turbulence data acquisition. Due to the multi-scale process of ocean turbulence and the complex motion of ocean circulation, analysis and research on turbulence interaction cannot be satisfied by only single-point observation.
By using multi-point, high resolution, spatiotemporal synchronous observations, the evolution of turbulence can be better analyzed. In order to meet the research requirements of three-dimensional and multidimensional synchronous observation in a small scale range, a matrix profile turbulence observation system is adopted to acquire ocean turbulence data, so that horizontal space-time synchronous three-dimensional observation of full sea depth turbulence mixing is realized, turbulent multi-scale coupling research is facilitated, and a new observation mode is provided for realizing ocean space-time multidimensional measurement.
However, due to the constraints of marine environmental complexity and measurement conditions, the matrix profile turbulence observation platform is constantly in various marine background noise, and the turbulence shear signal is inevitably contaminated by noise, which makes signal processing and analysis more difficult. Therefore, noise pollution in a complex ocean background is detected, interference of noise on observed data is eliminated as much as possible, and the method is an important way for guaranteeing stability of an observation platform and improving effectiveness of turbulent flow observed data. Noise pollution of turbulent shear signals has frequency band diversity, and for this purpose, the current common denoising method has mode decomposition denoising, wavelet transformation denoising and the like.
In the conventional decomposition method, empirical Mode Decomposition (EMD) can recursively decompose a noise-containing original signal into a fixed number of modal function (IMF) components, but more modal aliasing exists between the IMF components and false components are generated, which has a larger influence on the denoising effect. The Variational Modal Decomposition (VMD) is based on complete non-recursive decomposition, and can decompose a noisy original signal into a series of IMF components, calculate the center frequency of each IMF component, and can effectively solve the phenomenon of mode aliasing in the EMD, but has the defect that penalty factor alpha and IMF component decomposition layer number K need to be determined manually according to experience.
Disclosure of Invention
The invention aims to provide a turbulence denoising method based on a parameter optimization VMD combined wavelet, which can eliminate noise interference in turbulence shear signals so as to improve the accuracy of ocean turbulence data.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a turbulence denoising method based on a parameter optimization VMD combined wavelet comprises the following steps:
step 1, optimizing parameters of a VMD method through an NGO algorithm, and obtaining optimal parameters [ alpha, K ]]Performing variational modal decomposition on the measured original turbulence shear signal x (t), and calculating each modal component u K (t) and the correlation coefficient M of the original turbulence shear signal x (t), judging each modal component u K Effective component u in (t) m (t) and noise component u K-m (t)；
Step 2, obtaining the noise component u K-m (t) culling and thresholding the effective component u using wavelet m (t) denoising to obtain a denoised turbulence effective component y m (t)；
Step 3. Effective component y of turbulence m And (t) carrying out data reconstruction to obtain a denoised shear signal s (t).
In addition, on the basis of the turbulence denoising method based on the parameter optimization VMD combined wavelet, the invention also provides a corresponding turbulence denoising system based on the parameter optimization VMD combined wavelet, which adopts the following technical scheme:
a parameter-optimized VMD-joint wavelet-based turbulent denoising system, comprising:
a decomposition module for performing parameter on VMD method by NGO algorithmOptimizing the number according to the obtained optimal parameters [ alpha, K ]]Performing variational modal decomposition on the measured original turbulence shear signal x (t), and calculating each modal component u K (t) and the correlation coefficient M of the original turbulence shear signal x (t), judging each modal component u K Effective component u in (t) m (t) and noise component u K-m (t)；
A wavelet threshold denoising module for denoising the obtained noise component u K-m (t) culling and thresholding the effective component u using wavelet m (t) denoising to obtain a denoised turbulence effective component y m (t)；
And a data reconstruction module for reconstructing a turbulence effective component y m And (t) carrying out data reconstruction to obtain a denoised shear signal s (t).
In addition, on the basis of the turbulence denoising method based on the parameter optimization VMD combined wavelet, the invention also provides computer equipment for realizing the turbulence denoising method based on the parameter optimization VMD combined wavelet.
The computer device comprises a memory having executable code stored therein and a processor for performing the steps of the above-described method of turbulence denoising based on parameter optimized VMD joint wavelets when the processor executes the executable code.
In addition, on the basis of the turbulence denoising method based on the parameter optimization VMD combined wavelet, the invention also provides a computer readable storage medium for realizing the turbulence denoising method based on the parameter optimization VMD combined wavelet.
The computer readable storage medium has stored thereon a program for implementing the steps of the above-mentioned parameter-optimized VMD-joint wavelet based turbulent denoising method when the program is executed by a processor.
The invention has the following advantages:
as described above, the invention relates to a turbulence denoising method based on a parameter optimization VMD combined wavelet, which adopts a multimode decomposition denoising strategy, optimizes punishment parameters alpha and decomposition parameters K of variation modal decomposition by means of an NGO algorithm, eliminates noise pollution in a frequency band by combining a wavelet threshold method, improves the existing denoising technology, and can eliminate noise interference in a turbulence shear signal, thereby improving the accuracy of ocean turbulence data, and facilitating further analysis and research on the turbulence signal. The method is suitable for complex marine environments, and can realize the refinement, the three-dimensional and the synchronization of marine hydrologic information observation.
Drawings
FIG. 1 is a flow chart of a method for turbulence denoising based on parameter-optimized VMD joint wavelet in an embodiment of the present invention.
FIG. 2 is a graph comparing the simulated signal with the results of denoising according to the method of the present invention; wherein, (a) is an original noise-containing signal schematic, and (b) is an NGO-VMD combined wavelet denoising schematic.
FIG. 3 is a graph of turbulence shear signal denoising contrast in an embodiment of the present invention.
FIG. 4 is a graph of turbulence signal denoising contrast ratio in an embodiment of the present invention.
Fig. 5 is a graph of turbulence dissipation ratio denoising contrast ratio in an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
example 1
The embodiment describes a turbulence denoising method based on a parameter optimization VMD combined wavelet, which is characterized in that an original shearing signal is acquired through a matrix profile turbulator, parameters of an NGO algorithm are initialized, minimum envelope entropy is set to be an fitness value, alpha and K values corresponding to the minimum envelope entropy are obtained as optimal parameters of the VMD algorithm, the VMD algorithm of the optimal parameters is used for carrying out modal decomposition on the original turbulence shearing signal, noise components are removed according to correlation coefficients of modal components and the original turbulence shearing signal, effective components are reserved, primary denoising is completed, wavelet threshold denoising is carried out on the reserved effective components, secondary denoising is achieved, signal reconstruction is carried out on the denoised components, and a purer turbulence shearing signal is obtained.
The northern eagle algorithm (Northern Goshawk Optimization, NGO) is a swarm intelligent optimization algorithm proposed in 2021, and has the characteristics of high convergence rate and good stability. The algorithm is selected to optimize the parameters of the VMD, and the most suitable penalty factor alpha and the decomposition layer number K are screened out as the optimal parameters of the VMD. In order to improve the denoising effect of turbulent data, the NGO-VMD method is combined with the wavelet threshold method to perform secondary denoising, so that a more accurate turbulent shear signal can be obtained.
As shown in fig. 1, the turbulence denoising method based on the parameter optimization VMD combined wavelet in the present embodiment includes the following steps:
step 1, optimizing parameters of a VMD method through an NGO algorithm, and obtaining optimal parameters [ alpha, K ]]Performing variational modal decomposition on the measured original turbulence shear signal x (t), and calculating each modal component u K (t) and the correlation coefficient M of the original turbulence shear signal x (t), judging each modal component u K Effective component u in (t) m (t) and noise component u K-m (t)。
And acquiring turbulence information in the marine environment through a matrix profile turbulence observer to obtain an original turbulence shear signal x (t).
Optimizing parameters of the VMD method by NGO algorithm to minimize envelope entropy E Pmin As a fitness function, obtaining VMD optimal parameters [ alpha ] corresponding to minimum envelope entropy values through global search and local search 0 ,K 0 ]。
Specifically, with a minimized envelope entropy as a fitness function, the envelope entropy of a turbulent signal x (j) of length q is defined as:
；
；
wherein ω (j) is an envelope signal obtained by Hilbert demodulation of the signal x (j), p j Is a normalized form of ω (j), E P Is obtained according to the information entropy calculation rule and E P The decomposition effect of the VMD is measured.
After the turbulence signal is decomposed by the VMD method, if more noise is contained in a certain component, the sparsity of the component is weak, and the envelope entropy is large; conversely, if less noise is included in a component, the envelope entropy is smaller.
In the parameter combinations alpha and K, the minimum envelope entropy in K components is selected as the local minimum entropy E Pmin The component corresponding to the minimum envelope entropy value contains more pure turbulence information; the local minimum entropy value is used as the fitness function of the searching process, and the parameter combination [ alpha ] corresponding to the optimal component is searched 0 ,K 0 ]And (5) completing the parameter optimization process.
The specific calculation process of optimizing the punishment parameter alpha and the decomposition parameter K of the VMD by using the NGO algorithm is as follows:
step 1.1. Initializing population.
In the NGO algorithm, the population matrix X is:
。
wherein X is i The number is i is the position of the northern hawk, N is the population number, m is the dimension of the solution, the value is 2,the position of north hawk with the number i in the j-th dimension is j, and j is 1 or 2.
The calculation process is divided into a searching stage and a hunting stage.
Step 1.2. The search phase procedure is as follows:
；
；
；
wherein k is a random integer within the range of [1, N ];the updating position of the northern hawk with the number of i in the j-th dimension; f (F) i Is the objective function value, i.e. minimizes the envelope entropy;the updated envelope entropy value; p (P) i For the prey position numbered i, F Pi Objective function values corresponding to the prey; r is a random number between 0 and 1, the value of I is 1 or 2, and is used for updating the position of the northern hawk,the updated position of hawk in north is denoted by the number i. The searching stage is used for continuously updating VMD optimization parameters through global searching to obtain an optimal parameter range, and calculating envelope entropy E of turbulence signal VMD decomposition every time the position is updated P To optimize VMD parameters.
And step 1.3. Hunting stage, carrying out local search on the space to determine the optimal solution.
；
；
。
In the method, in the process of the invention,a new position of north hawk numbered i for hunting stage in the j-th dimension; r is the radius of the region; t is the current iteration number; t is the maximum iteration number;is the updated objective function value.
After all VMD parameters are updated according to the combination of global search and local search, all objective functions E are determined at this time P Current optimal solution [ alpha, K ]]Then the NGO algorithm goes to the followingOne iteration, population members continue to update according to the searching and hunting stages until the last iteration is completed, and the minimum envelope entropy E obtained in the whole iteration process Pmin Corresponding optimal solution [ alpha ] 0 ,K 0 ]As the optimal parameter for turbulent shear signal VMD decomposition. The various parameters in the NGO algorithm are set as follows:
in the searching process, the minimum decomposition layer number K is set min =2, maximum number of decomposition layers K max =12; minimum penalty factor alpha min =0, maximum penalty factor α max =8000, population number N is 10, maximum number of iterations T is 30.
At minimum envelope entropy value E Pmin VMD decomposition of turbulence signals for fitness function by substituting each time a different combination of alpha and K for E P Calculating, comparing and updating the current minimum envelope entropy value, and storing the global minimum envelope entropy E when the iteration number reaches the maximum iteration value Pmin And the corresponding parameter combinations alpha and K, finally determining that the penalty parameter alpha is 879, the decomposition parameter K is 9, and using the penalty parameter alpha as a corresponding parameter value of the turbulent flow signal VMD conversion, namely completing the VMD optimization process.
Taking the optimized parameters alpha and K as the optimal parameters of the VMD, decomposing the turbulence signal to obtain K IMF components u K (t); to determine each IMF component u K (t) introducing a pearson correlation coefficient into the correlation between the original turbulent shear signal x (t), and setting the correlation coefficient to be more than or equal to omega as an effective IMF component, wherein 0 < omega < 1; the definition formula is as follows:
。
in the method, in the process of the invention,representing the original turbulent shear signal x (t),is the mean value of the original turbulent shear signal x (t),representing the modal components resulting from the decomposition of the VMD,is the mean value of the modal components; in this embodiment, Ω takes a value of 0.5, for example.
Removing IMF component u with correlation coefficient smaller than 0.5 K-m (t) retaining IMF component u with correlation coefficient greater than or equal to 0.5 m And (t), wherein m is the number of turbulence effective components, and the primary noise reduction process of the turbulence signal is completed.
Step 2, obtaining the noise component u K-m (t) culling and thresholding the effective component u using wavelet m (t) denoising to obtain a denoised turbulence effective component y m (t)。
Step 2.1. Effective component u for retained turbulence m And (t) performing wavelet threshold denoising, wherein the calculation steps are as follows:
the wavelet threshold denoising method is a theory and method based on wavelet transformation, and the purpose of removing noise is achieved by thresholding coefficients of signals in a wavelet domain.
And 2.1.1. Determining a proper wavelet basis function, and carrying out wavelet decomposition on the turbulence effective component to obtain a wavelet coefficient.
。
Wherein a is β (n) is an approximation coefficient, b β (n) is a detail coefficient, h is a low-pass filter coefficient, g is a high-pass filter coefficient, β is the number of decomposition layers, n represents the number of sampling points, k=1, 2.
Step 2.1.2, selecting a proper threshold function to perform threshold processing on the wavelet coefficient, wherein the threshold function is as follows:
。
wherein U is the wavelet coefficient after denoising the turbulence signal, w is the wavelet coefficient before denoising the turbulence signal, and lambda is the wavelet threshold.
Step 2.1.3, carrying out signal reconstruction by utilizing the denoised wavelet coefficient to obtain a reconstructed turbulence effective component, namely a wavelet denoised turbulence signal; the reconstruction formula is:
。
wherein,representing the turbulence signal after wavelet denoising,represents the approximation coefficients of the block,representing the detail coefficients.
Step 2.2, selecting db5 wavelet by wavelet basis, defining the decomposition layer number as 4 layers, and carrying out noise reduction treatment on the effective IMF component to obtain an effective IMF component y after secondary denoising m (t)。
Step 3. Effective component y of turbulence m And (t) carrying out data reconstruction to obtain a more accurate denoised shear signal s (t), wherein the formula is as follows:
。
and (3) carrying out spectrum comparison and dissipation ratio comparison on the denoised turbulence signal s (t) and the non-denoised signal x (t), and verifying the denoising precision.
Fig. 2 shows a simulated signal of the present invention and a comparison of NGO-VMD joint wavelet denoising as proposed by the present invention. Wherein (a) in fig. 2 is an original noise-containing signal schematic, and (b) in fig. 2 is an NGO-VMD joint wavelet denoising schematic.
Before turbulent signal processing, the invention uses NGO-VMD to combine wavelet denoising, performs denoising comparison on analog simulation noise adding signals, and calculates three denoising indexes: signal-to-noise ratio (SNR), root Mean Square Error (RMSE), and cross correlation coefficient (cc) with the clean original signal. Through calculation, the signal to noise ratio of the denoised signal is found as follows: 24.5441; the root mean square error is: 0.1676; the cross-correlation coefficient is: 0.9966. from the comparison result of fig. 2 and the three denoising indexes, the NGO-VMD combined wavelet denoising effect provided by the invention is better, so that the method can be used for denoising turbulent signals.
Fig. 3 shows a comparison of turbulent shear signal denoising according to the present invention. The original turbulence shear signal is compared and analyzed with the shear signal after denoising by the method, and the purified time sequence also maintains the intermittence and cascade property of the turbulence sequence. The fluctuations of the original turbulent shear signal are significantly larger than the denoising signal. The time sequence of the denoising signal also presents the attribute of the original time sequence, and the consistency between the original shearing data and the reconstructed shearing spectrum is good.
Fig. 4 shows a turbulence signal denoising contrast spectrum of the present invention. The original turbulence shear signal is compared with the shear signal after denoising by the method, and is subjected to fitting analysis with a standard Nasmyth spectrum. According to the comparison result and the fitting condition analysis, the shear wave number spectrum formed by the denoising method is higher in fitting degree with the standard Nasmyth spectrum before the cut-off wave number (vertical dotted line in the figure), so that the signal-to-noise ratio of a turbulence signal can be effectively improved, and the turbulence data acquired in a complex marine environment is ensured to be more accurate.
Fig. 5 shows a turbulence dissipation ratio denoising comparison graph of the present invention. Comparing the original turbulence dissipation ratio with the turbulence dissipation ratio after denoising by the method of the invention, the fluctuation of the turbulence dissipation ratio after being treated by the novel denoising method provided by the invention is smaller than the original dissipation ratio, and the whole magnitude is more approximate to the standard microRIder dissipation ratio (about 10 -9 W kg -1 ) Proved by the method, accurate turbulence signal denoising can be realized, so that denoising data is more similar to pure ocean turbulence data, and reliable data guarantee is provided for subsequent research of turbulence space characteristic distribution and dynamic evolution rules.
In summary, the turbulence denoising method based on the parameter optimization VMD combined wavelet provided by the invention can intuitively acquire the optimal parameters of the signals in the variation modal decomposition, realize the accurate modal decomposition of the original turbulence shear signals, effectively separate the useful signals and noise signals from the mixed signals, simultaneously retain the original characteristics of the signals, perform secondary denoising by adopting a wavelet threshold method, ensure more accurate turbulence data, reduce the influence of complex ocean environments on turbulence observation data, and have important significance for researching complex space-time evolution and mutual relations of ocean turbulence and as the basis of model verification.
The turbulence denoising method based on the parameter optimization VMD combined wavelet provided by the invention can provide accurate data support for deep excavation of the dynamics mechanism of the complex ocean evolution process while promoting further optimization of the ocean signal measuring instrument.
Example 2
This embodiment 2 describes a parameter-optimized VMD-combined wavelet-based turbulence denoising system based on the same inventive concept as the parameter-optimized VMD-combined wavelet-based turbulence denoising method described in embodiment 1 above.
Specifically, a turbulence denoising system based on parameter optimization VMD joint wavelet comprises:
the decomposition module is used for optimizing parameters of the VMD method through an NGO algorithm and obtaining optimal parameters [ alpha, K ]]Performing variational modal decomposition on the measured original turbulence shear signal x (t), and calculating each modal component u K (t) and the correlation coefficient M of the original turbulence shear signal x (t), judging each modal component u K Effective component u in (t) m (t) and noise component u K-m (t)；
A wavelet threshold denoising module for denoising the obtained noise component u K-m (t) culling and thresholding the effective component u using wavelet m (t) denoising to obtain a denoised turbulence effective component y m (t)；
And a data reconstruction module for reconstructing a turbulence effective component y m And (t) carrying out data reconstruction to obtain a denoised shear signal s (t).
It should be noted that, in the turbulence denoising system based on the parameter optimization VMD combined wavelet, the implementation process of the functions and roles of each functional module is specifically shown in the implementation process of the corresponding steps in the method in the above embodiment 1, and will not be described herein.
Example 3
This embodiment 3 describes a computer apparatus for implementing the parameter-optimized VMD-combined wavelet-based turbulence denoising method described in embodiment 1 above.
In particular, the computer device includes a memory and one or more processors. Executable code is stored in the memory for implementing the steps of a parametric optimized VMD joint wavelet based turbulence denoising method when the executable code is executed by the processor.
In this embodiment, the computer device is any device or apparatus having data processing capability, which is not described herein.
Example 4
This embodiment 4 describes a computer readable storage medium for implementing the turbulence denoising method based on the parameter-optimized VMD joint wavelet described in embodiment 1 above.
Specifically, the computer readable storage medium in this embodiment 4 has a program stored thereon, which when executed by a processor, is configured to implement the steps of the above-described turbulence denoising method based on parameter optimization VMD joint wavelet.
The computer readable storage medium may be an internal storage unit of any device or apparatus having data processing capability, such as a hard disk or a memory, or may be an external storage device of any device having data processing capability, such as a plug-in hard disk, a Smart Media Card (SMC), an SD Card, a Flash memory Card (Flash Card), or the like, which are provided on the device.
The foregoing description is, of course, merely illustrative of preferred embodiments of the present invention, and it should be understood that the present invention is not limited to the above-described embodiments, but is intended to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.
Claims (10)
1. The turbulence denoising method based on the parameter optimization VMD combined wavelet is characterized by comprising the following steps of:
step 1, optimizing parameters of a VMD method through an NGO algorithm, and obtaining optimal parameters [ alpha, K ]]Performing variational modal decomposition on the measured original turbulence shear signal x (t), and calculating each modal component u K (t) and the correlation coefficient M of the original turbulence shear signal x (t), judging each modal component u K Effective component u in (t) m (t) and noise component u K-m (t)；
Step 2, obtaining the noise component u K-m (t) culling and thresholding the effective component u using wavelet m (t) denoising to obtain a denoised turbulence effective component y m (t)；
Step 3. Effective component y of turbulence m And (t) carrying out data reconstruction to obtain a denoised shear signal s (t).
2. The method for turbulence denoising based on the parameter-optimized VMD joint wavelet according to claim 1, wherein in step 1, the envelope entropy of the turbulence signal x (j) with length q is defined as:
；
；
wherein ω (j) is an envelope signal obtained by Hilbert demodulation of the signal x (j), p j Is a normalized form of ω (j), E P Is obtained according to the information entropy calculation rule and E P The decomposition effect of the VMD is measured.
3. The method for turbulence denoising based on parameter-optimized VMD combined wavelet according to claim 1, wherein in step 1, the minimum envelope entropy E is used Pmin To be adaptive toFunction, VMD decomposition of turbulent signal by substituting each time a different combination of alpha and K to E P Calculating, comparing and updating the current minimum envelope entropy value, and storing the global minimum envelope entropy E when the iteration number reaches the maximum iteration value Pmin And finally determining a punishment parameter alpha and a decomposition parameter K as corresponding parameter values of the VMD conversion of the turbulence signal, namely completing the VMD optimization process.
4. A turbulence denoising method based on a parameter optimization VMD joint wavelet according to claim 3, wherein in step 1, the process of optimizing the penalty parameter α and the decomposition parameter K of the VMD by the NGO algorithm is as follows:
step 1.1, initializing population;
in the NGO algorithm, the population matrix X is:
；
wherein X is i The number is i is the position of the northern hawk, N is the population number, Q is the dimension of the solution, m is 2 here,the position of the north hawk with the number i in the j-th dimension is j, and 1 or 2 is taken;
the calculation process is divided into a searching stage and a hunting stage;
step 1.2. The search phase procedure is as follows:
；
；
；
wherein k is a random integer within the range of [1, N ];the updating position of the northern hawk with the number of i in the j-th dimension; f (F) i Is the objective function value, i.e. minimizes the envelope entropy; />The updated envelope entropy value; p (P) i For the prey position numbered i, F Pi Objective function values corresponding to the prey; r is a random number between 0 and 1, and the value of I is 1 or 2, and is used for updating the position of northern hawk,>representing the updated position of the hawk with the number i north; the searching stage is used for continuously updating VMD optimization parameters through global searching to obtain an optimal parameter range, and calculating envelope entropy E of turbulence signal VMD decomposition every time the position is updated P To optimize VMD parameters;
step 1.3, in hunting stage, carrying out local search on the space to determine an optimal solution;
；
；
；
in the method, in the process of the invention,a new position of north hawk numbered i for hunting stage in the j-th dimension; r is the radius of the region; t is the current iteration number; t is the maximum iteration number; />Is the updated objective function value;
after updating all VMD parameters according to the combination of global search and local search, determining all objective functions E P Current optimal solution [ alpha, K ]]Then the NGO algorithm enters the next iteration, population members continue to update according to the searching and hunting stages until the last iteration is completed, and the minimum envelope entropy E obtained in the whole iteration process Pmin Corresponding optimal solution [ alpha ] 0 ,K 0 ]As the optimal parameter for turbulent shear signal VMD decomposition.
5. The method for denoising turbulent flow based on the combined wavelet of the parameter-optimized VMD according to claim 3, wherein in said step 1, the optimized parameters α and K are used as the optimal parameters of the VMD, and the turbulent flow signal is decomposed to obtain K IMF components u K (t); to determine each IMF component u K (t) introducing a pearson correlation coefficient into the correlation between the original turbulent shear signal x (t), and setting the correlation coefficient to be more than or equal to omega as an effective IMF component, wherein 0 < omega < 1; the definition formula is as follows:
；
where f represents the original turbulent shear signal x (t),is the mean value of the original turbulent shear signal, +.>Representing modal components of VMD decomposition, +.>Is the mean value of the modal components;
removing IMF component u with correlation coefficient smaller than omega K-m (t) retaining IMF component u with correlation coefficient greater than or equal to Ω m (t)，And m is the number of the turbulence effective components, so that the preliminary noise reduction process of the turbulence signal is completed.
6. The turbulence denoising method based on the parameter optimization VMD combined wavelet according to claim 1, wherein in the step 1, turbulence information in the marine environment is acquired by a matrix profile turbulence observer to obtain an original turbulence shear signal x (t).
7. The method for turbulence denoising based on parameter optimization VMD combination wavelet according to claim 1, wherein the step 2 is specifically:
step 2.1. Effective component u for retained turbulence m And (t) performing wavelet threshold denoising, wherein the calculation steps are as follows:
step 2.1.1, determining a wavelet basis function, and carrying out wavelet decomposition on the turbulence effective component to obtain a wavelet coefficient;
；
wherein a is β (n) is an approximation coefficient, b β (n) is a detail coefficient, h is a low-pass filter coefficient, g is a high-pass filter coefficient, β is the number of decomposition layers, n represents the number of sampling points, k=1, 2, n-1;
step 2.1.2. Selecting a threshold function to perform threshold processing on the wavelet coefficients, wherein the threshold function is as follows:
；
wherein U is a wavelet coefficient after denoising the turbulent flow signal, w is a wavelet coefficient before denoising the turbulent flow signal, and lambda is a wavelet threshold;
step 2.1.3, carrying out signal reconstruction by utilizing the denoised wavelet coefficient to obtain a reconstructed turbulence effective component, namely a wavelet denoised turbulence signal;
step 2.2, selecting db5 wavelet based on wavelet base, defining the decomposition layer number as 4 layers, and performing noise reduction treatment on effective IMF componentObtaining effective IMF component y after secondary denoising m (t)。
8. The method for denoising turbulence based on the parameter-optimized VMD combined wavelet according to claim 7, wherein in step 1, the formula for reconstructing the signal by using the denoised wavelet coefficients is:
；
wherein y represents a turbulence signal after wavelet denoising, a β+1 (k) Representing approximation coefficients, b β+1 (k) Representing the detail coefficients.
9. The method for denoising turbulence based on the parameter-optimized VMD combined wavelet according to claim 1, wherein in said step 3, the denoised effective modal component y m And (t) reconstructing to obtain a shear signal s (t), wherein the formula is as follows:
。
10. a parametric optimization VMD-joint wavelet-based turbulent denoising system, comprising:
the decomposition module is used for optimizing parameters of the VMD method through an NGO algorithm and obtaining optimal parameters [ alpha, K ]]Performing variational modal decomposition on the measured original turbulence shear signal x (t), and calculating each modal component u K (t) and the correlation coefficient M of the original turbulence shear signal x (t), judging each modal component u K Effective component u in (t) m (t) and noise component u K-m (t)；
A wavelet threshold denoising module for denoising the obtained noise component u K-m (t) culling and thresholding the effective component u using wavelet m (t) denoising to obtain a denoised turbulence effective component y m (t)；
And a data reconstruction module forTo the turbulence effective component y m And (t) carrying out data reconstruction to obtain a denoised shear signal s (t).
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