CN117421561A - Turbulence denoising method and system based on parameter optimization VMD (virtual machine direction detector) combined wavelet - Google Patents

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

Turbulence denoising method and system based on parameter optimized VMD combined wavelet

Technical field

The invention belongs to the technical field of ocean turbulence denoising, and in particular relates to a turbulence denoising method and system based on parameter optimized VMD combined wavelet.

Background technique

Ocean turbulence is a complex flow phenomenon in ocean circulation. Its distribution and evolution in the ocean environment are crucial to understanding ocean circulation, tides, mixing and other processes. Ocean turbulence is a nonlinear complex fluid motion. In the marine environment, the direction and velocity of ocean turbulence vary greatly and are random. Therefore, it is necessary to continuously optimize and design more scientific and precise turbulence observation instruments to obtain ocean turbulence data. Due to the multi-scale process of ocean turbulence and the complex motion of ocean circulation, analysis and research on turbulent interactions cannot be satisfied by relying solely on single-point observations.

By using multi-point, high-resolution spatio-temporal simultaneous observations, the evolution of turbulence can be better analyzed. In order to meet the research needs for three-dimensional and multi-dimensional synchronous observation in a small scale, a matrix profile turbulence observation system is used to collect ocean turbulence data, which realizes horizontal space-time synchronous three-dimensional observation of turbulent mixing in the whole sea depth, which is helpful to The study of multi-scale coupling of turbulence provides a new observation method for achieving multi-dimensional measurements of ocean space and time.

However, due to the complexity of the marine environment and limitations of measurement conditions, the matrix profile turbulence observation platform is always surrounded by various ocean background noises, and the turbulence shear signals are inevitably contaminated by noise, which makes signal processing and analysis more difficult. . Therefore, detecting noise pollution in complex ocean backgrounds and minimizing the interference of noise on observation data is an important way to ensure the stability of the observation platform and improve the effectiveness of turbulence observation data. The noise pollution of turbulent shear signals has frequency band diversity. For this, currently commonly used denoising methods include mode decomposition denoising and wavelet transform denoising.

Among traditional decomposition methods, empirical mode decomposition (EMD) can recursively decompose the noisy original signal into a fixed number of modal function (IMF) components, but there are many modal aliasing and Produces false components, which has a greater impact on the denoising effect. Variational mode decomposition (VMD) is based on completely non-recursive decomposition. It can decompose the noisy original signal into a series of IMF components and calculate the center frequency of each IMF component. It can effectively solve the phenomenon of modal aliasing in EMD. However, It has the disadvantage that the penalty factor α and the number of IMF component decomposition layers K need to be determined manually based on experience.

Contents of the invention

The purpose of this invention is to propose a turbulence denoising method based on parameter-optimized VMD combined wavelet, which can eliminate noise interference in turbulent shear signals, thereby improving the accuracy of ocean turbulence data.

In order to achieve the above objects, the present invention adopts the following technical solutions:

A turbulence denoising method based on parameter optimized VMD joint wavelet, including the following steps:

Step 1. Use the NGO algorithm to optimize the parameters of the VMD method, perform variational mode decomposition on the measured original turbulent shear signal x(t) based on the obtained optimal parameters [α, K], and calculate each modal component. The correlation coefficient M between u _K (t) and the original turbulence shear signal x (t) is used to determine the effective component u _m (t) and noise component u _Km (t) in each modal component u _K (t);

Step 2. Eliminate the obtained noise component u _Km (t), and use the wavelet threshold method to denoise the effective component u _m (t), and obtain the denoised turbulence effective component y _m (t);

Step 3. Reconstruct the data of the effective turbulence component y _m (t) to obtain the denoised shear signal s(t).

In addition, based on the above turbulence denoising method based on parameter optimized VMD joint wavelet, the present invention also proposes a corresponding turbulence denoising system based on parameter optimized VMD joint wavelet, which adopts the following technical solution:

A turbulence denoising system based on parameter optimized VMD combined wavelet, including:

The decomposition module is used to optimize the parameters of the VMD method through the NGO algorithm. According to the obtained optimal parameters [α, K], the measured original turbulence shear signal x(t) is subjected to variational mode decomposition, and each mode is calculated. The correlation coefficient M between the state component u _K (t) and the original turbulence shear signal x (t) is used to determine the effective component u _m (t) and the noise component u _Km (t) in each modal component u _K (t);

The wavelet threshold denoising module is used to eliminate the obtained noise component u _Km (t), and use the wavelet threshold method to denoise the effective component u _m (t), and obtain the denoised turbulence effective component y _m (t) ;

and a data reconstruction module, which is used to reconstruct the data of the effective turbulence component y _m (t) to obtain the denoised shear signal s(t).

In addition, based on the turbulence denoising method based on parameter optimized VMD joint wavelet, the present invention also proposes a computer device for implementing the turbulence denoising method based on parameter optimized VMD joint wavelet.

The computer device includes a memory and a processor, and executable code is stored in the memory. When the processor executes the executable code, it is used to implement the above-mentioned steps of the turbulence denoising method based on parameter-optimized VMD joint wavelet.

In addition, based on the turbulence denoising method based on parameter-optimized VMD joint wavelet, the present invention also proposes a computer-readable storage medium for implementing the turbulence denoising method based on parameter-optimized VMD joint wavelet.

The computer-readable storage medium has a program stored thereon, and when the program is executed by the processor, it is used to implement the above-mentioned steps of the turbulence denoising method based on parameter-optimized VMD joint wavelet.

The invention has the following advantages:

As mentioned above, the present invention describes a turbulence denoising method based on parameter optimized VMD combined wavelet. This method adopts a multi-modal decomposition denoising strategy and relies on the NGO algorithm to determine the penalty parameter α and decomposition parameters of variational mode decomposition. K is optimized, combined with the wavelet threshold method to eliminate noise pollution in the frequency band, and the existing denoising technology is improved to eliminate the noise interference in the turbulence shear signal, thereby improving the accuracy of ocean turbulence data and facilitating the analysis of turbulence signals. Further analysis and research. The method of the invention is suitable for complex marine environments and can achieve refinement, three-dimensionality and synchronization of marine hydrological information observation.

Description of the drawings

Figure 1 is a flow chart of the turbulence denoising method based on parameter optimized VMD combined wavelet in the embodiment of the present invention.

Figure 2 is a comparison chart between the simulated signal and the result after denoising by the method of the present invention; (a) is a schematic of the original noisy signal, and (b) is a schematic of NGO-VMD joint wavelet denoising.

Figure 3 is a comparison chart of turbulent shear signal denoising in the embodiment of the present invention.

Figure 4 is a comparative spectrum chart of turbulence signal denoising in the embodiment of the present invention.

Figure 5 is a comparison chart of turbulence dissipation rate denoising in the embodiment of the present invention.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments:

Example 1

This embodiment describes a turbulence denoising method based on parameter optimized VMD combined wavelet. This method collects the original shear signal through a matrix profile turbulence meter, initializes the parameters of the NGO algorithm, and sets the minimum envelope entropy to adapt degree value, obtain the α and K values corresponding to the minimum envelope entropy as the optimal parameters of the VMD algorithm, use the VMD algorithm with optimal parameters to perform modal decomposition of the original turbulence shear signal, and compare each modal component with the original turbulence shear signal. Cut the correlation coefficient of the signal to remove the noise component, retain the effective components, and complete the first denoising. Then, perform wavelet threshold denoising on the retained effective components to achieve the second denoising. Reconstruct the signal for each component after denoising, and obtain a more accurate signal. Pure turbulent shear signal.

The Northern Goshawk Optimization (NGO) is a population intelligent optimization algorithm proposed in 2021. It has the characteristics of fast convergence speed and good stability. This algorithm is used to optimize the parameters of VMD, and the most appropriate penalty factor α and decomposition layer number K are selected as the optimal parameters of VMD. In order to improve the denoising effect of turbulence data, the NGO-VMD method is combined with the wavelet threshold method for secondary denoising, which can obtain a more accurate turbulence shear signal.

As shown in Figure 1, the turbulence denoising method based on parameter optimized VMD joint wavelet in this embodiment includes the following steps:

Step 1. Use the NGO algorithm to optimize the parameters of the VMD method, perform variational mode decomposition on the measured original turbulent shear signal x(t) based on the obtained optimal parameters [α, K], and calculate each modal component. The correlation coefficient M between u _K (t) and the original turbulence shear signal x (t) determines the effective component u _m (t) and the noise component u _Km (t) in each modal component u _K (t).

The turbulence information in the marine environment is collected through the matrix profile turbulence observer, and the original turbulence shear signal x(t) is obtained.

The parameters of the VMD method are optimized through the NGO algorithm, and the minimum envelope entropy E _Pmin is used as the fitness function. The optimal VMD parameters [α ₀ , K ₀ ] corresponding to the minimum envelope entropy value are obtained through global search and local search. .

Specifically, taking the minimum envelope entropy as the fitness function, the envelope entropy of the turbulence signal x(j) with length q is defined as:

;

;

Among them, ω(j) is the envelope signal obtained after Hilbert demodulation of signal x(j), p _j is the normalized form of ω(j), E _P is obtained according to the information entropy calculation rule, and is measured according to E _P The decomposition effect of VMD.

After the turbulence signal is decomposed by the VMD method, if a certain component contains more noise, the sparsity of the component is weak and the envelope entropy is larger; conversely, if a certain component contains less noise, the envelope entropy is smaller .

In the parameter combination α and K, the minimum envelope entropy among the 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 As the fitness function of the search process, find the parameter combination [α ₀ , K ₀ ] corresponding to the best component to complete the parameter optimization process.

The specific calculation process of using the NGO algorithm to optimize the penalty parameter α and decomposition parameter K of VMD is as follows:

Step 1.1. Initialize the population.

In the NGO algorithm, the population matrix X is:

.

In the _formula , is the position of the northern goshawk numbered i in the jth dimension, j is 1 or 2.

The calculation process is divided into search phase and hunting phase.

Step 1.2. The search phase process is as follows:

;

;

;

In the formula, k is a random integer in the range of [1, N]; is the updated position of the northern goshawk numbered i in the jth dimension; F _i is the objective function value, which is to minimize the envelope entropy; is the updated envelope entropy value; _Pi is the position of the prey numbered i, F _Pi is the objective function value corresponding to the prey; r is a random number between 0 and 1, and the value of I is 1 or 2, used for Update northern goshawk location, Indicates the updated position of northern goshawk number i. In the search stage, the VMD optimization parameters are continuously updated through global search to obtain the optimal parameter range. Each time the position is updated, the envelope entropy E _P of the VMD decomposition of the turbulence signal is calculated to optimize the VMD parameters.

Step 1.3. In the hunting phase, a local search is performed on the space to determine the optimal solution.

;

;

.

In the formula, is the new position of the northern goshawk numbered i in the hunting stage in the jth dimension; R is the radius of the area; t is the current number of iterations; T is the maximum number of iterations; is the updated objective function value.

After updating all VMD parameters based on the combination of global search and local search, all objective functions E _P and the current optimal solution [α, K] are determined at this time. Then the NGO algorithm enters the next iteration, and the population members continue according to the search and hunting stages. Updated until the last iteration is completed, the optimal solution [α ₀ , K ₀ ] corresponding to the minimum envelope entropy E _Pmin obtained during the entire iteration process is used as the best parameter for VMD decomposition of the turbulent shear signal. The various parameters in the NGO algorithm are set as follows:

During the search process, set the minimum number of decomposition layers K _min =2, the maximum number of decomposition layers K _max =12; the minimum penalty factor α _min =0, the maximum penalty factor α _max =8000, the population number N as 10, and the maximum number of iterations T is 30.

Using the minimum envelope entropy value E _Pmin as the fitness function, perform VMD decomposition of the turbulence signal, calculate E _P by substituting different combinations of α and K each time, and then compare with each other to update the current minimum envelope entropy value. When the number of iterations When the maximum iteration value is reached, the global minimum envelope entropy E _Pmin and its corresponding parameter combination α and K are saved. The penalty parameter α is finally determined to be 879 and the decomposition parameter K is 9, which are used as the corresponding parameter values of the VMD transformation of the turbulence signal, that is, it is completed. Optimization process of VMD.

Using the optimized parameters α and K as the optimal parameters of VMD, the turbulence signal is decomposed to obtain K IMF components u _K (t); in order to determine the relationship between each IMF component u _K (t) and the original turbulence shear signal x ( t), introduce the Pearson correlation coefficient, and set the correlation coefficient to be greater than or equal to Ω as the effective IMF component, 0＜Ω＜1; the definition formula is as follows:

.

In the formula, represents the original turbulent shear signal x(t), is the mean value of the original turbulent shear signal x(t), Represents the modal components obtained by VMD decomposition, is the mean value of the modal components; in this embodiment, Ω takes a value of 0.5, for example.

IMF components u _Km (t) with correlation coefficients less than 0.5 are eliminated, and IMF components u _m (t) with correlation coefficients greater than or equal to 0.5 are retained, where m is the number of effective turbulence components, completing the preliminary noise reduction process of turbulence signals.

Step 2. Eliminate the obtained noise component u _Km (t), and use the wavelet threshold method to denoise the effective component u _m (t), and obtain the denoised effective turbulence component y _m (t).

Step 2.1. Perform wavelet threshold denoising on the retained turbulence effective component u _m (t). The calculation steps are as follows:

The wavelet threshold denoising method is a theory and method based on wavelet transform, which achieves the purpose of removing noise by thresholding the coefficients of the signal in the wavelet domain.

Step 2.1.1. Determine the appropriate wavelet basis function, perform wavelet decomposition on the effective component of turbulence, and obtain the wavelet coefficients.

.

In the formula, a _β (n) is the approximate coefficient, b _β (n) is the detail coefficient, h is the low-pass filter coefficient, g is the 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. Select an appropriate threshold function to perform threshold processing on the wavelet coefficients. The threshold function is as follows:

.

In the formula, U is the wavelet coefficient after denoising the turbulence signal, w is the wavelet coefficient before denoising the turbulence signal, and λ is the wavelet threshold.

Step 2.1.3. Use the denoised wavelet coefficients to reconstruct the signal to obtain the reconstructed turbulence effective component, which is the wavelet denoised turbulence signal; the reconstruction formula is:

.

in, represents the turbulence signal after wavelet denoising, represents the approximate coefficient, Represents the detail coefficient.

Step 2.2. The db5 wavelet is selected as the wavelet base, and the number of decomposition layers is defined as 4 layers. The effective IMF component is denoised to obtain the effective IMF component y _m (t) after secondary denoising.

Step 3. Reconstruct the data of the effective turbulence component y _m (t) to obtain a more accurate denoised shear signal s(t). The formula is as follows:

.

Compare the spectrum and dissipation rate of the denoised turbulence signal s(t) and the non-denoised signal x(t) to verify the denoising accuracy.

Figure 2 shows a comparison diagram of the simulated signal of the present invention and the use of NGO-VMD joint wavelet denoising proposed by the present invention. Among them, (a) in Figure 2 is a schematic of the original noisy signal, and (b) in Figure 2 is a schematic of NGO-VMD joint wavelet denoising.

Before performing turbulence signal processing, the present invention uses NGO-VMD joint wavelet denoising, conducts denoising comparison on the simulated noise signal, and calculates three denoising indicators: signal-to-noise ratio (SNR), root mean square error (RMSE) and cross-correlation coefficient (cc) with the pure original signal. Through calculation, it is found that the signal-to-noise ratio of the denoised signal is: 24.5441; the root mean square error is: 0.1676; and the cross-correlation coefficient is: 0.9966. From the comparison results in Figure 2 and the three denoising indicators, it can be seen that the NGO-VMD joint wavelet denoising effect proposed by the present invention is better. Therefore, the method of the present invention can be used for turbulence signal denoising.

Figure 3 shows a comparison diagram of turbulent shear signal denoising according to the present invention. Comparative analysis of the original turbulence shear signal and the shear signal after denoising by the method of the present invention shows that the purified time series also retains the intermittency and cascade properties of the turbulence sequence. The fluctuations of the original turbulent shear signal are significantly larger than those of the denoised signal. The time series of the denoised signal also exhibits the properties of the original time series, and the consistency between the original shear data and the reconstructed shear spectrum is good.

Figure 4 shows the turbulence signal denoising comparison spectrum of the present invention. The original turbulent shear signal and the shear signal after denoising by the method of the present invention are compared and analyzed, and the fitting analysis is performed with the standard Nasmyth spectrum. According to the comparison results and fitting analysis, it can be seen that the shear wave number spectrum formed by the denoising method of the present invention has a higher fitting degree to the standard Nasmyth spectrum before the cut-off wave number (vertical dotted line in the figure), which can effectively improve the accuracy of the turbulence signal. The signal-to-noise ratio ensures more accurate turbulence data acquired in complex ocean environments.

Figure 5 shows a comparison chart of turbulence dissipation rate denoising according to the present invention. Comparative analysis of the original turbulence dissipation rate and the turbulence dissipation rate after denoising by the method of the present invention shows that the fluctuation of the turbulence dissipation rate after processing by the new denoising method provided by the present invention is smaller than the original dissipation rate, and The overall magnitude is closer to the standard MicroRider dissipation rate (about 10 ^-9 W kg ^-1 ), which proves that the denoising method proposed in the present invention can achieve accurate turbulence signal denoising and make the denoised data closer to Pure ocean turbulence data provides reliable data guarantee for subsequent research on the spatial distribution of turbulence characteristics and dynamic evolution rules.

In summary, the turbulence denoising method based on parameter optimized VMD combined with wavelet proposed by the present invention can intuitively obtain the optimal parameters of the signal in the variational mode decomposition, achieve accurate mode decomposition of the original turbulence shear signal, and effectively The useful signal and noise signal are separated from the mixed signal, while retaining the original characteristics of the signal. The wavelet threshold method is used for secondary denoising, ensuring that the turbulence data is more accurate and reducing the impact of the complex ocean environment on turbulence. The influence of observation data is of great significance for studying the complex spatiotemporal evolution and interrelationships of ocean turbulence and as a basis for model verification.

The turbulence denoising method based on parameter optimized VMD combined with wavelet proposed by the present invention not only promotes the further optimization of ocean signal measurement instruments, but also provides accurate data support for in-depth exploration of the dynamic mechanism of complex ocean evolution processes.

Example 2

This embodiment 2 describes a turbulence denoising system based on parameter-optimized VMD joint wavelet. This system is based on the same inventive concept as the turbulence denoising method based on parameter-optimized VMD joint wavelet described in the above-mentioned embodiment 1.

Specifically, the turbulence denoising system based on parameter optimized VMD combined wavelet includes:

The decomposition module is used to optimize the parameters of the VMD method through the NGO algorithm. According to the obtained optimal parameters [α, K], the measured original turbulence shear signal x(t) is subjected to variational mode decomposition, and each mode is calculated. The correlation coefficient M between the state component u _K (t) and the original turbulence shear signal x (t) is used to determine the effective component u _m (t) and the noise component u _Km (t) in each modal component u _K (t);

The wavelet threshold denoising module is used to eliminate the obtained noise component u _Km (t), and use the wavelet threshold method to denoise the effective component u _m (t), and obtain the denoised turbulence effective component y _m (t) ;

and a data reconstruction module, which is used to reconstruct the data of the effective turbulence component y _m (t) to obtain the denoised shear signal s(t).

It should be noted that in the turbulence denoising system based on parameter optimized VMD joint wavelet, the implementation process of the functions and functions of each functional module is detailed in the implementation process of the corresponding steps in the method in the above-mentioned Embodiment 1, which will not be described again here.

Example 3

This embodiment 3 describes a computer device, which is used to implement the turbulence denoising method based on parameter-optimized VMD combined wavelet described in the above-mentioned embodiment 1.

Specifically, the computer device includes a memory and one or more processors. The executable code is stored in the memory, and when the processor executes the executable code, it is used to implement the steps of the turbulence denoising method based on parameter-optimized VMD joint wavelet.

In this embodiment, the computer device is any device or device with data processing capabilities, which will not be described again here.

Example 4

This embodiment 4 describes a computer-readable storage medium, which is used to implement the turbulence denoising method based on parameter-optimized VMD combined wavelet described in the above-mentioned embodiment 1.

Specifically, the computer-readable storage medium in Embodiment 4 has a program stored thereon, and when the program is executed by the processor, it is used to implement the above steps of the turbulence denoising method based on parameter-optimized VMD joint wavelet.

The computer-readable storage medium can be an internal storage unit of any device or device with data processing capabilities, such as a hard disk or a memory, or it can be an external storage device of any device or device with data processing capabilities, such as a plug-in device equipped with the device. Hard disk, Smart Media Card (SMC), SD card, Flash Card, etc.

Of course, the above descriptions are only preferred embodiments of the present invention. The present invention is not limited to the above-mentioned embodiments. It should be noted that all equivalent substitutions made by any person familiar with the art under the teaching of this specification , obvious deformation forms, all fall within the essential scope of this specification, and should be protected by the present invention.

Claims (10)

1. A turbulence denoising method based on parameter optimized VMD joint wavelet, which is characterized by including the following steps: Step 1. Use the NGO algorithm to optimize the parameters of the VMD method, perform variational mode decomposition on the measured original turbulent shear signal x(t) based on the obtained optimal parameters [α, K], and calculate each modal component. The correlation coefficient M between u _K (t) and the original turbulence shear signal x (t) is used to determine the effective component u _m (t) and noise component u _Km (t) in each modal component u _K (t); Step 2. Eliminate the obtained noise component u _Km (t), and use the wavelet threshold method to denoise the effective component u _m (t), and obtain the denoised turbulence effective component y _m (t); Step 3. Reconstruct the data of the effective turbulence component y _m (t) to obtain the denoised shear signal s(t). 2. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 1, characterized in that, in the step 1, minimizing the envelope entropy is used as the fitness function, and the turbulence signal x with a length of q ( The envelope entropy of j) is defined as: ; ; Among them, ω(j) is the envelope signal obtained after Hilbert demodulation of signal x(j), p _j is the normalized form of ω(j), E _P is obtained according to the information entropy calculation rule, and is measured according to E _P The decomposition effect of VMD. 3. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 1, characterized in that in step 1, the minimum envelope entropy E _Pmin is used as the fitness function to perform VMD decomposition of the turbulence signal, E _P is calculated by substituting different combinations of α and K each time, and then comparing with each other to update the current minimum envelope entropy value. When the number of iterations reaches the maximum iteration value, the global minimum envelope entropy E _Pmin and its corresponding parameter combination are saved. α and K, the penalty parameter α is finally determined, and the decomposition parameter K is used as the corresponding parameter value for the VMD transformation of the turbulence signal, that is, the VMD optimization process is completed. 4. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 3, characterized in that in step 1, the process of optimizing the penalty parameter α and decomposition parameter K of VMD using the NGO algorithm is as follows: Step 1.1. Initialize the population; In the NGO algorithm, the population matrix X is: ; In the _formula , is the position of the northern goshawk numbered i in the jth dimension, j is 1 or 2; The calculation process is divided into search stage and hunting stage; Step 1.2. The search phase process is as follows: ; ; ; In the formula, k is a random integer in the range of [1, N]; is the updated position of the northern goshawk numbered i in the jth dimension; F _i is the objective function value, which is to minimize the envelope entropy;/> is the updated envelope entropy value; _Pi is the position of the prey numbered i, F _Pi is the objective function value corresponding to the prey; r is a random number between 0 and 1, and the value of I is 1 or 2, used for Update Northern Goshawk location,/> Represents the updated position of the northern goshawk numbered i; in the search phase, the VMD optimization parameters are continuously updated through global search to obtain the optimal parameter range. Each time the position is updated, the envelope entropy E _P of the VMD decomposition of the turbulence signal is calculated to optimize the VMD parameters; Step 1.3. In the hunting stage, perform a local search on the space to determine the optimal solution; ; ; ; In the formula, is the new position of the northern goshawk numbered i in the hunting stage in the jth dimension; R is the radius of the area; t is the current number of iterations; T is the maximum number of iterations;/> is the updated objective function value; After updating all VMD parameters based on the combination of global search and local search, determine all objective functions E _P and the current optimal solution [α, K], and then the NGO algorithm enters the next iteration, and the population members continue to update according to the search and hunting stages until After completing the last iteration, the optimal solution [α ₀ , K ₀ ] corresponding to the minimum envelope entropy E _Pmin obtained during the entire iteration process is used as the best parameter for VMD decomposition of the turbulent shear signal. 5. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 3, characterized in that in step 1, the parameters α and K obtained by optimization are used as the optimal parameters of VMD, and the turbulence signal is Decompose to obtain K IMF components u _K (t); in order to determine the correlation between each IMF component u _K (t) and the original turbulent shear signal x (t), the Pearson correlation coefficient is introduced, and the correlation coefficient is set to be greater than Or equal to Ω is the effective IMF component, 0＜Ω＜1; the definition formula is as follows: ; In the formula, f represents the original turbulent shear signal x(t), is the mean value of the original turbulent shear signal,/> Represents the modal components obtained by VMD decomposition, /> is the mean value of the modal components; The IMF components u _Km (t) with correlation coefficients less than Ω are eliminated, and the IMF components u _m (t) with correlation coefficients greater than or equal to Ω are retained, where m is the number of effective turbulence components to complete the preliminary noise reduction process of turbulence signals. 6. The turbulence denoising method based on parameter optimized VMD combined wavelet according to claim 1, characterized in that in step 1, the turbulence information in the marine environment is collected by a matrix profile turbulence observer to obtain the original turbulence shear. Cut signal x(t). 7. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 1, characterized in that the step 2 is specifically: Step 2.1. Perform wavelet threshold denoising on the retained turbulence effective component u _m (t). The calculation steps are as follows: Step 2.1.1. Determine the wavelet basis function, perform wavelet decomposition on the effective component of turbulence, and obtain the wavelet coefficients; ; In the formula, a _β (n) is the approximate coefficient, b _β (n) is the detail coefficient, h is the low-pass filter coefficient, g is the 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. Select the threshold function to perform threshold processing on the wavelet coefficients. The threshold function is as follows: ; In the formula, U is the wavelet coefficient after denoising the turbulence signal, w is the wavelet coefficient before denoising the turbulence signal, and λ is the wavelet threshold; Step 2.1.3. Use the denoised wavelet coefficients to reconstruct the signal, and obtain the reconstructed turbulence effective component, which is the wavelet denoised turbulence signal; Step 2.2. The db5 wavelet is selected as the wavelet base, and the number of decomposition layers is defined as 4 layers. The effective IMF component is denoised to obtain the effective IMF component y _m (t) after secondary denoising. 8. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 7, characterized in that in step 1, the formula for signal reconstruction using denoised wavelet coefficients is: ; Among them, y represents the turbulence signal after wavelet denoising, a _β+1 (k) represents the approximate coefficient, and b _β+1 (k) represents the detail coefficient. 9. The turbulence denoising method based on parameter optimized VMD joint wavelet according to claim 1, characterized in that, in the step 3, the effective modal component y _m (t) after denoising is reconstructed to obtain Cut signal s(t), the formula is as follows: . 10. A turbulence denoising system based on parameter optimized VMD joint wavelet, which is characterized by: The decomposition module is used to optimize the parameters of the VMD method through the NGO algorithm. According to the obtained optimal parameters [α, K], the measured original turbulence shear signal x(t) is subjected to variational mode decomposition, and each mode is calculated. The correlation coefficient M between the state component u _K (t) and the original turbulence shear signal x (t) is used to determine the effective component u _m (t) and the noise component u _Km (t) in each modal component u _K (t); The wavelet threshold denoising module is used to eliminate the obtained noise component u _Km (t), and use the wavelet threshold method to denoise the effective component u _m (t), and obtain the denoised turbulence effective component y _m (t) ; and a data reconstruction module, which is used to reconstruct the data of the effective turbulence component y _m (t) to obtain the denoised shear signal s(t).