72 results about "Threshold function" patented technology

The invention relates to the technical field of ultrasonic signal processing, in particular to a wavelet and MAD adaptive threshold combined laser ultrasonic signal denoising method. The method comprises the following steps: firstly, preprocessing an acquired trigger channel signal and an ultrasonic channel signal, determining a signal starting point through differential positioning, and intercepting an effective signal segment; carrying out multilayer wavelet decomposition on the effective signal segment to obtain a wavelet coefficient of each layer; extracting a detail coefficient of the highest decomposition layer, and adaptively estimating a noise standard deviation based on a median absolute deviation criterion; calculating an adaptive threshold according to the noise standard deviation and the signal length, and processing each layer of wavelet coefficient by adopting a hard threshold function; and finally, carrying out wavelet inverse transformation reconstruction to obtain a denoised signal. According to the method, prior noise information is not needed, the noise level can be adaptively estimated, the optimal threshold value can be determined, the signal features are reserved while noise is effectively suppressed, and the signal-to-noise ratio is remarkably improved.

The invention provides a heterogeneous multi-agent system control method under a DoS attack based on an event triggering strategy. Robust cooperative control under a complex attack environment is realized through the following steps: S1, performing state description and topological structure modeling on a heterogeneous multi-agent system; s2, carrying out approximation on an unknown nonlinear function in the system; s3, restraining the error dynamic state through a performance function, and ensuring that the system response meets the preset precision; s4, designing a time-varying threshold function, and dynamically adjusting a trigger condition to reduce communication burden; s5, designing an anti-attack controller in combination with event triggering and a Lyapunov theory; s6, designing an adaptive law to carry out online estimation on disturbance; and S7, testing synchronization precision, communication efficiency and anti-attack performance based on a physical platform. According to the method, deep fusion of the event triggering mechanism and the preset performance constraint is realized for the first time, and the self-adaptive dynamic threshold strategy is introduced, so that malicious interference of DoS attacks on a communication link is effectively dealt with, and it is ensured that the heterogeneous nodes can still meet preset performance indexes in an attack scene.

The method comprises the following steps: obtaining a power quality signal containing noise; selecting permutation entropy as an adaptive function of genetic algorithm, calling variational mode decomposition through genetic algorithm, and iteratively optimizing a penalty factor α and a decomposition mode number k of the variational mode decomposition to determine optimal parameters; decomposing signal data into k mode components through the variational mode decomposition, and determining effective mode components and noise mode components through a correlation coefficient; for improved wavelet threshold, a parameter-adjustable threshold function is proposed, and the concept of wavelet energy entropy is introduced into the threshold function; the noise mode components are denoised through the improved wavelet threshold, and the effective mode components and the denoised noise mode components are reconstructed to obtain a denoised power quality disturbance signal. The method can effectively remove noise interference while retaining singular information of mutation points of the collected signal, and provides help for subsequent analysis and treatment of the power quality disturbance signal.

The invention discloses an abnormal sample detection and identification method and system based on evolutionary computation and multi-modal consistency constraint, and belongs to the field of network security and artificial intelligence security. The method comprises the following steps: S1, acquiring and preprocessing input data; s2, multi-modal feature extraction and unified characterization are carried out; s3, optimization feature selection and weight self-adaption are carried out; s4, constructing a cross-modal fusion detection model; s5, abnormal sample risk identification; and S6, result judgment output: generating a final detection label according to a threshold function. The method shows high accuracy and strong robustness in counterfeit image and video detection and abnormal sample elimination, and can be widely applied to the fields of multimedia authentic identification, intelligent security and protection and AI content traceability.

The invention provides a self-adaptive denoising method based on Meyer wavelets and an improved smooth threshold function in order to solve the problem that a transformer partial discharge high-frequency current sensor field calibration signal is seriously polluted by field electromagnetic noise interference. The method comprises the following steps: firstly, arranging a standard sensor according to an optimal cross-core distance to obtain an original noisy signal; then adaptively determining a decomposition series according to a data length, selecting Meyer wavelets to perform multi-scale decomposition, performing smooth contraction on wavelet coefficients by adopting a continuously derivable, progressive and unbiased improved threshold function, and finally reconstructing a high signal-to-noise ratio verification signal through wavelet inverse transformation; the method does not need manual intervention of the threshold value, keeps orthogonality and high-order derivability in the whole process, and is beneficial to improving the accuracy of the verification result of the high-frequency sensor to be tested.

The invention discloses a lightweight non-repudiation model fingerprint efficient traceability method, and belongs to the related technical fields of cryptography application, digital content traceability and the like. The method comprises the following specific steps: inputting an original image data set into a computer system; training and learning a lightweight encoder and a decoder, wherein the encoder maps a binary fingerprint and an image to generate a residual error; the residual error is constrained through a Sigmoid function to generate a fingerprint-containing image, and the model is optimized through dual loss; generating metadata of a PIT parameter binding salt value and a commitment value, and embedding the fingerprint into the image; during detection, a decoder is loaded to extract a decoded fingerprint, binaryzation is carried out through a threshold function, and dual verification is realized through fingerprint polynomial comparison and commitment value verification. According to the method, a fingerprint verification mode is converted into polynomial verification through a polynomial equivalent detection technology, the communication complexity is reduced to sub-linear overhead, lightweight efficient verification is realized, and the method is suitable for efficient traceability and authenticity verification of deep forged images.

The invention discloses a power servo knife rest health state assessment method based on an integrated multi-scale convolution attention network, and belongs to the technical field of power servo knife rest health state assessment, the method adopts a base classifier architecture of the multi-scale convolution attention network, and the base classifier architecture is composed of a multi-scale convolution noise reduction module and an attention enhancement module; the multi-scale convolution noise reduction module extracts rich discrimination features through a multi-scale kernel; the noise is further filtered through a soft threshold function, the attention enhancement module adopts a sparse improved channel attention mechanism to adaptively filter redundant features, important channel information is concerned, and the anti-noise performance and generalization of the model are improved; an interactive joint training strategy is adopted, a basic classifier automatically pays attention to a category with poorer classification through a designed Recall guide loss function, a balance training subset is constructed for each basic classifier, weighted voting is adopted to integrate the trained basic classifiers, and the robustness of an integrated model and the evaluation performance under data imbalance are improved.

The application discloses a water acoustic signal time-frequency spectrum line spectrum extraction method and system, and belongs to the technical field of signal processing. A noisy time domain signal is generated through simulation, and a mask label of a time-frequency spectrum of the noisy time domain signal belonging to a line spectrum is generated through a soft threshold function; a denoising model is trained according to the time-frequency spectrum and the mask label; a target water acoustic signal is acquired, the time-frequency spectrum of the target water acoustic signal is input into the denoising model, and a mask label corresponding to the time-frequency spectrum input is output through inference; the time-frequency spectrum of the denoised target water acoustic signal is acquired according to the mask label and the time-frequency spectrum; an initial candidate point set of the time-frequency spectrum of the denoised target water acoustic signal is acquired, and an initial candidate point of a current frame time-frequency spectrum in the initial candidate point set is acquired; a correlation cost matrix is constructed, the initial candidate point and a trajectory are correlated and matched with the minimum difference as a target, and a line spectrum of the trajectory and the candidate point dynamic correlation is acquired. The method can balance denoising fidelity, detection accuracy and real-time performance.

The application discloses an abnormal station identification method and system based on observation values and distance dynamic threshold values, and relates to the technical field of observation station data quality control. The method comprises the following steps: acquiring verified qualified long sequence of hourly precipitation data of observation stations and observation station position data; constructing a relationship function of the distance of adjacent observation stations and the difference threshold value of hourly precipitation, and a relationship function of the hourly precipitation of observation stations and the difference threshold value of hourly precipitation of adjacent observation stations; on the basis, calculating the data anomaly probability of all observation stations in the analysis period; and judging the observation station grade according to the data anomaly probability. The application breaks through the limitation of traditional fixed threshold value by fusing the observation value size and the station distance to dynamically construct a double threshold function, can realize accurate and adaptive identification of abnormal data of observation stations, and improves the reliability and business application value of ground observation data.

The invention discloses an aluminum plate ultrasonic Lamb wave denoising method based on ICPO-VMD combined improvement of a wavelet threshold, and the method comprises the steps: collecting an ultrasonic echo signal of an aluminum plate, carrying out the adaptive optimization through an improved crown porcupine optimization algorithm, and obtaining an optimal parameter combination; performing variational mode decomposition on the ultrasonic echo signal by using the optimal parameter combination to obtain a plurality of intrinsic mode function components; calculating a correlation coefficient of each intrinsic mode function component and the original ultrasonic echo signal, and dividing all the components into a signal dominant component, a mixed component and a noise dominant component according to a preset correlation coefficient threshold; de-noising processing is carried out on the mixed component by adopting an improved wavelet threshold function; and reconstructing the signal dominant component and the denoised mixed component to obtain a denoised Lamb wave signal. According to the invention, high-fidelity and high-reliability de-noising processing of the ultrasonic Lamb wave signal of the aluminum plate is realized, and the accuracy and robustness of subsequent defect detection are improved.

The invention provides a karst tunnel excavation support pressure reliability design method based on threshold gradient. According to the method, aiming at three random variables in an HB constitutive model, firstly, center composite sampling is carried out in a standard normal space to obtain seven test points, coordinates of the test points are converted into an original parameter space, a failure threshold value of each test point is obtained through calculation by establishing a numerical model, then a secondary response surface agent model without cross terms is established, and the failure threshold value of each test point is calculated. And establishing a tunnel face support force dominant threshold function by solving a linear equation set and determining seven coefficients of the proxy model, on this basis, proposing a standard normal space target reliability isoline checking point iteration method, performing checking point iteration on a sphere with the radius being the target reliability, and updating checking points in a gradient manner by adopting the threshold function. And calculating the collapse load of the design point during iterative convergence, so that the collapse load meets the requirement of a target reliable index. According to the method, the design complexity is reduced, and the design efficiency is improved.

The present disclosure provides a data reconstruction method based on Radon domain sparse representation and related equipment, relating to the technical field of communication. The method comprises: obtaining low-resolution Radon coefficients; inputting the low-resolution Radon coefficients into a neural network of an encoding-decoding structure to obtain first high-resolution Radon coefficients; and increasing the sparsity of the first high-resolution Radon coefficients through an adaptive soft threshold function cascaded at the back end of the neural network to obtain second high-resolution Radon coefficients. The method can reduce the data size, operation time, and operation cost of time-domain inversion of reduced-time variable Radon transform, and improve the stability and resolution of reconstructed data.

The application discloses an abnormal sample detection and identification method and system based on evolutionary calculation and multi-modal consistency constraint, and belongs to the field of network security and artificial intelligence security. The method comprises the following steps: S1, input data collection and preprocessing; S2, multi-modal feature extraction and unified representation; S3, optimized feature selection and weight self-adaptation; S4, cross-modal fusion detection model construction; S5, abnormal sample risk identification; S6, result judgment output: generating a final detection label according to a threshold function. The application has high accuracy and strong robustness in counterfeit image and video detection and abnormal sample elimination, and can be widely applied to the fields of multimedia authentication, intelligent security and AI content tracing.

This invention discloses a high-frequency piezoelectric signal denoising method based on WPD-AT-EMD, belonging to the field of signal processing technology. The method involves wavelet packet decomposition of the original noisy signal to obtain sub-signals in multiple frequency bands. For each high-frequency sub-signal, a threshold is calculated according to a locally dynamically adjusted adaptive threshold rule, and an improved threshold function is used for threshold processing. Then, all processed sub-signals are reconstructed to obtain a preliminary purified signal. The preliminary purified signal is then subjected to EMD decomposition to obtain several Intrinsic Mode Function (IMF) components. This invention employs a collaborative denoising mechanism combining wavelet packet decomposition, adaptive threshold processing, and empirical mode decomposition, which can accurately suppress high-frequency noise in strongly interfering and non-stationary high-frequency piezoelectric signals while completely preserving signal impulse characteristics and key waveform information. It effectively improves the signal distortion and feature loss problems that easily occur in traditional denoising methods and significantly alleviates the EMD mode aliasing phenomenon.

The application discloses a circuit breaker feature extraction method and device based on a wavelet threshold, equipment and a medium, and relates to the field of power data analysis.The method comprises the following steps: determining a wavelet base function according to the data characteristics of time-series current data of an intelligent circuit breaker to be analyzed; performing wavelet threshold denoising on the time-series current data under a plurality of preset decomposition layers respectively according to the wavelet base function, so as to obtain a plurality of groups of denoised current data; wherein, the wavelet threshold denoising is performed based on a wavelet threshold function with an adjustable threshold processing amplitude adjustment factor; determining the correlation degrees of the time-series current data and each group of denoised current data respectively according to a preset correlation degree screening function, and screening a plurality of groups of denoised current data according to the correlation degrees to obtain optimal denoised data; and extracting error features of the intelligent circuit breaker according to the time-series current data and the optimal denoised data. Through the implementation of the application, the accuracy of feature extraction of the data of the intelligent circuit breaker can be improved.

The application discloses a kind of based on optimization's guiding filter image fusion change detection method and device, method includes: by extreme minimum scale difference operator, pixel value difference value is to the picture of same scene different phase, obtains difference map;Using the way of guiding filter, difference map is fused on global scale, to obtain the optimal difference map;Mean filter is used to phase map I1 and I2, and the base layer difference map corresponding to each difference map is obtained, and the optimized brightness saliency map F1 and F2 are obtained by pca fusion as the image of generating saliency map, according to the principle of maximum saliency to determine weight map;Weight map is regularized for double-scale difference map reconstruction, and the final difference map is obtained;In clustering segmentation stage, by introducing soft threshold function, the final difference map is further processed, to suppress existing abnormal noise.The device includes: processor and memory.

The invention belongs to the technical field of nondestructive testing and signal processing, and particularly relates to a cam driven bearing quality detection method based on data analysis, which comprises the following steps of: acquiring a bearing vibration acceleration signal, and decomposing the bearing vibration acceleration signal into a detail coefficient and an approximation coefficient by using discrete wavelet transform; constructing pulse singularity, obtaining an enhancement coefficient by calculating the pulse singularity of the detail coefficient, and performing directional enhancement on the weak damage features; the enhancement coefficient is denoised by applying a nonlinear threshold function combined with index adjustment, and the pseudo-Gibbs phenomenon of a hard threshold is avoided while the constant deviation of the soft threshold is eliminated; through signal reconstruction and Hilbert envelope spectrum analysis, the damage characteristic frequency is detected to judge the quality of the bearing. According to the invention, weak impact characteristics can be effectively extracted under a strong noise background, and the detection accuracy is improved.

This invention discloses a memory-based dynamic event triggering control method for a single-link robotic arm system based on spoofing attacks. The method first establishes a Markov model of the single-link robotic arm system based on Markov jump system theory, considering a system model with a general transition rate. Next, a mode-dependent memory controller is designed to overcome the influence of spoofing attacks and external disturbances on the system. A memory-based dynamic event triggering mechanism is also designed to reduce communication transmission frequency. Compared with existing memoryless event triggering schemes, this scheme utilizes a series of recently released signals and introduces a threshold function and an internal dynamic factor, which can automatically adjust according to the triggering error. Finally, vertex separator processing is introduced to address the uncertainty in the Markov transition rate. A mode-dependent state feedback controller is designed to control the stochastic stability of a single-link robotic arm system. When applied to a single-link robotic arm system, this method ensures the normal operation of the system under spoofing attacks and disturbances.

The invention discloses an intelligent labeling method and system based on an oral cavity CBCT image, and belongs to the technical field of oral cavity medical image processing, and the method comprises the steps: obtaining CBCT voxel data of a target tooth region; performing sliding window analysis on the CBCT voxel data to generate a local gray statistical feature of each sliding window; and for each sliding window, constructing a dual-modulation adaptive threshold function comprising a main threshold and an auxiliary threshold based on the local gray statistical characteristics, and carrying out joint judgment on the CBCT voxel data to form a preliminary tooth region. According to the scheme, through cooperative work of the main threshold value and the auxiliary threshold value, the judgment condition is dynamically adjusted in combination with the gray distribution similarity, the gray overlapping areas of the enamel, the dentin, the alveolar bone and other tissues can be effectively distinguished, and the problems that in the prior art, fixed threshold value segmentation is sensitive to local noise and cannot adapt to gray distribution changes are solved; and the accuracy and robustness of tooth region preliminary labeling are obviously improved.

This invention claims protection for an improved wavelet thresholding denoising method. The method decomposes a noisy signal through several layers of wavelet decomposition, obtaining the corresponding high-frequency wavelet coefficients for each layer. The amplitude of the corresponding wavelet coefficients is analyzed; those for useful signals are lower, while those for noise are higher. An improved threshold function is applied to process the wavelet coefficients at each layer, retaining coefficients below the threshold and discarding those above. These wavelet coefficients are then reconstructed using inverse wavelet transform, outputting the denoised signal. This invention constructs a new threshold function to address the shortcomings of traditional wavelet denoising algorithms in noise reduction. Experimental data processing results show that the new threshold function effectively suppresses the influence of random errors in the inertial measurement unit. Compared to the original compromise threshold algorithm, the signal-to-noise ratio is improved, the root mean square error is reduced, and the scheme of this invention achieves better signal smoothness and excellent denoising effect compared to traditional denoising schemes.

The invention relates to the field of circuit breakers, in particular to a wavelet transform-based high-voltage circuit breaker signal feature extraction optimization method, which comprises the following steps of: calculating a wavelet transform-based high-voltage circuit breaker signal feature extraction optimization capability index; constructing a related parameter time sequence; calculating an influence factor of the relevant parameters of the current time period on the relevant parameters of the next time period; predicting numerical values of relevant parameters at the next moment; calculating a signal feature extraction optimization capability index at the next moment; a threshold function of wavelet transform is selected. The method has the advantages that the signal feature quality at the next moment is predicted based on historical and current data, so that the system does not process the current signal statically and passively, but can predictively pre-judge the signal state and dynamically adjust the threshold function of wavelet transform according to the signal state, and the dynamic closed-loop optimization mechanism can improve the reliability of the system. The problem that in the prior art, due to the fact that threshold selection is fixed and adaptability is lacked, noise suppression and signal shape preservation are not compatible is effectively solved.

This invention discloses a pruning method, apparatus, and electronic device for spiking neural networks. The method includes: initializing an initial vector composed of the weights of each connection in the synaptic connection layer to obtain a weight vector; when pruning the spiking neural network using a backpropagation-based algorithm, calculating the gradient of each loss function value with respect to the hidden parameter vector using a predefined derivative function; updating the gradient of the hidden parameter vector using gradient descent, and calculating a target threshold for subsequent gradient updates using a preset incrementing function; based on the target threshold, mapping the hidden parameter vector back to the weight vector using the soft threshold function; and obtaining a trained spiking neural network model when the number of pruning training rounds reaches a preset number. This invention solves the technical problem of effectively deploying spiking neural networks on neuromorphic computing chips in related technologies.

The application discloses a wavelet denoising multi-path extraction method with improved threshold function, which can be used for effective extraction and weakening of global navigation satellite system (GNSS) multi-path error, solves the problem of signal discontinuity or distortion of wavelet coefficients at a preset threshold, improves traditional wavelet denoising effect and multi-path error modeling precision, effectively weakens the influence of multi-path error on GNSS positioning and orbit determination precision, provides a feasible solution for realizing high-precision positioning and orbit determination in a complex environment, and lays a solid foundation for establishing a 1mm space-time reference in the future.

The application discloses a wide field of view video image change detection method and device, and the method comprises the following steps: using an improved adaptive fast guided filter for video image change detection; after filtering the multi-temporal video image, a logarithmic ratio operator considering neighborhood information is proposed, the mean ratio difference graph generation mode is improved, and a difference graph is generated; the improved MR image and the improved LR image are subjected to image fusion through discrete wavelet transform, and a fused difference graph is obtained; a soft threshold function is used to perform initial classification on the fused difference graph, and an initial change area and an unchanged area are obtained; a cumulative distribution function is used to compress the pixel value of the classification result from [0, 255] to [0, 1]; a super-fast robust constraint fuzzy C-Means clustering algorithm is proposed, and an improved adaptive median filter is used for denoising processing. The device comprises a processor and a memory.

The invention discloses a federated learning privacy protection and fair incentive method and system based on grey wolf optimization, and relates to the technical field of federated learning and distributed security computing, and the method comprises the steps: receiving multi-dimensional attribute parameters and preset initial global model parameters of candidate clients; based on the multi-dimensional attribute parameters of the candidate clients, screening the candidate clients by using a grey wolf optimization algorithm to obtain an optimal client; inputting a preset initial global model parameter into the optimal client for training, and performing gradient encryption based on a decentralized threshold function encryption method to obtain encrypted gradient data; performing security aggregation to obtain a security aggregation result, updating a preset initial global model parameter based on the security aggregation result, and performing quality evaluation on the updated global model to obtain a global model quality voucher; and performing consensus verification on the global model quality voucher, and triggering an intelligent contract by the verified global model quality voucher to generate an incentive distribution result.

The present disclosure provides an ultrasonic bolt pretightening force detection method and system based on improved CEEMDAN-wavelet threshold, relating to the technical field of bolt pretightening force detection, comprising: obtaining ultrasonic echo original signals under different loads; performing improved CEEMDAN decomposition on the ultrasonic echo original signals to obtain IMFs (Intrinsic Mode Functions) without modal aliasing; calculating MPE values of each IMF, classifying the components through a double-threshold quantization method to obtain to-be-de-noised components, effective reserved components and baseline drift components; introducing an improved threshold function with smoothing characteristics, accurately de-noising the to-be-de-noised components through the improved threshold function, and reconstructing to obtain de-noised IMFs; fusing the de-noised IMFs and the effective reserved components, reconstructing a pure ultrasonic echo signal and extracting a time-of-flight feature; calculating the acoustic time difference between a reference signal and a measurement signal using a cross-correlation method, and finally obtaining an accurate bolt pretightening force. The present disclosure ensures high-precision dynamic monitoring of bolt pretightening force in harsh engineering environments.

The present application relates to the technical field of visual identification, in particular to a kind of engine cylinder cover defect detection method and system, first to gray cast iron cylinder cover fire surface image pre-processing, obtain denoising gray scale chart;Two rows of gas hole profile are extracted and the hole center coordinates, connecting line unit vector and pixel length are calculated. Along the hole center connecting line equidistant sampling, take the gray scale maximum value of local strip in vertical direction as local background gray scale, combined with the preset crack gray scale upper limit, the local threshold value of sampling point is calculated, and the piecewise linear threshold function is generated by linear interpolation. The projection coordinates of cylinder cover nose bridge area pixels on connecting line are solved, the adaptive threshold is obtained by calling threshold function, and binary image is generated by pixel by pixel segmentation. The binary image is denoised and the skeleton is extracted, the skeleton pixel length is calculated using chain code, and the actual length of crack is obtained combined with camera correction factor. The present scheme can solve the fixed threshold mis-detection and missed detection problem caused by the gray scale gradient of the nose bridge area of the cylinder cover fire surface under the condition of no pre-cleaning.

The present application relates to ultrasonic radar technology, provide a kind of ultrasonic threshold calibration method, device, electronic equipment and computer storage medium, the method comprises: the target detection distance is divided into multiple continuous detection intervals;According to the initial threshold function of each detection interval that is fitted out according to preset threshold model, preset threshold model is obtained according to the multiple historical sampling echo points in target detection distance, the initial threshold function of each detection interval that is fitted out according to the modeling of target detection distance segmentation;For the last detection interval, the initial threshold function of each detection interval is sequentially smoothed according to the initial threshold function of the next detection interval adjacent to each detection interval, and the segmented threshold function of each detection interval is obtained.The present application improves the accuracy of target detection by calibrating reasonable ultrasonic threshold.

This invention discloses a method, system, and device for denoising electrocardiogram (ECG) signals based on a threshold contraction network model. The method inputs the ECG signal into a threshold contraction network model for denoising, obtaining a denoised ECG signal output by the model. The denoising process includes: feature extraction from the ECG signal to obtain waveform features; feature extraction from the waveform features to obtain low-frequency signal features, and threshold learning and slope learning from these low-frequency features to obtain a first threshold and a first slope; constructing a threshold function based on the first threshold and the first slope; obtaining a first denoised signal feature based on the threshold function; performing feature enhancement on the first denoised signal feature to obtain a second denoised signal feature; and performing a residual operation between the second denoised signal feature and the ECG signal to obtain the denoised ECG signal. This invention improves denoising performance and reduces the loss of useful signals.

The invention relates to a primary harmonic noise reduction method for a tunable diode laser absorption spectroscopy (TDLAS) system based on mayfly naiad-optimized VMD parameter-wavelet transform, and belongs to the technical field of spectrum detection and signal processing, and the method comprises the following steps: S1, preprocessing a noisy primary harmonic signal output by a tunable diode laser absorption spectroscopy (TDLAS) system; s2, optimizing variational mode decomposition (VMD) parameters by using a mayfly naiad optimization algorithm (MOA); s3, performing VMD decomposition on the preprocessed noisy first harmonic signal by using the VMD optimal parameter, and screening out an effective intrinsic mode IMF component; and S4, carrying out wavelet transformation on the screened effective IMF components, processing a wavelet coefficient through an improved threshold function, and reconstructing to obtain a first harmonic signal after noise reduction.