31 results about "Wavelet filter" patented technology

The invention discloses a photovoltaic power ultra-short-term prediction method, system and device and a storage medium, and relates to the technical field of new energy prediction. The method comprises the following steps: constructing an improved ensemble empirical mode decomposition algorithm to decompose original photovoltaic power data, and distinguishing and reconstructing a plurality of decomposed intrinsic mode functions to obtain reconstruction sequences of high, medium and low frequency components; for high-frequency and intermediate-frequency components, carrying out dynamic denoising by adopting an extended Kalman filtering algorithm; and inputting the original data, the low-frequency component, the denoised high-frequency component and the denoised intermediate-frequency component into a WTConv1d module, extracting multi-scale features through a wavelet filter and convolution operation, and inputting a multi-dimensional input matrix constructed by the multi-scale features and meteorological data into a Transform model for prediction. According to the method, noise interference can be effectively suppressed, multi-time scale feature expression is enhanced, and a self-attention mechanism is fully utilized to capture a cross-scale dependency relationship, so that the prediction precision and stability are improved.

The invention provides a hot rolled steel strip surface defect detection method based on target detection, and aims to solve the problems of difficulty in multi-scale small target recognition, insufficient feature fusion and complex background interference in hot rolled steel strip surface defect detection. Extracting multi-scale defect features by using wavelet convolution through decomposition, convolution and reconstruction processes; multi-scale features are adaptively fused based on channels and space attention through a dynamic feature fusion module (DFF); a dynamic hyperbolic tangent function (DyT) is used to replace a normalization layer to dynamically adjust the input range and compress extreme values to reduce the amount of calculation. The method has the effects of high detection average precision (mAP), low missing report rate and remarkably reduced calculation complexity, effectively improves the efficiency and reliability of identifying the surface defects of the hot rolled steel strip in a complex industrial environment, and is suitable for intelligent steel manufacturing quality control.

The application discloses a kind of based on adaptive wavelet decomposition's rotating machinery fault analysis method and system, wherein, method includes the vibration signal of the rotating machinery to be analyzed is collected and data preprocessing is carried out, obtains data sample;Depth feature extraction processing is carried out to the data sample by feature extraction module, and depth feature is obtained;The depth feature is input to adaptive wavelet decomposition model, and target decomposition feature is output and obtained;The target decomposition feature is subjected to fault type discrimination processing, and obtains fault analysis result.The adaptive wavelet decomposition model is carried out in the embodiment of the application wavelet filter and the automatic learning of wavelet decomposition route, and then separates fault component and interference component in rotating machinery fault analysis task, effectively improves the anti-noise performance of method, and can be widely applied in intelligent monitoring technical field.

The invention belongs to the technical field of inertial navigation, and provides an inertial instrument anti-disturbance alignment method based on sliding window wavelet filtering to solve the problems that measurement information cannot be fully utilized, an alignment result cannot be globally optimal and is seriously influenced by sudden change interference in an existing alignment method. The anti-disturbance alignment method comprises the following steps: extracting a speed error of an inertial instrument; based on a sliding window wavelet filtering method, carrying out noise reduction processing on system noise of the inertial instrument; and carrying out anti-disturbance self-alignment on the inertial instrument according to the speed error and the noise-reduced output data of the inertial instrument. According to the method, the output data of the inertial instrument can be subjected to noise reduction processing, the holding time of the alignment precision of the inertial measurement unit is prolonged, the dispersion of a convergence value is reduced, and the navigation precision is improved.

The application discloses a kind of traffic image segmentation control method and system, it is related to image processing field, wherein the method comprises: the camera of interactive target vehicle is obtained first traffic image;According to wavelet filtering algorithm, first traffic image is carried out denoising processing, and second traffic image is obtained;Based on the second traffic image, ORB feature recognition is carried out, and ORB feature recognition result is obtained;Based on the ORB feature recognition result, clustering is carried out, and ORB feature clustering result is obtained;Based on the ORB feature clustering result, the second traffic image is carried out background isolation, and third traffic image is obtained, and, third traffic image has ORB feature mark;Traffic image segmentation channel is built;Based on the ORB feature mark, according to the traffic image segmentation channel, third traffic image is carried out segmentation control, and target traffic image segmentation result is obtained.The technical problem that present technique is low in accuracy for traffic image segmentation is solved.

The embodiment of the specification provides a bearing fault diagnosis method and system of optimized filtering MCKD, wherein the method comprises the following steps: collecting a vibration signal, performing Fourier transform on a frequency spectrum of the vibration signal to obtain a key function; setting an initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a frequency spectrum trend, taking a minimum value of the trend spectrum as a boundary of spectrum segmentation to divide a filtering frequency band with different bandwidths and center frequencies, increasing the truncation length, repeating the above steps, and recording all results; designing a Mayer wave filter based on the obtained filtering frequency band, and taking the designed Mayer wave filter coefficient as an initial filter coefficient of the MCKD; calculating MCKD filtering results of different initial filter coefficients, quantifying fault characteristics of all filtering signals by a harmonic spectrum kurtosis (HSK), selecting a filtering result corresponding to a maximum value of the HSK as a global optimal solution of the MCKD, and completing fault diagnosis.

The invention discloses a GPS (Global Positioning System) and inertial navigation fused high-precision navigation method, aiming at the problems that a GPS signal is easily shielded and interfered and an inertial navigation error is accumulated along with time, high-precision navigation is realized through data preprocessing, a self-adaptive fusion algorithm, GPS signal interruption processing and fusion result optimization. In the data preprocessing, multi-path effect detection and correction are performed on GPS data, and zero offset error compensation is performed on inertial navigation data; the adaptive fusion algorithm constructs a model based on a fuzzy logic system, and updates a fusion weight in real time; when a GPS signal is interrupted, map matching is adopted to assist navigation, and the prediction model is utilized to compensate an inertial navigation error; and a fusion result is subjected to wavelet filtering and smoothing processing, and the accuracy is ensured by using consistency check. The method can effectively overcome the limitation of independent use of GPS and inertial navigation, realizes high-precision, continuous and reliable navigation in a complex environment, and improves the adaptability and stability of a navigation system.

This invention belongs to the field of video coding and decoding technology, and relates to a three-dimensional wavelet scalable video bitrate control method and system based on video content-aware characteristics. This method fully considers the impact of wavelet filter type, subband coupling phenomenon, and temporal subband content-aware characteristics on reconstruction error propagation, and proposes an adaptive Lagrange multiplier selection algorithm suitable for the wavelet domain. Then, an R-λ model based on video content-aware characteristics is established. Finally, based on the proposed bitrate control model, the coding parameters are adaptively adjusted to achieve the bitrate control target. Experimental results show that, compared with the bitrate control methods used in current classic three-dimensional wavelet video coding and decoding schemes, the method proposed in this invention achieves accurate bitrate control without significantly increasing complexity, while significantly improving the rate-distortion performance of the codec, better maintaining the continuity of video quality, and obtaining a more stable reconstructed video quality.

According to the switch cabinet vibration signal denoising method, the switch cabinet vibration signal denoising device, the storage medium and the computer equipment provided by the invention, before the vibration signal is denoised, direct current removal processing and normalization processing can be firstly performed on the vibration signal, so that the signal precision is improved; in order to accurately identify a key part carrying fault information in a signal, the vibration signal variation mode can be decomposed into a plurality of connotation mode components, and each connotation mode component is divided into an effective component and a background component according to the kurtosis value of each connotation mode component; according to the method, a target wavelet filter is used for carrying out targeted denoising on effective components, and the filter is constructed by adopting a golden proportion optimization algorithm to carry out iterative optimization on parameter vectors by taking minimization of a root mean square error as a target, so that subjectivity and blindness of parameter selection can be avoided, and the denoising effect is further improved; and finally, performing signal reconstruction on the background component and the filtered effective component to ensure the integrity and high signal-to-noise ratio of the denoised vibration signal.

The invention provides an easily-confused emotion recognition method based on multi-path feature extraction and gating fusion. The method can be applied to scenes such as human-computer interaction, emotion calculation and psychological health auxiliary diagnosis. According to the method, a feature path is constructed for each group of emotions aiming at three groups of easily-confused emotions of happiness-angry, fear-surprising and sadness-neutral, each path adopts different wavelet parameter sets to generate a multi-scale wavelet filtering kernel, and multi-path time-frequency features are extracted from the same voice signal. And inputting each path output into a gating attention mechanism module, calculating a path weight and performing weighted summation to obtain a fusion feature for easily confused emotion discrimination, and inputting the fusion feature into a classifier to output an emotion category. According to the method, a wavelet filtering kernel group emphasizing different emotion categories is constructed, a multi-path feature extraction structure is generated, and fusion is performed by using a gating mechanism, so that the model can realize easy-to-confusion emotion recognition according to feature contributions of different paths, and the easy-to-confusion emotion recognition accuracy of the model is improved.

The invention belongs to the field of fault diagnosis, and particularly relates to a bearing single-source-domain generalization fault diagnosis method based on wavelet adaptive causal decoupling, and the method comprises the steps: carrying out the time domain decomposition and adaptive feature extraction of a source domain sample based on a training set and a test set, and obtaining a global feature; in the second convolutional neural network, introducing a causal decoupling strategy based on a binary mask, and displaying and decoupling the extracted global features into causal features and non-causal features; and performing second convolutional neural network training, establishing a fault classifier, and determining causal purification loss based on the fault classifier. In the feature extraction stage, multi-scale time-frequency decomposition of mechanical vibration signals is achieved by adaptively optimizing wavelet filter parameters, low-frequency stable information and high-frequency impact features can be effectively separated, the stability and discrimination of feature expression can still be kept even in a strong-noise and multi-component coupling environment, and the method is suitable for large-scale popularization and application. Therefore, the accuracy and robustness of fault feature extraction are remarkably improved.

The invention relates to the technical field of crossing of artificial intelligence and tobacco processing, discloses a cut tobacco feeding moisture regulation and control method and system based on LightGBM, and aims to solve the problems of moisture signal distortion and low regulation and control precision caused by dust interference. The method comprises the following steps: acquiring dust concentration and noise-containing moisture signals of a production line in real time; dynamically generating an optimal wavelet filtering parameter through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict a moisture value; and generating a regulation and control instruction according to the deviation between the predicted value and the target value to realize closed-loop control. The system comprises a working condition sensing module, a signal acquisition module, a filtering parameter dynamic generation module, an adaptive filtering module, a moisture prediction module and a regulation and control instruction generation module. Through decoupling integration of dynamic filtering and an existing LightGBM model, on the premise that an original model is not changed, the moisture regulation and control precision and the response speed are remarkably improved, and the engineering transformation cost is reduced.

This invention relates to a hyperspectral image demixing method based on a graph wavelet transform convolutional network, belonging to the field of hyperspectral image demixing technology. The method includes: using a Mexican Hat graph wavelet filter to perform frequency domain decomposition on the undirected graph of the original hyperspectral image, obtaining low-frequency and high-frequency components, which are then input into a parallel dual-branch feature encoder network for feature learning, extraction, and adaptive recalibration. Enhanced low-frequency and high-frequency features are obtained and fused element-wise to obtain multi-frequency comprehensive features, which are then input into an endmember constraint and reconstruction module for abundance estimation. This yields a reconstructed hyperspectral image, which is compared with the original hyperspectral image. A multi-dimensional joint loss function is constructed and optimized to obtain a hyperspectral demixing model for hyperspectral image demixing. This method aims to solve the technical problem of existing technologies' inability to effectively distinguish the mixing characteristics of high-frequency boundary regions and low-frequency homogeneous regions, leading to boundary blurring and spectral distortion.

The invention discloses a method for accurately calculating passive discharge time, and the method comprises the steps: S1, carrying out the wavelet filtering processing, obtaining a time sequence tk and a voltage sequence vk of a passive discharge curve of a passive discharge resistor collected by an oscilloscope, carrying out the wavelet filtering of the voltage sequence vk, and obtaining a voltage sequence vfk after the wavelet filtering; s2, determining a passive discharge starting point, and obtaining a time point tstart corresponding to the starting point; s3, determining a passive discharge termination point and a time point tend corresponding to the termination point; and S4, calculating the passive discharge time. According to the method for accurately calculating the passive discharge time, the defects that in the prior art, manual point taking errors are large, and the accuracy of actually measured passive discharge time cannot be guaranteed are overcome.

The present application relates to a hyperspectral image unmixing method based on graph wavelet transform convolution network, belonging to the technical field of hyperspectral image unmixing. The method comprises: using a Mexican Hat graph wavelet filter to perform frequency domain decomposition on the undirected graph of the original hyperspectral image, obtaining low-frequency components and high-frequency components and inputting them into a parallel double-branch feature encoder network for feature learning and extraction and adaptive recalibration, obtaining enhanced low-frequency features and enhanced high-frequency features and performing element-by-element addition fusion, obtaining multi-frequency comprehensive features and inputting them into an endmember constraint and reconstruction module for abundance estimation, thereby obtaining the reconstructed hyperspectral image and comparing it with the original hyperspectral image, and constructing and optimizing a multi-dimensional joint loss function to obtain a hyperspectral unmixing model for hyperspectral image unmixing. The present application aims to solve the technical problem that the existing technology cannot effectively distinguish the mixed characteristics of high-frequency boundary regions and low-frequency homogeneous regions, resulting in blurred boundaries and spectral distortion.

The invention discloses an infrared small target detection method based on feature enhancement modulation, and the method comprises the steps: designing a dynamic wavelet feature enhancement module, extracting the high-frequency texture features of an image through a Haar wavelet filter in the shallow features, and further extracting the dynamic weight information of a feature map through a multi-layer perceptron, the weight information and the high-frequency texture features are combined and then added into the deep features, so that the problem of texture feature deficiency in the deep features is solved; according to the method, an adaptive feature modulation module is constructed, adaptive pooling of different factors is utilized to operate feature maps after feature pyramid, feature maps of different space sizes are generated, feature information of different space sizes is extracted, and meanwhile, target information is more prominent in combination with different feature information. According to the method, the infrared small target detection model based on feature enhancement modulation is constructed, so that the problem of small target information loss caused by texture feature loss in deep features and up-sampling in a feature pyramid is effectively solved.

The invention relates to the technical field of multi-source acoustic signal separation, in particular to a multi-source acoustic signal separation method based on Doppler wavelet filtering, and the method comprises the following steps: 1, collecting a trackside mixed acoustic signal: arranging a single microphone at a vertical distance d from a track, and collecting a mixed acoustic signal s (t) containing at least two sound sources; 2, calculating pseudo time-frequency distribution: performing improved Doppler wavelet transform (IDT) on s (t) to obtain pseudo time-frequency distribution FTFD: t0 is a time center, f0 is a characteristic frequency, and D (tau) is a dynamic distance between a sound source and a microphone; 3, sound source parameters are extracted, wherein time centers t01, t02,..., t0n and corresponding characteristic frequencies f0j of all sound sources are extracted from FTFD; and step 4, constructing a Doppler wavelet filter: aiming at the target sound source i. The invention provides a multi-source signal separation technology based on pseudo time-frequency distribution and Doppler wavelet filtering, can realize rail-side multi-source acoustic signal separation, and has a better application prospect in wayside acoustic fault diagnosis.

The invention discloses a frequency domain-space domain combined filtering enhanced anti-interference visual detection method, which comprises the following steps of S10, performing frequency domain direction enhancement on an input image through a Gabor multi-scale wavelet filter to obtain a frequency response characteristic graph; s20, constructing spatial domain saliency feature mapping by adopting an attention mechanism, and improving weak target area response through channel weighting; and S30, combining the frequency domain feature map and the spatial attention feature map to construct a mixed feature representation, and inputting the mixed feature representation into a deep convolutional neural network for training and detection.

A system and method for reducing frame-to-frame variations at the pixel level during noise reduction includes determining the noise source and the magnitude of those sources in the system. Once the system noise is characterized a selection of filters can be tailored to target the noise present in the system. The large temporal spikes, or impulse responses, in pixel value are considered to be discrete photon events. The system identifies pixels that have recently received a photon, preserving what little signal is present, while also suppressing Gaussian dark noise. The system implements a multi-level temporal wavelet filter.

The invention provides a non-contact blood pressure prediction method in a natural state based on a multi-level model, which comprises the following steps: sequentially performing face recognition, region of interest selection, color signal extraction, signal enhancement, abrupt change noise removal and wavelet filtering on each frame of picture to obtain a filtered IPPG signal; carrying out expansion and label classification on the data set; constructing a residual bidirectional long-short-term memory network, wherein the network comprises a convolutional neural sub-network, a residual sub-network and a bidirectional long-short-term memory sub-network; inputting the data set with the label into the network, and training the network by using a callback function to obtain the trained network; inputting a one-dimensional signal obtained through image processing into the trained network so as to predict the blood pressure value of the human body; according to the distance and the attitude angle, the blood pressure value is corrected through a Kalman filtering algorithm, and the corrected blood pressure value is obtained. In this way, the situation of overfitting can be avoided, the method is suitable for more real scenes, and the accuracy of the predicted blood pressure value is high.

The application relates to the technical field of artificial intelligence and tobacco processing, and discloses a tobacco cut filler moisture control method and system based on LightGBM, which aims to solve the problems of distorted moisture signal and low control precision caused by dust interference. The method comprises the following steps: acquiring the dust concentration of a production line and a moisture signal with noise in real time; generating optimal wavelet filtering parameters through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict the moisture value; generating a control instruction according to the deviation between the predicted value and a target value to realize closed-loop control. The system comprises six modules, namely, working condition sensing, signal acquisition, dynamic filtering parameter generation, adaptive filtering, moisture prediction and control instruction generation. The application realizes decoupling integration of dynamic filtering and the existing LightGBM model, significantly improves the moisture control precision and response speed without changing the original model, and reduces the engineering transformation cost.

This invention discloses a method, apparatus, and computer-readable storage medium for generating defect samples, relating to the field of deep learning technology. The method includes the following steps: inputting a preset defect image and a preset target image into a trained convolutional neural network to obtain defect image samples; the convolutional neural network includes a wavelet filtering module, a downsampling layer, and an upsampling layer connected in sequence, wherein the wavelet filtering module is used to perform preset wavelet filtering processing on the input image; wherein the downsampling layer includes at least one downsampling module and at least one wavelet filtering module alternately arranged; the upsampling layer includes at least one upsampling module and at least one wavelet filtering module alternately arranged; the number of downsampling modules in the downsampling layer is equal to and corresponds to the number of upsampling modules in the upsampling layer. This invention provides effective defect samples for training defect detection models.

The invention discloses a GIS isolation switch fault diagnosis method. According to the method, a voltage signal and a current signal of the GIS isolation switch are obtained and then noise reduction processing is carried out by using a wavelet filtering algorithm; modal function components of the current signals in different states are extracted through EMD to serve as frequency domain features, and the frequency domain features and time domain features are fused through a PCA algorithm to obtain a fused feature set; dividing the fusion feature set into a training sample set and a test sample set; optimizing kernel parameters and penalty factors of the support vector machine model by improving an original whale optimization algorithm, and training and testing the support vector machine model according to the training sample set and the test sample set to obtain a GIS isolation switch fault diagnosis model; inputting the fusion feature set of the GIS disconnecting switch into a GIS disconnecting switch fault diagnosis model, and identifying a GIS disconnecting switch fault; the high-efficiency fault diagnosis of the high-voltage isolation switch is realized by adopting the finite characteristic quantity when the fault samples are few.

The invention discloses a speech recognition sampling method based on multichannel learnable wavelet transform, and belongs to the technical field of automatic speech recognition. The method comprises the following steps: acquiring a fbank spectrogram after voice signal calculation; constructing a speech recognition model, and optimizing the speech recognition model by adopting a learnable wavelet transform method; and completing speech recognition sampling based on the optimized speech recognition model. According to the method, by utilizing the characteristic that wavelet transform has multi-resolution analysis capability, the signal can be decomposed into sub-signals with different scales and directions, and feature fusion is performed after noise reduction processing, so that the model can capture the features of the audio in different scales and directions; the automatic learning optimal wavelet filter is used, so that different audio data features are better adapted, the expression ability and generalization performance of the model are improved based on a learnable nonlinear threshold, and the ability of the model to suppress noise and unimportant features can be improved.

A system and method for reducing frame-to-frame variations at the pixel level during noise reduction includes determining the noise source and the magnitude of those sources in the system. Once the system noise is characterized a selection of filters can be tailored to target the noise present in the system. The large temporal spikes, or impulse responses, in pixel value are considered to be discrete photon events. The system identifies pixels that have recently received a photon, preserving what little signal is present, while also suppressing Gaussian dark noise. The system implements a multi-level temporal wavelet filter.

The invention relates to a traffic fusion data acquisition and processing method and a data acquisition system. The method comprises the following steps: acquiring multi-dimensional multi-path signal data acquired by an intelligent lamp post integration system; carrying out data preprocessing on the collected original data; for the preprocessed data, an orthogonal rational wavelet group filter bank is matched with a sensor array and a network to carry out multipath separation on multipath signal data to form multi-dimensional wavelet filter array data; and performing data fusion on the separated multi-path signal data, and performing optimization in the range of the whole network by adopting a wavelet probabilistic neural network model to obtain a numerical value closest to a real value. Compared with the prior art, the method has the advantages that the problem of multi-path separation is solved, multi-dimensional multi-path signals are fused and optimized in the range of the whole network, data accuracy is improved, and urban managers are effectively helped to detect traffic conditions and deal with possible problems.

The invention relates to the technical field of welding seam detection, in particular to a steel structure welding seam defect identification method and system based on a visual function, and the method comprises the following steps: S1, obtaining an assembly type steel structure welding seam image, and identifying a welding seam defect area which comprises fine crack features through a CCD camera acquisition module; s2, extracting initial noise pixel points in the defect area, and performing division processing on each initial noise pixel point according to an AI noise adaptive filtering strategy to obtain a plurality of initial noise pixel areas; the initial noise pixel region comprises a plurality of initial noise pixel points for determining the size of a filtering window; according to the method, accurate suppression of complex field noise is realized through an AI-driven noise adaptive weighted wavelet filtering strategy, accurate identification of fine crack features in the assembly type steel structure welding seam image is realized in combination with the U-Net deep segmentation network, and the assembly type steel structure welding seam defect identification precision and robustness are improved.

The present application belongs to the field of power distribution network management, and relates to a power distribution network harmonic optimization method and system based on electrochemical energy storage, comprising: collecting initial harmonic data and preprocessing the initial harmonic data to obtain processed harmonic data; the harmonic data includes power grid current, energy storage system parameters, PCS power and power grid voltage; the preprocessing includes sequentially performing wavelet filtering processing and direct current component filtering processing on the initial harmonic data; based on the processed harmonic data, fundamental wave parameters are calculated; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power; based on the fundamental wave parameters, fundamental wave components are calculated, and based on the fundamental wave components, a compensation current target value is determined; the fundamental wave components include fundamental wave active component, fundamental wave reactive component and harmonic component; based on the compensation current target value, control instructions are generated through model predictive control and fast repetitive control coordination; the overall power quality and the reliability of operation of the power distribution network are improved.

The application discloses a deep fake image detection method fusing content-independent features and content-dependent features. Firstly, smooth images, texture images and detail images are obtained through Gaussian filters, Gabor filters and wavelet filters respectively, and after fusion, the true and false probability based on frequency domain features is obtained through feature pyramid network processing. Secondly, difference images and noise images are obtained through unsharp mask and DnCNN denoising model, and after fusion, the true and false probability based on spatial domain features is also obtained through feature pyramid network processing. The frequency domain and spatial domain features jointly constitute the content-independent features of the image. In addition, the content-dependent features of the image are extracted through a ViT model to obtain the true and false probability based on the content-dependent features. Finally, the three kinds of true and false probabilities are weighted and fused to output the true and false judgment result of the image. The application performs excellently in cross-generation model and different data source fake image detection, and has strong generalization and universality.

The application discloses an ECG signal adaptive threshold wavelet filtering method and device, which is applied to ECG signal measurement, and adjusts the wavelet threshold of each layer of wavelet filtering from an initial value by a set step length, and calculates a self-correlation function according to a fast algorithm of the self-correlation function. After each filtering, the self-correlation value of the filtering result is recorded, and when the self-correlation value reaches a maximum value, the corresponding wavelet threshold is recorded as an optimal wavelet threshold. The self-correlation function is calculated from the sequence correlation of the signal itself after denoising according to the correlation and self-correlation characteristics of adjacent time instants of the ECG signal. Finally, the optimal wavelet threshold is used for adaptive filtering of the ECG signal. The application selects the optimal wavelet threshold of the wavelet filtering through the calculation result of the self-correlation function, can evaluate the filtering effect of the wavelet filtering method in the case that there is no clean and noise-free reference signal, and further optimizes the wavelet threshold parameter, and can better retain useful information and obtain a higher signal-to-noise ratio.