741 results about "Signal reconstruction" patented technology

The present invention relates to the technical field of cross-modal image signal reconstruction. Disclosed is a cross-modal joint source-channel coding and decoding method adaptable to changeable scenarios. The method comprises: first designing a Transformer encoder-based cross-modal channel coding and decoding optimization solution, so as to achieve the performance improvement and robustness of a channel encoder and a channel decoder; then designing a cross-modal source coding and decoding optimization solution for haptic-to-image generation based on a latent diffusion model, so that under an image signal loss scenario, haptic information is used to guide image generation; and finally, incorporating transfer learning technology, so as to reduce additional training costs caused by a system facing changeable cross-modal communication scenarios such as a changeable channel signal-to-noise ratio and different transmission tasks. Under cross-modal changeable communication scenarios, the joint source-channel coding and decoding method provided in the present invention can solve the problems of the inability of a receiving end to well complete image reconstruction, and additional model training costs caused by changeable channel environments and scenarios.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention discloses a signal reconstruction denoising method for circuit breaker mechanical state evaluation and a storage medium. In order to solve the problems of multi-source signal aliasing and strong noise interference of the double-break vacuum circuit breaker, a technical route combining mechanical transfer function modeling and a double-flow deep network is innovatively adopted. A complex vibration source is analyzed by constructing a signal coupling model, environmental noise is accurately filtered by using a multi-stage noise reduction strategy, and a multi-dimensional state vector is constructed by fusing time-frequency domain features, so that accurate evaluation of the health state of the equipment is realized. Experiments show that the method is remarkably superior to the prior art in signal denoising, feature extraction precision and diagnosis accuracy, and an efficient solution is provided for intelligent operation and maintenance of power equipment.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention provides a magnetotelluric data denoising method and device based on a depth fusion model, is applied to a magnetotelluric data denoising system, and relates to the technical field of magnetotelluric data processing. A deep fusion model with a'noise feature recognition-signal selective reconstruction 'framework is established, input signals are pre-classified through a front noise detection module, the problem of false attenuation of effective signals in traditional end-to-end denoising is effectively avoided, a denoising auto-encoder containing a residual structure is utilized, and the denoising accuracy is improved. The deep feature extraction capability of the residual structure and the signal reconstruction advantage of the denoising auto-encoder are combined, the fidelity and integrity of the reconstructed signal are remarkably improved, the denoising process does not depend on manual intervention, and the efficiency and accuracy of data processing are greatly improved.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

The invention provides an aero-engine blade damage detection method based on an improved ultrasonic sparse reconstruction algorithm, and relates to the technical field of aero-engine damage detection positioning, and the method comprises the steps: S1, setting a sensor array and a detection region of a to-be-detected blade, and obtaining an actual measurement damage reflection signal dictionary through a sensor; s2, wave number distribution of a detection area is determined according to a variable thickness structure Lamb wave propagation model, signal reconstruction is carried out, and a theoretical damage reflection signal dictionary is obtained; s3, an aero-engine blade damage detection model is constructed, an improved sparse reconstruction algorithm is adopted to solve and optimize, and damage judgment is carried out; and S4, according to the pixel value distribution condition of the aero-engine blade detection area, generating an aero-engine blade damage detection image, and positioning a damage position. According to the method, the reflected wave amplitude information can be obtained from the array sensor signals, and the reflected wave amplitude information is compared with the reconstructed theoretical reflected signals, so that the damage condition of the aero-engine blade is identified and positioned.

The invention relates to the technical field of rotary steerable drilling system dynamics analysis and control, in particular to a rotary steerable drilling system dynamics analysis system which comprises a data acquisition module used for acquiring ground engineering parameters and underground dynamic parameters in real time and intercepting drilling parameter adjusting instructions; the digital twin modeling module is used for outputting a complete state vector of a digital twin model; the noise prediction module is used for generating a control source noise signal; the signal reconstruction module is used for executing self-adaptive hedging processing on the original mixed signal so as to reconstruct a pure geological signal; the cooperative control module is used for predicting the vibration risk caused by the change of the front stratum and generating an optimal control instruction for actively avoiding the vibration risk; the occurrence probability of malignant vibration is remarkably reduced, the drilling efficiency is improved, and the service life of a drilling tool is prolonged.

The invention discloses a radar signal noise reduction method for deep learning, and relates to the technical field of radar signal processing, and the method comprises the steps: converting a time domain radar signal into a two-dimensional time-frequency graph through short-time Fourier transform; frequency domain feature extraction is carried out on the two-dimensional time-frequency graph, and frequency domain feature representation is output through a DnCNN and a cascade CNN in sequence; performing time domain feature extraction on the time domain radar signal and outputting time domain feature representation; fusing the frequency domain features and the time domain features based on a multi-head attention mechanism to generate cross-domain joint features; and inputting the cross-domain joint feature into a residual shrinkage network for signal reconstruction, and outputting a denoised time domain signal. A time-frequency double-domain collaborative learning framework is constructed, and signal high-fidelity reconstruction in a complex noise environment is realized through deep fusion of time domain characteristics and a frequency domain structure distribution rule. According to the noise reduction method, an extremely low phase error and an extremely high feature retention rate can be kept in a strong noise environment.

The invention belongs to the technical field of intelligent wireless sensing and radar signal processing, and particularly relates to a millimeter wave radar gesture recognition feature processing method, which is particularly suitable for scenes with environment interference (such as walking of others and static clutter), and specifically comprises the following steps: S1, preliminary filtering by a self-adaptive filter; s2, carrying out improved I CEEMDAN decomposition; s3, I MF component classification and processing; s4, signal reconstruction; the experimental result shows that the method provides a robust and efficient solution for gesture recognition, and can be widely applied to the fields of man-machine interaction, virtual reality, intelligent equipment control and the like.

The invention discloses an MFL signal reconstruction method based on channel mutual fusion and a global attention mechanism, relates to the technical field of nondestructive testing, and particularly discloses a magnetic flux leakage signal MFL reconstruction method based on a neural network. In order to solve the problem of interference of external noise on defect characteristic signals, a signal channel recombination, shuffling and aggregation optimization method based on CycleGAN is adopted, a GSoP optimization covariance matrix is introduced, through PSNR and LPIPS index comparison, the reconstruction precision of the MFL defect signals can be greatly improved, and the reconstruction precision of the MFL defect signals is improved. And the data processing precision and speed are improved (the time cost and the space cost of data processing are reduced), so that the defect detection efficiency is improved.

The invention belongs to the technical field of fault positioning, and discloses a Beidou satellite time service-based power distribution network fault section positioning method, which comprises the following steps of: acquiring three-phase current, and performing phase synchronization alignment on the three-phase current by using a Beidou satellite to obtain a three-phase current synchronization data sequence; performing modulus transformation on the three-phase current synchronous data sequence to obtain a zero-mode current component and a line-mode current component; performing adaptive waveform decomposition on the zero mode current component and the line mode current component to obtain an intrinsic mode component, and performing signal reconstruction on a target component to obtain an enhanced fault traveling wave signal; performing waveform curvature sudden change detection on the enhanced fault traveling wave signal to obtain a wave head arrival time and a time difference observation sequence; performing geometric analysis on the topological structure based on the time difference observation sequence and the traveling wave propagation speed to obtain a suspected fault section; and comprehensively studying and judging the suspected fault section to obtain an actual fault section. The power distribution network fault positioning efficiency can be improved.

The invention discloses a state monitoring method for a bridge structure, and belongs to the technical field of bridge measurement and monitoring, and the method comprises the steps: obtaining an original data set of the inclination angle of a bridge main tower and the tension of an inhaul cable, analyzing a signal time feature representation factor, and carrying out the linear interpolation adjustment information judgment; and based on the original data set, analyzing an inhaul cable tension update value, then analyzing an inhaul cable tension residual error and an interpolation adjustment effect label, and carrying out signal reconstruction effect judgment. According to the method, the bridge structure can be continuously monitored, linear interpolation adjustment information is carried out by analyzing signal time characteristic characterization factors, whether adjustment is carried out or not is judged, calculation power waste is avoided, and the inhaul cable tension is aligned with a main tower inclination timestamp by analyzing inhaul cable tension update values through linear interpolation adjustment. A stay cable tension update value is analyzed to eliminate a cross-sensor clock asynchronous error, signals are classified by analyzing a frequency coherence factor, mutual interference of different frequency components in broadband signals is avoided through classification processing, and the structural health monitoring precision is remarkably improved.

The invention discloses a state detection method, a related device, equipment, a medium and a program product. The state detection method comprises the following steps: decomposing a first electric signal of an electric drive system into a plurality of first modal components; selecting at least one noise mode component from the plurality of first mode components; performing filtering processing on the at least one noise mode component to obtain at least one second mode component; performing signal reconstruction by using the at least one second modal component and each unfiltered modal component in the plurality of first modal components to obtain a second electric signal; and determining a state detection result of the electric drive system at least by using the second electric signal. In this way, the accuracy of state detection of the electric drive system can be improved.

The invention relates to the technical field of signal reconstruction, in particular to a complex electromagnetic environment-oriented millimeter wave signal blind source separation and reconstruction system, which comprises a frequency spectrum trend division module, a path fading construction module, an initial cluster label generation module, a multi-solution path screening module and a fusion reconstruction execution module. According to the method, power spectral density sequence processing is carried out on millimeter wave frequency domain data, a multi-dimensional feature group is formed according to a path loss factor, an angle of arrival and the like, and an initial feature cluster is screened through an Euclidean distance, so that the accuracy of signal source classification is effectively improved; after the frequency domain response and the phase contour are continuously subjected to point comparison, path screening is completed by integrating a mean square error residual value, the path misjudgment probability is reduced, time domain resampling and phase frequency offset standardization are executed in fusion reconstruction, the consistency and fidelity of signal reconstruction are enhanced, and the reconstruction precision is improved. The whole process improves the separation accuracy and reconstruction precision of mixed signals in a complex electromagnetic environment.

The invention relates to the technical field of data fusion, in particular to a multi-source electrocardiosignal correction method and system based on adaptive fusion, and the method comprises the following steps: constructing a multi-channel input tensor, extracting local features through a weight calculation network, carrying out the adaptive weight fusion and dimension reduction of multiple paths of signals, and carrying out the correction of the multi-source electrocardiosignal. A nonlinear mapping relation is established through a deep reconstruction network, a standard waveform is reconstructed, and network parameters are optimized based on reconstruction error reverse iteration. According to the method, local neighborhood features of multichannel signals are extracted by constructing a weight calculation network, a dynamic channel weight sequence reflecting the real-time contribution degree of a signal source is constructed, the amplitude intensity is adaptively adjusted according to the signal quality, unstable channel noise interference is effectively inhibited, and high-quality signal components are enhanced; a deep reconstruction network is used for carrying out nonlinear feature transformation on a fusion sequence, accurate mapping from non-standard input to standard lead waveforms is established, and weight distribution and optimization of signal reconstruction parameters are achieved in combination with an error back propagation mechanism.

The invention particularly relates to an electroencephalogram data enhancement method based on a conditional diffusion model and a graph neural network. The electroencephalogram data enhancement method comprises the following steps: constructing an EEG signal reconstruction model; the EEG signal reconstruction model is trained, the two processes of noise diffusion and reverse denoising are included, and in the noise diffusion process, real EEG signals are subjected to T-step noise-by-noise superposition to be converted into pure Gaussian distribution; in the reverse denoising process, a U-Net model of an embedded graph neural network is used for conducting T-step denoising on Gaussian noise obtained in the diffusion process, and EEG signals are reconstructed; performing sampling by using the trained EEG signal reconstruction model to generate new EEG signal data; the generated EEG signal and the original EEG signal are mixed and input into an EEG decoder together, an original training set is expanded, and the performance of the EEG decoder is enhanced. According to the method, complex spatial-temporal characteristics of the EEG can be effectively captured by using the conditional diffusion model of the fusion graph network, and a high-fidelity EEG signal with neuroscience significance is reconstructed from Gaussian noise.

The invention discloses a hydropower station signal source side data anti-interference acquisition method and system. The method comprises the following steps: constructing a multi-source interference feature recognition network according to an environment state of a hydropower station, obtaining an interference source distribution feature spectrum, and obtaining an original signal through space-frequency domain joint filtering preprocessing based on the spectrum; performing recursion processing on the original signal by adopting an adaptive Kalman filtering algorithm to obtain a high-fidelity target signal; processing the high-fidelity target signal based on a multi-scale feature extraction and verification mechanism to obtain a credibility quantitative purification signal; and processing the credibility quantitative purification signal based on a compensation strategy to obtain a high-reliability output signal. According to the method, the influence of interference signals is gradually removed from interference source identification, filtering processing, signal reconstruction and quality evaluation to signal compensation, and finally, a result subjected to multiple anti-interference processing is obtained, so that various interferences in a complex environment of a hydropower station can be effectively resisted, and high-reliability data acquisition is realized.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

The invention relates to a pipeline leakage detection method based on acoustic signal spectrum noise reduction, and belongs to the technical field of pipeline leakage monitoring. The method comprises the steps that pure background noise signals are collected through an acoustic sensor to serve as noise analysis samples, noise power spectrum estimation is conducted, and average noise power spectrum estimation is obtained; collecting an original acoustic signal containing potential leakage; performing spectrum analysis and phase reservation on the original acoustic signal after framing processing; a continuous noise reduction acoustic signal is output through spectral subtraction operation and signal reconstruction; performing feature extraction on the continuous noise reduction acoustic signal, performing anomaly detection on a current signal segment through a pipeline leakage detection model, and outputting a leakage signal segment; and leakage point positioning is carried out according to the time difference of the leakage signal segment reaching different sensors. Efficient and accurate detection of pipeline leakage is realized, and interference of background noise on a detection result is effectively overcome.

A power transmission tower grounding resistance intelligent measurement method based on ground potential field distributed sensing comprises the steps that a ground potential sensing network is arranged around a power transmission tower grounding device, and a ground potential field distributed measurement system with an iron tower as the center is established; a broadband characteristic coding current signal is injected into the grounding system through a weak current injection module; acquiring potential response data of each node of the ground potential sensing network excited by the feature coding current signal by adopting a distributed synchronous acquisition technology, and reconstructing complete ground potential field spatial distribution from sparse node observation data based on a compressed sensing theory through a signal reconstruction algorithm; and inputting the reconstructed complete ground potential field spatial distribution map into a pre-trained map neural network model, and outputting an intelligent identification result and an anomaly diagnosis conclusion of the ground resistance. According to the invention, non-contact, distributed and high-precision grounding resistance measurement under the condition that a grounding lead does not need to be disconnected is realized, and the measurement precision and reliability under a complex working condition are remarkably improved.

The invention discloses a signal reconstruction and noise suppression method and system for a distributed Raman temperature measurement sensing system, and the method comprises the steps: obtaining original anti-Stokes light and Stokes light time domain signals of the distributed Raman temperature measurement sensing system, and constructing a space-time signal matrix; performing preprocessing and data enhancement on the space-time signal matrix; constructing a dual-path feature fusion network for Raman signal reconstruction and noise suppression, and initializing the network; defining a loss function fusing temperature physical constraints, and training the dual-path feature fusion network; and inputting an original Raman signal to be processed into the trained network, outputting the reconstructed anti-Stokes and Stokes signals with the high signal-to-noise ratio, and carrying out temperature demodulation according to the anti-Stokes and Stokes signals with the high signal-to-noise ratio. While the spatial resolution is maintained or improved, the noise of the Raman temperature measurement system is effectively suppressed, especially the real signal of a temperature abrupt change point can be recovered, the temperature demodulation precision, stability and reliability are improved, and reliable data are provided for infrastructure safety monitoring.

The invention relates to the technical field of voice semantics, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a high-fidelity music reconstruction method, device, equipment and medium, and the method comprises the steps: obtaining a to-be-processed music signal, carrying out the signal coding of the to-be-processed music signal, and obtaining a low-dimensional music signal; performing multi-scale convolution on the low-dimensional music signal to obtain a multi-scale music feature; performing multi-head self-attention calculation on the multi-scale music features to obtain attention values of the multi-scale music features; performing feature fusion on the multi-scale music features according to the attention values to obtain fused music features; performing non-autoregression decoding on the fused music features to obtain target acoustic features; and performing waveform reconstruction on the to-be-processed music signal according to the target acoustic feature to obtain a target music signal. According to the invention, the music signal reconstruction quality and reconstruction efficiency can be improved.

The invention discloses a secondary modulation signal identification method and system based on joint model deep learning, and relates to electronic countermeasure, wireless communication and signal processing technologies, and the method comprises the steps: obtaining a sample secondary modulation signal, and carrying out the preprocessing; for the preprocessed sample secondary modulation signal, extracting a spatial-temporal characteristic sample based on signal reconstruction and phase space statistical analysis permutation entropy; a lightweight deep learning model is adopted to jointly construct a data local spatial characteristic perception model and a time sequence backtracking control model, and the extracted spatial-temporal characteristic samples are utilized to train the joint model; and extracting spatial-temporal characteristics of the secondary modulation signal of the target, and outputting an identification result by using the trained joint model. The method can solve the problems that a secondary modulation signal method in the field of signal and information processing is low in recognition accuracy and large in calculation amount, and the number of needed prior samples is large.

The invention discloses a millimeter wave electrocardiogram reconstruction system and method based on adaptive MODWT and CNN-BiLSTM-CA, and the method comprises the steps: collecting a tiny phase displacement signal of a target thoracic cavity region, and carrying out the multi-band decomposition and key component adaptive screening of an original signal through combining with a multi-scale stationary wavelet decomposition algorithm; and introducing a channel attention mechanism to enhance key information representation, inputting a reconstruction signal into a deep learning model combining a convolutional neural network and a bidirectional long-short term memory network, completing time sequence modeling and nonlinear mapping, and outputting a reconstruction waveform highly consistent with a standard electrocardiogram. According to the method, the signal reduction capacity under the complex interference condition is remarkably improved, high-precision and privacy-friendly remote physiological signal monitoring can be achieved under the condition of not depending on a lead electrode, and the method is superior to a traditional baseline model in the aspects of waveform reduction precision, signal time sequence consistency, model generalization capacity and the like; the method is suitable for various application scenes such as intelligent medical treatment.

The invention discloses a fault diagnosis method, device and equipment and a computer readable storage medium, which are applied to the technical field of fault diagnosis, and the method comprises the steps: carrying out the feature extraction of each decomposed time domain signal, obtaining a feature sequence corresponding to each decomposed time domain signal, and determining the stability feature corresponding to each feature sequence; screening the decomposition time domain signals based on the stability characteristics to obtain screened decomposition time domain signals, and performing signal reconstruction based on the screened decomposition time domain signals to obtain reconstructed fault signals; and performing full-band envelope spectrum analysis on the reconstructed fault signal to obtain an envelope spectrum analysis result, and performing fault diagnosis based on the envelope spectrum analysis result. According to the method, the reconstructed fault signal closely related to the fault can be extracted based on the stability feature, the full-band envelope spectrum is carried out based on the reconstructed fault signal, and due to the fact that the full-band envelope spectrum is used, the fault detection accuracy of envelope spectrum analysis based on the fault information can be improved.

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention discloses a complex nonlinear MIMU (Micro Inertial Measurement Unit) signal noise reduction method based on improved modal decomposition. Dynamic adaptive optimization of noise parameters is realized by introducing Hilbert spectrum analysis into a CEEMDAN (Complex Empirical Empirical Mode Decomposition Number) algorithm; an IMF component discrimination model is constructed based on multi-dimensional feature fusion, and accurate classification of IMF components is realized; and aiming at a discrimination result, adopting a hierarchical processing strategy of combining variational mode decomposition and empirical wavelet transform for different types of mode components to realize high-quality signal reconstruction. Compared with the prior art, the method has the advantages that the signal decomposition quality is remarkably improved, the multi-feature fusion discrimination model is excellent in performance when the boundary fuzzy region is processed, the problem of discontinuity of a traditional hard threshold method at the feature boundary is solved, the signal-to-noise ratio is remarkably improved, the root-mean-square error is greatly reduced, and the noise reduction effect is remarkably improved.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is improved.