337 results about "Noise component" patented technology

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

A leakage sound signal denoising method based on a combination of optimized VMD and an improved wavelet threshold, for use in solving the problem in existing noise processing methods of low identification accuracy in processing leakage sound signals of water supply pipe networks. The present invention comprises: acquiring leakage sound signals of a real water supply pipe network, and analyzing noise components and ranges of the leakage sound signals; on the basis of a goshawk optimization algorithm, performing parameter optimization on the number K of decomposition modes and a penalty factor α of VMD to obtain optimal parameters, and using the optimal parameters to construct a variational model; using the variational model to decompose the leakage sound signals to obtain a plurality of intrinsic mode components; using a correlation coefficient method to screen the plurality of intrinsic mode components to obtain high-frequency components and low-frequency components; performing wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; and reconstructing the low-frequency components and the denoised high-frequency components to obtain denoised leakage sound signals. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the identification accuracy is improved.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention discloses a high-reliability planetary gearbox signal denoising method, system, medium and equipment, and the method comprises the steps: obtaining original vibration signals of a nuclear power circulating pump planetary gearbox in different health states, and carrying out the detrending and demean preprocessing of the signals; performing modal decomposition on the original vibration signal by adopting an empirical mode decomposition (EMD) algorithm, an ensemble empirical mode decomposition (EEMD) algorithm and a variational mode decomposition (VMD) algorithm to obtain a plurality of different modal components; iteratively optimizing a hyper-parameter value in the variational mode decomposition algorithm VMD by adopting a sparrow search algorithm SSA so as to realize the self-adaptive decomposition of the variational mode decomposition algorithm VMD on the signal; and carrying out modal decomposition on the gearbox vibration signal by adopting a variational modal decomposition algorithm VMD after iterative optimization, removing noise components in modal components, and reconstructing the signal to realize gearbox vibration signal denoising.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

The invention relates to the field of electrical measurement and testing, in particular to a fault monitoring method and early warning system for a bottom loading and unloading crane pipe, and the method comprises the steps: obtaining and preprocessing the current, voltage and vibration sequence of a servo motor, and obtaining a corresponding IMF component and an energy spectrum; and in combination with historical normal operation data and to-be-monitored data, the importance degree of the IMF component is evaluated, and the time domain anomaly degree is obtained based on the standard deviation of the sliding window variance and the DTW distance. Odd harmonic information is extracted from the energy spectrum, and the time domain anomaly degree is combined to obtain the comprehensive anomaly degree. And obtaining a fault degree through weighted integration, and inputting the fault degree into a control system to judge a fault. According to the method, the energy spectrum difference of the IMF components, the first-order difference sequence difference and the time-frequency domain abnormal degree are comprehensively analyzed, the operation state of the servo motor is comprehensively evaluated, key fault information loss and single time domain feature monitoring limitation caused by elimination of high noise components in a traditional method are avoided, and the accuracy and sensitivity of fault monitoring are remarkably improved.

The invention discloses a data full-link dynamic anti-interference optimization transmission method. The method comprises the following steps: S1, constructing a data transmission system architecture comprising a sending end, a transmission link and a receiving end; the sending end detects environment disturbance characteristics in real time, and the receiving end collects receiving signals containing background noise and link distortion; s2, designing a self-adaptive waveform coding strategy at a sending end; s3, performing active channel compensation on a transmission link; channel characteristics are extracted, and channel interference is counteracted in real time through pre-distortion waveform adjustment; s4, deploying an intelligent noise reduction decoding algorithm at a receiving end; a depth feature separation network is constructed, and link residual interference is filtered out by analyzing the time-frequency domain difference between a target signal and a noise component. Through a three-level anti-interference mechanism of adaptive coding of the sending end, active compensation of a transmission link and intelligent noise reduction of the receiving end, the problem that the transmission reliability of signals is reduced due to environment disturbance, channel distortion and noise pollution in a complex environment is solved.

The invention relates to the technical field of sensing data processing, and discloses a sensing data chip method based on multi-source information fusion and dynamic parameter adjustment. The method comprises the following steps: collecting original data streams of a plurality of heterogeneous sensors, and generating synchronized sensing data through timestamp alignment and format standardization processing; extracting an environment noise component, and dynamically suppressing noise by using an adaptive filtering algorithm to obtain de-noised sensing data; performing multi-dimensional feature decomposition on the de-noised data, calculating time domain, frequency domain and space domain feature vectors, and generating a multi-dimensional feature set; dynamically adjusting a feature weight distribution strategy in combination with a current system load state and a multi-dimensional feature set historical distribution rule, and generating an optimized fusion weight matrix; and carrying out weighted aggregation on the multi-dimensional feature set and the optimized fusion weight matrix, and outputting a multi-source fusion feature vector. According to the method, the problems of data synchronization, noise suppression and feature fusion suitability in multi-source heterogeneous sensing data processing can be solved, and the processing requirements of different scenes are met.

The invention discloses a wind turbine generator blade vibration mode feature recognition method, which mainly comprises the following steps of: arranging three-axis piezoelectric acceleration sensors at equal intervals from a blade root to a maximum chord length position, and adaptively selecting an optimal channel as a target input vibration signal according to envelope dispersion and a period proportion; introducing a vibration signal denoising method based on regenerative phase shift sine-assisted empirical mode decomposition, and constructing a Fisher ratio-based multi-dimensional fusion index to remove a noise component; estimating a system order range according to a singular entropy jump value theory, and designing modal similarity and a confidence index to accurately estimate a real order of a blade system; introducing three types of constraints of structure maintenance, modal sparsity and energy smoothness to jointly optimize a low-rank approximation strategy so as to realize optimal reconstruction of the Hankel matrix; constructing a fitness function selected by a clustering center by combining the point set density of the sample and Euclidean distance information, and optimizing a modal extraction result by adopting inter-class dispersivity and an intra-class sample number; according to the method, the environmental noise can be effectively removed, the system order can be accurately determined, and finally the modal parameters of the system can be accurately identified.

The invention provides a full-spectrum water quality multi-parameter dynamic inversion method and a model construction method thereof, and belongs to the technical field of spectral analysis based on machine learning. By constructing a closed-loop optimization architecture of noise reduction, wavelength optimization, turbidity correction and adaptive modeling, high-precision detection of four water quality parameters of chemical oxygen demand, total organic carbon, total nitrogen and nitrate nitrogen is realized. According to the method, a variational mode decomposition and improved threshold translation invariant wavelet combined noise reduction algorithm is provided, a kurtosis-correlation coefficient dual index is adopted to screen noise components, a multi-translation average strategy is combined to suppress a pseudo-Gibbs phenomenon, and meanwhile, a global parameter collaborative tuning mechanism based on Bayesian optimization is designed; a cross-module hyper-parameter coupling space is established in the whole process of noise reduction, wavelength optimization, turbidity correction and modeling. Through a multi-dimensional feature enhancement mechanism, the model feature representation capability is improved by 35%, and an innovative solution is provided for multi-parameter online detection in a complex water quality scene.

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

The invention relates to the technical field of speech enhancement, in particular to a time-frequency domain speech enhancement method based on KAN channel attention, which comprises the following steps: processing a speech data set to obtain frequency domain representation; extracting local features in the frequency domain representation through an encoder to obtain output features; the output features are input into a TF-Transform block, noise components are recognized and suppressed, and output components are obtained; inputting the output component into a KAN-based channel attention module, sequentially obtaining a channel feature map and a spatial feature map, and successively multiplying the output component by the channel feature map and the spatial feature map and introducing jump connection to obtain refined features; and obtaining a time domain voice signal based on refined feature recovery, and processing the recovered time domain voice signal through an amplitude decoder and a phase decoder to correspondingly obtain an amplitude spectrum and a phase spectrum. And through a KAN-based channel attention module, the denoising and reconstruction performance of sparse and sensitive high-frequency components in the voice is improved.

This disclosure provides methods, devices, and systems for audio signal processing. The present implementations more specifically relate to speech enhancement techniques for separating microphone signals into speech, echo, and noise signals. In some aspects, a speech enhancement system may include a delay estimator and an acoustic echo and noise (AEN) decoupling filter. The delay estimator receives a microphone signal via a microphone and a far-end audio signal for output via a speaker and estimates a reference audio signal based on a delay between the microphone signal and the far-end audio signal. In some aspects, the AEN decoupling filter may determine a speech mask, an echo mask, and a noise mask based on the microphone signal and the reference audio signal and may suppress an echo component and a noise component of the microphone signal based on the determined set of masks.

The invention relates to the technical field of noise recognition, and discloses an intelligent recognition method and system for Bluetooth headset environment noise, and the method comprises the steps: collecting two paths of original audio of a Bluetooth headset, determining the dynamic evolution characteristics of environment noise components, carrying out the frequency domain cepstrum transformation of the environment noise components, and obtaining the frequency domain noise characteristics; carrying out attention fusion on the dynamic evolution features and the frequency domain noise features to obtain fused audio features, and constructing an association feature matrix of the fused audio features; constructing a noise scene candidate set of the Bluetooth headset, and performing noise type screening on the noise of the Bluetooth headset by using the associated feature matrix and the noise scene candidate set to obtain a noise type candidate list; and performing adversarial enhancement processing on the noise corresponding to each noise type in the noise type candidate list to obtain an enhanced noise set, extracting a dynamic characteristic spectrum of the enhanced noise set, and identifying the environmental noise of the Bluetooth headset. According to the invention, the recognition precision of the environment noise of the Bluetooth earphone can be improved.

The invention discloses a robot inspection control method for textile equipment monitoring, and relates to the technical field of textile equipment monitoring, and the method comprises the following steps: a robot scans an environment along a preset path at a variable sampling rate through a mobile noise fingerprint collection method, synchronously collects working condition associated noise decomposition of textile equipment, and constructs a dynamic noise substrate; performing space-time-frequency domain constraint blind source separation by taking a dynamic noise base as a physical constraint and combining a sound vibration sensor, decoupling a background noise component, an equipment global vibration component and a residual signal, performing kurtosis detection on the residual signal, and performing preliminary screening on a fault signal by combining a preset abnormal triggering mechanism; according to the method, dynamic reconstruction of the noise base is achieved through multi-sensor cooperative working condition correlation modeling, the problems of noise time-varying characteristics and spatial isomerism are solved, an accurate reference is provided for subsequent noise suppression, the noise base serves as a physical anchor point for separation, the fault component separation purity is improved, and suspicious fault signals are effectively and preliminarily screened out.

The invention discloses a coal mine tunneling roadway advanced geological exploration and ground stress inversion method, particularly relates to the technical field of seismic signal processing in geophysical exploration, and is used for solving the problems of geological exploration signal distortion and insufficient stress inversion precision caused by strong noise interference of tunneling equipment in the prior art. Rock mass vibration signals are collected through an optical fiber sensor arranged on a roadway wall, equipment noise characteristics are identified through spectral analysis, a quantitative mapping relation between working condition parameters and noise characteristics is established, a noise reference time period is determined according to the quantitative mapping relation, a spatial coherence characteristic template is constructed, high-coherence noise components are inhibited through template comparison, and the noise reference time period is determined according to the spatial coherence characteristic template. Finally, the geological structure and stress field distribution in front of the working face are inverted based on the purified seismic wave signals, a stress concentration area and a disaster risk area are identified, the signal-to-noise ratio of the seismic signals in the strong noise environment and the reliability of the geological inversion result are effectively improved, and accurate technical support is provided for safe mining of a coal mine.

The invention provides an intelligent charging pile management system and method with a remote diagnosis function, and relates to the technical field of power control, and the method comprises the steps: carrying out the one-dimensional variational mode decomposition and multi-dimensional variational mode decomposition of load data and operation state signals of a charging pile, and obtaining a stationary component, a noise component and a state sub-mode signal set; determining load identification characteristics according to the stationary component, the external environment data and the noise component, and performing joint analysis processing on the state sub-mode signal set to obtain fault diagnosis characteristics of the charging pile; carrying out feature fusion on the load identification features and the fault diagnosis features to obtain a modal fusion matrix, and then carrying out joint diagnosis analysis on the modal fusion matrix to obtain a load prediction curve and a fault diagnosis result of the charging pile; according to the method and the device, the charging piles can be subjected to collaborative analysis on the basis of load prediction and fault diagnosis, so that the accuracy of remote scheduling control is improved.

This disclosure provides methods, devices, and systems for audio signal mixing. The present implementations more specifically relate to mixing audio signals from a microphone array by performing fixed beamforming to generate beams, reducing noise on the beams, and mixing the beams to generate a final audio signal for playback. In some aspects, an audio mixing system includes a fixed beamformer to generate beams from audio signals from a microphone array and noise reduction units (NRUs) to reduce a noise component of each audio beam. The system also includes logic to calculate a signal characteristic of each reduced noise audio beam to determine, based on the signal characteristics, the reduced noise audio beams that include a speech component. The logic also generates a gain for each audio beam based on the selection, with the gains used in beam mixing. In some aspects, the NRU includes a neural network noise reduction unit.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention discloses a PET detector signal simulation generation method based on artificial intelligence, and the method comprises the steps: generating an initial two-photon signal pair which meets the physical constraints of initial energy and time difference through a two-photon signal generator which fuses Transform and U-Net; decoupling the mixed noise into a plurality of independent physical noise components according to a PET noise physical priori library by using a multi-branch decoupling network based on an attention mechanism, and outputting a pure signal and a noise component map; performing linkage adjustment on the pure signal and the noise component according to a target parameter set by a user through a space-time-noise cooperative regulator; and finally, a customized signal data format is adaptively output according to the downstream task type. According to the method, high-fidelity, interpretable and adjustable coincidence event-level signal simulation is realized, the core problems of complex modeling, noise distortion, lack of relevance and poor scene adaptability of a traditional method are effectively solved, and the efficiency and precision of PET detector research, development and test are remarkably improved.

The invention relates to the technical field of electro-oculogram signal processing, and discloses a medical-grade electro-oculogram signal noise reduction filtering processing method and system, and the method comprises the steps: obtaining a to-be-processed electro-oculogram signal and a synchronous electroencephalogram signal, and marking a target signal segment; separating an electro-oculogram noise component and an electro-oculogram effective component by an electro-oculogram separation algorithm based on mutual information combination; performing decomposition and weighted fusion on the electro-oculogram noise component by adopting a double-coefficient fusion mode denoising model to generate a preliminary denoising signal; a special high-pass convolution noise reduction model for the EEG channel is used for filtering, and low-frequency noise is suppressed; signals are detected according to medical-grade parameter requirements, and if the signals do not reach the standard, the signals are returned for reprocessing; and checking the channel consistency of the qualified signal, and integrating to form a final signal. The system comprises six units. The method and the system can accurately separate the noise, guarantee the signal quality, solve the problems of inaccurate noise processing and poor signal stability in the prior art, and meet the high-quality requirement of clinical medical treatment on the electro-oculogram signal.

The invention discloses an image defogging method and system based on a physical guide diffusion model, and relates to the technical field of image processing. Comprising the following steps: inputting a to-be-processed fog image into a trained physical perception defogging model to obtain a pseudo clear image (namely a preliminarily estimated fog-free image) and a transmissivity image (representing the attenuation degree of a medium to scene light); inputting the to-be-processed fog image into a pre-trained physical guide diffusion model, and gradually adding Gaussian noise to the clear image through a Markov chain to generate a noise image; the pseudo clear image and the transmissivity image serve as input through condition guidance, and feature splicing is adopted to be injected into a diffusion model so as to guide the denoising direction; then capturing the characteristics of noise distribution by using a diffusion model, predicting and removing noise components in the current time step, and obtaining an intermediate image; and taking the intermediate image corresponding to the final time step as a defogged clear image. According to the method, the accuracy of the denoising direction is emphasized, and the definition and quality of the defogged image can be improved.

The invention provides a method, a device and a system for audio signal mixing. The present implementation more particularly relates to mixing audio signals from a microphone array to generate beams by performing fixed beamforming, reducing noise on the beams, and mixing the beams to generate a final audio signal for playback. In some aspects, an audio mixing system includes a fixed beamformer to generate beams from audio signals from a microphone array and a noise reduction unit (NRU) to reduce noise components of each audio beam. The system also includes logic to compute a signal characteristic of each de-noised audio beam to determine a de-noised audio beam including a speech component based on the signal characteristic. The logic also generates a gain for each audio beam based on the selection, wherein the gains are used in beam mixing. In some aspects, the NRU includes a neural network noise reduction unit.

Disclosed is a method comprising: receiving captured seismic data and generating first velocity and first pressure data therefrom; transforming, from a first data domain to a second data domain, the first velocity and first pressure data and thereby generate second velocity and second pressure data; mapping wavenumber gather data from the second data domain to a third data domain; determining envelope ratio scaling data and threshold value data using the second velocity and second pressure data; generating, threshold envelope data using a thresholding operator associated with the threshold value data; using coefficient data associated with the threshold envelope data to estimate vertical component data; transforming, from the third data domain to the first data domain, the vertical component data to generate temporal-spatial data; and generating, based on the temporal-spatial data, noise component data comprised in the captured seismic data.

The invention discloses a video pulse wave extraction method and system based on automatic noise recognition. The method comprises the steps that a face area is divided into a plurality of sub-areas; calculating the signal-to-noise ratio of each sub-region in the pulse frequency band, and performing weighted fusion on the original pulse signals of each sub-region according to the signal-to-noise ratio; performing ensemble empirical mode decomposition on the fusion signal; according to the peak-to-peak interval variation coefficient, the amplitude stability index and the spectrum purity, determining a noise component dominated by the motion artifact; performing adaptive filtering processing on the fused signal by using an adaptive filter and taking a reference noise signal as input; carrying out distortion detection on the filtered fusion signal, and carrying out signal restoration processing on a detected distortion region to obtain an extracted pulse signal; in order to solve the problem that in the prior art, when video pulse signals are extracted, motion artifacts and physiological signal frequency spectrums are overlapped, and consequently signal separation of a traditional frequency domain filtering method fails, effective signal separation and accurate pulse signal extraction are achieved.

The invention relates to the technical field of transformer substation monitoring, and discloses a partial discharge real-time monitoring system and method for a transformer substation switch cabinet. The method comprises the following steps: capturing an original signal data set which is generated by partial discharge in the switch cabinet and comprises a timestamp sequence and spectrum distribution; processing the set to segment the discharge pulses and background noise components, calculating the rise time, the energy integral value and the frequency bandwidth of each discharge pulse, and forming a set of discharge characteristic parameters; identifying a potential discharge risk mode based on the parameter set, and generating a dynamic configuration instruction containing monitoring priority ranking and parameter adaptive requirements; calling the instruction to modify the sampling interval and the detection sensitivity of the monitoring unit in real time, and executing the optimized signal acquisition process; and integrating and optimizing the real-time data flow in the acquisition process, updating the discharge characteristic parameter set, and iteratively generating a new dynamic configuration instruction. The method can improve the comprehensiveness, accuracy and real-time performance of partial discharge monitoring, and is suitable for the complex operation environment of the switch cabinet.

The invention discloses a sound signal processing method and device, equipment and a storage medium, and belongs to the technical field of audio processing. According to the invention, sound signal noise reduction with noise suppression and target signal reservation is realized. The method comprises the following steps: after acquiring a sound signal collected in a running state of mechanical equipment, firstly performing time-frequency analysis on the sound signal; then, a noise determination threshold is automatically determined based on the logarithmic magnitude spectrum of the sound signal, and noise estimation is performed based on the determined noise determination threshold. According to the scheme, a completely data-driven parameter selection mechanism is realized, and manual parameter or threshold setting is not needed, so that the automation degree is improved, the unreliability of manual parameter or threshold setting is avoided, and the accuracy and robustness of noise estimation are enhanced. In addition, the noise-reduced sound signal does not comprise noise components, so that the accuracy and reliability of subsequent operation state recognition and fault diagnosis of the mechanical equipment are ensured.

The invention discloses an improved SVMD combined SOBI bridge dynamic strain signal adaptive noise reduction method, which comprises the steps of obtaining an original signal of bridge dynamic strain through a strain sensor, improving an SVMD algorithm by taking kurtosis and a decomposition error as stopping criteria, and further decomposing the original signal; classifying the intrinsic mode function components through a K-means clustering algorithm so as to reconstruct a noise component signal and an initial noise reduction signal; constructing a three-channel hybrid observation matrix containing an original signal, a noise component signal and an initial noise reduction signal; performing blind source separation on the hybrid observation matrix by using an SOBI algorithm, and estimating to obtain a separated source signal and a hybrid matrix; noise components are identified based on kurtosis clustering, signals are reconstructed after frequency points are set to be zero, and high-precision noise reduction is achieved. According to the method, a deep collaborative architecture is constructed by introducing an adaptive decomposition mechanism, intelligent clustering recognition and accurate reconstruction are adopted, and efficient suppression of noise and high-fidelity retention of signal features are realized.

The invention relates to a landslide deep deformation monitoring data noise reduction method based on an HBP-VMD combined improvement wavelet threshold. The method comprises the steps of collecting a landslide deep deformation original signal; the sample entropy is used as a fitness function, a badger optimization algorithm is adopted to optimize VMD decomposition parameters, and an optimal combination parameter combination is obtained; substituting the optimal combination parameter into the VMD, and performing VMD decomposition on the original signal to obtain K intrinsic mode components IMF of different frequencies; calculating a variance contribution rate and a correlation coefficient corresponding to each obtained IMF component, and dividing the IMF components into an effective component, a noisy component and a noise component; retaining the obtained effective component, abandoning the noise component, and carrying out noise reduction processing on the noisy component by using an improved wavelet soft threshold; and reconstructing the IMF component after noise reduction and the effective IMF component, and finally realizing signal noise reduction. According to the method, the deformation monitoring signal of the deep part of the landslide can be efficiently stripped from the noisy signal, and the waveform is clearer than that before noise reduction; the SNR of the signal after noise reduction is the highest, the SMES is the lowest, and the excellent noise reduction effect is achieved.

The invention relates to a belt weigher weighing precision compensation method based on multi-sensor data fusion. According to the method, an original weighing signal and an electromagnetic interference signal of the belt weigher in a running state and a baseline noise signal in a no-load state are synchronously acquired, a feature vector is extracted based on the electromagnetic interference signal, and an intermodulation noise component in the weighing signal is predicted by using a nonlinear system identification model in combination with the baseline noise signal. Then, the noise component is subtracted from the original weighing signal through an adaptive cancellation algorithm to obtain a local compensation weighing signal, and finally, a final weighing signal is generated based on a plurality of local compensation weighing signals through a signal-to-noise ratio weighted fusion algorithm, so that intermodulation noise caused by electromagnetic interference generated by the variable frequency driver is effectively suppressed. The weighing precision and the anti-interference capability of the belt weigher are improved.