166 results about "Variational mode decomposition" patented technology

The application discloses a variational mode decomposition high-voltage switchgear signal denoising method and system, which comprises the following steps: firstly, collecting the mechanical signal of the high-voltage switchgear; then, performing self-adaptive global optimization on the key parameters of the variational mode decomposition by using an improved sparrow search algorithm, wherein the improved strategy comprises chaotic disturbance reverse learning initialization, dynamic step control and Levy flight mechanism; then, performing variational mode decomposition on the signal by using the optimized parameters to obtain a series of intrinsic mode function components; then, screening the effective components by calculating the Pearson correlation coefficients of the components and the original signal; finally, reconstructing the effective components to obtain the denoised signal. The application overcomes the problems of blind parameter setting, mode aliasing and serious noise residue in the traditional method, and can adaptively realize high-precision signal denoising, significantly improves the signal-to-noise ratio and waveform fidelity, effectively retains the fault characteristics, and provides a reliable data basis for accurate fault diagnosis of the high-voltage switchgear.

The present invention discloses a method and system for blur removal and jitter correction in visual monitoring of super high-rise buildings, comprising the following steps: a visual sensor acquires a video stream at a fixed frame rate, and locates an initial region of interest (ROI) by mapping prior information; the spatial gradient field is calculated for the ROI image of the current frame, and a Gaussian-weighted two-dimensional gradient structure tensor matrix is ​​constructed; for the clear image sequence that passes blur detection, variational mode decomposition is then used in the time domain to decouple the displacement time series into eigenmode functions of different frequencies, and the low-frequency components are reconstructed to preserve the true deformation of the building; the reconstructed low-frequency displacement signal is output as the final monitoring result. The present invention employs a lightweight algorithm design throughout, significantly reducing the matrix operation dimension of subsequent processing through a dynamic ROI clipping mechanism; the structural tensor eigenvalues ​​are solved using a direct algebraic analytical method, avoiding complex matrix iterative decomposition.

The present application belongs to the technical field of glass product production management and data processing, and relates to a glass product production management method and system. The method comprises: collecting glass surface ripple signals and process parameters of a production line in real time; calculating a local curvature sequence and a local curvature entropy reflecting the sharpness of the micro-waveform of the signals, and adaptively determining a dynamic bandwidth adjustment factor of a variational mode decomposition algorithm based on the local curvature entropy; using the dynamic bandwidth adjustment factor to perform mode decomposition on the glass surface ripple signals, and extracting a characteristic component matching the frequency of a roller fault; calculating a physical real wear index after eliminating the influence of the speed according to the characteristic component and the glass drawing speed, and making a device maintenance decision based on the index. The present application can accurately identify device defects under complex working conditions by adaptively adjusting algorithm parameters and decoupling the influence of speed, thereby improving the accuracy of maintenance decisions.

The application discloses a motion electrocardio processing method and system, and relates to the technical field of electrocardio signal processing. The motion electrocardio processing method provided by the application comprises the following steps: acquiring electrocardio signals under different motion states; performing empirical mode decomposition on the motion electrocardio to decompose the electrocardio signals into a plurality of intrinsic mode functions, and performing high-pass filtering operation on the low-frequency intrinsic mode functions to suppress low-frequency noise in the motion electrocardio; then, performing variational mode decomposition on the remaining components to more accurately extract variational mode functions of different frequencies, and performing center frequency judgment on each variational mode function; for the mode functions smaller than the center frequency, non-local mean noise reduction is adopted; for the mode functions greater than or equal to the center frequency, singular value decomposition is adopted to reduce noise and realize baseline interference suppression; finally, the mode functions after noise reduction are reconstructed, the wavelet threshold method is adopted to reduce noise for the electromyographic interference noise in the motion electrocardio, so that a clean motion electrocardio signal waveform after noise interference suppression is obtained, and subsequent further analysis is facilitated.

This invention discloses a capacity optimization configuration method for a photovoltaic-energy storage system, belonging to the field of photovoltaic power generation. The method includes: acquiring the time series of photovoltaic output and load demand, aligning the timestamps, and subtracting to obtain the net load vector; using a variational mode decomposition model to decouple and obtain multiple intrinsic mode function components; calculating the sample entropy value of each component, and using the K-means algorithm to cluster and divide it into low-entropy and high-entropy subsets, respectively reconstructing energy-type and power-type net load demand matrices; constructing a comprehensive cost model with photovoltaic installation capacity, conventional energy storage, and transient buffer unit configuration capacity as variables; setting dual-track hard isolation constraints so that the charging and discharging variables of conventional energy storage and buffer units are only limited by the energy-type and power-type matrices, respectively; and finally, finding the optimal solution to output the configuration array. This invention effectively reduces the interference of mixed signals on the algorithm and alleviates battery life degradation.

This application discloses a digital twin control method for facility poultry farming environments based on deep spatiotemporal data fusion, relating to the field of poultry farming technology. It proposes a spatiotemporal sequence prediction model for environmental parameters using a bidirectional long short-term memory network (VMD-Attention-BiLSTM) that integrates variational mode decomposition and attention mechanisms, effectively overcoming the system's large time lag. A digital twin decision engine based on nonlinear model predictive control is constructed, and the optimal control sequence for energy consumption and environmental quality is solved under multiple constraints by establishing a joint state equation for thermodynamics and gas diffusion. Specifically, for complex winter conditions, a ventilation-heating collaborative game strategy based on real-time heat loss compensation is designed. Adaptive sliding mode control with radial basis function neural network compensation is introduced into the underlying execution unit, significantly enhancing the system's anti-disturbance capability. This achieves precise environmental control under complex conditions, improving poultry welfare and energy efficiency.

The present application provides a kind of vital signs detection signal denoising method and device, it is related to physiological perception and signal processing technical field, including: based on millimeter wave radar's Doppler technique, obtains the original phase signal containing physiological signal;Original phase signal is carried out variational mode decomposition based on the search algorithm of variational mode decomposition super parameter optimization algorithm optimized by particle swarm optimization algorithm, obtain the vibration modal function information corresponding to the original phase signal;Wherein, the particle swarm optimization algorithm uses permutation entropy and fuzzy entropy as fitness function;After denoising processing is carried out to the vibration modal function, the vibration modal function after denoising processing is recombined, and high-precision denoising processing is obtained after physiological signal;From the high-precision denoising processing physiological signal, extract respiratory and heartbeat physiological signal.

This application discloses an integrated diagnostic method, apparatus, and device for early wear of rollers in a floating stack control rod drive mechanism. It addresses the problems of low accuracy and poor generalization ability in early wear diagnosis caused by weak fault features, scarcity of real hot-state samples, and feature redundancy in existing technologies. First, this application uses variational mode decomposition guided by natural frequencies and Hilbert demodulation to effectively separate and enhance weak modulation features related to wear from the vibration signal. Then, it calculates the multivariate initial features of the envelope spectrum and uses its Pearson correlation coefficient with the roller stroke to screen out strongly correlated degradation features, thereby improving feature interpretability. Finally, based on the screened features, it uses a Bagging or Stacking ensemble learning framework to fuse the decisions of multiple heterogeneous individual diagnostic models, constructing a high-precision, highly robust ensemble diagnostic model.

This invention relates to a method and system for short-term load forecasting across seasons in the electricity spot market. The method first collects electricity load, meteorological, and temporal characteristic data to construct a multi-dimensional input feature set, which is then divided into a training set and a validation set. Next, variational mode decomposition is used to decompose the original load data into three modal components, corresponding to three types of input feature sets. Then, with the goal of maximizing the validation set determination coefficient, seasonally differentiated hyperparameter optimization is performed using a particle swarm optimization algorithm. Temporal dependency prediction sub-models and multi-feature association prediction sub-models are constructed based on the optimized Long Short-Term Memory Network and Random Forest, respectively. The three types of input feature sets are then input into their respective sub-models, outputting two types of prediction results. Finally, the two types of results are weighted and integrated to obtain the short-term load forecast result. Compared with existing technologies, this invention has advantages such as significantly improved prediction accuracy.

This invention discloses a method for separating vital signs based on millimeter-wave radar signal enhancement, comprising the following steps: acquiring the original radar signal of the target being monitored by millimeter-wave radar and preprocessing it; extracting six types of features from the preprocessed signal and classifying the preprocessed signal into four scene categories based on the six types of features; according to the classified scene categories, calling the corresponding signal processing model to enhance or reconstruct the preprocessed signal to obtain the enhanced radar signal; performing signal quality assessment on the enhanced radar signal, and inputting the enhanced signal that meets the quality assessment requirements into a variational mode decomposition module to separate the respiratory and heartbeat signals of the target being monitored. This invention improves the accuracy of vital sign detection in complex environments by classifying millimeter-wave radar vital sign signals in complex scenes, adaptively calling the model to enhance the signal, and using variational mode decomposition to separate respiration and heartbeat.

The present application belongs to the technical field of hydrological prediction and water resources management, and specifically relates to a prediction method and system based on multi-scale decomposition of runoff sequence and dynamic weight reconstruction. The method first constructs a physical hydrological model based on meteorological driving data and generates a runoff simulation sequence; the simulation sequence is subjected to multi-scale decomposition by using variational mode decomposition, the decomposition parameters are adaptively determined by particle swarm optimization, and a plurality of mode components are obtained; a long short-term memory network is constructed to establish a mapping relationship between the contribution weights of the mode components, and dynamic weights varying with time and normalized are output; the mode components are weighted and reconstructed according to the weights, so as to realize deviation correction of the simulated runoff of the physical hydrological model on different time scale structures. In the prediction stage, the same decomposition is performed on the future runoff simulation sequence, and the trained model is directly used to output the runoff prediction result. The present application converts the runoff prediction problem into a dynamic weight distribution problem of multi-scale structure components, improves the prediction precision and migration ability while maintaining physical interpretability, and is suitable for scenarios such as basin runoff prediction, flood simulation, water resources scheduling and the like.

The application discloses a mud pump accessory fault intelligent prediction method and system, belonging to the technical field of fault diagnosis and prediction. The method comprises the following steps: constructing a multi-dimensional heterogeneous perception array unit to synchronously collect multi-dimensional physical state parameters; performing adaptive time-frequency feature enhancement processing, enhancing weak fault features through variational mode decomposition, envelope spectrum entropy and correlation coefficient double screening, and Hilbert transform; establishing a multi-physical field coupling mechanism mapping, constructing a hydraulics mechanism based on the Bingham model, calculating the pressure residual and flow residual of the measured value and the theoretical value, and fusing the enhanced time-frequency features to construct a physical consistency feature vector; using a long short-term memory network with an integrated attention mechanism to identify the evolution trend; implementing variable working condition migration adaptation and dynamic compensation; and outputting fault diagnosis and life assessment results. Through deep fusion of physical mechanism and data driving, the application effectively suppresses strong background noise interference, and improves the prediction accuracy and generalization ability under variable working conditions.

This invention discloses a partial discharge (PD) signal denoising method based on image information entropy and multivariate variational mode decomposition (MMD). The method involves converting the noisy signal into a grayscale image, calculating the image information entropy, and optimizing the number of modes K in the MMD by combining Pearson correlation coefficient and execution efficiency to determine the optimal value. The noisy PD signal is then decomposed. The kurtosis value of each intrinsic mode component is calculated, and the nature of the mode component is determined based on a threshold, classifying it as either a dominant PD component or a noise component. A mathematical statistical method using the 3σ criterion is employed to filter out normally distributed white noise. The reconstructed signal is then denoised using an improved wavelet thresholding method to obtain the denoised PD signal. This denoising method accurately reduces noise in noisy PD signals, achieving good noise suppression and restoring the waveform characteristics of the PD signal while maintaining high execution efficiency.

This invention relates to the field of photovoltaic data prediction technology, specifically disclosing an integrated platform and data processing method for photovoltaic power plant energy management and power prediction. By acquiring the discrete-time series of photovoltaic power output from the platform and the platform's low-frequency sampling period, while simultaneously extracting the high-frequency disturbance period from the inverter control logic, the theoretical aliasing center frequency is calculated. The system can accurately determine the anchoring point of aliasing artifacts in the spectrum. Subsequently, by constructing a variational mode decomposition model, the power sequence is decomposed into multi-mode functions. Based on the center frequency matching, candidate aliasing artifact data with the smallest difference is extracted, and their permutation entropy is calculated. By subtracting these artifact data and analyzing the Pearson correlation of the remaining signal, the model dynamically adjusts to find the true signal, ensuring that only effective power change data is extracted, and ultimately outputting high-precision photovoltaic power prediction, improving the safety and economy of grid dispatch.

The embodiment of the specification discloses a kind of electric gate valve fault diagnosis method, device and equipment based on strong noise background, belong to the field of fault diagnosis, method includes: obtaining the acceleration signal of electric gate valve under strong noise background;First noise in electric gate valve acceleration signal is determined using variational mode decomposition model optimized based on satin blue gardener bird optimization algorithm;Remove first noise, obtain first reorganization acceleration signal;Second noise is removed, and second reorganization acceleration signal is obtained;Wherein, the frequency band where second noise is located is less than the frequency band where first noise is located;Based on second reorganization acceleration signal, the electric gate valve is fault diagnosed.This embodiment can accurately determine the first noise in electric gate valve acceleration signal using variational mode decomposition model optimized based on satin blue gardener bird optimization algorithm, and then most of noise can be removed from electric gate valve acceleration signal, improve the accuracy of fault diagnosis to electric gate valve.

This invention proposes a method for multi-target recognition, tracking, and vibration extraction of bridge cable-stayed bridge videos based on unmanned aerial vehicles (UAVs). The method includes: Step 1: Constructing a refined tilted and slender target detection model for bridge cable-stayed bridges based on the YOLOv11 model; Step 2: Proposing a multi-target tracking algorithm that integrates the tilted and slender target detection model and the StrongSORT algorithm; Step 3: Improving the displacement extraction method by combining SIFT / ORB feature point matching and sub-pixel refinement techniques; Step 4: Designing a UAV motion correction algorithm based on variational mode decomposition and time-frequency domain joint screening; Step 5: Constructing a joint working mode analysis algorithm that combines natural excitation technology and random subspace recognition algorithm. This method achieves high-precision extraction of cable-stayed bridge vibration signals and identification of cable-stayed bridge modal parameters, providing technical support for health monitoring of long-span cable-stayed bridges.

The application discloses a kind of hybrid energy storage microgrid capacity configuration methods based on double-layer optimization decomposition, comprising the following steps: based on annual wind light load data extraction typical day, construct the double-layer optimization model of capacity configuration with minimum as target full life cycle cost;Inner layer uses intelligent hybrid algorithm to optimize variational mode decomposition parameter, and the power shortage signal is decomposed, and the adaptive distribution of high and low frequency power is realized by energy and frequency joint clustering, respectively by super capacitor and battery bear;Outer layer is based on the allocation scheme, and the capacity of wind light storage is globally optimized by using optimization solver, and finally the optimal configuration scheme of economy that meets operation constraint is output.The application realizes the full life cycle economic optimal configuration of microgrid under the condition of meeting reliable operation constraint by constructing the capacity configuration optimization model of inner and outer layer coupling, combining signal decomposition parameter adaptive optimization algorithm and objectively distributing hybrid energy storage output according to energy and frequency characteristics.

The application provides a farm load prediction method, device and equipment based on multi-source data, and relates to the field of power load prediction.The method comprises the following steps: obtaining historical load data, breeding animal growth data, environmental data and equipment data of a farm; decomposing the historical load data according to a variational mode decomposition algorithm to obtain a plurality of load components; wherein the variational mode decomposition algorithm optimizes parameters through a sparrow search algorithm, and the sparrow search algorithm establishes a fitness function about the load components according to the breeding animal growth data; after the plurality of load components, the breeding animal growth data, the environmental data and the equipment data are spliced, the spliced data is input into a preset Transformer model to obtain a power load prediction curve of the farm.The application can improve the accuracy and availability of the power load prediction result of the farm.

This invention relates to the field of mixed-flow turbine technology, specifically to a method and system for identifying risk areas during wide-load operation of mixed-flow turbines. The method includes: acquiring unit operating status parameters and pressure pulsation signals from the draft tube and bladeless zone; decomposing the signals based on variational mode decomposition, filtering vortex band components within a preset frequency band, and calculating the effective amplitude value; obtaining the pressure wave velocity by querying the wave velocity characteristic curve using the guide vane opening, calculating the pressure wave wavelength and geometric position difference, and obtaining the phase resonance risk coefficient; calculating the draft tube vortex band intensity index based on the operating head and the effective amplitude value; comparing the phase resonance risk coefficient and vortex band intensity index with preset thresholds to determine the risk area type of the current operating condition. This invention eliminates the interference of head on amplitude determination through dimensionless processing and corrects the pressure wave velocity, achieving accurate identification of risk areas under wide-load, full-condition operation of mixed-flow turbines.

The present application belongs to the technical field of fault diagnosis, and particularly relates to a belt conveyor bearing fault diagnosis method and system based on temperature and vibration signal analysis. The method comprises the following steps: removing outliers and correcting baseline drift of the collected temperature and vibration signals respectively; using kurtosis and envelope entropy to optimize variational mode decomposition to extract fault components and generate time-frequency diagrams, and using temperature change rate to map weighting coefficients for reconstruction and high-frequency gain compensation; splicing the weighted time-frequency diagram and the two-dimensional temperature diagram into a three-dimensional tensor, inputting the residual network of the band coordinate attention and the deformable convolution branch, and using the temperature energy proportion to adjust the joint loss output to output the diagnosis result. The present application effectively enhances the multi-modal feature expression capability of the model under complex variable working conditions by establishing the physical correlation between temperature change and high-frequency vibration, and significantly improves the diagnosis accuracy and generalization performance.

This invention discloses a method and system for monitoring equipment status based on vibration signals, relating to the field of mechanical equipment life prediction technology. The invention simultaneously collects vibration and temperature signals; employs adaptive variational mode decomposition to extract degradation-sensitive features; estimates instantaneous rotational speed based on the vibration signal itself, converting it into angular domain signals through order tracking; constructs an overcomplete dictionary for sparse decomposition, automatically determining the first prediction time based on the sparse energy ratio; fuses multimodal features to construct normalized health indicators; uses a temporal fusion prediction network to predict remaining life and generate graded maintenance recommendations; and continuously optimizes the model through incremental learning. This invention, through the synergy of adaptive decomposition, rotational speed-free order tracking, angular domain sparse decomposition, and lightweight networks, achieves accurate remaining life prediction without the need for speed sensors and a large number of fault samples, significantly improving early fault warning capabilities and the reliability of predictive maintenance decisions under varying load conditions.

This invention provides a method, apparatus, device, and medium for predicting joint angles. It relates to the field of electromyography (EMG) data processing technology. The method includes: acquiring surface EMG signals of a target lower limb; introducing an adaptive adjustment coefficient positively correlated with the number of iterations into the original particle swarm optimization (PSO) algorithm to adaptively adjust the update step size of the particle positions representing the parameter combination (K, α), so that the particle search range transitions from global exploration to local development as the iteration progresses, thus obtaining an improved PSO algorithm; optimizing the parameter combination (K, α) of a variational mode decomposition (VMD) algorithm using the improved PSO algorithm to obtain the optimal parameter combination (K, α); decomposing the surface EMG signals of the target lower limb into K intrinsic mode components using the VMD algorithm with the optimal parameter combination (K, α); extracting features from each of the K intrinsic mode components, and obtaining a joint angle prediction result based on the feature extraction results.

The application provides a full-user voltage overrun automatic identification method, system, device and medium, fuses multiple power grid data and generates a typical scene library through clustering, solves the problem of incomplete scene coverage under high proportion of new energy, uses expert rules and particle swarm optimization variational mode decomposition-Stacking integrated learning double-path identification to greatly improve efficiency, and uses a combination weighting method to construct a voltage overrun sensitivity model and quantify the risk level, realizes fine diagnosis of user overrun, and completely overcomes the defects of incomplete scene coverage, low identification efficiency and poor pertinence of traditional artificial experience methods; the method comprises the following steps: acquiring multiple power grid data and preprocessing; clustering to generate a typical voltage overrun scene library; extracting key factors and weighting to construct a sensitivity model; double identification through expert rules and PSO-VMD+Stacking integrated learning; calculating a risk value and outputting a report.

This invention discloses a machine learning-based method and system for predicting groundwater level changes, relating to the field of hydrogeology. The method includes: standardizing multi-source time-series data to obtain a standardized multivariate time-series data matrix; constructing a supervised learning sample set; inputting the groundwater level sequence from the supervised learning sample set into a physically-guided variational mode decomposition network; decomposing the groundwater level sequence into K intrinsic mode component sequences and a residual term sequence using a loss function with physical-driven consistency constraints; for each of the K intrinsic mode component sequences, dynamically assembling a differentiable simulator from a library of differentiable simplified physical simulators, and co-training these simulators with the goal of approximating each intrinsic mode component sequence and reconstructing the original water level sequence as a whole, resulting in K fully trained assembled differentiable simulators. This invention generates reliable, visualized prediction results through multi-simulator collaborative extrapolation and uncertainty quantification.

The application discloses a centrifugal pump off-design condition diagnosis method and system based on a vibration signal, and the method comprises the following steps: collecting vibration signals of a centrifugal pump under multiple flow conditions, and constructing a vibration sample sequence with a working condition label; reconstructing the vibration signal by using a variational mode decomposition (VMD); performing a continuous wavelet transform (CWT) on the reconstructed signal, and mapping a one-dimensional vibration signal into a two-dimensional time-frequency graph; constructing a Light-CBAM-CNN diagnosis model, and training the same; inputting the vibration signal collected in real time into the trained diagnosis model after pretreatment, VMD and CWT, outputting a working condition category and a diagnosis confidence, and outputting an "uncertain" label and triggering a retest mechanism or an alarm when the confidence is lower than a threshold value. The application can realize online diagnosis of high-energy consumption off-design conditions of the centrifugal pump only by relying on the shell vibration signal, and has the advantages of small modification workload, low deployment cost, high identification reliability and strong energy-saving scheduling guiding significance.