900 results about "Modal decomposition" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention discloses an insurance intelligent decision engine system based on a multi-modal user portrait, and relates to the technical field of insurance industry, the system comprises a semantic representation space construction unit used for performing feature decoupling on collected multi-modal data by using variational modal decomposition, extracting modal features, and constructing a semantic representation space based on each modal feature; a user portrait generation unit; a user classification result acquisition unit; and the insurance decision-making unit is used for analyzing the multi-source health data in the user portrait by using a cross-modal alignment technology, constructing a layered health risk assessment model, and generating an insurance recommendation strategy and a risk avoidance scheme in combination with a user classification result. According to the method, resource waste and efficiency loss caused by scattered storage and repeated development of data are avoided through multi-modal data fusion and construction of a unified semantic representation space; and in combination with a hierarchical label system, the user portrait can be automatically generated, and the intelligent level of insurance business is enhanced.

The invention discloses a harbor shore power system oscillation damping suppression method based on heuristic probability optimization. According to the method, sliding window preprocessing is carried out on an alternating current bus side current time domain oscillation signal, modal decomposition is carried out based on a Prony algorithm, and modal energy is calculated to judge an oscillation mode; the discriminated oscillation modes are aggregated and mapped into equivalent circuit elements, a corresponding three-order circuit state matrix is constructed, and the damping ratio of each mode is determined according to the characteristic value of the matrix; taking a damping ratio as an evaluation index, introducing an influence factor to construct a multi-objective optimization function, parameterizing the parallel adjustable impedance, and limiting an adjustable range; and searching the control parameters by adopting a heuristic probability optimization strategy to obtain final damping control parameters, and downloading the final damping control parameters to the parallel adjustable impedance device to realize online dynamic adjustment. According to the method, multi-modal identification, physical magnitude mapping and online adaptive optimization can be realized, and the suppression capability and the operation stability of the shore power system on sub / super synchronous oscillation are improved.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses a primary equipment state monitoring method and system in a new energy power system, and the method comprises the following steps: deploying a multi-mode sensor array on primary equipment, and synchronously collecting a voltage signal, a current signal, a temperature signal, a vibration signal and an environment parameter; inputting the collected data into an edge computing node for preprocessing to obtain a multi-modal signal sequence; variational mode decomposition is carried out to form a multi-dimensional feature vector; inputting a long-short-term memory neural network model containing an attention mechanism, performing training and reasoning by using an AdamW optimizer and a cosine annealing learning rate strategy, and outputting the health degree of equipment; determining the weight of each monitoring index based on an analytic hierarchy process, and dividing the equipment into a plurality of state grades; the fault probability is obtained through fuzzy Petri net reasoning, and a corresponding early warning mechanism is triggered according to a preset threshold value. According to the invention, through multi-dimensional data fusion and intelligent analysis, the accuracy and real-time performance of state monitoring are significantly improved.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

The invention discloses a multi-source observation data dam automatic monitoring and early warning method fused with deep learning, and relates to the technical field of hydraulic engineering, a grey wolf optimization algorithm is used to optimize variational mode decomposition parameters, and an envelope entropy is used as a fitness function to obtain an optimal parameter combination; introducing a multi-scale permutation entropy as a standard for screening signals, determining an effective modal component, and reconstructing the effective modal component to obtain an effective signal; and according to the optimal parameter combination and the effective signal, fusing a deep learning algorithm long-short term neural network to carry out multi-source observation data dam deformation feature learning, taking a mean absolute error and a root-mean-square error as evaluation indexes of GWO-VMD-LSTM prediction accuracy, introducing a decision coefficient to judge the performance of a prediction model, and carrying out multi-source observation data dam deformation feature prediction. And accurate prediction of dam deformation displacement is realized. The prediction result has higher accuracy and precision, and a reliable basis is provided for automatic monitoring and early warning of the dam.

The invention belongs to the technical field of radar signal processing, and discloses a sea surface small target detection method based on optimized characteristic mode decomposition, which comprises the following steps: S1, acquiring to-be-detected signal data; s2, decomposing an original signal into a plurality of modal components by using FMD, and selecting an envelope spectrum entropy as a fitness function; s3, performing global optimization on the fitness function in the FMD by using an SOS algorithm; s4, introducing a PSO algorithm to carry out local optimization on key parameters of the FMD; s5, components with low envelope spectrum entropy values and correlation coefficients larger than a threshold value are reserved; s6, extracting an envelope spectrum entropy and frequency band energy ratio feature from the screened modal components, introducing a Gini coefficient as a weighting factor, and constructing a GSEBE joint feature; and S7, inputting the entropy value of the envelope spectrum into a DELM classifier with a controllable false alarm, and realizing target detection based on comparison between a predicted value and a judgment threshold. According to the invention, the capability of distinguishing sea clutters and target echoes is enhanced, and more accurate classification detection is realized.

The invention discloses a micro-seismic signal arrival time pickup method based on fuzzy clustering and variational mode decomposition. The method comprises the following steps: firstly, calculating characteristic functions of attribute characteristics such as a micro-seismic signal mean value, power and kurtosis and normalizing the characteristic functions to obtain a characteristic matrix, then primarily picking up a micro-seismic P-wave initial movement position and a first arrival moment through a fuzzy clustering algorithm, and extracting an effective time window by taking the first arrival moment as a reference point; a variational mode decomposition algorithm is utilized to decompose signals in a time window into K intrinsic mode function components, AIC function values of the components are calculated by means of an akaike information criterion algorithm, a minimum value point is picked up to serve as first arrival time, energy ratios of all the components are calculated, and final micro-seismic P-wave first arrival time is obtained through weighted calculation. The method effectively deals with the low signal-to-noise ratio environment of the underground coal mine, has higher pickup precision and reliability compared with a traditional pickup method, can provide accurate micro-seismic occurrence time and position information for mine safety early warning, and powerfully guarantees the safety production of the coal mine.

The invention discloses a traveling wave head fault positioning method and system based on modal decomposition and a neural network, and the method comprises the steps: obtaining a first traveling wave signal on a power transmission line, and extracting a waveform feature corresponding to the first traveling wave signal; inputting the waveform characteristics into a preset parameter optimization model, determining a corresponding optimal decomposition parameter, performing multi-scale variational mode decomposition on the first traveling wave signal by using the optimal decomposition parameter to obtain a plurality of initial modes, and determining a corresponding key mode based on each initial mode; inputting the key mode into a preset distortion elimination model, determining a distortion result of each sampling point in the key mode, and filtering a distortion section of the first traveling wave signal based on the distortion result and the key mode to obtain a second traveling wave signal; and determining a traveling wave head abrupt change point based on the second traveling wave signal, and determining a corresponding fault position based on the traveling wave head abrupt change point. According to the invention, the accuracy of traveling wave head fault positioning can be improved.

The invention relates to the technical field of power quality monitoring, and discloses a method for identifying power quality disturbance characteristics of a distributed photovoltaic interference power supply, which comprises the following steps: S1, data acquisition and preprocessing: acquiring a power quality signal of a photovoltaic system and carrying out denoising, normalization and time alignment; and S2, carrying out disturbance signal adaptive decomposition based on improved variational mode decomposition, and automatically selecting a mode number by utilizing information geometric optimization. An improved variational mode decomposition method is adopted, information geometric optimization and Bayesian optimization are combined, self-adaptive mode decomposition of disturbance signals is achieved, the optimal mode number and penalty factors can be automatically determined through improved variational mode decomposition, decomposition errors caused by manual parameter setting in traditional variational mode decomposition are avoided, and the method is suitable for large-scale popularization and application. Compared with a fixed parameter variational mode decomposition method in the prior art, the method has the advantages that the analysis capability of complex disturbance signals is improved, and the disturbance mode in the photovoltaic system is accurately decomposed.

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 belongs to the technical field of deep learning, and particularly relates to a multi-modal gait recognition method based on SMPL modal decomposition and embedding fusion, and the method comprises the following steps: constructing a multi-modal gait recognition model DFGait fusing an SMPL model and a contour; providing an adaptive frame joint attention module, and adaptively extracting important joint information of a gait sequence key frame in an SMPL posture branch; a modal embedding fusion module is provided, and efficient fusion of two kinds of modal information is achieved by aligning and fusing SMPL model features and contour features in a unified semantic space; and jointly supervising the training of the DFGait model by using a joint loss function combining triple loss, cross entropy loss and modal consistency loss. According to the method, the SMPL human body model is decomposed, and gait information of dynamic postures and static shapes contained in the SMPL model is fully extracted. Branch optimization is carried out through an adaptive frame joint attention module, and finer spatial-temporal feature extraction of gait information is realized.

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention belongs to the technical field of vibration analysis and testing of bearings, and particularly relates to a cam driven bearing wear degree monitoring method and system, and the method comprises the steps: carrying out the equal-angle resampling processing of a vibration signal through a rotating speed signal, decomposing an obtained angular domain vibration signal into a plurality of mode components through a variational mode decomposition algorithm, and carrying out the measurement of the vibration signal; according to the kurtosis value of each modal component and the correlation coefficient of each modal component and the original vibration signal, evaluating the impact saliency weight of each modal component, and performing weighted summation on the energy of each modal component to obtain comprehensive impact energy; calculating to obtain a speed decoupling wear index without the influence of the rotating speed by utilizing the comprehensive impact energy and the vibration energy calculated by the physical mapping model; and the speed decoupling wear index is compared with a preset self-adaptive alarm threshold value, and the wear state of the cam driven bearing is judged according to a comparison result. According to the invention, the problems of false alarm and missing alarm under the variable-speed working condition are solved.

The invention relates to a short-term load prediction method based on a VMD-NRBO optimization neural network fusion method, belongs to the field of power systems, and aims to solve the problem of insufficient load prediction accuracy of the power systems. The method comprises the following steps: firstly, decomposing historical load data by using variational mode decomposition (VMD), effectively removing high-frequency noise and redundant information, and retaining key signal components; and then, inputting the decomposed modal components into a Transform encoder-bidirectional long short-term memory network BiLSTM decoder, fusing the modal components with a neural network model for training, and optimizing hyper-parameters of the neural network model through a Newton-Raphson optimization algorithm NRBO (Newton-Raphson Optimization). And finally, using the trained neural network model to predict future load data, and carrying out reverse normalization processing on a prediction result to obtain a final prediction value.

The invention discloses a distribution network fault positioning method, system and device based on variational mode decomposition and a medium, and relates to the technical field of power system fault positioning, and the method comprises the steps: collecting a traveling wave signal of a power distribution network, and carrying out the preprocessing of the collected traveling wave signal; performing modal decomposition on the preprocessed traveling wave signal, and outputting a plurality of modal components; based on the mode component obtained through decomposition, wave head arrival time is extracted; performing correction compensation based on the wave head arrival time difference, the phase difference and the propagation characteristics, and calculating the relative distance between the fault point and the known node; calculating the position coordinates of the fault point in the distribution network according to the geographic coordinates and the direction vectors of the nodes in combination with the relative distance; and constructing a visual interface, displaying the position of a fault point, and storing the traveling wave signal data and a fault positioning result into a database. According to the method, the adaptive correction function and the time difference compensation model are constructed, so that time difference errors caused by factors such as frequency change and phase nonlinearity can be corrected, and the accuracy of fault positioning is improved.

The invention provides a prediction method for a sudden change type signal of gas dissolved in oil. The prediction method comprises the following steps: firstly, acquiring a time sequence signal of transformer gas monitoring; processing the time sequence signal according to a variational mode decomposition algorithm to obtain a plurality of mode components; according to the variational mode decomposition algorithm, the sum of confusion entropies of all mode components is used as an objective function of parameter optimization; and finally, according to a pre-established prediction model, performing prediction and superposition reconstruction on each modal component to obtain a prediction result of the abrupt change type signal of the gas dissolved in oil. According to the method, the VMD decomposition architecture optimized by the frost ice algorithm is introduced, the non-stationarity of the signal is quantified and obviously reduced through the chaos entropy index, higher prediction precision and robustness of the gas signal in the mutation type oil are realized, and the method has obvious innovativeness and superiority in the field of transformer fault trend prediction.

The invention relates to the technical field of machine learning, and discloses a load prediction method based on dual feature processing and error correction. The method comprises the following steps: acquiring multi-source time sequence data; decomposing the historical load data into a plurality of modal components by adopting a variational modal decomposition algorithm; classifying each modal component into different frequency levels according to the size of the sample entropy; performing phase-space reconstruction according to the modal component of each frequency level and the corresponding external influence factor data, and generating a multivariable phase-space data set of each frequency level; respectively inputting the multivariable phase space data set of each frequency level into the corresponding load prediction sub-model, generating prediction output results, and superposing the prediction output results; constructing a residual sequence based on the historical load data and the initial load prediction result; inputting the residual error sequence into a residual error prediction model to obtain a load residual error prediction value; and compensating the initial load prediction result through the load residual prediction value. According to the scheme, the load prediction accuracy can be improved.

The invention discloses a rainstorm torrential flood disaster-causing element prediction method and system based on modal decomposition and deep learning, and solves the problems that a traditional model is insufficient in non-linear time sequence feature capture, and a physical model depends on complex data and is weak in generalization ability. Comprising the steps of collecting flow, flow velocity and water level data of an upstream site as input, and taking downstream disaster point data as output; preprocessing the data; a frost ice optimization algorithm is adopted to optimize variational mode decomposition parameters; a Fourier transform high and low frequency feature enhanced attention network is constructed, low-frequency and high-frequency components are divided, trend features are extracted through a fluctuation enhancement module, dynamic changes are captured through a multi-path difference calculation unit, self-perception attention is introduced to achieve feature weighted fusion and long-term memory, and a downstream hydrological state prediction result is output. By optimizing a modal decomposition and deep learning cooperation mechanism, the precision, robustness and generalization ability of sudden mountain torrent prediction are significantly improved, and the method is suitable for disaster emergency management in complex scenes of small and medium watersheds.

A lithium battery life prediction method based on modal decomposition and Informer-LSTM belongs to the field of battery life prediction, and adopts a CEEMDAN method to decompose a battery capacity data sequence into a plurality of intrinsic modal components serving as high-frequency components and a residual component serving as a low-frequency component, so as to reduce the influence caused by the irregular recovery phenomenon of the battery capacity; inputting the low-frequency component into the LSTM network model to obtain a low-frequency component prediction result; a high-frequency component is secondarily decomposed into a trend term and a residual term by adopting a Dlinear method, and the fluctuation degree of the high-frequency component is reduced while key information is reserved, so that the prediction stability is improved. Respectively inputting a trend term and a residual term decomposed by each high-frequency component into an Informer network model for prediction, then superposing results, extracting implicit features in the complex fluctuation data, and obtaining a prediction result of the high-frequency component; and carrying out weighted fusion on the low-frequency component prediction result and the prediction results of the plurality of high-frequency components to obtain a final prediction result.

The invention discloses a multipath interference suppression method and device during in-pipe detection and a storage medium, and belongs to the technical field of radar signal processing. According to the method, a CFAR detection and clustering algorithm combined method is adopted, and the target number and the center frequency are automatically extracted from a radar amplitude-frequency signal; an original noisy signal is decomposed into a target number of modal components by using an improved variational modal model, and an improved power reference analysis method is proposed to divide the modal components into effective modals and ineffective modals; sVD noise reduction is carried out on the effective mode; and performing signal reconstruction on the plurality of modal components after SVD noise reduction to obtain a signal after multipath interference suppression. According to the method, the problem that the decomposition number and the center frequency of the variational mode decomposition algorithm are difficult to determine is effectively solved, the influence of inaccurate parameters is reduced, multipath interference suppression during complex environment detection is facilitated, and the effectiveness of the algorithm is verified through actual measurement.

The invention provides an axial flow pump cavitation stage identification method based on high-speed photography and vibration signal double-flow convolution fusion. The method comprises the following steps: synchronously acquiring vibration acceleration signals on an inlet and outlet pipeline in three stage states of non-cavitation, cavitation inception and serious cavitation and a cavitation image in a high-speed use shooting pump stage; performing feature extraction of fine composite multi-scale dispersion entropy improved by a granulation strategy of variable mode decomposition and local mean filtering on the vibration acceleration signal, and performing feature importance scoring by using a Laplacian fraction method to obtain a one-dimensional feature cavitation feature data set; performing image processing on the cavitation image to obtain a cavitation dynamic feature image data set; and establishing an axial flow pump cavitation diagnosis model, and outputting an identification type by the diagnosis model. According to the method, the cavitation state of the axial flow pump can be accurately identified, and the anti-interference capability is high.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.