213 results about "Dictionary learning" patented technology

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention discloses a data processing method for industrial equipment for industrial control Internet of Things, which comprises the following steps: acquiring industrial equipment sensor data, and carrying out standardization processing to obtain a standardized matrix; initializing a sparse dictionary learning model, setting an initial dictionary structure and a sparse coefficient, and executing preliminary sparse decomposition; constructing a flower pollination algorithm search space, optimizing a layering number, a regular weight and a switching threshold, and updating dictionary parameters; calculating an optimal sparse coefficient matrix, and performing sparse reconstruction processing; and carrying out data denoising, compression and feature extraction, and outputting a final processing result. According to the method, the flower pollination optimization algorithm and the sparse representation dictionary learning model are fused, so that efficient denoising, compression and abnormal feature extraction processing of the industrial equipment data are realized.

The invention provides a power big data-based sparsity feature adaptive compression method, which comprises the steps of adaptively optimizing a sensing matrix through a dictionary learning method aiming at a screened multi-granularity feature subset, introducing a sparse regularization item to enhance the sparsity of data reconstruction, and according to a missing mode of a missing value and context information, carrying out adaptive compression on the sparse feature of the power big data. Establishing a mapping relationship among the compression ratio, the reconstruction error and the feature subset, and determining a self-adaptive compression strategy; based on a self-adaptive compression strategy, a compressed sensing method is adopted to carry out self-adaptive compression on the multi-time-granularity power data, the compression ratio is dynamically adjusted according to reconstruction error feedback, the data compression and reconstruction quality is balanced, and the reconstructed multi-granularity power data is synchronized according to timestamp precision.

The invention belongs to the field of magnetic resonance measurement signal noise filtering, and particularly relates to a ground magnetic resonance signal sparse denoising method of a combined dictionary, which comprises the following steps: reconstructing a power frequency harmonic component of a magnetic resonance signal according to frequency bands, and eliminating power frequency harmonics from the magnetic resonance signal to obtain a power frequency-removed signal; constructing a track matrix Z by adopting a Gaussian random matrix, constructing a track matrix Y by adopting a power frequency-removed signal, taking first K columns of the track matrix Z and normalizing to obtain an initial dictionary D, and using the track matrix Y as an original sample for constructing a sparse coefficient matrix X; updating the initial dictionary D and the sparse coefficient matrix X through K-SVD dictionary learning, and reconstructing a track matrix by adopting the updated dictionary # imgabs0 # and the sparse coefficient matrix # imgabs1 # to obtain a track matrix W; and obtaining a pure magnetic resonance signal by taking the first row of the track matrix W, and realizing effective removal of MRS signal noise in a complex electromagnetic interference scene under the condition of single signal acquisition.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

Methods and apparatus are provided for encoding and subsequent decoding of video data by using a learnt video specific dictionary for implicit neural compression. An implicit neural representation comprising a head layer and a tail layer is used with approximations to the head layer parameters. The approximations are determined with combinations of atoms of a learnt video specific dictionary. In one embodiment, the head layer approximations and tail layer parameters are encoded in a bitstream. The dictionary is learnt at the decoder. In another embodiment, a dictionary is sent in the bitstream. At decoding, a reconstructed image is computed using transmitted INR parameters.

The invention discloses a face recognition method based on sparse coding and dictionary learning. The method comprises the following steps: S1, collecting and preprocessing a standardized training image and a to-be-recognized face image; s2, training a dictionary set, labeling categories, and initializing and optimizing the dictionary; s3, performing sparse coding, and solving a sparse coefficient by using the optimization dictionary; s4, carrying out regional feature division and fusion of multi-region sparse coding, weighted compression and interactive modeling; s5, performing fusion feature normalization and classification, and performing joint discrimination by a reconstruction error and a discrimination classifier; s6, performing confidence evaluation and dictionary updating, and triggering an incremental learning updating mechanism with low confidence; and S7, performing model deployment and real-time identification, and deploying an optimization dictionary and a classification module to perform identification output. The invention provides a face recognition method suitable for a complex environment, and the method has the advantages of high precision, strong robustness and good practicability.

The invention discloses an industrial fault detection method based on structured augmented dictionary learning, and the method comprises the steps: firstly processing a fault-free component through a basic dictionary, and introducing an additional low-dimensional sparse dictionary for precisely reconstructing a fault related part, thereby avoiding the decentralization of fault information. Secondly, in order to ensure that a fault-free part is in a statistical control state, a steady-state statistical characteristic and a manifold structure constraint are embedded into a basic dictionary coefficient, and meanwhile, a fault-related characteristic variable is accurately selected through a hard sparse constraint, so that a fault-free signal in an industrial process is effectively prevented from being interfered; therefore, accurate separation of the fault and the normal component of the signal is realized in sparse decoding, so that the fault mode is accurately revealed; in addition, an aggregation method based on a moving window is adopted, the sensitivity and detection efficiency of initial faults in the industrial process are remarkably improved, and an efficient and reliable fault diagnosis solution is provided for industrial application.

The invention discloses a multi-frequency near-field millimeter wave super-resolution imaging method based on a compressed sensing model, and relates to the field of super-resolution imaging. Aiming at the problems in the prior art, the invention provides a multi-frequency near-field millimeter wave super-resolution imaging method, the method is based on a compressed sensing model, the selection of multi-frequency near-field millimeter waves is used for improving the stability of imaging quality, and the compressed sensing model is used for constructing a mixed sparse function. And a wavelet transform domain norm and a TV (Total variation) operator are taken as optimization targets, so that the reconstruction of the high-resolution image is realized. Aiming at a subsequent image processing process of a two-dimensional near-field millimeter wave imaging system, under the condition of shortening data sampling time, a high-resolution image is reconstructed from low-resolution sampling data, and redundant hardware, a large number of image data sets and a dictionary learning process are not needed.

The invention discloses a foundation pit support deformation real-time monitoring method based on edge calculation, and aims to solve the problems of multi-node time synchronization and clock drift compensation caused by unavailability or poor quality of satellite navigation and precise time protocols in an underground or shielded environment. According to the method, through event dictionary learning, partition time slot scheduling and safety coding micro excitation time synchronization, matched filtering decoding, multi-scale arrival time estimation, paired time difference robust fusion, frequency priority and phase speed limit correction and network disconnection time holding, the time synchronization efficiency is improved; the technical effects that multi-source data alignment, deformation field reconstruction and graded early warning are completed on the unified virtual reference time scale, and high-precision synchronization and online drift compensation are obtained without depending on the external time service condition are achieved.

The invention relates to a fault arc-caused fire early warning method based on multi-sensor fusion, and belongs to the field of fault arc fire. The method comprises the following steps: establishing an arc electrical fire cause fault simulation test platform, selecting a typical single load and a combined load to perform a test before normal operation of a line and the occurrence of an arc cause fire, and establishing a fault arc waveform database and an arc fire database; the power-on detection method for the fault arc detection device is based on dictionary learning, current characteristics are represented through a sparse matrix, and fault classification is carried out in combination with an SVM. The fluctuation range of a plurality of characteristic quantities of the line in a normal state and when a fault arc occurs is analyzed, a threshold value is determined according to the periodic increase ratio of the characteristic quantities in different states, a final combined load detection threshold value is formed by complementation of a plurality of threshold values, and fault arc detection is realized; and based on the arc fire database, training a double-layer LSTM model under Bayesian optimization, and identifying multi-sensor signals in a time window to realize fire early warning.

The invention provides a time-varying frequency spectrum map compression surveying and mapping method based on dictionary learning and smooth regularization, and the method comprises the steps: building a sparse base psi through the path loss between two points in a spatial frequency domain, carrying out the sparse representation of a signal in the spatial frequency domain, and building a compression sensing model of a three-dimensional space through combining with a measurement matrix; a measurement matrix in the compressed sensing model is optimized, singular value decomposition (SVD) is used for decomposing psi, sparse bases psi m corresponding to the first m maximum eigenvalues are selected, and an optimized measurement matrix is obtained after m times of iteration; the optimized measurement matrix is substituted into a compressed sensing model, an O-KSVD dictionary is adopted to learn and update a sparse base psi, and spectrum data are recovered; finally, sparse regularization and weighted smoothing regularization are combined to achieve the minimum Euclidean distance between adjacent points in the reconstructed spectrum map, smoothing regularization is used for driving the adjacent points in the reconstructed spectrum map to be similar, sparse representation in the spectrum map is updated through iteration till convergence, and actual spectrum mapping is obtained.

The invention discloses a multi-working-condition process monitoring method and system based on zero-forgetting continuous dictionary learning, and the method comprises the steps: carrying out the offline modeling: firstly, carrying out the dictionary learning through employing an initial working condition data set, obtaining an initial dictionary, carrying out the decomposition, obtaining a low-rank matrix of an initial working condition, and calculating the control limit of the initial working condition through calculating a sample reconstruction error; performing incremental updating on the low-rank matrix obtained by learning the old working condition by using the new working condition data set to obtain a low-rank matrix of a new working condition, and calculating a control limit of the new working condition; on-line monitoring comprises the following steps: firstly, a weight selector is used for distributing weights for a low-rank matrix of a learned working condition according to monitoring data, and a monitoring dictionary is constructed in a self-adaptive manner; reconstructing the monitoring data by using the monitoring dictionary, solving a reconstruction error, and further judging the working condition of the monitoring data; and finally, calculating a fault detection statistic according to the reconstruction error and the control limit of the corresponding working condition, and judging whether the monitoring data is abnormal or not according to the statistic. According to the invention, accurate monitoring of a multi-working-condition process is realized.

The invention relates to the technical field of data processing, in particular to a covariance inverse matrix recursive updating method for on-satellite hyperspectral anomaly detection, which comprises the following steps: acquiring a hyperspectral data stream; carrying out adaptive dimension reduction processing on the data stream and loading an initial background model; extracting local background statistics by adopting a sliding window mechanism, generating a background spectrum dictionary by utilizing online dictionary learning, calculating a reconstruction error between a current pixel and the dictionary and a Mahalanobis distance between the current pixel and a background model, and fusing to generate an abnormal score; performing abnormal confidence coefficient evaluation by combining the spatial context information and the spectral angle matching degree, and updating a spectral mean vector by using an exponential weighted moving average algorithm based on non-abnormal pixel data; and the updated model is injected into the next round of processing to form a recursive chain. According to the method, the high calculation complexity of direct inversion of a covariance matrix is avoided, real-time anomaly detection on a satellite is realized, the downloading amount of original data is remarkably reduced, and the congestion of a satellite-ground communication link is relieved.

The invention discloses an abnormal waveform matching method for electric energy meter voltage abnormity identification, and relates to the technical field of waveform matching, and the method comprises the steps: collecting a historical abnormal waveform, extracting a feature vector, and constructing an abnormal waveform dictionary through a dynamic dictionary learning algorithm; performing coverage analysis on historical abnormal waveforms, and screening core features to establish a simplified feature set; collecting a to-be-recognized voltage waveform in real time, extracting a feature vector to be matched with the simplified feature set, and primarily judging whether dominant anomaly exists or not; if yes, candidate template types are screened through the characteristic deviation ratio, and abnormal types are judged; if the dominant anomaly does not exist, a normal waveform feature baseline model is constructed, deviation analysis is carried out to judge whether the implicit anomaly exists or not, and if the implicit anomaly exists, the abnormal waveform dictionary is iteratively optimized in combination with the to-be-recognized waveform features; the method can effectively identify the voltage abnormity of the electric energy meter, balance the identification precision and the calculation efficiency, and adapt to a novel abnormal mode and equipment gradual change fault characteristics.

The invention relates to a deep energy level transient spectrum data processing method based on dictionary learning. The method comprises the following steps: acquiring transient capacitance signals of a sample at different temperatures by using deep energy level transient spectrum equipment; establishing an Arrhenius equation that the defect emissivity changes along with the temperature; the defect emissivity under each group of values is calculated according to all possible values of the coefficient of the exhaustion Arrhenius equation; constructing a dictionary matrix based on the defect emissivity amplitude introducing Arrhenius constraint, and reconstructing a transient capacitance signal by using the dictionary matrix; and constructing a convex optimization problem, solving a dictionary index variable which enables a reconstruction signal of the transient capacitor to be closest to an acquisition signal, and extracting a non-zero solution in the dictionary index variable to obtain a corresponding defect emissivity amplitude spectrum. According to the method, the detection precision of the deep energy level defect can be improved by introducing the Arrhenius constraint relation.

The invention discloses an X-ray diffraction spectrum sparse reconstruction method and system based on a dynamic dictionary, and the method comprises the steps: generating an initial dictionary, and carrying out the dynamic optimization and updating of the dictionary, and obtaining a sparse dictionary; selecting an X-ray diffraction scanning angle subset based on the sparse dictionary, constructing an observation matrix, encoding an observation process, and obtaining compressed and sampled X-ray diffraction projection data; and sparse vectors are obtained from the obtained X-ray diffraction projection data after compressed sampling, and a reconstruction spectrum is obtained. And the accuracy of sparse representation is improved. The invention provides a high-throughput XRD (X-Ray Diffraction) material spectrogram analysis method combining dynamic dictionary learning, a compressed sensing theory and deep learning, and aims to improve the analysis efficiency and precision of crystal structure recognition and phase composition in new material research and development.

The present invention discloses a sparse aperture ISAR maneuvering target imaging and calibration method, comprising the following steps: establishing a maneuvering target ISAR sparse aperture signal model, characterizing the maneuverability of the target by constructing a variable scale non-uniform Fourier two-dimensional dictionary containing MTRC; constructing a parameterized sparse imaging model by maximum a posteriori estimation, expressed as a cost function of joint target parameter estimation and sparse imaging; finding the target rotation parameter by the minimum entropy method, and implementing a rough estimation of the maneuvering target rotation parameter by performing parameter iteration by the coordinate descent method; and implementing high-quality imaging with MTRC correction and accurate parameter estimation by combining sparse ISAR imaging and target rotation parameter estimation, decomposing into sparse imaging and dictionary learning, and iterating and solving in sequence. The present invention realizes the use of sparse aperture echo data to solve the problem of high-resolution imaging of ISAR maneuvering targets, greatly increasing the implementation scope and application scenarios of radar imaging.

The invention discloses a power distribution network fault signal reconstruction method, system and device based on K-SVD and orthogonal matching pursuit, and a medium, and belongs to the technical field of power systems, and the method comprises the steps: obtaining a fault signal from a monitoring device of a power distribution network, and carrying out the preprocessing of the signal; a K-SVD algorithm is used for dictionary learning, a sparse dictionary suitable for power distribution network fault signals is generated, and the dictionary is optimized through training of a signal data set; performing sparse representation on the signal by adopting an orthogonal matching pursuit algorithm, and realizing sparse decomposition of the signal by searching an optimal sparse representation coefficient; based on the sparse representation coefficient, reconstructing the signal from the sparse dictionary, calculating a reconstruction error, comparing the reconstruction error with an actual fault signal, evaluating a reconstruction effect and an error, and performing parameter optimization adjustment; and accurately judging the type of the fault according to the reconstructed signal. According to the method, the key information of the fault signal is effectively extracted in a complex noise environment, and the signal reconstruction precision is remarkably improved.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.

This disclosure relates generally to the field of image processing, and, more particularly, to a method and system for Multimodal Image Super-Resolution (MISR) using convolutional dictionary learning. Existing sparse representation learning based techniques for MISR have certain limitations which impact quality of the reconstructed image. The present disclosure performs MISR using convolutional dictionaries which are translation invariant. Low-resolution image of the target modality and high-resolution image of the guidance modality are modelled using their respective convolutional dictionaries and associated coefficients. Additionally, two coupling convolutional dictionaries are learned to model the relationship between them and synthesize the high-resolution image of the target modality more efficiently.

The invention belongs to the technical field of image analysis and processing, and discloses a woven fabric texture reconstruction method based on deep shared convolutional dictionary learning, and the method specifically comprises the steps: (1) constructing a deep shared convolutional dictionary learning model; (2) decomposing an overall optimization problem of the constructed deep shared convolutional dictionary learning model into N independent optimization problems; (3) rewriting the N independent optimization problems into three sub-optimization problems with constraints, and converting the three sub-optimization problems into optimization problems without constraints according to an alternating direction multiplier method; (4) inputting woven fabric texture sample images in the training set, and iteratively solving an optimization problem without constraints by adopting a multi-stage training strategy; and (5) inputting the test set image into the deep shared convolutional dictionary learning model, outputting the corresponding adaptive convolutional dictionary and convolutional coding coefficient, and finally calculating to obtain a reconstructed image. According to the method, high-quality reconstruction of a complex woven fabric texture image and general representation of an untrained woven fabric texture image are realized.

The invention relates to a compressed sensing-based adaptive wireless sensor network optimization method, which comprises the following steps of: constructing a sensor data transmission tree, and collecting historical data of a sensor network; constructing a sparse dictionary based on the collected historical data of the sensor network; and performing compression processing on the real-time sensor network data by using the sparse dictionary, and performing recovery of the sensor network data in the sink node. According to the compressed sensing algorithm based on adaptive dictionary learning, a mechanism is designed to meet the adaptive requirement of the compressed sensing algorithm in a large-scale sensor network, and the limitation of sparse base selection is overcome to a certain extent.

The invention relates to the technical field of fiber bragg gratings, and particularly provides a birefringent FBG-based fiber environment event identification method, which comprises the following steps: constructing a spectral pulse matrix according to fast axis spectrums and slow axis spectrums of a plurality of spectral pulses, the spectral pulses being echo spectral pulses transmitted by a birefringent FBG; solving a joint optimization problem, and separating a first matrix and a second matrix from the spectral pulse matrix; solving a first optimization problem of the first matrix based on a first dictionary learning algorithm to obtain a vibration event recognition matrix used for representing a vibration event signal; and solving a second optimization problem based on a second dictionary learning algorithm to obtain a temperature offset feature vector used for representing the temperature change signal, the second optimization problem being determined according to the merged vector of the second matrix. The problem of difficulty in signal decoupling of the FBG in a complex environment in which temperature change and vibration events exist at the same time in the prior art is solved.

The invention relates to the technical field of voice signal processing, and particularly provides a voice separation method, device and system irrelevant to array geometry. The method is suitable for various microphone array structures, adopts a virtual microphone estimation mechanism to generate a virtual channel signal for enhancing spatial information density, combines frequency spectrum-time features and spatial direction features, and extracts multi-modal representation through a spatial dictionary learning and attention fusion module. And the extracted features are further input into a layered double-path modeling network, and global dependency relationships are respectively modeled on a time axis and a frequency axis, so that high-precision separation of voices of multiple speakers is realized. The system has good array structure adaptivity, can adapt to channel number changes and array shape differences, and has good application value in scenes such as teleconferences, voice recognition front ends and vehicle-mounted voice processing.

The invention provides a foundation pit support structure lateral displacement prediction method based on dictionary learning fusion monitoring data. The foundation pit support structure lateral displacement prediction method comprises the steps that S1, a physical parameter database is established; s2, performing conversion to obtain weak prior distribution of a compressibility parameter ES; s3, sampling by using Latin hypercube to obtain N alternative parameter combinations of the soil body; s4, establishing a finite element model of the target foundation pit; s5, inputting a parameter combination to calculate corresponding side displacement data of the enclosure structure, and forming an over-complete dictionary D; s6, reading measured side displacement data of the enclosure structure to form a monitoring data matrix Y; S7, extracting corresponding data in the over-complete dictionary D, and forming a dimension transformation matrix A matched with the dimension of the monitoring data matrix Y; s8, obtaining an important atom set and a sparse solution vector x by using an orthogonal matching pursuit algorithm program; and S9, obtaining a side displacement prediction curve of the building envelope corrected by combining the monitoring data. The method has the advantages of high prediction precision, low calculation cost and high response speed.

The invention belongs to the technical field of data processing, and particularly discloses a spectral data fusion method and system based on dictionary learning, and the method comprises the following steps: S1, carrying out the adaptive pixel clustering of hyperspectral data and multispectral data, and obtaining an ideal cluster number; s2, calculating a spectral dictionary of each cluster of hyperspectral data by using an online dictionary learning algorithm; s3, calculating a sparse coefficient of each cluster of multi-spectral data after clustering in combination with the spectral dictionary; s4, calculating fusion data corresponding to each cluster according to the spectrum dictionary and the sparse coefficient, and integrating the fusion data of each cluster to obtain a fusion image; according to the method, a more accurate and more stable clustering result can be obtained when a complex data set is processed; and meanwhile, a non-negative constraint condition is applied to the coefficient matrix, so that the robustness of the result is enhanced.

The present invention provides a wireless audio sound effect processing method and system, relating to the technical field of audio processing. The method includes: collecting wireless audio data; determining whether there is noise in the wireless audio data through an autoregressive prediction algorithm, and if there is, extracting the noise signal in the wireless audio data; combining a sparse dictionary learning algorithm, and generating a reverse noise signal for canceling the noise signal through a filter with an adaptive filtering step size; aligning the noise signal and the reverse noise signal based on a dynamic phase compensation order; synthesizing the wireless audio data and the aligned reverse noise signal to obtain optimized wireless audio data; otherwise, marking the wireless audio data as optimized wireless audio data and outputting the optimized wireless audio data. While improving the noise reduction efficiency, it can effectively cope with complex and variable noise information and improve the quality of wireless audio.

This invention discloses a noise reduction method, a bearing fault diagnosis method, and a system based on dual sparse dictionary adaptive learning. The method performs wavelet decomposition on the signal to be denoised to obtain high-frequency and low-frequency signals, constructing high-frequency and low-frequency matrices. Then, threshold-adaptive DDTF dictionary learning is applied to both the high-frequency and low-frequency matrices. Specifically, an initial dictionary is set and learned to obtain an initial sparse coefficient matrix. An adaptive threshold update is then applied to the initial sparse coefficient matrix, selecting sparse coefficients from the initial sparse coefficient matrix in descending order as dynamic thresholds during the iteration process to update the initial sparse coefficients. After determining the final threshold, the final sparse coefficient matrix is ​​obtained, and the dictionary is updated again to obtain sub-signals. Finally, inverse transformation and matrix rearrangement inverse operations are performed on the obtained sub-signals to obtain the denoised signal. This invention combines wavelet decomposition and DDTF to construct a dual sparse pattern, effectively improving the sparse representation capability of a fixed basis.

This invention discloses a high-overload data analysis method and system based on multi-dimensional buffer protection, relating to the field of data acquisition and storage technology. The system consists of several functional modules, including: a transient impedance module, which acquires stress wave propagation parameters and material strain data collected in real time by the recorder node, constructs a dynamic digital twin model of the buffer structure, and outputs impact feature vectors and stress field distribution matrices; a signal reconstruction module, which dynamically configures the sensing signals based on the impact feature vectors, including the gain, bandwidth, and filtering parameters of the analog front end, conditions and performs analog-to-digital conversion on the sensing signals, and uses compressed sensing and dictionary learning algorithms to sparsely reconstruct abnormal data, generating a high-fidelity data stream and storage data integrity identifiers for each storage block; and a federated storage module, which inputs the stress field distribution matrix and storage data integrity identifiers into the edge-side reinforcement learning decision engine to calculate the failure risk probability of each storage block.