221 results about "Sparse constraint" patented technology

The invention provides a knowledge base question-answering system optimization method and device based on hybrid fine tuning and multi-dimensional evaluation and a readable storage medium thereof, and provides the following scheme: constructing a hybrid progressive fine tuning framework, fusing low rank adaptation (LoRA) and direct preference optimization (DPO), and realizing domain knowledge migration through hierarchical dynamic parameter configuration; establishing a logic-semantic-knowledge three-dimensional quantitative evaluation system, and forming closed-loop optimization by using dynamic weight fusion and a visual decision system; and designing a multi-domain prompt template library with layered parameter freezing, sparse constraint and attention driving, and realizing model lightweight and cross-domain logic constraint. According to the method, the adaptive bottleneck of a general model and domain characteristics is broken through, the small sample training efficiency and the generated content compliance are improved, the computing resource consumption is reduced, an efficient and reliable knowledge service base is provided for professional scenes such as laws, medical treatment and finance, and the technical advantages of specialization, light weight and interpretability are achieved.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The invention discloses a road cavity volume calculation method and device based on a three-dimensional FK migration algorithm, equipment and a medium, and relates to the technical field of road cavity detection, and the method comprises the steps: obtaining three-dimensional ground penetrating radar data, and carrying out the preprocessing of the data, and obtaining target radar data; converting the target radar data to a frequency wavenumber domain to obtain frequency wavenumber domain radar data; establishing a road hierarchical velocity model, and performing hierarchical wave field extrapolation and double weight correction based on the frequency wave number domain radar data and the road hierarchical velocity model to obtain corrected wave field data; performing sparse constraint optimization imaging processing on the corrected wave field data to obtain a target imaging result; and carrying out boundary extraction processing on the target imaging result to obtain a three-dimensional cavity boundary point set, and carrying out voxelization processing based on the three-dimensional cavity boundary point set to obtain a road cavity volume. According to the invention, high-precision restoration of the shape of the road cavity and automatic extraction of the volume can be realized in three-dimensional GPR imaging.

The invention provides an oil film detection method and system based on a hierarchical self-organizing network and scale adaptation, and relates to the technical field of oil film detection. According to the technical key points, the method comprises the following steps: preprocessing an obtained original radar image; performing oil film region identification on the preprocessed radar image by using a trained oil film identification model based on the improved growth layered neural gas network to obtain an oil film candidate region; and segmenting the oil film candidate region by using an improved multi-scale threshold segmentation algorithm to obtain a final oil film detection result. According to the method, a local feature enhancement mechanism, dynamic level adjustment, adaptive error attenuation, sparse constraint optimization and a real-time adaptive learning mechanism are introduced, so that the adaptivity, robustness and calculation efficiency of the detection model are remarkably improved. The method can be integrated in a shipborne radar system, is high in calculation efficiency, can meet the real-time processing requirements of shipborne equipment, and is suitable for practical application scenes such as marine oil spill monitoring and emergency response.

The invention provides an anomaly detection method and device for image data and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: carrying out the feature extraction of the image data, fusing the spectrum and spatial features to obtain a joint feature matrix, and inputting an anomaly detection model; the model adopts an alternating direction multiplier algorithm to solve a low-rank sparse decomposition problem, and an objective function comprises a data fidelity item, a regularization item and a waveband weight item; the regularization item comprises a low-rank constraint and a sparse constraint, and the wave band weight item acts on the low-rank constraint in a weighting form; updating a background low-rank tensor, an abnormal sparse tensor, a Lagrange multiplier, a sparse constraint weight, a wave band weight item and penalty parameters of an algorithm by adopting an iteration mode in a solving process; and repeating iteration until a preset termination condition is reached, calculating an abnormal score graph pixel by pixel based on the abnormal sparse tensor, and comparing to determine an abnormal target. The problem that an abnormal target is difficult to accurately recognize in a complex scene can be solved, and detection precision and efficiency are improved.

The invention provides an airport parking space allocation method and system based on gridding space-time layering, and the method comprises the steps: building an airport space-time grid model through obtaining and space-time correlation analysis of multi-source basic data, such as real-time flight dynamic data, parking space physical attributes, historical operation efficiency and ground guarantee resource states; then, on the basis of multi-level feature constraint fusion, adaptive weight dynamic adjustment and sparse constraint low-dimensional reconstruction, adaptive optimization facing complex space-time requirements and delay disturbance is achieved, and an airport parking space distribution matrix is obtained; generating an anti-conflict camera configuration set in combination with dynamic coupling and conflict verification of ground support resources; and finally, through physical constraint real-time inspection and historical efficiency mode matching, generating an optimal distribution matrix of the airports, so that the overall use efficiency and the operation guarantee capability of airport airport resources are remarkably improved, and the airport operation efficiency and the service quality are improved.

The invention belongs to the technical field of geophysics, and particularly discloses a magnetization intensity vector sparse inversion method under a strong residual magnetism condition. The method comprises the following steps: firstly, subdividing an underground space, and simulating an underground magnetic anomalous body; obtaining a sensitivity matrix according to partial derivatives of observed magnetic anomaly values of the magnetic anomaly body in each unit magnetization direction, and constructing a total objective function of magnetization intensity vector inversion based on the sensitivity matrix; taking magnetization intensity amplitude as a parameter to introduce sparse constraint for the total objective function; converting the total target function into a standard L2 regularization form, and calculating a sensitivity matrix by using a singular value analysis method so as to obtain target parameters of the total target function; and finally, obtaining three components of the magnetization intensity vector from the target parameters, and further solving the magnetization intensity amplitude and the total magnetization direction. According to the scheme, the magnetization intensity inversion precision under the influence of strong residual magnetization is effectively improved.

The invention discloses a power adaptive digital pre-distortion method, system and device based on sparse GRU and a medium. The method comprises the steps that input and output signals under different powers are collected, a composite feature vector is constructed through amplitude normalization and phase alignment, and a pre-distortion training target is generated in combination with indirect learning; constructing a sparse GRU neural network, introducing L1 regularization constraint, and obtaining a sparse pre-distortion model through joint optimization of mean square error and sparse constraint; partial weight updating is realized based on power grading, and weight fusion output is carried out during power interval switching so as to complete power adaptive optimization; pruning and compressing the sparse GRU model and then deploying the sparse GRU model in a pre-distortion module to compensate the nonlinearity of the power amplifier; according to the method, the model complexity is reduced through rarefaction, the dynamic working condition stability is improved through a power adaptive mechanism, pruning compression adapts to low-power-consumption hardware, high precision, low complexity and adaptive capacity are considered, and the method is suitable for the low-power-consumption and high-performance requirements of a broadband wireless communication system.

The invention discloses an automatic business travel itinerary optimization method, and relates to the technical field of intelligent itinerary planning, and the method comprises the steps: integrating the multi-source heterogeneous data of enterprise policies, personal preferences and real-time traffic through a federated learning framework, and achieving the cross-domain knowledge sharing; the method comprises the following steps: constructing a staged optimization engine by adopting an attention mechanism to dynamically balance cost, time, comfort and sustainability targets: in the first stage, modularly disassembling a travel through sparse constraint linear programming, and quickly generating a Pareto frontier candidate set; in the secondary stage, on the basis of a multi-agent reinforcement learning framework, complex interaction is simulated through a Markov decision process, and strategy iteration is driven through a special reward function for quantifying a comfort index; in order to cope with real-time disturbance, event-driven edge computing nodes are deployed, flight delay and traffic jam emergencies are responded in real time, an incremental topology updating algorithm is triggered, and only affected sub-modules are reconstructed to reduce computing complexity. According to the invention, the bottleneck of dynamic adjustment efficiency and multi-target balance capability is solved.

The invention discloses a sparse Bayesian learning shallow sea target depth and horizontal distance passive estimation method, and the method comprises the steps: calculating a modulus depth function hypothesis space through employing a finite difference method based on a prior sound velocity profile and a horizontal wavenumber hypothesis interval; preprocessing the vertical array data to a frequency domain, constructing a linear underdetermined equation set about the frequency domain vertical array data and a modulus depth function of a hypothesis space under sparse constraint, and solving by using a sparse Bayesian learning algorithm to obtain estimation of a horizontal wave number spectrum; performing spectrum peak search on the horizontal wave number spectrum, estimating the order and the horizontal wave number of an actual excitation mode, and estimating a mode depth function of each order of mode by using a finite difference method; vertical array copy sound fields under different depths and horizontal distances are calculated according to a normal wave sound field formula; and respectively performing conjugate transpose multiplication on the vertical array copy sound field and actual data to obtain a depth-horizontal distance ambiguity surface, and searching the position of the maximum surface value as the estimation of the depth and horizontal distance of the source.

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 provides a tensor completion method based on low-rank and sparse combined constraint, and the method is applied to radar high-resolution two-dimensional sparse reconstruction imaging so as to enhance sparse imaging performance. Radar high-resolution two-dimensional imaging is realized through the following steps: firstly, constructing a tensor model of radar data through a sliding window method to capture an internal structure of high-dimensional data and mine low-rank characteristics of the data; secondly, representing enhanced low-rank and sparse characteristics of the constructed radar tensor data by using a tensor low-rank and sparse combined constraint model represented based on a Kronecker basis; and then an optimal solution of the constraint model is efficiently solved in an iteration mode by using an alternating direction multiplier method, and related parameters are updated through a closed-form solution method. According to the method, the superiority of the tensor completion method based on the low-rank and sparse joint constraint in radar high-resolution two-dimensional imaging is verified through comparison experiments on electromagnetic simulation and actually measured radar data.

The invention relates to the field of network link quality evaluation, and discloses a link quality evaluation method based on test data fusion statistics, which comprises the following steps: S1, collecting time delay, flow and topological parameters of a network link, and carrying out sliding window standardization; s2, constructing a four-dimensional non-negative tensor comprising a link number, a parameter dimension, a network slice and time; s3, features are extracted through non-negative Tucker decomposition with sparse constraints; s4, solving a dynamic weight based on a three-party game model of time delay sensitivity, bandwidth competition and reliability guarantee; s5, calculating a link quality score in combination with the weight and the tensor core; and S6, generating a thermodynamic diagram. Through the technical scheme of sliding window standardization and four-dimensional non-negative tensor construction, the technical effects of eliminating parameter dimension differences and adapting to topology dynamic changes are achieved by utilizing window dynamic calculation of mean values and standard deviations and sparse constraint modeling, and the input data quality of a subsequent game model and weight calculation is improved.

The invention relates to the technical field of satellite navigation signal processing, and discloses a signal noise suppression method in a Beidou navigation system, which comprises the following steps of: performing three-dimensional modeling on navigation signals, and extracting time-frequency characteristics to generate a signal model; constructing an interference heat prediction map, and dynamically updating and identifying a high-interference area; analyzing a signal phase change, calculating a phase rate, and identifying abrupt change interference; adopting adaptive filtering and L1 norm sparsification to remove multi-source interference; correcting deviation in signal modeling by using a redundant navigation module; and adjusting a signal processing strategy based on a real-time control and scheduling mechanism. According to the method, a signal modeling guide mechanism based on an interference heat prediction map is introduced, and the dynamic change of electromagnetic environment interference is combined with a signal modeling process, so that an interference sensitive area is recognized in advance, and structural sparse constraint is applied; and a blind area lacking sensing of interference at the initial modeling stage in the traditional technology is eliminated.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

The invention discloses a million-frame-level industrial vision system and method based on event driving and compressed sensing, and the system is characterized in that an event camera imaging module in the system captures the brightness change of each pixel in a field of view of the event camera imaging module in an asynchronous manner, and generates an event containing a pixel coordinate, a timestamp and change polarity for each change; a compressed sensing coding module constructs sparse image vectors for events in a time window, and the sparse image vectors are projected to low-dimensional observation vectors through an observation matrix phi; the sparse image reconstruction module is used for optimizing an objective function through sparse constraint and total variation regularization; a dynamic ROI compression module controls a compression mask function according to the event density and a gradient threshold. The method can break through the limitation of the traditional frame rate, has the advantages of high precision, high efficiency, low power consumption, strong robustness and the like, and has a wide industrial application prospect.

The invention discloses a dual-channel label-guided multi-label feature selection method and system, and belongs to a feature engineering technology. The method mainly comprises the steps of obtaining a feature matrix and a positive label matrix of a multi-label data set, performing logic negation on the positive label matrix to generate a mirror image negative label matrix, and constructing a graph Laplacian matrix based on the feature matrix; constructing a multi-label model based on the preprocessed data, wherein an objective function of the multi-label model at least comprises a positive label regression loss item, a negative label regression loss item, a label alignment constraint item, a graph regularization item and a sparse constraint item; constructing an optimization function through relaxation processing constraint and in combination with a Lagrangian multiplier method, iteratively solving the objective function according to a KKT condition, and evaluating feature importance based on a projection matrix for associating features and positive tags after iterative convergence; according to the method, the requirement of multi-label learning for accurate and efficient feature screening is met, label information can be comprehensively utilized, the anti-interference capability is enhanced, and the efficiency is considered.

The present invention discloses a remote sensing image declouding method using a group sparse prior tensor model, which belongs to the field of information processing technology and comprises the following steps: step 1, obtaining remote sensing images of continuous time nodes, stacking them in sequence along the spectral direction according to the time series, and forming a third-order original tensor with spatial and spectral smoothness; step 2, based on the low-rank tensor representation, characterizing the low-rank property of the clean component, decomposing the clean component into a spatial factor and a time-spectral factor according to the set rank through the low-rank tensor representation, and writing this representation relationship as a constraint condition; step 3, based on l 2,1 The norm regularization characterizes the group sparsity of the spatial factors of the clean component. The spatial factors are subjected to differential operations along two spatial directions to obtain the differential image. 2,1 The norm constructs a group sparse constraint term based on the spatial factor. The invention is used to realize efficient and accurate cloud removal of multi-temporal remote sensing images in complex scenes.

The present invention relates to the technical field of enterprise big data recommendation, and discloses an enterprise data intelligent display and recommendation method, S1: collecting feedback data of users on the market on enterprise projects, performing user interest modeling based on the feedback data, and obtaining the user's preference potential factors for the projects; S2: applying sparse constraints to non-zero elements in the preference potential factors, and constructing a sparse latent factor model; S3: obtaining similarity vectors between projects, and performing diversity penalty term calculation based on the similarity vector to obtain the diversity penalty term; S4: constructing a final objective function based on the sparse latent factor model and the diversity penalty term to obtain the final objective function, performing recommendation optimization based on the final objective function to obtain the final recommendation result, and balancing the prediction error and different types of regularization terms based on the objective function, which can effectively solve the problem of hot project bias in the recommendation system, thereby improving the diversity and novelty of the recommended content.

The invention discloses a multi-view clustering method based on tensor feature extraction, and the method comprises the steps: inputting a multi-view data matrix, constructing a similarity matrix of each view through a K-NN algorithm and a Gaussian kernel function, and carrying out the spectral clustering to obtain a sample embedding matrix; performing singular value decomposition on an original data matrix of each view, taking first c left singular vectors to construct a feature embedding matrix, applying 2, 1 norm group sparse constraint on the feature embedding matrix, and connecting a sample embedding matrix through a bigraph to extract features; and normalizing the sample embedded matrix, and reconstructing a block diagonal matrix into a third-order tensor. And integrating the sample embedding matrix, the feature embedding matrix and global tensor learning to construct a target function, and optimizing through an alternating direction multiplier method until convergence. And finally, the normalized samples are embedded into the matrix to form block diagonals to form a consistent similarity graph, and a clustering result is obtained by using an N-Cut or k-means algorithm.

The invention discloses a three-dimensional Gaussian splashing method and system for sparse view curve reconstruction, and the method reconstructs a three-dimensional curve scene through a sparse image: firstly, obtaining a dense point cloud and a camera pose, extracting a two-dimensional line segment graph, employing a line segment guide Gaussian initialization strategy, and obtaining a three-dimensional curve scene; generating an initial Gaussian set on the basis of the dense point cloud and the two-dimensional line segment graph; structure perception Gaussian pruning is executed in the optimization process of the initial Gaussian set, and spatial outliers and visibility redundant gauss are removed; and finally, calculating the total loss containing sparse regular terms through the difference between the rendered image and the sparse image, and iteratively optimizing Gaussian parameters until the total loss converges, thereby obtaining a final three-dimensional curve reconstruction result. According to the method, the problem of geometric prior deficiency under a sparse view is solved through line segment guide initialization, overfitting and artifacts are effectively inhibited by using structure pruning and sparse constraint, and high-quality three-dimensional curve reconstruction is realized.

The invention discloses an infrared small target detection method based on sparse perception global channel pruning, and the method comprises the steps: rapidly achieving the sparsification of a feature map through introducing a sparse constraint interpretable layer, learning robust features, and effectively maintaining the detection performance of a model; according to the method, an infrared small target detection network pruning problem is formalized into a sparse representation problem, extra standards do not need to be designed to identify redundant channels, the sparsity of the feature map is directly utilized, the method is more in line with the characteristics of the target, and the detection performance and the reasoning efficiency can be effectively balanced.

The invention relates to the technical field of machine learning feature engineering, in particular to an image data robust discrimination feature selection method and system based on an improved trace difference criterion, and the method comprises the steps: randomly selecting sample data with a label type from an image source sample data set as training sample data, and enabling the remaining sample data to serve as to-be-processed data; constructing a target function by adopting a trace difference criterion and introducing L2, p norm sparse constraints, training a feature selection model based on the target function by utilizing training sample data, and obtaining model optimal hyper-parameters and a row sparse projection matrix through cross validation and grid search; the trace difference criterion is constructed by an intra-class divergence matrix, an inter-class divergence matrix and a projection matrix of sample data; and performing feature screening on the to-be-processed data based on the optimal hyper-parameter and the row coefficient projection matrix, and outputting an optimal feature subset. The method can solve the problem that the feature selection effect on a small sample or a poor-quality data set is not ideal, and has a good application prospect in the field of image classification and the like.

The invention discloses a screening method of a protein marker combination, which is used for risk prediction of abdominal aortic aneurysm or ruptured abdominal aortic aneurysm, and optimizes the stability of a protein expression profile by constructing an initial protein expression profile; screening differential expression proteins by adopting statistical analysis, and preliminarily locking candidate proteins remarkably associated with a disease endpoint based on survival analysis or a risk regression model; performing cross validation by combining a sparse constraint algorithm and a nonlinear feature selection algorithm, and extracting a robust core marker; the independent contribution degree of each marker is evaluated through the standardized weight, and finally the optimal protein combination for disease prediction or diagnosis is determined. According to the method, six core proteins obtained through multi-algorithm cross screening can be detected in serum, multivariable logistic regression and external verification set performance both show good robustness, and an aorta protein fingerprint feature set which can be popularized and explained is formed.

The invention discloses a high-resolution video generation method and system based on multi-modal semantic guidance, and belongs to the technical field of video generation. According to the method, firstly, a low-resolution video is generated according to a text prompt, an edge map, a depth map and optical flow features of the low-resolution video are extracted through a space-time perception module, the features are combined with the text prompt, and a saliency region mask is generated after saliency weighting. And then the edge map, the depth map, the optical flow features and the text prompt are input into a DiT model. In each layer of Transform of DiT, after input is converted into corresponding embedded representation, structural feature embedding and text embedding are aligned in a space-time dimension, global semantic association is established through a cross attention mechanism, text embedding is processed by MLP to form conditional constraints, and sparse constraints and mask guidance are applied to an attention matrix. And finally, after multi-layer Transform processing, the model outputs a high-resolution video. In the whole process, the generation from the text to the high-resolution video is realized, and the details of the saliency region are emphatically protected.

The invention provides a photoelectric volume pulse wave identity recognition method and system, and relates to the technical field of identity recognition, and the method comprises the steps: obtaining a to-be-recognized PPG signal; and inputting the PPG signals into a trained identification model, firstly extracting linear features and nonlinear features, then projecting the features fused by the two features into a feature space by using a learned discriminant projection matrix to obtain multi-view features, and finally classifying the PPG signals by using the multi-view features to obtain an identity identification result. According to the method, manifold regularization and inter-class-error double sparse constraints are combined, the problems of intra-class discretization and inter-class overlapping of a linear model are solved, a graph structure learning method for adaptive local density adjustment is provided, and the manifold modeling precision of non-stationary PPG signals is improved.

The invention provides a machine tool bearing fault diagnosis method and system, a storage medium and equipment, and relates to the technical field of rotating machine fault diagnosis. Vibration signals are input into the fault diagnosis model, firstly, the vibration signals enter a sparse wavelet convolution module, the sparse wavelet convolution module carries out limitation by introducing spectral kurtosis constraint and sparse constraint, and initial features are obtained through output; inputting the preliminary characteristics into a gating pulse module, in the gating pulse module, releasing pulses by adjusting gating factors according to pulse neuron characteristics, outputting the pulses, converting the pulse output at the previous moment and the preliminary characteristics at the current moment as input, and outputting to obtain a peak sequence; and inputting the spike sequence into the deep neural network module again, outputting to obtain a fault information feature vector, inputting the fault information feature vector into a classifier, and finally outputting to obtain a fault classification result to realize fault diagnosis.

The invention provides a seismic data high-resolution processing method for solving the problem that existing seismic data is insufficient in resolution for underground structures in complex regions. According to the method, a sparse seismic reflection coefficient is obtained by solving an optimization problem of sparse constraint to carry out frequency expansion on seismic data, and the method comprises the following steps: obtaining a low-frequency wavelet function according to an average spectrum of the seismic data, and obtaining an initial seismic reflection coefficient through channel-by-channel deconvolution seismic data and the low-frequency wavelet function; and iteratively extracting numerical reconstruction sparse seismic reflection coefficients with dominant values in the initial reflection coefficients by using the sparsity of the reflection coefficients, and carrying out convolution on the sparse seismic reflection coefficients and the high-frequency seismic wavelets to obtain seismic data after frequency expansion. According to the method, a seismic section with higher resolution can be provided through signal processing, and description and interpretation of a thin-layer oil and gas reservoir structure are facilitated.

The invention provides an efficient algorithm for large-scale sparse matrix decomposition, and the algorithm comprises the steps: finding out two low-rank matrixes W < Rm * k and H < Rk * n for a given large-scale sparse matrix A < Rm * n, and enabling A to be approximately equal to WH; the method specifically comprises the following steps: generating a random matrix R belonging to Rn * l; calculating a matrix # imgabs0 # after dimension reduction to construct an optimization problem with sparse constraint; solving by adopting an alternating direction multiplier method; and post-processing the result. According to the method, random projection dimensionality reduction is performed on the original sparse matrix, so that the calculation complexity is reduced; a sparse regularization item is introduced, and the sparsity of the matrix is kept; and solving by adopting an alternating direction multiplier method, and accelerating convergence. According to the method, the calculation and storage cost can be greatly reduced while the decomposition precision is guaranteed, and the method is suitable for large-scale data processing in the fields of machine learning, data mining and the like.

The invention relates to the technical field of intelligent power distribution operation and maintenance, in particular to an Internet of Things power distribution box monitoring method, which comprises the following steps of implementing multi-domain active excitation in a safety interlocking time window, acquiring multi-domain response, and generating an excitation-response observation packet; carrying out feature distribution alignment on the observation package, obtaining a degradation state field and an uncertainty field on a node-edge graph model through joint inversion containing group sparse constraint and physical prior, and generating a sensitivity field through a risk density function; establishing model predictive control under action mutual exclusion and resource constraint, and jointly arranging a source channel, an environment channel and a local channel to output an optimal action trajectory; and single-channel amplitude difference comparison is implemented in adjacent micro-regions, and an acquisition time slot selection scheme and a channel priority of a next period are generated in combination with an evidence ratio, an action Shapley attribution and a second-order optimal transmission distance write-back weight, so that in-service interpretable monitoring and self-calibration control are realized.