169 results about "Nonnegative matrix factorisation" patented technology

The invention discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention belongs to the technical field of sensor error correction, and discloses a data synchronization and error correction method based on multi-source gas detection, and the method comprises the steps: firstly achieving the time-space synchronization of a multi-source sensor through the combination of GPS / Beidou hardware time service and a gas diffusion transmission model; non-negative matrix factorization (NMF) is adopted to decouple sensor cross interference, and an LSTM neural network is utilized to predict a sensor drift amount caused by environmental temperature and humidity changes; and finally, carrying out dynamic weight fusion based on the real-time confidence of each sensor, and outputting a high-precision gas concentration value. The problem of data distortion caused by time sequence dislocation, cross sensitivity and environment drift in a multi-sensor system is effectively solved, the accuracy of detection data and the reliability of the system are remarkably improved, and the method is particularly suitable for multi-component gas detection scenes in the fields of chemical engineering, environment monitoring and the like.

The invention provides a semi-supervised non-negative matrix factorization multi-view clustering method based on double constraints, which is realized by adopting a double-constraint algorithm, the double-constraint algorithm comprises a point constraint propagation algorithm and a pairwise constraint propagation algorithm, and specifically, when limited label information is obtained, the point constraint propagation algorithm and the pairwise constraint propagation algorithm are combined to form a semi-supervised non-negative matrix factorization multi-view clustering algorithm. According to the double-constraint algorithm, not only is label regularization constructed by using a point constraint propagation algorithm, but also multi-view hypergraph regularization is constructed by using a hypergraph pairwise constraint propagation algorithm, so that the detection accuracy of the model on multi-view clustering is improved. According to the method, the performance of multi-view clustering can be improved by utilizing the label information and the paired supervision information.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention discloses a Chinese and college entrance examination student learning condition diagnosis method based on multi-source data fusion, and the method comprises the following steps: S1, collecting and standardizing multi-source data of examination, behavior, psychology and the like, and constructing a unified student data set; s2, non-negative matrix factorization is adopted to extract fusion factors of all dimensions, and student feature expression is formed; s3, multi-view clustering is carried out based on the fusion factors, and student learning condition category labels are generated; s4, constructing a student portrait model according to category labels, and depicting group feature distribution; s5, comparing the individual features with the portrait, identifying abnormal differences, and generating a diagnosis conclusion; and S6, according to a diagnosis result matching rule base, outputting personalized intervention suggestions to assist teaching intervention. According to the invention, accurate learning condition diagnosis and intelligent personalized intervention driven by multi-source data are realized, and the teaching decision-making efficiency is improved.

A subway station environment control wind and water linkage energy-saving optimization control method based on deep learning comprises the steps that the train operation state, the station environment data and the environment control equipment data are preprocessed, and a time sequence data matrix is obtained; and decomposing the time sequence data matrix through non-negative matrix decomposition, extracting disturbance characteristics in the time sequence data matrix, and outputting a blind source separation result matrix. And performing convolution operation on the blind source separation result matrix through a convolutional neural network, extracting a spatial pattern of disturbance characteristics, and generating a spatial feature vector through pooling dimension reduction. And capturing a time change rule of dynamic disturbance through a bidirectional GRU structure in the recurrent neural network, and generating an implicit state sequence. And an attention mechanism is introduced into an output layer of the recurrent neural network, an implicit state when the train enters the station is concerned, and optimized disturbance feature representation is generated through weight weighting. And taking the optimized disturbance characteristic representation as the input of a full connection layer, and generating and outputting a corresponding energy-saving strategy category.

The invention relates to the field of retrieval enhancement, in particular to a multi-view knowledge-intensive retrieval enhancement generation system and method, which are characterized in that structural vectors and semantic topics are extracted from professional corpora through principal component analysis and non-negative matrix factorization technologies, and a multi-dimensional professional view set is constructed. After a user query is received, a potential intention is identified, a view angle weight vector is generated, the query is rewritten according to the view angle weight vector, multiple groups of view angle retrieval requests are constructed, targeted document retrieval is executed, and a structured prompt input language generation model is constructed based on a view angle weight reordering result and fusion of an original query and a multi-view angle retrieval result. The method is suitable for scenes of law assistance, intelligent diagnosis, academic questions and answers and the like, so that the accuracy, the interpretation and the reliability of retrieval and generation in the complex field are remarkably improved.

The invention discloses a multi-channel fusion neural network equipment state diagnosis method and system, electronic equipment and a computer readable medium, and the method comprises the steps: collecting data of a sensor disposed on the equipment, carrying out the preprocessing, and constructing equipment state multi-channel data; inputting the equipment state multi-channel data into a multi-component signal analysis model, wherein an empirical mode decomposition model is used to obtain an equipment state decomposition signal; inputting the equipment state decomposition signal into a non-negative matrix decomposition model, and separating out an independent source equipment state signal; performing time-frequency domain feature extraction on the independent source equipment state signal in combination with improved Hilbert-Huang transform to obtain a time-frequency domain feature result, and reconstructing an equipment state time-frequency graph from the time-frequency domain feature result; and inputting the equipment state time-frequency graph into the multi-scale sparse time-frequency convolutional network model to obtain an equipment state diagnosis result. According to the invention, equipment fault diagnosis is realized by acquiring the multi-channel data of the equipment, and the diagnosis accuracy is improved.

The invention relates to the technical field of optical detection, in particular to a notebook computer shell injection molding part surface quality detection method and system, and the method comprises the steps: irradiating a standard part with a preset wave band according to preset parameters through an infrared spectrometer to obtain a first spectrogram, and irradiating the injection molding part with the preset wave band according to the preset parameters to obtain a second spectrogram. Through the synergistic effect of a non-negative matrix factorization algorithm and a Transform fusion model, the problem of spectrum overlapping caused by addition of a flame retardant is effectively solved, through seven-degree polynomial and smooth second derivative processing, the carbonyl peak position measurement error is further reduced, and compared with a traditional Fourier transform infrared spectroscopy, the feature parameter extraction precision is further improved. The number of the carbonyl absorption peaks and the positions of the carbonyl absorption peaks are compared with the standard threshold values twice, so that the problem that some qualified products are mistakenly considered as non-qualified products easily due to deviation during judgment of the carbonyl content of the injection molding shell of the notebook computer is further solved.

The application discloses a cell heterogeneity analysis method, device and computer readable storage medium for large-scale single cell sequencing data. The application combines kernel non-negative matrix factorization with deep neural network to construct a clustering method called KNMF-DNN. The method selects some representative subsets from the overall data, and uses the kernel non-negative matrix factorization method to cluster the subsets; then, the representative subsets are used as a training set, the remaining data is used as a test set, the labels obtained by clustering are used to classify the remaining data through the deep neural network, so that the clustering of the whole data is realized. In addition, the application also uses the idea of stratified sampling to select the most representative samples, and the best division of the clustering samples and the classification samples is determined by the KL divergence. Experimental results show that, compared with other latest methods, the KNMF-DNN has more excellent results on three real scRNA sequencing data sets.

The invention provides a vibration signal identification method and system based on waterfall plot enhancement, and relates to the technical field of signal identification, and the method comprises the steps: obtaining a to-be-identified vibration signal, and generating an initial waterfall plot; calculating a gradient matrix to construct an edge feature map, and extracting a vibration feature region; feature sub-regions are divided, and an enhanced waterfall plot is obtained through non-negative matrix factorization and nonlinear transformation; extracting scene nodes based on the instantaneous energy value, and constructing a scene topological structure to generate a vibration mode descriptor; and carrying out vibration signal identification by using a preset classifier. According to the method, the distinguishability of weak vibration signal features can be effectively improved, and the recognition accuracy is improved.

The invention belongs to the technical field of image classification, and discloses a clustering method and device for graph regular non-negative matrix factorization and a medium, and the method comprises the steps: obtaining matrix information data and a to-be-clustered number; defining four initialized sparse matrixes, respectively and correspondingly constructing a matrix E, a weight matrix U, a coefficient matrix V and an assistance matrix Z, and setting an initial hyper-parameter value mu; determining a target equation and the maximum number of iterations, and iteratively solving a construction matrix E, a weight matrix U, a coefficient matrix V, an assistance matrix Z, a Lagrange operator Y1 and a Lagrange operator Y2 according to an updating formula; and outputting a final coefficient matrix V under the condition that the number of iterations is maximum or iteration converges, and obtaining a clustering result. According to the method, the target function construction method is improved, and better constraint conditions are added to capture deep information features, so that the robustness and accuracy of image classification are improved, and a better clustering effect is obtained.

The invention discloses a spectrum aliasing wireless signal depth identification method, which comprises the following steps of: performing non-negative matrix factorization on M monitored spectrum aliasing signals, constructing an objective function Q1 and solving the objective function Q1 to obtain a coefficient matrix Am and a signal type consensus matrix A * containing type information of the mth spectrum aliasing signal; and a target function Q2 is constructed, the Am is further decomposed, the position and the transmitting power of each signal in the spectrum aliasing signals are obtained, and the positions of the signals are grid positions obtained through grid division of the monitoring area. According to the invention, signal identification, signal position estimation and transmitting power estimation are integrated in a depth identification framework, and decoupling estimation is carried out on multiple parameters of a spectrum aliasing signal through a multi-layer NMF; according to the invention, deep identification can be carried out on the spectrum aliasing signal, and the type, the position and the transmitting power of the unknown signal in the spectrum aliasing signal can be identified.

The invention discloses a thin film spectrum door lock management system and method based on data analysis, and relates to the technical field of data analysis, and the method comprises the following steps: 1, collecting same-batch intact thin film spectrum data, disturbance spectrum samples and corresponding environment parameters; step 2, carrying out denoising smoothing, baseline correction, derivative operation and normalization processing on the spectral data; step 3, extracting spectrum key features to construct vectors, combining the vectors, and decomposing the vectors into a substrate and a coefficient matrix by using a non-negative matrix; step 4, respectively calculating intact and disturbance thresholds, and constructing a regression model by using environmental parameters and coefficient offset; and 5, collecting real-time spectrum and environmental parameters, carrying out projection solution on a coefficient vector after preprocessing, and dynamically adjusting a judgment reference and carrying out decision making. The method can effectively improve the situation that in the prior art, thin film spectrum door lock management mainly depends on a fixed threshold value and is difficult to adapt to disturbance and environmental changes.

The invention relates to the technical field of learning style recognition, and provides a learning style recognition method and system based on deep learning, a medium and equipment, and the method comprises the steps: obtaining learning behavior features of a learner, obtaining embedded features through preprocessing, gradually decomposing the embedded features into non-negative matrix factorization factor features through multi-level non-negative matrix factorization operation, and obtaining a non-negative matrix factorization factor features; the embedded features and the non-negative matrix factorization factor features are fused through an adaptive attention fusion mechanism to obtain fused features; wherein the multi-layer non-negative matrix factorization operation adopts a multi-layer factorization structure, and potential factors are gradually increased layer by layer; and on the basis of the fusion features, learning styles are predicted through a deep classification network. And the learning style identification accuracy is improved.

The invention belongs to the technical field of information and communication, and relates to a dynamic spectrum mapping method based on non-negative matrix factorization and deep learning. The invention provides a frequency spectrum charting method suitable for a dynamic electromagnetic environment, which combines non-negative matrix factorization and deep learning, models a dynamic frequency spectrum situation based on dynamic radiation source separation and space-time data representation, decomposes the dynamic frequency spectrum situation into space-time-frequency related low-dimensional feature factors and estimates the space-time-frequency related low-dimensional feature factors respectively, and further recovers a complete RM based on a representation model. According to the method, the problem that the accuracy of a traditional frequency spectrum mapping method is reduced in a dynamic electromagnetic environment is solved by mining the time domain characteristics of the radiation source, and compared with an existing frequency spectrum mapping method, the method has obvious estimation performance advantages in the scene of space sparse sampling and dense frequency spectrum occupied by the radiation source.

The invention discloses a gas detection precision improving method based on a multi-algorithm fusion framework. A photonic crystal resonant cavity and a tunable band-pass filtering structure are integrated in a Fourier transform spectrometer light path; collecting a wide-spectrum light intensity signal, and constructing a spectral signal database in combination with the enhancement characteristic and the filtering characteristic of the resonant cavity; zero calibration and dynamic baseline deduction operation are carried out, and self-adaptive variational mode decomposition and wavelet transform are adopted to carry out signal denoising on the spectral signals; an environment compensation model is established, and an Arrhenius type correction factor is adopted to suppress water vapor cross interference; constructing an RLS and fuzzy control combined hybrid adaptive filter; separating aliasing spectral signals by adopting a non-negative matrix factorization algorithm; establishing a quantitative relation model; in the online concentration prediction process, the initial concentration value is subjected to recursive optimization by using a hybrid adaptive filter, the deviation is continuously corrected through an environment compensation model, and an accurate concentration value is output. The method can significantly improve the accuracy and stability of multi-gas detection.

The invention relates to the technical field of territorial space planning, and discloses a territorial space planning environmental influence monitoring method, which comprises the following steps of: performing radiometric calibration and atmospheric correction on an original remote sensing image, converting the original remote sensing image into surface reflectance, screening a heavy metal sensitive wave band, eliminating scattering noise and estimating an inter-wave band noise covariance matrix; constructing an end member spectrum library, optimizing end members through non-negative matrix factorization, inverting abundance by using an alternating direction multiplier algorithm and meeting physical constraints, and calculating covariance of unmixing residual errors; and designing a deep neural network model, and inputting abundance and reflectivity characteristics. According to the method, the timeliness of pollution diffusion early warning is improved through construction of a multi-source data collaborative treatment platform, dynamic tracking of a pollution migration path, an intelligent prediction model and a real-time calculation algorithm, the spectrum interference bottleneck of multi-metal combined pollution is broken through through a spectrum analysis method, and decision support is provided for territorial space environment treatment.

The invention relates to the technical field of community detection, and discloses a robust semi-supervised community detection method and system based on correlation entropy symmetric non-negative matrix factorization, and the method comprises the following specific steps: collecting community nodes, and constructing a community detection network model; constructing an initial adjacency matrix based on a community detection network model; on the basis of a pairwise constraint propagation algorithm, supervised information learning is carried out on the adjacency matrix to obtain a new adjacency matrix; a Gaussian kernel is adopted as a kernel function, and correlation entropy is constructed; constructing an optimization problem of non-negative matrix factorization of a new adjacent matrix based on correlation entropy and graph regularization; and solving an optimization problem by adopting a semi-quadratic optimization method to obtain an optimization matrix, and obtaining a final community detection result. The method solves the problem that a constraint propagation mechanism from constraint nodes to unconstraint nodes is lacked in the prior art, and has the characteristic of high anti-noise performance.

A robust nonnegative matrix factorization (RNMF) method, in which an image sample set is split into a training set and a test set. The training set and the test set are normalized to map the image data from [0, 255] to [0, 1]. The training set matrix is pretrained by RNMF for decomposition. l2,1-deep incremental nonnegative matrix factorization (l2,1-DINMF) model is construed. The l2,1-DINMF model is configured to decompose the training set matrix into l+1 factors. After the basis matrix has been updated, and the samples of the training set and samples to be recognized are projected into a feature space. Feature representations of the test set are classified by a trained SVM classifier to obtain a predicted label, and the predicted label is compared with an actual label of the test set to calculate a recognition accuracy.

The invention belongs to the technical field of 5G mine underground positioning, and particularly relates to a 5G mine underground positioning method based on a convex relaxation constraint consistency specificity multi-view subspace clustering algorithm. Firstly, in a sampling part, sampling point position information and multiple signal intensities of a 5G access point are obtained, and high-dimensional data points are combined; in the clustering stage, the data are combined into a matrix, clustering is conducted through a non-negative matrix factorization multi-view clustering algorithm, and a clustering result is obtained through a specific optimization process and convex relaxation constraint processing; in the positioning stage, the position of the to-be-measured point is estimated according to the RSSI. According to the method, the problems that a traditional positioning method only uses a single signal parameter and a multi-view clustering algorithm does not consider data characteristics are solved, multiple signal parameters are comprehensively utilized, the algorithm is optimized, the positioning precision is improved, experiments prove that the positioning success rate of the method is higher than that of a traditional algorithm, the method is suitable for 5G mine personnel positioning, and powerful support is provided for mine safety production.

The invention aims to provide a teaching-assisting knowledge graph and a construction method thereof, and relates to the technical field of education data processing. The method comprises the following steps: constructing a teaching aid evaluation matrix according to learning behavior data, teaching aid resource data and learning stage data of a user; decomposing the teaching-assistant evaluation matrix into an educational knowledge mastery degree matrix and an educational knowledge association degree matrix through a non-negative matrix factorization algorithm; and constructing a knowledge graph according to the teaching aid evaluation matrix, the educational knowledge mastery degree matrix and the educational knowledge association degree matrix. According to the knowledge graph construction method, the relationship among the educational knowledge nodes, the learning stage nodes and the teaching-assisted resource nodes is integrated, the influence degrees of different teaching-assisted resources on the educational knowledge nodes in different learning stages of the user can be distinguished by analyzing knowledge mastering and resource use conditions, the teaching-assisted resources can be efficiently screened, and the teaching-assisted resources can be conveniently and rapidly screened. A user is helped to discover weak links in a learning process, and a learning plan and a learning method are optimized.

The invention provides a damping and noise-reducing installation method for electromechanical equipment, and belongs to the technical field of electromechanical equipment installing.The damping and noise-reducing installation method comprises the steps that firstly, a multi-dimensional vibration noise collection system is constructed, and comprehensive vibration noise characteristic data is collected; then extracting vibration noise features through wavelet transform and non-negative matrix factorization; constructing a small-scale model, and analyzing the influence of the installation parameters by adopting an orthogonal test method; obtaining a vibration noise variation matrix by using a singular value decomposition method; constructing a multi-objective optimization model based on the optimal installation parameters, and solving the optimal installation parameters by adopting a particle swarm algorithm; the parameter rationality is verified by applying an elastic damping dynamics equation set; matched damper materials and installation positions are selected; actual installation is carried out according to the optimal parameters, and the effect is verified; and finally, a parameter correction model is established, a complete knowledge base is formed, and accurate optimization of damping and noise reduction installation of the electromechanical equipment is realized.

The application discloses a kind of frequency spectrum aliasing wireless signal depth identification method, including the following steps: to the M frequency spectrum aliasing signal monitored by non-negative matrix decomposition, construct objective function Q1 and solve, obtain the coefficient matrix A of the kind information that the mth frequency spectrum aliasing signal contains m , signal kind consensus matrix A * ;Objective function Q2 is constructed, and A m Further decomposition is carried out, and the position and transmitting power of each signal in the frequency spectrum aliasing signal are obtained, wherein the position of signal is the grid position obtained by grid division to monitoring area.The application integrates signal identification, signal position estimation and transmitting power estimation in a depth identification framework, and multiple parameters of frequency spectrum aliasing signal are decoupled and estimated by multiple NMFs;The application can perform depth identification on frequency spectrum aliasing signal, and identify the kind, position and transmitting power of unknown signal in frequency spectrum aliasing signal.

This invention relates to the field of resource scheduling, and proposes a task scheduling optimization method and system based on dynamic task profile modeling. The method includes the following steps: real-time collection of task metadata of the target task and computing node operation data of the intelligent computing center, and standardization processing; extraction of discriminative features from the standardized task metadata; input of the discriminative features into the dynamic task profile modeling model to generate a dynamic task profile; wherein the dynamic task profile modeling model is configured with a spatiotemporally coupled tensor model for multi-dimensional representation of the target task, and an online non-negative matrix factorization algorithm for dynamically updating the feature matrix of the discriminative features; based on the standardized computing node operation data and the dynamic task profile, a Pareto optimal solution set is generated based on a multi-objective optimization algorithm, and the task scheduling optimization scheme is output.

The application provides a non-negative matrix factorization community detection method based on latent structure and attribute, and relates to the field of community network detection. The method mainly solves the problem that the existing method does not sufficiently utilize community network information. The method comprises the following steps: firstly, preprocessing community network data, and representing information in the network; calculating an adjacency matrix, an attribute matrix and a latent structure matrix; establishing a community detection model mainly based on direct topological structure and attribute information and supplemented by latent structure information; calculating an iterative updating rule; setting the number of iterations, initializing a community member matrix, two community-community matrices and the latent structure matrix, and adjusting a weight parameter; performing iteration to obtain a target matrix; and finally discovering a community according to the iteration result community member matrix.

A radar system for vital signs monitoring such as at least heart rate or breathing and a method for mitigating random body movement and external interference effects in vital signs monitoring such as at least heart rate or breathing by employing a radar system that is configured for providing in-phase and quadrature signals from reflected and received radar waves are provided. The problem of random body movement effects and external interference affecting vital signs monitoring is at least mitigated by decomposing and filtering a reconstructed displacement signal using a time and frequency analysis technique such as Short-Time Fourier Transform (STFT), followed by a Non-negative Matrix Factorization (NMF) operation. This decomposition allows the identification of time and frequency basis components containing the random body movement interference. Hence, the filtered signal can be reconstructed after removing the random body movement components, thus enabling reliable and robust vital-signs parameter estimation.

The present disclosure discloses a method for encrypting a visually secure image based on adaptive block compressed sensing and non-negative matrix decomposition. Firstly, the Tetrolet transform is performed on the plain image, then the sparsity degree is optimized on the sparsity matrix and the matrix scrambling is performed, such that the sparsity degree in each block region of the image matrix is equalized. Then according to the image information, the sampling number of the block region is calculated, the measurement matrix is constructed and optimized, and the image is compressed by using the optimized measurement matrix. The compressed image is then scrambled and diffused to complete the encryption process. Finally, the image information is embedded into the carrier image through non-negative matrix decomposition to obtain a visually safe ciphertext image. The decryption process is the inverse of the encryption process.