1080 results about "Compressed sensing" patented technology

The invention relates to the technical field of multi-axis linkage numerical control machining, and discloses a multi-axis linkage numerical control machining state monitoring method and system.The method comprises the steps that vibration, acoustic emission and cutting force signals in the multi-axis linkage numerical control machining process are collected and preprocessed, and first multi-source sensor data are obtained; performing feature extraction and compressed sensing to obtain a low-dimensional feature subset; performing fusion judgment to obtain an abnormal state detection result; training a deep learning model comprising a backbone network and a plurality of branch networks to obtain a state evaluation model; and inputting second multi-source sensor data collected in real time into the state evaluation model for state evaluation to obtain a processing state classification result and a parameter estimation value. According to the method, cooperative state evaluation and prediction simulation of multiple machine tools are realized.

The present invention provides a flight attitude control method based on inertial navigation technology, which relates to the field of aircraft control technology and includes an attitude control system. The attitude control system includes a quantum inertial navigation fusion module, a distributed multi-source collaborative inertial navigation module, a real-time error compensation module based on deep learning, an energy collection and self-powered inertial system module, a distributed attitude data management module based on blockchain, a multimodal fusion and virtual twin collaborative control module, and a terahertz communication and inertial navigation fusion anti-interference module. The present invention uses a quantum annealing algorithm combined with a compressed sensing algorithm to optimize quantum sensor data, and then combines the D-S evidence theory and the particle swarm optimization algorithm in the distributed multi-source collaborative inertial navigation module to fuse multi-source data, which can deeply mine the deep connection and complementary information between different sensor data, break the simple data level superposition limitation, and greatly improve the accuracy of the attitude information after fusion.

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system is improved.

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

The invention discloses a target positioning method and system based on a single reference station, and relates to the field of wireless positioning, and the method comprises the steps: generating vortex electromagnetic waves carrying orbital angular momentum based on a transmitting end of a reference station, and generating a time-varying phase modulation transmitting signal and a metasurface time-varying coding sequence through a programmable metasurface; a time-varying phase modulation emission signal forms a multipath superposition effect in a propagation environment, a receiving end obtains a mixed signal of a direct wave and a reflected wave, and the receiving end carries out OAM mode separation on the mixed signal of the direct wave and the reflected wave through a spiral phase plate and an annular array to obtain signal components in different modes; and based on the signal component and the metasurface time-varying coding sequence, reconstructing an equivalent virtual array response by adopting a compressed sensing algorithm. According to the invention, the orbital angular momentum modal separation technology and the metasurface dynamic coding technology are creatively fused, and high-precision real-time direction finding in a complex multi-path environment is realized under a single base station architecture.

The invention discloses a special environment laser radar system and method based on polarization modulation and laser coding. The laser radar system comprises a polarization modulation laser emitting unit, a multi-channel receiving unit and a compressed sensing reconstruction unit. The polarization modulation laser emission unit comprises a laser, a beam expanding module, a spatial light modulator, a micro galvanometer array, a spatial filter and a first focusing lens which are arranged in sequence; the multi-channel receiving unit comprises a second focusing lens, a polarization beam splitter and a single-pixel detector array which are arranged in sequence; and the compressed sensing reconstruction unit comprises a compressed sensing processing module, an image reconstruction module and a loop adaptive control module which are arranged in sequence. The anti-interference capability in a complex environment is enhanced through polarization modulation, the detection resolution and the signal-to-noise ratio are improved in combination with laser coding, the imaging quality and stability of the laser radar in a special environment are greatly improved, and the method can be widely applied to the fields of military reconnaissance, disaster rescue, unmanned driving and the like.

The invention discloses an integrated sensing and backscatter communication method and device based on backscatter, and relates to the technical field of wireless communication, and the method comprises the steps: receiving an open-circuit pilot frequency receiving voltage and a short-circuit pilot frequency receiving voltage generated by a receiving antenna based on a pilot frequency signal; determining a tag antenna mode pilot frequency voltage component and a clutter tag structure combined pilot frequency voltage component, and correspondingly determining a tag antenna mode reflection coefficient and a clutter tag structure combined reflection coefficient based on the tag antenna mode pilot frequency voltage component and the clutter tag structure combined pilot frequency voltage component by adopting a compressed sensing algorithm; and transmitting a data signal to a receiving antenna through the tag by adopting binary phase shift keying, collecting a data receiving voltage, and completing signal demodulation by adopting the data receiving voltage, a tag antenna mode reflection coefficient and a combined reflection coefficient to obtain a bit mapping symbol of the data signal. The backscatter communication is realized through combination of backscatter and tag impedance modulation, and the backscatter communication performance is improved.

The invention discloses an Internet of Things panoramic perception data conversion method. The method comprises the following steps: acquiring a multi-source heterogeneous Internet of Things perception data set; performing space-time alignment preprocessing on the multi-source heterogeneous Internet of Things sensing data set to generate a standardized space-time data set; inputting the standardized spatio-temporal data set into a feature fusion model, and generating a panoramic perception feature vector set through a dynamic weight distribution algorithm; performing adaptive compressed encoding on the panoramic sensing feature vector set to generate a compressed sensing data stream; and inputting the compressed sensing data stream into the multi-modal decision model to generate a real-time abnormal decision instruction. The method has the following advantages and effects that the problem of panoramic feature expression distortion caused by dynamic value imbalance of the multi-source heterogeneous sensing data is effectively solved, and then the reliability of real-time decision making is improved.

The invention provides a training method of a document image tampering detection model and a tampering detection method and device.The training method of the document image tampering detection model comprises the steps that multi-scale visual domain features are extracted from a sample document image, and multi-scale frequency domain compressed sensing features are extracted from frequency domain information; acquiring tampered area edge mask data from the document image; fusing the multi-scale visual domain features and the multi-scale frequency domain compressed sensing features to obtain multi-modal fusion features; performing semi-supervised training on the multi-scale sensing network by taking the multi-scale visual domain feature as a sample feature of a first prediction head, taking the multi-modal fusion feature as a sample feature of a second prediction head, taking a real label or a pseudo label as a sample label and taking joint loss as a loss function to obtain a document image tampering detection model; according to the method provided by the invention, document image tampering pixel-level detection under low labeling cost is realized, and the detection precision of a document image tampering detection model is improved.

The invention discloses a multi-water quality factor prediction method based on feature compression and interpretable feature fusion. The method comprises the following steps: acquiring multi-sensor data, and calculating to obtain a physical derivative variable based on the multi-sensor data; multi-sensor data and physical derivative variables serve as input variables to train a CS-CNN-Transform prediction model, local features of the input variables are extracted through CNN, and a pooling layer in the CNN is replaced by a compressed sensing layer; introducing a multi-layer attention mechanism to carry out adaptive feature fusion on the input local features to obtain fusion features; inputting the fusion feature into a Transform, and outputting a predicted value; key features influencing water quality changes are dynamically recognized in the model training process through a multi-layer attention mechanism; the contribution degree of the input variables and the interaction between the input variables are calculated through the SHAP algorithm, and input variable screening is carried out. The prediction precision and the interpretability of the model can be improved.

The invention relates to the technical field of geological monitoring, and discloses a self-adaptive phase unwrapping foundation SAR deformation monitoring method and system. The method comprises the steps of collecting ground-based SAR echo data, extracting phase gradient features and noise distribution, constructing a model based on a compressed sensing theory to solve a phase unwrapping result, establishing a deformation field dynamic evolution model fusing multiple factors, and generating a deformation monitoring value through an adaptive weighted fusion algorithm. And an abnormal deformation detection model is also constructed to realize adaptive re-calibration of model parameters, and a multi-sensor collaborative calibration network is constructed to improve the monitoring precision. The problem of phase unwrapping is effectively solved, multiple influence factors are comprehensively considered, surface deformation can be accurately monitored in real time, abnormity can be timely found, the model can be calibrated, multi-sensor data can be fused, and the method has important application value in the fields of geological disaster early warning, engineering structure safety assessment and the like.

The invention belongs to the technical field of signal processing, and particularly relates to an image compressed sensing reconstruction method based on generation prior diffusion. According to the method, the problem of insufficient utilization of prior information in an existing data-driven image compressed sensing method is solved, a deep expansion network and a diffusion generation model are fused, and a two-section image reconstruction method based on generation of prior diffusion is provided. In the training of the first stage, a variation encoder is used for learning compact prior expression of a natural image, and a dynamic near-end mapping module is designed in a deep expansion reconstruction network to embed prior information. And in the second stage, freezing parameters of the prior learning model, inferring image prior by applying the lightweight diffusion model, and embedding the image prior into the deep expansion network to complete image reconstruction. Experimental results on a plurality of reference sets show that compared with the existing method, the image compressed sensing reconstruction method provided by the invention can improve the reconstruction quality of the natural image.

The invention relates to the technical field of ground resistance detection, in particular to a single-core cable ground resistance detection method capable of resisting strong power frequency interference. High-frequency signals are injected in a non-contact mode through a coupling type capacitor clamp, background voltage and current signals are collected firstly, and injection frequency far away from power frequency and harmonic waves is selected through Fourier analysis; cancelling background noise by adopting a normalized least mean square adaptive filter to generate an enhanced signal; the signal is down-converted to a base band through digital quadrature demodulation, a Kalman filter estimates the amplitude and the phase in real time, and the state and noise parameters are periodically calibrated through a compressed sensing algorithm; and finally, loop impedance is calculated according to the voltage and current amplitudes and the phase difference to obtain grounding resistance. The method has the advantages of being non-contact, high in safety, strong in anti-interference capability, high in measurement precision and the like, and is suitable for monitoring the grounding state of the cable in a complex electromagnetic environment.

The invention provides a hyperspectral snapshot compression imaging method and system based on a space-spectrum prior decoupling model, high-quality reconstruction is realized through decoupling optimization and a deep expansion network, and the method comprises the following steps: constructing a training data set containing a compression measurement image and a corresponding reconstruction spectrum; establishing an objective function fusing space and spectrum prior, converting the objective function into constrained optimization, and converting the constrained optimization into three sub-problems of linear reconstruction, space prior and spectrum prior by adopting a semi-quadratic splitting method; a deep expansion network is designed to alternately solve sub-problems: a linear sub-problem is solved through analysis, and a space / spectrum sub-problem is subjected to implicit prior modeling through a private network, so that end-to-end reconstruction is realized; a mixed loss function is adopted to optimize model parameters, and images can be reconstructed in real time after training is completed. Space and spectrum prior decoupling is carried out, space structure details and spectrum features are respectively captured through an independent network architecture, the problem of mutual interference of joint modeling in a traditional method is solved, and high-quality spectrum image reconstruction is realized.

The invention relates to an end-to-end deep learning-based phase mask constrained image reconstruction method, apparatus and device, and a storage medium. The method comprises the following steps: acquiring hyperspectral image data, inputting a single two-dimensional hyperspectral image into a CASSI imaging system, and constraining the single two-dimensional hyperspectral image by using a binary phase mask; performing spectral information compression on the constrained single two-dimensional hyperspectral image, performing image reconstruction through a compressed sensing algorithm, and outputting a measurement value; a measurement value output by the CASSI imaging system is constrained by using regularization constraint; randomly initialized or pre-trained weights are used to initialize the U-Net network, the training set and the verification set are used to carry out joint training and verification on the phase mask and the U-Net network, and joint optimization of the phase mask and the U-Net network is realized; and carrying out hyperspectral image reconstruction by using a U-Net network, and evaluating the quality of a reconstructed image by continuously iterating and jointly optimizing parameters of a phase mask and a network model.

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 relates to the technical field of computers, in particular to a hyperspectral compressed sensing reconstruction method based on depth double-prior progressive fusion, which comprises the following steps of: 1, shooting to obtain an original compressed measurement image, and normalizing the original compressed measurement image to obtain a normalized compressed measurement image; 2, constructing an objective function based on a compressed sensing reconstruction problem, and optimizing the objective function to obtain a final optimized objective function; 3, establishing a self-supervised learning model for hyperspectral compressed sensing reconstruction according to the normalized compressed measurement image; the self-supervised learning model comprises outer loop iterative optimization and two pieces of inner loop network training; and 4, solving the optimized final objective function by using a self-supervised learning model, and obtaining a final reconstructed image by fusing depth priori items obtained in two internal loop network training processes. The problem that an existing self-supervised spectrum reconstruction method is low in imaging quality is solved.

The invention discloses a compressed sensing MRI (Magnetic Resonance Imaging) reconstruction method and system based on a double-domain fusion expansion model. The method comprises the following steps: acquiring an MRI image, preprocessing the MRI image to obtain a preprocessed image and a corresponding compressed sensing measurement value, and constructing a data set; training, testing and verifying the double-domain fusion expansion model by using the data set; and outputting a reconstructed image from the MRI image after mask sampling by using a double-domain fusion expansion model. According to the double-domain fusion expansion model, a complex convolutional neural network and a K-space attention mechanism are introduced for complex value data processing and double-domain information fusion in compressed sensing MRI reconstruction. The complex value data is processed through the complex network, the amplitude and phase information of the complex field is fully utilized, the detail recovery capability and the noise suppression effect of the model are enhanced, and particularly, excellent robustness is shown at a low sampling rate. The method is excellent in performance in complex MRI data reconstruction, and has high reconstruction precision and generalization ability.

The invention discloses a circulating fluidized bed combustion reduced-order prediction method based on enhanced compressed sensing and a time convolutional neural network. In the off-line preparation stage, firstly, original variable information in a multi-phase combustion field is obtained, the time average value of grid variables is calculated, and a result snapshot matrix is decentralized in time; and obtaining a POD mode and a corresponding mode coefficient thereof, and obtaining the optimal arrangement positions of the time convolutional neural network and the sensor. In the online prediction stage, the modal coefficient at the current moment is reconstructed based on the position of the sensor, the future modal coefficient is predicted based on the time convolutional neural network, and the flow field is reconstructed through linear combination with the POD modal. According to the method, on-line rapid prediction of full-field variables at the future moment is realized by utilizing sparse sensor data at the current moment, the defect that a traditional CFD method cannot be applied to industrial production rapid prediction is overcome, and the blank of multiphase combustion in the circulating fluidized bed in the aspect of on-line flow field rapid prediction is filled.

A secret image sharing method based on deep compressed sensing image steganography belongs to the field of image information security, and comprises the steps of training set preprocessing and model training; performing compressed sensing sampling on the carrier image and the secret image by using a sampling network to obtain corresponding measurement values; mapping the measured value into a proxy image through a forward projection process; hiding the proxy secret image into the proxy carrier image by using a reversible neural network to generate a high-dimensional proxy steganographic image; mapping the proxy steganographic image back to a low-dimensional measurement value through a back projection process, and mapping the proxy steganographic image into a high-dimensional proxy steganographic image through a forward projection process; performing reverse revelation of secret information by using a reversible neural network to recover a proxy image; and reconstructing the recovered proxy image through the reconstruction network to obtain a recovered carrier image and a secret image, and completing sharing. According to the invention, the storage and transmission overhead is reduced, the transmission efficiency is improved, the shared image capacity is increased, and safe and efficient secret image sharing is realized.

The invention relates to a wavenumber domain compressed sensing damage detection method suitable for a plate-shaped structure. The method comprises the following steps: S1, constructing a compressed sensing equation; s2, solving the compressed sensing equation, and reconstructing to obtain a wave number domain signal of a full wave field; and S3, selecting a threshold value for the reconstructed wavenumber domain data for filtering, returning to a spatial domain through inverse Fourier transform, and carrying out merging processing on results by adopting a root mean square method to obtain a damage morphology graph. According to the method, the sparse basis matrix meeting the compressed sensing requirement is constructed by optimizing the sparse representation of the signal, so that the precision of full-wave field signal reconstruction is remarkably improved; the number of sampling points is greatly reduced, the full-wave-field data acquisition efficiency is improved, and the potential of breaking through the limitation of the Nyquist sampling law is achieved; the position and the shape of the damage are detected, the detection effect is good, and the detection efficiency is high.

A heavy traffic abnormal event detection and state discrimination system belongs to the field of intelligent traffic and comprises a data acquisition and labeling module, an image processing module, a traffic state analysis module, a state determination and abnormal event identification module and a transfer learning module. The data acquisition and labeling module comprises a data acquisition unit and a data labeling unit; the image processing module comprises an image enhancement unit and an image compression and reconstruction unit; the traffic state analysis module comprises a traffic bottleneck identification unit and a traffic flow wave analysis unit; the state judgment and abnormal event recognition module comprises a vehicle statistical unit, a congestion state recognition unit, a state transition analysis unit and a compressed sensing model establishment unit; and the transfer learning module comprises a transfer learning unit. According to the method, the false detection rate of the traffic flow abnormal events in bad weather can be reduced, so that the traffic abnormal events can be detected all day long, and the occurrence probability of the traffic events in bad weather conditions can be reduced.

The invention relates to the technical field of safety and environmental protection, in particular to a negative-pressure circulating explosion-control dust removal method suitable for combustible dust places, which comprises the following steps: firstly, sealing a bin body and injecting carbon dioxide-nitrogen inert gas to generate a low-oxygen environment; then, a flow guide angle-adjustable cyclone separator, a dielectric barrier discharge plasma reactor and a waste heat thermo-acoustic transducer are driven to cooperatively capture dust, and sound node amplitude synthesis capture data are obtained through compressed sensing of a microphone; inputting the environment data and the capture data into a reinforcement learning process containing a micro-thermochemistry model, and outputting instructions for adjusting negative pressure pulse, inert flow, sound field intensity and cyclone geometry; in combination with distributed radio frequency imaging data, an adversarial digital twinborn model is generated to predict explosion free energy, and automatic rollback or shutdown is carried out when the explosion free energy exceeds a threshold, so that a safe, efficient and low-energy-consumption closed loop is formed.

The invention discloses an observable monitoring method for an edge computing network, which comprises the following steps of: acquiring the running states of a plurality of eBPF probes on edge equipment, analyzing the access condition of the eBPF probes to the same kernel mounting point, identifying and monitoring conflicts and generating a probe conflict mapping table; based on the conflict relation, an improved distributed mutual exclusion algorithm is combined with a data fingerprint mechanism, sampling authority coordination between probes is carried out, repeated collection is avoided, and a probe sampling scheduling sequence is generated; predicting a future key event according to a historical event mode, and dynamically adjusting a time sequence interleaving strategy of each probe to form an optimized time slice distribution table; and each probe executes cooperative sparse sampling according to the time slice distribution table and the data fingerprint database, and reconstructs complete monitoring information from sparse data based on a compressed sensing theory to realize fault diagnosis. According to the invention, the system overhead of multi-probe monitoring can be obviously reduced, and low-overhead and high-precision network observability is realized.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention discloses an image super-resolution reconstruction method based on compressed sensing, and belongs to the field of image reconstruction. The problems that the updating speed of existing hardware cannot meet the requirement for signal precision, and on the premise that the hardware cost is not increased, an image with higher definition cannot be reconstructed are solved. The invention discloses an image super-resolution reconstruction method based on compressed sensing, and the method comprises the steps: designing a dictionary learning process, and obtaining high-frequency component parameters needed in image restoration; comprising the steps of constructing a training data set and performing dictionary training; wherein a corresponding low-resolution image is obtained by carrying out quality reduction and scale reduction calculation on an image so as to carry out a process of training a data set; constructing an over-complete dictionary pair by using a K-SVD algorithm, and training the dictionary; firstly, a reduced low-frequency component is obtained, and then the resolution is reconstructed. According to the method, the GPSR algorithm is utilized, the limitation of fixed sparseness of a traditional OMP algorithm is overcome, a more accurate sparse coefficient is obtained, and the precision of the reconstructed image is further improved by adopting a bilateral filtering mode.

The invention discloses an air federated learning realization method for collaborative optimization of client scheduling and model compression. The invention realizes the air federated learning realization method for collaborative optimization of client scheduling and model compression. Along with rapid development of federated learning in a wireless network, air computing based on a multiple-input-multiple-output technology is widely concerned due to high communication efficiency of the air computing. However, in a resource-limited large-scale device scene, channel interference, device heterogeneity and limited spectrum resources significantly restrict the convergence rate and model performance of federated learning. In order to deal with the challenges, the invention provides an air federated learning framework combining compressed sensing and client scheduling, and by collaborative design of model parameter compression, multi-antenna beam forming and dynamic equipment selection strategies, the total communication overhead of each round of training is minimized, and meanwhile, the convergence of a global model is guaranteed. In order to balance training cost and model quality, a joint optimization problem based on calculation-communication cost and model precision loss is provided. In order to solve the non-convex optimization problem, an original problem is decoupled into two sub-problems. Firstly, an optimization problem of a pre-coding and post-processing matrix is designed for a given user scheduling result to minimize a gradient aggregation error. Then, a novel user scheduling algorithm based on channels and data is provided to obtain an air aggregation result.

The invention discloses a low-rank DCT (Discrete Cosine Transform) compressed sensing algorithm realized based on an FPGA (Field Programmable Gate Array), and relates to the technical field of image data processing, and the compressed sensing algorithm comprises the following specific steps: carrying out blocking processing on an input signal, dynamically adjusting the blocking granularity based on a calculation result of a signal local gradient entropy, and when the gradient entropy is higher than a set threshold value, adopting fine granularity blocking, when the frequency is lower than a threshold value, coarse granularity partitioning is adopted, the partitioning granularity is dynamically adjusted within a preset range, discrete cosine transform is conducted on the partitioned signals, the spectrum entropy of coefficients after transform is calculated, and the number of the reserved low-frequency coefficients is dynamically adjusted according to the magnitude of the spectrum entropy. According to the dynamic blocking strategy based on the gradient entropy, the compressed sensing algorithm can automatically adjust the blocking granularity according to the local features of signals, so that the compression ratio of a complex texture region and a smooth region is improved, the blocking boundary is dynamically judged through the gradient entropy, it is ensured that pixel affiliation of an edge region is more accurate, and the blocking effect is reduced; and complete structure information is reserved for subsequent reconstruction.

The invention relates to an electric power system load fluctuation section data compression method based on compressed sensing, which is characterized by comprising the following steps of: 1, dividing an event section and a non-event section based on available load power consumption data; 2, dividing a non-event section into a fluctuation section and a stationary section; 3, performing compression processing on the fluctuation segment data extracted in the step 2 by adopting a compressed sensing-based data compression and reconstruction method; 4, reconstructing the original data of the fluctuation section data by adopting an optimized SAMP method; and 5, evaluating the reconstruction precision by taking the RMSE and the MPPE as evaluation indexes. According to the method, the data compression efficiency can be further improved while certain data reconstruction precision is ensured.