905 results about "Compressed sensing" patented technology

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 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 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 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 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.

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 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.

The invention discloses an air quality monitoring data processing transmission method and device, and relates to the technical field of data transmission, and the method comprises the steps: collecting multi-modal environment parameters through a distributed sensor array, calculating a pollutant concentration covariance matrix of adjacent monitoring nodes, and judging an abnormal monitoring event according to a condition number; hierarchical compression is carried out by adopting a compressed sensing algorithm driven by space-time correlation; calculating a channel comprehensive quality index, predicting a channel state in combination with a deep learning model, dynamically selecting a transmission protocol combination, and obtaining a multi-protocol data stream; performing space-time interpolation reconstruction, calculating a reconstructed hash, writing the reconstructed hash into the block chain, and performing integrity verification; the weight of the deep learning model is updated, the compressed sensing algorithm is optimized, energy distribution is optimized according to the transmission load, and iterative optimization of the deep learning model is carried out. According to the invention, data compression is carried out by using the spatial-temporal correlation driven compressed sensing algorithm, so that the data transmission burden is reduced.

The invention provides a transcranial alternating current stimulation method and system with an automatic adjustment function, and relates to the technical field of transcranial alternating current stimulation, and the method comprises the steps: obtaining, compressing and sampling a multi-channel electroencephalogram signal, carrying out the multi-channel reconstruction through the physiological priori and the related information of adjacent brain regions, extracting the feature parameters of target brain waves, and carrying out the reconstruction of the target brain waves. Based on the parameters, dual-stage transcranial alternating current signals of prior stimulation and post stimulation are output; by introducing an adjacent matrix or a graph Laplacian operator in a compressed sensing acquisition and reconstruction link, high-fidelity recovery of cross-brain region oscillation can be maintained under the conditions of low sampling rate and noise; performing layered screening and dynamic fine tuning on characteristic frequency bands of different brain regions by combining general and individualized prior; the target brain wave phase is locked in advance in the first stimulation stage, and the stimulation intensity of each brain region is independently or cooperatively adjusted in the later stimulation stage, so that the requirement of coupling regulation and control of multiple brain regions is met, and the closed-loop intervention effect on nerve states such as insomnia and anxiety is improved.

The invention provides a tight gas micro-crack detection method and system based on seismic data, and relates to the technical field of tight gas micro-crack detection, and the method comprises the steps: obtaining seismic wave data, and obtaining the preprocessed seismic wave data through wavelet transform denoising, compressed sensing reconstruction and median filtering smoothing processing. Using a preset multi-dimensional feature extraction model to extract micro-crack related features from the preprocessed data, including crack edge, curvature, frequency and similarity features, and constructing a multi-dimensional feature matrix; and based on the feature matrix, modeling and training are carried out by adopting a convolutional neural network model of a spatial attention mechanism added with a channel, and then the crack probability of each to-be-detected position is predicted. And carrying out smoothing, denoising and connectivity analysis on the crack probability image through image conversion and optimization processing to obtain a final crack detection result. According to the invention, the precision of micro-crack detection can be effectively improved.

The invention discloses a laser ultrasonic defect imaging method based on sampling-imaging double-order compressed sensing, and relates to the technical field of laser ultrasonic detection and material defect detection, and the method comprises the following steps: carrying out sparse scanning on a to-be-detected sample through a random sparse sampling strategy; according to a compressed sensing principle, global ultrasonic intensity distribution of full-matrix scanning under a high sampling rate is reconstructed, and global sparse positioning is realized through extreme value detection; then, on the basis of global sparse positioning, carrying out local fine scanning by adopting high spatial resolution; and generating a sparse imaging result with low spatial resolution by using global sparse positioning and local fine scanning data through a synthetic aperture focusing technology, and performing high-resolution compressed sensing reconstruction imaging by using a compressed sensing principle again. Therefore, by adopting the laser ultrasonic defect imaging method based on sampling-imaging double-order compressed sensing, efficient imaging of tiny defects can be realized, and scanning points are reduced while the imaging precision is improved.

The invention discloses a quantum measurement and control data processing method and device, and belongs to the technical field of data processing.The quantum measurement and control data processing method and device comprises the following specific steps that firstly, wavelet packet decomposition is conducted on an original measurement signal through a quantum noise feature extraction module, and a quantum noise feature extraction module is obtained; carrying out matched filtering with a preset quantum noise fingerprint database; and 2, performing quantum state reconstruction on the filtered signal by adopting a compressed sensing algorithm, wherein a measurement matrix is dynamically generated according to the Hamiltonian of a quantum system. Through combination of wavelet packet decomposition (time-frequency locality) and a dynamic measurement matrix (Hamiltonian relevance), compared with a traditional Fourier method, quantum state reconstruction errors are greatly reduced, time sequence dependence is captured through an LSTM network, physical legality is guaranteed through a quantum main equation, control parameter speed is optimized, 2ns-level time sequence precision is achieved through an FPGA segmentation correction algorithm, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. And the real-time requirement of the quantum error correction code is met.

The invention discloses an adaptive blocking compressed sensing image super-resolution reconstruction method and system. The method comprises the following steps: acquiring a low-resolution image and carrying out adaptive blocking on the low-resolution image; establishing a sparse representation model of the high-resolution image by using a discrete cosine transform basis; establishing an observation dimension reduction model by using the binary block diagonal matrix to obtain a relationship between a high-resolution image and a low-resolution image; performing compressed sensing super-resolution reconstruction solution on the low-resolution sub-block images by using a weighted iterative least square algorithm to generate high-resolution image blocks, and performing constraint by using Shearlet transformation and dual-scale total variation regularization in the reconstruction process; and splicing the generated high-resolution image blocks according to the position indexes to obtain a complete high-resolution image meeting the requirements. According to the method, the problems of unreasonable blocking caused by single blocking basis and limited reconstruction effect caused by insufficient consideration of prior information in the existing method are solved.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention discloses a cable accessory nondestructive testing method based on image recognition, and relates to the technical field of data encryption, and the method comprises the steps: extracting a photon signal from an anti-noise coding control template, generating an anti-interference partial discharge light spot image in combination with a compressed sensing reconstruction algorithm, and carrying out the alignment fusion of the anti-interference partial discharge light spot image and LiDAR point cloud data of a cable accessory, generating spatial registration feature data; based on the neural radiation field network, constructing a three-dimensional radiation field model, inputting spatial registration feature data, generating time-space domain deformation field data and partial discharge hot spot distribution data, and generating a defect candidate region by using a spatial weighted fusion algorithm; a deep residual network is used as a framework, a transfer learning strategy is combined, a cable accessory defect model is constructed, a defect candidate area is input, and a cable accessory health state report is generated. According to the method, the three-dimensional radiation field model is constructed by using the neural radiation field network as a framework, accurate positioning and characterization of defects are realized, the detection reliability is improved, and the method is suitable for micro-defect identification.