735 results about "Time resolution" patented technology

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The present invention relates to the technical field of very-short-term photovoltaic power forecasting, and in particular to a very-short-term photovoltaic power forecasting method and system for real-time control, which intend to improve precision and real-time performance in photovoltaic power forecasting. The method comprises the following steps: performing normalization processing on meteorological data, and performing a feature correlation analysis; using a BP neural network to perform short-term photovoltaic power forecasting, inputting the meteorological data and historical output data, and outputting a short-term forecasting value with a resolution of 15 minutes; and performing spline interpolation and outlier removal on an upper-layer result of the BP neural network, and using same as a long short-term memory recurrent neural network input, so as to improve a temporal resolution of forecast data and obtain very-short-term photovoltaic power forecast data with a resolution of 1 minute. The method comprehensively considers meteorological factors and uses advanced neural network models and data processing techniques to achieve photovoltaic power forecasting on a very short temporal scale while ensuring forecasting precision, making the method suitable for the real-time control and optimized operation of photovoltaic power stations.

The invention relates to the technical field of territorial space planning, and discloses a monitoring and evaluation method based on multi-source remote sensing images and geographic information vector data. Comprising the following steps: acquiring a multi-source remote sensing image and geographic information vector data of a target area; performing preprocessing and fusion analysis on the multi-source remote sensing image, generating a dynamic earth surface change detection model with high time resolution, and constructing an initial monitoring baseline in combination with geographic information vector data; according to the method, a high-frequency change detection condition and a semantic segmentation condition are loaded on the basis of an initial monitoring baseline to form a comprehensive interpretation model, a real-time early warning evaluation index is output, and a dynamic earth surface change detection model is generated by preprocessing a remote sensing image. And generating an illegal behavior distribution result by using the key identification parameters, establishing a task distribution model to optimize a supervision scheduling scheme, and finally realizing accurate management and control. According to the invention, the efficiency and accuracy of territorial space planning monitoring are improved, and technical support is provided for intelligent supervision.

The invention discloses a desertification monitoring method and system based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the weighted scoring of multi-source satellite remote sensing data according to the spectrum, cloud coverage and time resolution, and automatically switching to a data source with a higher priority when the score is lower than a threshold value. Thirdly, performing initial segmentation on the image by using a multi-scale segmentation algorithm in combination with spectrum and texture features, optimizing boundaries through a region growing algorithm, and merging regions with small spectrum differences into desertification plaques; and extracting construction land expansion and agricultural activity intensity indexes from the time series data, and establishing a quantitative relation model with desertification plaque change. And adopting a Kriging space-time interpolation algorithm for missing data to generate a continuous curved surface. And finally, fusing a multi-period segmentation result and human factor data, and outputting a desertification boundary dynamic change diagram and a human influence weight distribution diagram. According to the invention, accurate monitoring of the desertification process and quantitative evaluation of human influence are realized, and a scientific basis is provided for desertification control.

The present invention relates to the field of pipeline leakage monitoring. Disclosed is an oil and gas pipeline leakage wave identification and monitoring system. In the present invention, an mCNN is combined with LFLBs for performing feature extraction on an acoustic wave signal collected by a DFB, and the collected data improves information completeness; a three-way parallel one-dimensional CNN used in the present invention exhibits good temporal resolution and sensitivity to high-frequency feature transformations in signals; and the present invention integrates advantages of different scales, enabling the algorithm to learn more features, and incorporating the LFLBs to further extract high-level local features. An mCNN-LFLBs network model of the present invention exhibits significant innovation and advancement on the technical level, and also demonstrates extremely high value in actual application. The network model not only provides a novel and efficient technical means for critical fields such as natural gas pipeline inspection, but also introduces new ideas and methods to research fields related to deep learning and signal processing.

The invention discloses a high-efficiency time sequence optical flow method, system and device for fusing event and image information and a medium. The method comprises the following steps: constructing a time sequence feature extraction module; constructing a double-branch collaborative iteration optical flow module of the event and the image; constructing a fusion modeling and iterative optimization optical flow module based on cross attention; training a dual-branch collaborative iteration optical flow module of the event and the image and a fusion modeling and iterative optimization optical flow module based on cross attention on a training set of an MVSEC public data set to obtain weights, and testing on a test set of the MVSEC public data set to obtain an optical flow calculation result; the system, the equipment and the medium are used for implementing the method. The method has the advantages of high time resolution, high dynamic modeling capability, fine multi-modal fusion, high estimation precision and the like, the motion sensing performance in a complex scene is remarkably improved, and powerful technical support is provided for an intelligent visual system.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

The invention discloses a Chinese electronic medical record data processing method and system based on a large language model. The method comprises the following steps: acquiring a Chinese electronic medical record, and performing data preprocessing to obtain a preprocessed Chinese electronic medical record; inputting the preprocessed Chinese electronic medical record into a multi-agent based on the fine-tuned large language model, and combining the multi-agent with the input data according to different processing tasks of the multi-agent to obtain a first cue word; retrieving a medical extraction knowledge base according to the first cue word to obtain corresponding medical knowledge, and combining the first cue word to obtain a second cue word; each agent calls the fine-tuned large language model according to the corresponding second cue word, information extraction and standardization processing are carried out, and extracted medical information is output. According to the method, the accuracy of medical entity and attribute extraction can be improved, combined extraction of complex medical entities and attributes thereof is achieved, standardized mapping of medical terms and time expressions is completed, and the term ambiguity and fuzzy time analysis problems are solved.

The invention discloses a thunderstorm and gale dynamic extrapolation forecasting method based on multi-task MSTA-ConvLSTM, and relates to the technical field of atmospheric sciences, and the method comprises the steps: constructing a multi-source fusion spatio-temporal data set and a gale process record file, and obtaining a data set; a thunderstorm gale nowcasting model based on an MSTA-ConvLSTM model is constructed; according to the method, a dynamic gating mechanism of lightning data is introduced, whether a lightning probability prediction task is started or not is intelligently judged according to the lightning activity intensity, thunderstorm, gale and co-evolution characteristics of the lightning process are considered, 0-3-hour high-temporal-spatial-resolution nowcasting is achieved, the limitation of traditional single-task forecasting is broken through, a multi-task learning structure is adopted, and the prediction efficiency is improved. The model is guided to recognize the lightning occurrence probability during main task gale prediction, and the overall perception capability of a severe convection system is improved; extreme sample learning is enhanced through a weighted loss function, and output is optimized in combination with a gust coefficient model, so that the forecasting precision and practicability are effectively improved.

The invention discloses a total primary productivity estimation method and system based on multi-model coupling deep learning, and the method comprises the steps: obtaining the multi-source data of meteorological data, remote sensing images, latent heat flux, sensible heat flux and solar radiation, carrying out the quality control, missing value processing and nearest neighbor interpolation of different data sources, and carrying out the prediction of the total primary productivity. Unifying to a target spatial resolution and a time resolution; in a light energy utilization rate (LUE) model family, a solar radiation phase factor is introduced into a photosynthetically active radiation absorption ratio (FPAR) to obtain a phase modulation type FPAR, drought duration is introduced into a water stress function f (W) to obtain an exponential decay type f (W), and a GPP time sequence of a plurality of improved mechanism models is calculated according to the exponential decay type f (W); extracting spatial texture features from a remote sensing image stack by using a convolutional neural network (CNN), and performing cross-modal fusion on the spatial features and the GPP estimated by the plurality of improved mechanism models in a gating mode to form fusion representation; carrying out learning and collaborative optimization on the fusion representation of the GPP and CNN spatial features estimated by the plurality of improved mechanism models by adopting a gradient lifting tree model; and high-precision estimation of the GPP is realized through multi-model collaborative optimization. The method aims at solving the problem that a traditional light energy utilization rate model is insufficient in response under the extreme environment conditions of drought and intense radiation, the adaptability limitation of a traditional single model under the complex environment is broken through, and therefore high-precision GPP estimation under the complex environment is achieved.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention discloses a high-speed imaging method, system and device based on an SPAD array and a time counting circuit. The method comprises the following steps: acquiring original data of a photon event to obtain an original photon counting matrix of each frame; performing statistical modeling based on the original photon counting matrix to obtain a dynamic threshold matrix; performing threshold filtering based on the dynamic threshold matrix in combination with the original photon counting matrix to obtain a sparse matrix; and performing accumulation and image reconstruction based on the multi-frame original photon counting matrix to output a final image. The high time resolution of the SPAD array and the TCSPC is combined with the BNQSA algorithm, the noise problem of high frame rate imaging under extremely low illumination is solved, the system has the characteristics of high sensitivity, real-time performance and low power consumption, and a breakthrough solution is provided for a weak light scene.

The invention relates to the technical field of Tokamak plasma diagnosis and data fusion, in particular to a multi-modal fusion Tokamak high-frequency Thomson scattering reconstruction method. According to the technical scheme, the method comprises the following steps: acquiring multi-modal diagnosis data on a Tokamak device, wherein the multi-modal diagnosis data comprises high-frequency auxiliary diagnosis data and low-frequency Thomson scattering data; and performing feature engineering and physical enhancement processing on the high-frequency auxiliary diagnosis data. According to the method, through multi-modal diagnosis data fusion and interpretable KAN network modeling, the Thomson scattering time resolution is greatly improved on the premise that hardware is not transformed, and high-fidelity reconstruction of sub-millisecond transient processes such as ELM and magnetic island evolution is achieved; the method has physical interpretability, can reveal the implicit relationship between diagnostic quantities, enhances the robustness under complex experimental conditions through strategies such as modal loss, and provides key high-frequency diagnostic support for Tokamak high-performance operation and real-time control.

The invention discloses a single-frame three-dimensional imaging method based on multi-frequency RGB (red, green and blue) stripe compression. According to the method, three groups of structured light stripe patterns with relatively prime frequencies and different directions are respectively embedded into RGB channels by utilizing channel characteristics of a color camera, the patterns are switched at a high speed by a projector, dynamic superposition is completed within single-frame exposure time of the camera, space compression acquisition of time domain information is realized, and a color superposition image fused with multiple pieces of time slice information is obtained. According to the invention, a multi-stage deep neural network architecture is designed to realize separation of fringe images of a color superposition image at different moments, phase information is accurately extracted from the decomposed fringe images, and finally unambiguous high-speed three-dimensional imaging in dynamic scenes at different moments is realized. According to the method, the time resolution of three-dimensional imaging can be remarkably improved without improving the frame rate of the camera, meanwhile, the space precision and the system stability are considered, and good universality and expandability are achieved.

Systems and methods are provided for time resolution of signals produced by the emission of energy. More particularly, systems and methods are provided for measuring distance using photons propagating in a scattering medium to produce multi-dimensional, measurements of objects in a media and / or the media itself by virtue of the character of light spatially scattered and absorbed in the media resulting from the transmission of light into the media, such transmitted light having some temporal character that distinguishes it from background light, e.g., ambient sources. A method for obtaining terrestrial LiDAR data generally includes: providing a LiDAR system moving traverse to a ground canopy; emitting light pulses from the LiDAR system toward the ground canopy and terrain such that the light pulses reflect therefrom; and receiving the reflected light pulses at the LiDAR system; wherein the LiDAR system includes a monolithic module comprising a photonic device, a sampling module, a digitizing module, and a readout integrated circuit (ROIC).

The invention discloses an earth surface downlink short-wave radiation space downscaling method based on a super-resolution reconstruction technology, belongs to the technical field of meteorological data analysis, and solves the problems of insufficient solar radiation data space resolution and weak earth surface heterogeneity characterization capability in the prior art. Carrying out super-resolution reconstruction on the radiation data set by adopting a super-resolution combination model, and carrying out weighted fusion on a reconstructed data set output by the super-resolution combination model; according to the method, the output results of the multiple models are subjected to weighted fusion, weight optimization of the super-resolution sub-models is performed in combination with the high-resolution terrain factors, spatial downscaling from 5km to 1km resolution is finally realized, the spatial precision of the solar radiation data is remarkably improved on the basis of keeping the hour-level time resolution of the original data, and the accuracy of the solar radiation data is improved. And more earth surface heterogeneity information can be captured, and meanwhile, the high-time-resolution application requirement is met.

The invention relates to the technical field of clean energy, and discloses a photovoltaic power prediction method based on foundation cloud picture prediction and computer equipment, and the method comprises the steps: obtaining historical cloud picture data of a target photovoltaic field station; interpolating the historical cloud picture data to obtain interpolated cloud picture data with a preset time resolution; constructing a multi-dimensional space-time condition vector based on the motion trail feature and the shape feature of the interpolation cloud picture data; performing conditional diffusion based on the interpolation cloud picture data by taking the multi-dimensional space-time condition vector as a reference to obtain predicted cloud picture data; acquiring historical power data and historical meteorological data corresponding to the time sequence resolution of the interpolation cloud picture data; and performing power prediction based on the cloud cluster characteristics of the prediction cloud picture data, the power change characteristics of the historical power data and the meteorological change characteristics of the historical meteorological data to obtain a power prediction result.

The invention relates to a coastal city flood multi-system coupling evaluation method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the unified projection and time resolution normalization processing of the data; constructing an upstream river network one-dimensional hydrodynamic model, an urban drainage pipe network one-dimensional hydrodynamic model and an urban two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions; coupling each model and the tide boundary; performing model calibration and error constraint on the coupled multi-dimensional hydrodynamic coupling model; constructing a composite scene, taking each group of scenes as an input boundary condition to drive the multi-dimensional hydrodynamic coupling model, and obtaining a flood evolution result; and performing risk assessment and sensitivity analysis based on a flood evolution result. According to the method, a unified river network-pipe network-earth surface-tide multi-scale coupling framework is constructed, so that the defect that a traditional single model or result superposition method is difficult to reflect multi-system interaction is effectively overcome.

The invention discloses a vector flow velocity measuring device and method based on a bidirectional scanning spectral domain optical coherence tomography technology. Comprising a super light-emitting diode light source, an optical fiber coupler, a polarization controller, an interferometer comprising a sample arm and a reference arm, and a collimating lens, the collimating lens, the grating, the focusing lens group and the linear array camera are sequentially connected through a light path; the linear array camera, the computer and the two-dimensional galvanometer are sequentially connected through a line; according to the method, the fixed bias vector velocity is introduced by utilizing the bidirectional scanning technology, the dynamic light scattering optical coherence tomography method and the Doppler optical coherence tomography method are combined, decoupling of each parameter of the vector flow velocity is efficiently and accurately realized, and a quantitative index is provided for vector flow velocity analysis. The vector flow velocity measuring device based on the bidirectional scanning spectral domain optical coherence tomography technology is simple in system setting and low in cost, the time resolution is improved by about 2.5 times, and the measuring time is shorter.

The invention relates to the technical field of optical precision measurement, in particular to an ultra-high time resolution camera time characteristic detection device and method, and the device comprises a laser source which is used for generating pulse laser; the polarization modulation element is arranged on an emergent light path of the laser source and is used for modulating the pulse laser into circularly polarized light; the spectroscope is arranged on an emergent light path of the polarization modulation element and is used for decomposing the circularly polarized light into a first linearly polarized light beam and a second linearly polarized light beam; the first optical path unit is used for guiding the first linearly polarized light beam to the cylindrical optical element along a fixed optical path; the second optical path unit is used for adjusting the optical path of the second linearly polarized light beam and guiding the second linearly polarized light beam to the cylindrical optical element; and the cylindrical optical element is used for receiving the first linearly polarized light beam and the second linearly polarized light beam and respectively projecting the first linearly polarized light beam and the second linearly polarized light beam to an imaging surface of a camera to be detected to form two spatially separated light spots. The objective of the invention is to improve camera time characteristic calibration precision and efficiency.

The invention discloses a Tor network exit traffic identification system and method fusing a multi-scale LSTM and a Transform network, and belongs to the technical field of anonymous network traffic analysis and network security. The system comprises five core components, namely a multi-scale feature extraction module, a feature fusion module, a global dependency modeling module, a dynamic weighted aggregation module and a classification module. The multi-scale feature extraction module adopts parallel bidirectional LSTM branches with different time resolutions to capture a microcosmic burst mode and a macroscopic session behavior at the same time; the feature fusion module unifies the scale features to the same time sequence length and splices the scale features; the global dependence modeling module utilizes a multi-head self-attention mechanism to learn long-distance time sequence dependence; the dynamic weighted aggregation module highlights a key time slice through adaptive weight pooling; and the classification module outputs website category labels. According to the system, the recognition accuracy on a GTT23 data set is remarkably improved compared with that of an existing method, and good recognition capability and robustness are shown for various flow defense mechanisms.

The invention discloses an unmanned aerial vehicle real-time video reality augmentation method and system suitable for browser playing, and belongs to the technical field of video augmentation processing, and the method comprises the steps: in the shooting process of an unmanned aerial vehicle, obtaining a video image shot by the unmanned aerial vehicle and the pose information of an unmanned aerial vehicle camera in real time, and transmitting the video image and the pose information to a local browser; extracting the real-time resolution of the current video frame, selecting the internal reference and distortion coefficient of the camera according to the real-time resolution, calling the LLA coordinate information of various targets in the shooting area of the unmanned aerial vehicle pre-stored locally, and calculating the pixel coordinate points of the various targets in the current video frame through a target coordinate conversion strategy carried in a local browser; and based on the picture size of the current video frame, target pixel coordinate points in the picture are screened, and the target pixel coordinate points are marked in the current video frame picture after being clipped. According to the method, the problems of labeling deviation, low labeling accuracy and the like caused by network problems of traditional video reality augmentation can be solved.

The invention relates to an NDVI product generation method and device based on a diffusion model, equipment and a medium, and relates to the technical field of remote sensing data processing, and the method comprises the steps: obtaining a plurality of NDVI images corresponding to a target time period; each NDVI image is input into a pre-constructed diffusion generation model, and an NDVI product corresponding to the NDVI image is generated; wherein the diffusion generation model which is constructed in advance comprises a Stable Diffusion model and a LoRA fine tuning module; the Stable Diffusion model comprises a VAE module and a diffusion module constructed on the basis of a U-Net framework; and the LoRA fine tuning module is embedded in a Query linear layer, a Key linear layer and a Value linear layer in the attention structure. The method has the technical effect of effectively generating the high-time-resolution NDVI product by using the diffusion model.

The invention relates to the field of medical electronics and rehabilitation engineering, and discloses a throat electromyographic signal synchronous swallowing action monitoring and aspiration early-warning device which comprises a shell, a display screen is fixedly connected to the top of the shell, a connecting end is arranged outside the shell, one end of a connecting wire is arranged in the middle of the connecting end, and the other end of the connecting wire is connected with the display screen. A signal collecting assembly is arranged outside the connecting line at the other end, the collecting assembly is electrically connected with a signal processing assembly and used for receiving collected throat electromyographic signals, analyzing the signals and outputting the signals, and the signal processing assembly comprises a main control circuit board. According to the invention, through the multi-source signal synchronization module, a mechanical acceleration peak value of laryngeal knot movement is captured by using an inertial sensor to serve as a hard time reference point, and a time window is constructed to segment and align throat electromyographic signals, so that the time resolution and the analysis accuracy of swallowing action feature extraction are improved.

The invention relates to the technical field of indoor positioning, in particular to a millimeter wave CSI (Channel State Information) indoor positioning method and system based on a generative adversarial network, and millimeter waves are large in bandwidth, narrow in beam, high in time resolution and space angle resolution and capable of reducing the influence of a multipath effect. Position estimation is directly obtained from CSI data directly obtained in the millimeter wave communication process, complex signal processing and filtering steps of obtaining other parameters from CSI are avoided, and the system calculation complexity is greatly reduced. The information of the AP position is extracted from the high-dimensional observation vector by using the generative adversarial network, so that complex positioning algorithms such as Kalman filtering or graph optimization are avoided, and the calculation complexity is reduced; according to the method, AP position estimation is obtained through confrontation training of the generator and the discriminator, position coordinates of reference points do not need to serve as labels, input data are all label-free data, dependence on marked data is avoided, and prior knowledge such as the AP or the reference points is not needed.

The invention discloses a full-automatic high-flux space-time resolution fluorescence spectrum imaging system and method, and relates to the field of fluorescence signal spectrum. Comprising a working table, a sample table, an objective lens, a mechanical arm sample injection module used for grabbing the sample table loaded with a sample in advance and placing the sample table on the working table, and a picosecond pulse laser output module used for outputting a laser beam to the objective lens, the fluorescence signal detection module and the control module are used for simultaneously collecting spectral data and dynamic data of a fluorescence signal generated after the sample is focused by the laser beam; the sample table and the objective lens are sequentially arranged on the workbench at intervals in the vertical direction from top to bottom; the mechanical arm sampling module, the picosecond pulse laser output module and the fluorescence signal detection module are respectively in communication connection with the control module. According to the invention, simultaneous acquisition of spectrum data and dynamic data of fluorescence signals can be fully automatically realized.

The invention discloses a true triaxial hydraulic fracturing multi-scale monitoring system fusing acoustic emission, ultrasonic wave and strain linkage, and relates to the technical field of true triaxial hydraulic fracturing, which comprises a device main body, a horizontal stress loading system, a hydraulic servo pump pressure module, an acoustic emission monitoring system, a crack strain monitoring system and an ultrasonic monitoring system, according to the true triaxial hydrofracture multi-scale monitoring system integrating acoustic emission, ultrasonic wave and strain linkage, the hydrofracture process is comprehensively monitored through the acoustic emission monitoring module, the ultrasonic wave monitoring module and the strain gauge array monitoring module in an experiment, and the three monitoring means are matched with one another; and a data support with high temporal-spatial resolution is provided for the whole crack evolution process.

The invention relates to the technical field of remote sensing and computer vision, and discloses a multi-source image time domain super-division method and system for giant constellations, and the method comprises the steps: obtaining multi-source remote sensing image data which comprises an SAR image, an infrared image and a visible light image; constructing a cross-modal feature space, mapping multi-source remote sensing image data to a unified semantic space, and realizing cross-modal feature alignment; the SAR image or the infrared image is converted into a visible light modal image based on a neural Schrodinger bridge model, and the neural Schrodinger bridge model is decomposed into a plurality of Markov chain sub-problems and solved through adversarial learning and a staged optimization strategy; two-way semantic constraints are introduced into the neural Schrodinger bridge model, and the two-way semantic constraints comprise visual feature matching constraints and text guiding constraints, so that semantic consistency of the generated image in a CLIP feature space is optimized; and the visible light image sequence after time domain super-division is output, and the time resolution is improved.

The invention discloses an awareness perception quantum gravitational nerve acceleration unified computing platform and method based on formalized verification, and belongs to the field of high-performance computing, medical AI and quantum information processing. A quantum gravitational coupling neural processing unit (QGCNPU); a consciousness information integral calculation core (phi ICC); formalizing a receipt chain verifier (FRCV); and the mirror reflection symmetry correction module (MRSCM) is used for eliminating the continuous projection mistake and the discrete Boolean mistake through topological equivalence verification of a local observer and a global GodCam visual angle. The system adopts an FPGA + ASIC + quantum processor three-layer heterogeneous architecture, and supports real-time pathological modeling (precision gt; gt) of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ALS; 99.7%), drug target discovery (acceleration by 50 times) and consciousness state monitoring (time resolution lt; and meanwhile, the mathematical proving performance and the clinical auditing performance of the calculation process are ensured, and the requirements of the global 152 billion dollar neural science and technology market and the 89 billion dollar quantum calculation industry are met.

The invention relates to the technical field of remote sensing data fusion, and discloses a remote sensing enhanced time sequence prediction method based on a physically-assisted multi-modal Transform, which utilizes the macroscopic coverage advantage of remote sensing data and the high time resolution characteristic of time-space sequence data. Complementary information of the two is fully utilized to avoid information loss and reconstruction errors caused by up-sampling or down-sampling, and the downstream task effect is remarkably improved. According to the method, time sequence features are efficiently processed through cross Transformer, meanwhile, the efficiency of remote sensing feature processing is also very high, in order to effectively maintain feature distribution characteristics of different modal data, a special parameter configuration scheme specially arranged for all modals is adopted, directional information exchange is achieved through multi-head cross attention inside, and the method has the advantages of being simple in structure and convenient to operate. One mode is allowed to selectively pay attention to the key features of the other mode, the fusion efficiency is remarkably improved, and deep fusion of the features of the two modes is achieved.