4030 results about "Very high resolution" patented technology

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention belongs to the technical field of remote sensing super-resolution images, and particularly relates to a remote sensing image super-resolution system and method based on an adaptive Mamba-attention network. Comprising a feature extraction module used for carrying out shallow feature extraction on an input low-resolution image to obtain shallow features; the multiple cascaded adaptive state space blocks are used for processing the shallow layer features to obtain reconstruction features; and the reconstruction module maps the reconstruction features to a target resolution space through sub-pixel rearrangement operation to obtain a high-resolution remote sensing image. High-frequency details and a low-frequency structure are cooperatively processed in a feature space by using the remote sensing frequency sensing modulation module, and high-resolution output is generated by combining sub-pixel rearrangement up-sampling, so that high-quality reconstruction of a complex remote sensing scene is realized.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention belongs to the technical field of super high-rise building three-dimensional reconstruction, and particularly relates to a multi-source data fusion super high-rise building group live-action three-dimensional model construction method. According to the method, an initial three-dimensional model is generated through a series of processing such as aerial triangulation encryption and triangulation network construction based on multi-source image data, in the process of recognizing a fuzzy region and performing data supplementary collection, regions with texture loss and structure distortion in the initial three-dimensional model can be positioned, supplementary collection requirements are determined according to characteristics of different regions and a preset threshold value, and the recognition accuracy of the initial three-dimensional model is improved. The method comprises the following steps of: performing oblique photography on an unmanned aerial vehicle to acquire data in a supplementary manner, fusing the data with original data, extracting a building structure contour, matching high-resolution texture data, performing texture binding and processing and the like to form a building monomer model, and performing spatial position and texture fusion on the building monomer model and a process three-dimensional model to generate a regional three-dimensional live-action model. And finally, splicing and fusing the three-dimensional live-action models of all the areas to form a complete super high-rise building group live-action three-dimensional model.

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention discloses a concrete internal defect quantitative detection method and system based on reflected waves, and belongs to the technical field of nondestructive testing. A reflected wave data matrix is obtained through multi-angle excitation and synchronous receiving; calculating energy characteristics of each channel, and constructing an energy response residual field; extracting waveform offset, spectrum jitter and phase change caused by defects by adopting a disturbance comparison algorithm to form a disturbance feature vector set; a defect-response function curved surface is further constructed, a defect topological structure is inversed based on gradient and curvature analysis, and defect geometric parameters are output; and finally, inputting the multi-moment defect parameters into the recurrent neural network, and predicting a defect evolution path and a failure risk. The method has high resolution and trend prediction capability, and is suitable for detection and early warning of concrete structures in bridges, tunnels and nuclear power projects.

The invention relates to the technical field of road engineering safety monitoring, in particular to an expressway slope crack identification method based on unmanned aerial vehicle laser point cloud visualization, which comprises the following steps: multi-source data collaborative acquisition: an unmanned aerial vehicle carrying a laser radar scanner and a high-resolution optical camera flies along a multi-angle combined route, and the unmanned aerial vehicle carries a laser radar scanner and a high-resolution optical camera; synchronously acquiring side slope three-dimensional laser point cloud data and an orthoimage sequence; data fusion preprocessing: denoising and filtering the data to generate an exposed slope triangular mesh curved surface model, and mapping textures to generate a high-precision live-action three-dimensional model; performing multi-dimensional feature fusion recognition, extracting curvature and texture features, inputting the curvature and texture features into a pre-training classification model, judging and outputting a crack pixel-level position; and carrying out visual output, superposing crack information rendering and generating a quantitative report containing the length and width of the spatial position. The method is high in coverage precision and identification accuracy, and provides a reliable basis for slope safety assessment.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention discloses a radiator micro-channel structure integrity detection method based on acoustic measurement, and relates to the technical field of information, the method couples acoustic propagation measurement with density inversion and particle transport observation, firstly screens a key observation object according to density gradient change, and then constructs a hydrodynamic equilibrium graph by using auxiliary particle flow, and finally obtains the structural integrity of the radiator micro-channel. And channel-by-channel difference is carried out with reflection measurement of standard acquired data, so that defects are indicated by three domains of energy, time and space together. According to the closed-loop link, the sensitivity to tiny blockage, slight deformation and early crosstalk is remarkably improved, the false alarm rate that single measurement is easily influenced by noise and working condition fluctuation is reduced, fine positioning of channel-level abnormal positions and ranges is achieved, and the closed-loop link is suitable for high-resolution detection of dense micro-channel arrays without disassembly or destructive operation.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention relates to the technical field of transformer detection, and discloses a nondestructive testing optimization method and system for an oil-immersed power transformer, and the method comprises the steps: building a three-dimensional dielectric response coordinate system, and generating a preliminary defect positioning map; aging-dominated and damp-dominated defects are detected and identified through spiral frequency sweep excitation; establishing a temperature gradient excitation scheme based on the defect type to generate a defect degree quantitative evaluation index; designing a sound wave modulation excitation scheme to generate a high-resolution defect characteristic spectrum; constructing a multi-mode intelligent sensor network, and combining a defect development trend prediction model to realize defect evolution prediction and generate a graded early warning signal; according to the method, the whole-process accurate detection of the insulation defect of the transformer from positioning, classification and quantitative evaluation to evolution prediction is realized, and a reliable basis is provided for operation and maintenance.

The invention discloses a high-feature-resolution underwater weak target parameter estimation method based on NOMP, and the method comprises the steps: obtaining a receiving array signal, carrying out the preprocessing of the receiving array signal, carrying out the azimuth angle estimation through employing a conventional beam forming algorithm, and extracting a beam output signal in a main target direction; rough search is carried out on discrete time delay and Doppler factor grids, initial parameters of a current most significant path are estimated, continuous optimization is carried out through a Newton iteration method, and optimized path parameters are obtained; constructing a continuous path echo signal according to the received signal, deducting the contribution of the current path to the received signal from the received signal, updating a residual signal, and continuously searching the next path by taking the residual signal as a new input signal until an iteration termination condition is met; and outputting parameter estimation results of all paths. According to the method, high-precision and high-resolution extraction of weak target parameters can be realized in an environment with a low signal-to-noise ratio and remarkable underwater multipath interference.

According to the spectrum-to-mass concentration imaging method and device based on physical mechanism deep learning provided by the invention, the actually measured spectrum and the reference spectrum of the pollution gas smoke plume are collected, the spectrum data set is constructed after differential processing, the meteorological data and the online mass concentration label are synchronously collected, and meanwhile, the spectrum-to-mass concentration imaging method and device based on physical mechanism deep learning are provided. A high-resolution gas absorption section is obtained and is convolved into a matrix; and constructing a deep learning model fusing a feature extraction module, an expanded least square module and a full connection module, taking the spectral data set, the meteorological data and the absorption cross section matrix as input, performing training in combination with labels to obtain an optimization model, and predicting the mass concentration of the target gas. According to the method, the problems of error accumulation, low calculation efficiency and poor interpretability caused by dependence on a complex physical model in a traditional method are solved, and high-precision, high-efficiency and interpretable real-time imaging of the mass concentration of the smoke plume of the pollution gas is realized.

The invention discloses a wetland carbon sink benefit evaluation method and system based on remote sensing data, and relates to the technical field of carbon emission evaluation. A wetland carbon sink benefit evaluation system based on remote sensing data comprises a carbon sink evaluation zoning module and a carbon sink benefit evaluation module. According to the invention, through multi-modal remote sensing data fusion and fine grid division, high-resolution analysis of spatial heterogeneity of a wetland region is realized, different types of carbon sink regions and distribution boundaries thereof can be accurately identified, and scientificity and particles of carbon sink partitioning are improved; a dynamic index characteristic graph layer is constructed through a remote sensing time sequence, a hydrological pulse response factor graph layer is constructed in combination with hydrological data, and clustering division is performed on the hydrological pulse response factor graph layer, so that dynamic identification and pixel level benefit calculation of a dynamic carbon sink area are realized.

The embodiment of the invention provides a wind field correction method based on a PPWNet model, and is applied to the technical field of wind field correction. The method comprises the steps of obtaining high-resolution grid wind field data, low-resolution grid wind field data, three-dimensional geographic coordinates and neighborhood data of a target prediction point and wind field data actually observed by a sounding point; wherein the neighborhood data comprises high-resolution grid wind field data and three-dimensional geographic coordinates of K nearest neighbor points around the target prediction point, and K is a positive integer; and inputting the acquired data into a pre-trained PPWNet model, and outputting three-dimensional corrected wind field data covering a target prediction point and a wind field divergence deviation index. In this way, the problem of data fusion of different sources, resolutions and structures can be solved, it is ensured that the output wind field is not only accurate in numerical value but also reasonable in physics, prediction results violating physical intuition are reduced, and wind field data are closer to the real situation.

The invention discloses a remote sensing image space-time fusion method and device based on a selective state space model, and belongs to the technical field of remote sensing image processing and computer vision crossing. The method comprises the following steps: acquiring high-resolution and low-resolution image input, and extracting multi-scale features through a multi-layer encoder; capturing an anisotropic space structure in the remote sensing image by using a four-way two-dimensional selective scanning mechanism; designing a state space fusion module, decoupling and cooperatively processing space details and time dynamic information through a space and time sequence selective scanning fusion sub-module, and performing feature fusion by adopting adaptive gating parameters; and finally, reconstructing a high-resolution image through a symmetric decoder, and carrying out model optimization by adopting a composite loss function. On the premise of ensuring the linear calculation complexity, the spatial detail fidelity, the time continuity and the overall efficiency of the fused image are remarkably improved, and the method is suitable for large-scale remote sensing data processing.

The invention discloses a regional wind power plant group multi-scale meteorological data assimilation and space-time reconstruction method, and aims to solve the problems that existing wind power plant meteorological prediction precision is insufficient, multi-source data fusion is difficult and the like. According to the method, effective assimilation of multi-source heterogeneous meteorological data is realized by constructing a deep convolution variational auto-encoder, space-time reconstruction of a high-resolution three-dimensional wind field is realized in combination with a heterogeneous graph network and a Shenchang differential equation technology, and physical constraint conditions are introduced to ensure physical consistency of output results. Experimental results show that the method can significantly improve the meteorological prediction precision of the wind power plant, and provides reliable meteorological data support for fine operation management of the wind power plant group.

The invention discloses a forest carbon sink acquisition method and system for driving an ecological carbon mode through remote sensing observation, and the method and system are corresponding schemes: in the scheme, a set of high-precision and multi-dimensional satellite-ground collaborative observation data system is formed by combining multi-source observation data, and a solid foundation is laid for subsequent carbon sink inversion; a high-resolution prior carbon concentration field of a research area can be generated by using a machine learning method, and a high-precision posterior carbon concentration field can be obtained; on the basis, through inversion analysis and a correction link, high-precision, wide-coverage and low-uncertainty analysis of forest carbon sequestration in a research area is finally realized, and the problems that an existing carbon sequestration estimation method is insufficient in space coverage, high in model uncertainty, limited in multi-source data fusion and the like are solved.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

The invention discloses a complex water body pollution detection method and system based on an adaptive cruise technology, and belongs to the technical field of environment monitoring. The method overcomes the inherent defect that an existing monitoring scheme based on a static preset path cannot adapt to a dynamic pollution environment by constructing a closed-loop feedback control architecture of real-time sensing, central decision making and dynamic execution. Generating initial global pollution distribution estimation and planning an initial path by the main aircraft; the subordinate vehicle sails along the path and estimates the local pollution concentration and the gradient direction in real time; and the main aircraft fuses all data to update global estimation, and dynamically generates an instruction for guiding the subordinate aircraft to move towards a pollution concentration gradient rising direction after identifying a pollution plume or a hot spot, thereby realizing active tracking and high-resolution surveying and mapping. According to the invention, the efficiency, precision and intelligent response capability of pollution monitoring are significantly improved, and accurate and adaptive tracking of a dynamic pollution field is realized.

The invention relates to a road dielectric constant inversion and disease discrimination method based on a neural network. The method comprises the following steps: expanding the number of jump connections based on a TransUNet baseline, introducing a gating attention mechanism, and additionally arranging a multi-scale feature fusion module, an asymmetric depth-space attention module and a deep supervision solution terminal; an AdamW optimizer and a Warmup-cosine annealing learning rate scheduling strategy are adopted to construct an adaptive region weighted loss function so as to amplify dielectric constant mutation boundary loss weight. After a model is trained through a road GPR simulation data set and parameter adjustment of the set is verified to be convergent, a to-be-detected road GPR-B-scan image is input, dielectric constant distribution is output, and according to dielectric constant characteristics of different diseases, accurate discrimination of hidden diseases such as cavities, voids, loose bodies and water-rich bodies is achieved. By adopting the method, high-resolution dielectric constant end-to-end inversion can be realized, and multi-scale disease characteristics can be accurately captured.

The invention discloses a summer rainfall forecast correction downscaling method and system based on deep learning, and belongs to the technical field of meteorological prediction and climate simulation. In order to solve the technical problems of systematic deviation and spatial detail missing in forecasting, a deep learning downscaling model is constructed for summer multi-mode climate forecasting data published six months ahead of time in combination with high-resolution observation data and topographic data, deviation correction and spatial super-resolution reconstruction are performed on a summer rainfall forecasting result, and a deep learning downscaling model is constructed. And outputting the kilometer-level summer precipitation field of the target area. The model improves the spatial feature capture capability of multi-scale climate elements through an attention mechanism, and realizes the adaptive fusion of meteorological elements and topographic factors through a topographic perception module, thereby enhancing the medium and long term prediction precision of summer rainfall in a complex topographic region. According to the method, global climate information and local topographic features can be effectively integrated, and high-precision technical support is provided for medium-and-long-term prediction of regional summer rainfall, disaster prevention and reduction in flood seasons and water resource scheduling.

The invention relates to the technical field of remote sensing image change detection, in particular to a remote sensing image change recognition method and system fusing time sequence alignment and semantic perception, and the method comprises the steps: obtaining remote sensing images of different time phases in the same region, carrying out the multi-scale feature extraction, and achieving the feature alignment through optical flow estimation under the same scale, performing weighted fusion in combination with an attention mechanism to obtain a fused multi-scale feature group; further through multi-scale convolution and channel and spatial attention enhancement context and detail expression, generating a high-resolution feature map, and finally outputting a pixel-level change recognition map for indicating whether a corresponding geographic position is changed or not; by means of the synergistic effect of optical flow estimation and an attention mechanism, false changes caused by geometric displacement, shadow drifting and seasonal spectral difference can be effectively inhibited, and the stability and reliability of a detection result are improved; and meanwhile, under the support of multi-scale convolution and attention weighting, the recognition capability of the small target and the boundary region is enhanced.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention provides a remote sensing semantic segmentation method and system based on an improved TransUNet algorithm, and relates to the technical field of deep learning and computer vision. The method comprises the following steps: acquiring a high-resolution remote sensing image data set, and performing multi-scale data enhancement; an improved TransUNet neural network model comprising an encoder, a Transform encoder, a context enhancement module CAEM, a decoder, a multi-scale feature fusion module MFFM and a segmentation head is constructed; wherein the CAEM intensifies a global context through multi-granularity pooling and cross-branch attention, and the MFFM fuses multi-scale features at a jump joint and introduces lightweight channel attention; and inputting the processed remote sensing image data set into a pre-trained improved TransUNet neural network model to execute semantic segmentation, and outputting a semantic segmentation result. The method effectively improves the small target recognition rate and the edge segmentation accuracy, and is suitable for fine ground feature interpretation of a complex remote sensing scene.

The invention discloses a multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method, and belongs to the field of atmospheric pollution monitoring. According to the method, the accuracy and physical interpretability of emission estimation are improved through adaptive fusion and high-precision complementation of multi-source observation data in combination with collaborative assimilation of a physical model and a deep intelligent model; and by adopting multi-scale feature dynamic fusion and a hierarchical graph neural network, high-resolution and refined emission spatial-temporal distribution modeling is realized. And meanwhile, Bayesian optimization and transfer learning are adopted to realize continuous adaptive optimization and knowledge generalization of model parameters. The method effectively solves the problems of heterogeneity, data missing, model migration, insufficient generalization ability and the like of multi-source data, and can be widely applied to the fields of atmospheric environment management, carbon emission monitoring and the like.