3624 results about "Anisotropy" patented technology

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention relates to a multi-modal sparse fusion three-dimensional target detection method in an unstructured environment, and the method is based on a complexity perception candidate region dynamic sampling strategy, and through the cross-domain self-compensation of point cloud geometric features and image spectral features, and the fusion of multi-view sparse features, the detection of a multi-modal sparse fusion three-dimensional target is realized. The perception capability and the recognition precision of the obstacle area, and the remote dynamic target detection performance and the real-time performance are improved; an anti-deformation convolution group and a terrain gradient perception attention module are introduced into a ParScaleNet image backbone network, so that the extraction of edge features of a complex scene is enhanced, and the cross-scale characterization capability of the features is improved; according to the method, a ContraSpaceOpt module with semantic maintenance capability is deployed behind a Transform, excessive smoothness of obstacle features is inhibited through class comparison loss functions and feature space anisotropy constraints, the space separability of obstacles is maintained, and the problems of point cloud fracture and texture misalignment caused by the obstacles are effectively solved.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention provides a medical image segmentation method based on adaptive anisotropic convolution, and the method comprises the steps: obtaining a three-dimensional medical CT data set comprising images and labels of a plurality of abdominal organs and kidney tumors, and carrying out the preprocessing of the data set; dividing a data set into a training set and a test set for model training and evaluation; designing a three-dimensional medical image segmentation network model based on an adaptive anisotropic convolutional layer, and inputting the preprocessed training set into the three-dimensional medical image segmentation network model, the three-dimensional medical image segmentation network model is trained through parallel multi-modal convolution, adaptive attention weight generation, weighted feature dynamic fusion and multi-stage deep supervision, and model parameters are optimized; and applying the optimized three-dimensional medical image segmentation network model to a test set, generating a three-dimensional segmentation result with clear boundary and complete reserved details, and providing support for clinical diagnosis and treatment planning.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention relates to the technical field of glacier information processing, in particular to a glacier surface moraine identification method based on multi-source remote sensing parameters, which comprises the following steps: performing radiation-atmosphere joint correction and geometric-reprojection on optical, thermal infrared, synthetic aperture radar and digital elevation data to generate physical consistency data; deducing the surface temperature under the driving of short-wave albedo, downward radiation and turbulent flux by using a cross-modal visual conversion network and a symmetric neural operator, and obtaining a thickness-existence degree posterior through a normalized flow; an uncertainty field is constructed according to information entropy and variance, a representative pixel unmanned aerial vehicle radar sampling and value measurement backflow fine tuning model is selected by means of quantum annealing, and then a spatial continuous thickness field is output through anisotropic diagram diffusion. According to the method, the future thickness is integrated according to mass conservation in combination with day-by-day weather forecast, and the future thickness is coupled with the existence degree index to form a burst risk grid, so that high-precision and iterable moraine dam early warning is realized.

The invention relates to the technical field of image processing, in particular to a craniocerebral disease area identification and detection method and system based on an MRI (Magnetic Resonance Imaging) image, and the method comprises the steps: obtaining a plurality of sub-images with different scales according to a gray level image of the craniocerebral MRI image; performing multi-scale analysis on the gradient value of any pixel point according to each sub-image to obtain a multi-scale gradient coefficient of any pixel point; obtaining a multi-scale local anomaly degree according to the gray values of the pixel points in different local ranges of any pixel point and the distribution in the gradient direction; optimizing the gradient value of any pixel point according to the multi-scale gradient coefficient and the multi-scale local anomaly degree to obtain a self-adaptive gradient value, and performing image enhancement on the grayscale image by using an anisotropic diffusion filtering algorithm according to the self-adaptive gradient value of each pixel point so as to identify a craniocerebral disease region. And the effect of performing image enhancement on the MRI image by using the anisotropic diffusion filtering algorithm is improved.

The invention relates to the technical field of control or regulation systems, and discloses a cooperative control method for a photovoltaic intelligent manufacturing equipment production line, and the method comprises the steps: a control unit collects the stock and change rate of materials in a physical cache region in real time; establishing and mapping the physical cache region into a virtual viscoelastic dynamic model with non-Newtonian fluid characteristics; calculating a virtual elastic restoring force enabling the stock to return to a balance point and a virtual viscous damping force preventing the stock state from changing based on the model; wherein an asymmetric anisotropic damping generation strategy is executed, and a damping coefficient is dynamically split according to a material flowing trend; and finally, superposing the virtual adjustment correction after vector synthesis to the basic transmission speed to generate a dynamic speed instruction, and by constructing a virtual dynamic field with rheological characteristics and an asymmetric damping mechanism, the problem of nonlinear cascade oscillation in discrete logistics transmission is solved, and differential self-adaptive suppression of accumulation and evacuation risks is realized.

The invention relates to the technical field of artificial intelligence and remote sensing image processing, and particularly provides a hyperspectral image and laser radar data classification method based on a dynamic fusion network. The method comprises the following steps: preprocessing acquired multi-modal data, and constructing multi-scale input; a dual-scale local attention module is designed, and context information of different scales is fused in a self-adaptive weighted mode through gating soft pooling; a dynamic down-sampling feature enhancement module is designed, the down-sampling rate is dynamically adjusted according to the complexity of the feature map, and deep multi-scale interaction is carried out based on a Mama backbone; constructing a directional interactive attention module, extracting features in horizontal, vertical and diagonal directions through directional gating convolution, and capturing an anisotropic structure of a linear ground feature; through the design of a double-path classifier, fusing shallow space details and deep semantic information; and the model is trained, optimized and reasoned to obtain data classification, and the method improves the classification precision and the calculation efficiency.

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

The invention provides an electrolytic bath three-dimensional temperature field reconstruction method and system based on multi-physics field coupling, and belongs to the technical field of energy equipment thermal management, and the reconstruction method is applied to a reconstruction system comprising an acquisition module, a first processing module, a second processing module, a third processing module, a fourth processing module and a rendering module. The method specifically comprises the following steps: layering a three-dimensional model of the electrolytic cell according to a Z-axis direction, establishing a hierarchical database and setting a reference layer; generating initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm; and establishing an electric-thermal-flow coupling equation, solving according to the hierarchical database, obtaining temperature field distribution of a reference layer, mapping the temperature field distribution to a three-dimensional space, correcting the temperature field distribution by combining interlayer temperature attenuation and a transverse thermal diffusion effect, obtaining target temperature field distribution, and carrying out visualization processing on the target temperature field distribution. Nonlinear temperature distribution of the electrolytic cell can be completely reproduced, and the accuracy of three-dimensional temperature field reconstruction of the electrolytic cell is guaranteed.

The invention provides a magnetic resonance image self-supervision super-resolution reconstruction method and system based on a prior guide diffusion model. The method comprises the steps of image preprocessing, pseudo-pairing data set construction, prior condition construction, model training, model testing and three-dimensional isotropic volume reconstruction. According to the method, the dependence on a real high-resolution image is remarkably reduced by constructing a self-supervised pseudo-pairing mechanism, and high-quality reconstruction from anisotropic MRI to an isotropic image is realized; meanwhile, by introducing multiple medical priori including high-frequency residual, edge structure and region-of-interest guidance, detail retention and structural boundary definition of the image are effectively improved, the convergence speed and robustness of the model are improved while anatomical fidelity is guaranteed, and the method is suitable for large-scale popularization and application. And a technically feasible and clinically applicable solution is provided for high-resolution reconstruction of medical images.

The invention provides a visual communication advertisement design system and method, and the method comprises the steps: carrying out the cross-modal feature alignment of a background voiceprint feature extracted from environment voiceprint data of an advertisement putting scene and a frequency domain feature of illumination intensity in illumination intensity data, and obtaining an environment perception state tensor; generating a dynamic illumination matrix by combining the spectral irradiance distribution in the environmental perception state tensor with the anisotropic highlight coefficient of each sub-region in the target advertisement picture; determining an audience space domain according to the space depth data of the advertisement putting scene and the audience motion trail, and performing space audio redirection on the target advertisement based on the audience space domain and the sound field energy distribution in the environment perception state tensor to obtain an acoustic beam width angle; and performing acousto-optic synchronous dynamic rendering on the advertisement content of the target advertisement according to the dynamic illumination matrix and the acoustic beam width angle. By adopting the scheme of the invention, acousto-optic collaborative rendering and acousto-optic parameter dynamic adaptation can be performed on the target advertisement so as to realize directional propagation to audiences.

The invention relates to the field of carbon fiber composite material mechanics, in particular to a fine evaluation method for the strength of a small-size large or full-opening CFRP (carbon fiber reinforced plastic) shell, which comprises the following steps: establishing a microscopic damage model containing material anisotropy, defining damage variables and evolution equations, uniformly quantifying multi-scale damage behaviors such as fiber fracture, matrix cracking and layered expansion, and evaluating the strength of the small-size large or full-opening CFRP shell. The problem that a traditional single-scale model cannot represent the multiphase coupling effect is solved; through cross-scale transmission of damage variables, whole-process prediction from microscopic damage initiation to macroscopic failure is realized, and the precision of strength evaluation of the complex opening structure is significantly improved. The method realizes refinement, reliability and engineering practicability of strength evaluation of the small-size large-opening or full-opening CFRP shell, is particularly suitable for the fields of aerospace and the like with extremely high requirements on structural weight and safety, and has remarkable technical leading and application value.

The invention provides a topology and morphology integrated optimization design method for a thin-walled structure. The method comprises the steps that 1, a finite element model is established according to design requirements; 2, extracting a thin-wall structure morphology distribution field # imgabs0 # through two-step partial differential equation filtering and Heavide projection operation; 3, constructing a material attribute rational approximation model in combination with the thin-wall morphology distribution field # imgabs1 # and the topological density field v obtained in the step 2, thereby obtaining an elastic modulus E and mass rho in a design domain; 4, performing finite element analysis in combination with the material interpolation model, and calculating the flexibility of the structure; 5, solving to obtain the sensitivity of the structure flexibility and the quality relative to the design variables; step 6, updating the morphology design variable mu and the topology design variable v of the thin plate by adopting a moving asymptote method; and step 7, when a convergence condition is satisfied, stopping optimization iteration and outputting an optimization result. According to the method, the maximum forming angle of the thin-wall structure is controlled by adjusting the filtering radius of the anisotropic filter, and the stamping forming process is met.

The invention provides a coal mine high-precision three-dimensional geological modeling method and system based on multi-source heterogeneous data fusion, and relates to the technical field of coal mine geological modeling. The method comprises the following steps: establishing a unified semantic reference system and a time-space coordinate reference, and generating an alignment anchor point set; mapping the multi-source observation data to form an alignment feature set and calculating a source weight; according to a multi-evidence consistency rule and geomechanical prior inversion, a discontinuous geological structure is obtained; constructing an implicit stratigraphic function and applying a boundary jump condition to solve a stratigraphic boundary set; and taking the stratum boundary as a framework, and executing anisotropic joint estimation to obtain a property distribution model and an uncertainty result. According to the method, integrated modeling of the geological structure and the property of the complex structure area is achieved, and the precision, the stability and the credibility of the three-dimensional geological model are remarkably improved.

The invention discloses a rapid polarized light acoustic imaging device which comprises an excitation source, a light beam shaping module, a rapid polarization modulation module, an energy feedback module, a rapid scanning module and an acquisition processing module. And receiving photoacoustic signals corresponding to the exciting light in four different polarization states for calculation, and extracting anisotropy information of the sample. Compared with multiple times of scanning a sample or modulating the polarization state by rotating a half-wave plate, the problems of dislocation of multiple times of polarization acquisition points and false positive calculation are avoided, meanwhile, the time interval generated by continuously modulating the laser pulses is short, and energy fluctuation among the four pulses and errors caused by fluctuation are reduced. According to the invention, the limitation of the precision and speed of polarized light acoustic microscopic imaging is overcome, the microstructure characteristics in the target sample can be rapidly, accurately and highly sensitively detected, and the method has great application prospects in biomedical imaging and high-precision material detection.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.

The invention discloses an orthotropic rock physical modeling method. The method comprises the following steps: S1, obtaining tight reservoir parameters; s2, estimating the elastic modulus of the mixed minerals; s3, enabling the mixed mineral to be equivalent to a VTI background rock matrix caused by a thin layer; s4, matrix pores are added into a VTI background rock matrix; s5, embedding the vertical crack into the VTI background'dry 'rock skeleton to obtain an OA medium'dry' rock skeleton; s6, calculating the bulk modulus and saturated rock density of the mixed fluid; s7, adding the mixed fluid into the OA medium'dry 'rock skeleton to obtain OA medium saturated rock; s8, calculating longitudinal and transverse wave velocities and anisotropy parameters of OA medium saturated rocks; and S9, calculating OA medium saturated rock. According to the method, the tight reservoir with the orthotropic characteristic can be simulated, the Thomsen parameter and the fracture weakness parameter of the tight reservoir can be obtained, and data support is provided for fracture evaluation, anisotropic parameter inversion, permeability prediction and the like of the tight reservoir.

The invention belongs to the technical field of light polarization analysis, and discloses a method for determining the sex of an embryo in an egg based on polarization image recognition. Comprising the following steps: acquiring a transmitted spectrum image sequence of a hatching egg to be detected at a plurality of polarization angles, wherein the transmitted spectrum image sequence covers a transmitted spectrum image from visible light to a near-infrared band; extracting a polarization sensitive wave band set of the embryonic tissue according to the polarization state change characteristics of the embryo area in the transmitted spectrum image at different polarization angles; constructing an embryo gender characteristic spectrum based on the polarization degree distribution difference and the spatial texture characteristics of each wave band in the polarization sensitive wave band set; determining an optimal wave band combination for sex determination by analyzing the relevance between a polarization anisotropy index of a specific wave band in the embryo sex characteristic spectrum and an embryo development stage; according to the polarization response mode corresponding to the optimal wave band combination, the sex of the embryo in the egg is judged, and the resource investment and the waiting cost are greatly reduced.

The invention discloses a landslide construction simulation method based on finite elements, particularly relates to the field of construction simulation, and realizes whole-process dynamic simulation through GTS-NX software. Firstly, a three-dimensional finite element model is established based on geological exploration data, a Mohr-Coulomb elastic-plastic constitutive model is adopted, and the bulk modulus and the shear modulus are calculated through the elastic modulus and the Poisson's ratio. The initial ground stress is simulated by adopting a self-weight stress field method, and the initial displacement influence is eliminated by adopting a displacement difference method. In construction process simulation, the unit passivation / activation technology is adopted to accurately reproduce the excavation and support time sequence, and cyclic calculation of excavation-stress release-support-balance is achieved. In nonlinear analysis, superposition of an elastic stress field and a plastic stress field is considered, and an unloading influence factor is introduced to correct anisotropy of a deformation modulus. A safety coefficient is calculated through a strength reduction method, a multi-working-condition evaluation system is established, a monitoring data fusion and parameter inversion mechanism is developed, and a three-level early warning system is formed.

The invention discloses a self-adaptive visual admittance control method fusing fluid characteristics and multi-modal perception, which comprises the following steps: designing a self-adaptive Bingham-shear thickening fluid virtual damping coefficient through nonlinear mapping based on sigmoid, and combining a threshold triggering behavior of a Bingham fluid and a sudden stiffening characteristic under the impact of the shear thickening fluid; the flexibility is enhanced under the action of small force, and the anti-interference capability is improved under impact. Besides, a force auxiliary function based on force amplitude is introduced, an anisotropic compliance strategy is combined, rigidity and damping are dynamically adjusted by identifying the main force direction, and the mechanism can reduce sensitivity to noise of a micro sensor and ensure stability and accuracy in the task execution process. Meanwhile, an environment attraction domain model is established in a feature space, Lyapunov analysis shows that the system has consistent final boundaries, stable convergence is ensured, and secondary correction is supported.

The invention relates to the field of X-ray image processing, and discloses an X-ray image reconstruction method based on direction perception tensor modeling. The method comprises the following steps: acquiring a plurality of X-ray projection images with different incident angles, and establishing a projection direction mapping matrix in combination with tissue direction distribution; constructing a high-order direction perception tensor field based on the mapping matrix and the structure prior information, wherein the high-order direction perception tensor field is used for describing the dominant structure direction and anisotropic response characteristics of each image position; a direction enhancement response diagram is generated through tensor-guided gradient calculation, and dynamic enhancement of local edges is achieved; then structure response intensity is extracted to construct a structure sensitive function graph, and the structure sensitive function graph serves as a weighting factor in image reconstruction; based on the weighted graph and the tensor field, an image reconstruction objective function containing direction consistency constraint is constructed, joint optimization is executed, a regularization item is dynamically updated, and high-fidelity reconstruction of the image under the low-dose condition is achieved. According to the method, the image detail keeping and noise suppression capabilities are improved, and the method has relatively high adaptability and robustness.

The invention belongs to the field of computer vision image signal processing, and relates to a multi-robot collaborative indoor scene semantic segmentation method and system based on multivariate interactive learning. The method comprises the steps of adopting an image feature encoder based on a double attention integration module to realize cross-modal feature fusion on an RGB image and a depth image; a dimension anisotropy attention enhancement module is adopted, and feature optimization is carried out through a multi-dimensional direction attention mechanism; a cross-task collaborative interaction module is adopted, and information is shared among different tasks by utilizing an interaction mechanism based on a feature level; in a multi-task heterogeneous decoding stage, each robot uses a decoder with a specific task to execute a specific task, and semantic segmentation, instance segmentation and scene classification are realized; and a joint optimization loss function is adopted to perform end-to-end joint training on multiple tasks, so that overall optimization and collaborative awareness performance improvement are realized. According to the method, the precision and robustness can be improved when complex indoor scene semantic segmentation and other visual tasks are executed.

The invention discloses a volume cloud rendering method and system based on three-dimensional Gaussian splash, and the method comprises the steps: carrying out the feature matching and structure reconstruction of a volume cloud image through an SfM algorithm, obtaining a sparse point cloud in a scene, and representing the sparse point cloud as an anisotropic Gaussian ellipsoid; decomposing the illumination transmission process of the volume cloud into two parts of forward single scattering and internal multiple scattering based on a radiation transmission equation to obtain an illumination modeling formula embedded volume cloud rendering process; determining the contribution of each three-dimensional Gaussian ellipsoid to a rendering result according to the opacity index of the three-dimensional Gaussian ellipsoid, and dynamically cutting the low-contribution three-dimensional Gaussian ellipsoid by adopting a delay deletion strategy to reduce redundancy; a comprehensive loss function is constructed, so that the three-dimensional Gaussian model is focused on the volume cloud region and the structure detail reduction capability is enhanced; and outputting the optimized volume cloud rendering result and the three-dimensional Gaussian model. According to the method, the volume cloud rendering efficiency and quality are effectively improved on the premise of ensuring real-time rendering.

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 three-dimensional seismic high-resolution strengthening method based on an adaptive reflection coefficient. The method comprises the following steps: S10, acquiring a three-dimensional seismic data volume; s20, constructing a reflection coefficient prior probability model through multi-scale dictionary construction and statistical feature extraction; s30, adaptive matched filtering: performing time-varying filter and directional compensation on the original three-dimensional seismic data volume and the Q-field model to obtain a data volume subjected to Q compensation and reflection feature enhancement; s40, three-dimensional anisotropy enhancement: carrying out structure tensor field calculation and tensor driving diffusion on the data volume in the step S30 to obtain enhanced data; and S50, joint inversion optimization: performing joint inversion by using the reflection coefficient prior probability model and the enhanced data to obtain a final high-resolution seismic body. According to the three-dimensional seismic data processing method, a physical mechanism and data driving are fused, a three-dimensional processing technology considering vertical and transverse resolution improvement is adopted, and the contradiction between resolution improvement and geological authenticity maintenance in three-dimensional seismic data processing is solved.