947 results about "Spatial structure" patented technology

The invention provides a real-time face blind restoration method and device based on identity constraint and frequency domain enhancement, and relates to the technical field of image processing, and the method comprises the steps: S1, generating an identity code through the fusion of CLIP and ArcFace, extracting spatial structure features in combination with ControlNet, and obtaining a high-frequency component through the decomposition of learnable wavelets; s2, an input image is mapped to a potential space, and identity, space and high-frequency features are fused to construct a condition vector; s3, performing iterative denoising based on the consistency model, and superposing lightweight wavelet residual correction to enhance high-frequency details; s4, according to the high-frequency energy dynamic distribution weight, reconstructing a multi-scale fusion image through inverse wavelet transform; according to the method, the problems of identity distortion, detail loss and low calculation efficiency of a low-quality face image in restoration are solved, and semantic consistency, detail fidelity and real-time performance of a restoration result are improved through identity-space-frequency domain ternary constraint, lightweight residual correction and degradation perception enhancement.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention provides a fire early warning method and system based on three-dimensional modeling, and the method comprises the steps: determining the transaction indexes of fire danger at different space units through the spatial structure characteristics of a three-dimensional digital twin model in combination with the state evolution mode of a physical field, and determining a risk cloud picture through all transaction indexes; extracting a risk sub-domain of the fire according to the risk cloud picture, and further generating a heat flux density field matrix among different space units; determining risk fitting degrees of different space units by combining the heat flow diffusion information with the monitoring information of the fire behavior in the target building, and performing time sequence aggregation on the fire risk in the target building according to all the risk fitting degrees and the local temperature field of each space unit to obtain fire danger propagation potential energy; and performing fire diffusion situation monitoring on the target building according to the fire danger propagation potential energy, and outputting a fire diffusion early warning signal. By adopting the scheme of the invention, the diffusion track of the fire heat flow can be accurately identified in the three-dimensional model.

The invention discloses a point cloud registration method, system and device and a medium, and relates to the technical field of three-dimensional reconstruction of scenes, and the method comprises the steps: obtaining a source point cloud and a target point cloud; semantic segmentation is carried out according to spatial structure characteristics of the point clouds, and plane points and non-plane points of the source point clouds and the target point clouds are obtained respectively; two-way distance search constraints are applied to planar points and non-planar points in the source point cloud and the target point cloud respectively, and a corresponding relation of two-way matching constraints is established; point-to-surface registration and point-to-point registration based on the maximum correlation entropy criterion are carried out on the plane points and the non-plane points after bidirectional constraint, and rotation matrixes and translation vectors of the plane points and the non-plane points are respectively obtained through multiple iterations; weighting the rotation matrix and the translation vector according to an adaptive weight, and applying the weighted rotation matrix and translation vector to the source point cloud to obtain the source point cloud after pose transformation; according to the method, the nearest neighbor corresponding relation from the source point cloud to the target point cloud and from the target point cloud to the source point cloud is considered, and the matching accuracy of the corresponding points is improved.

The invention relates to the technical field of BIM building optimization, in particular to a BIM-based prefabricated building design construction optimization management system and method. The method comprises the following steps: acquiring a panoramic monitoring image of a building area and a construction design initial BIM model; carrying out regional space structure evolution and construction scene three-dimensional simulation on the panoramic monitoring image of the building region, and constructing a three-dimensional construction scene graph; identifying assembly type building component information according to the construction design initial BIM model so as to generate building component assembly demand characteristics; performing dynamic construction sequence simulation optimization on the three-dimensional construction scene graph and the construction design initial BIM model based on building component assembly demand characteristics, and constructing a dynamic optimization construction sequence; and carrying out available material transportation path analysis on the three-dimensional construction scene graph so as to generate an optimal operation path of each material. According to the invention, real-time collaborative management based on the BIM and the cloud is realized, the building construction quality is improved, and the construction design decision is optimized.

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 invention relates to the technical field of artificial intelligence, in particular to a spatio-temporal context extraction method and system for real world model training, and the method comprises the steps: extracting all image information and text semantic information through content feature vectors; structural information such as spatial layout of a scene and relative positions of people and objects in the collected image is extracted through the spatial structure feature vector, and feature information such as appearance, posture and action of a target object is extracted through the entity feature vector; obtaining a time sequence feature vector according to the content feature vectors, the spatial structure feature vectors and the entity feature vectors in a plurality of preset time periods, capturing multi-dimensional dynamic changes of the target physical place in a period of time, and analyzing the extracted vectors to obtain scene analysis feature data; all feature information is integrated to obtain the spatio-temporal context, the representation richness and comprehensiveness of the target physical place are improved, and the representation degree and adaptability of the real world model obtained through subsequent training to the target physical place are achieved.

The invention discloses a container number tallying identification method, and relates to the technical field of container intelligent tallying. According to the technical key points, the method comprises the following steps: deploying a multi-modal sensing device, carrying out initial calibration, and pre-training an improved YOLOv7-tiny target detection model, a transformer-based cGAN character segmentation model and a lightweight CNN-transformer-capsule network integrated hybrid recognition model; through cooperative collection of multi-modal data of the multi-modal sensing device, the system can effectively integrate texture information of RGB images, a space structure of depth images and three-dimensional coordinates of point cloud data, the problem that traditional single visual recognition is affected by illumination, shielding and the like is solved, a self-calibration mechanism corrects space-time deviation of a sensor in real time, and the accuracy of recognition is improved. The system can still accurately extract container number characteristics in complex scenes such as strong light reflection, low illumination at night, character abrasion and the like.

The invention discloses a warehouse-in and warehouse-out path planning system and method applied to stored goods, and relates to the technical field of intelligent storage. The method comprises the steps of establishing a three-dimensional space structure of a warehouse, generating a menu to be stored and matching various storage schemes, carrying out importance degree assignment and priority ranking on goods, calculating the residual space and the total weight of the warehouse, judging and optimizing the space and the weight, and planning and optimizing warehouse-in and warehouse-out paths. Through the mathematical planning method, the total cost is minimized, and the space utilization rate and the access efficiency are maximized. And meanwhile, the operation condition in the warehouse is monitored in real time, the in-out path and the in-out path are dynamically adjusted, and congestion and conflicts are avoided. In addition, a high-definition camera and a sensor are further installed, and safety management in the warehouse is ensured. According to the invention, intelligent warehouse-in and warehouse-out path planning of the stored goods is realized, the warehouse management efficiency and the space utilization rate are improved, and safe storage and efficient warehouse-in and warehouse-out of the goods are ensured.

The invention discloses a distribution line unmanned aerial vehicle edge end real-time visual identification and target detection method and system based on lightweight AI. The method is based on a YOLOv8 architecture, and constructs a complete lightweight detection framework comprising a feature extractor, an enhancement module and a simplified detection head by introducing a space structure maintaining assembly, a separated large kernel convolution and a weighted reconstruction feature pyramid. A cross-dimension semantic relation model is constructed by utilizing a combined attention structure, multi-level feature fusion is realized by reconstructing a feature pyramid network, model training is performed by adopting a hybrid optimization function and a dynamic sample adjustment mechanism, and the model is deployed on a mobile computing platform after being optimized by a hierarchical knowledge transfer method. According to the method, the detection speed is remarkably increased while high precision is kept, and real-time identification and anomaly analysis of the power line element are effectively realized.

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

The invention belongs to the technical field of display design evaluation, and particularly relates to an exhibition display layout design effect analysis and evaluation method, which comprises the following steps of: constructing a dynamic spatial topology network by collecting audience moving tracks, exhibit information and spatial structure data in real time; the problem of insufficient attention on space utilization influenced by dynamic factors such as human traffic fluctuation is solved. And dynamic changes in the space are converted into quantifiable network nodes and connection relationships, so that the space utilization state is changed from static design to dynamic monitoring, accurate capture of the real-time use condition of the exhibition space is realized, a comprehensive and real-time data basis is provided for subsequent analysis, and the real-time monitoring of the exhibition space is realized. Meanwhile, the dynamic space topology network is analyzed by means of spatial-temporal feature fusion to determine the edge weight and the exhibition area correlation degree, the spatial efficiency index and the dynamic line rationality index are generated in combination with an analytic hierarchy process and multi-dimensional analysis, and the defect that a space utilization rate core evaluation index is not established in the prior art is overcome.

The invention relates to the technical field of artificial intelligence, in particular to a sheet part performance rapid prediction method based on deep learning, which comprises the following steps: collecting multi-working condition simulation data to generate a training sample, constructing and coding a grid topological structure to extract multi-dimensional features, and inputting a perceptron to predict stress and evaluate errors after feature fusion and self-attention mechanism processing. According to the method, a structured training sample set is constructed by introducing simulation information, a geometric structure feature set is formed by combining node space coordinates, boundary constraints and a connection relation, so that mutual positions and constraint conditions among nodes are completely expressed in a graph structure, and through node-level feature extraction and feature fusion processing, a graph structure is obtained. According to the method, deep embedding of node geometric layout and boundary interrelation is realized, learnable expression of a stress evolution path in a space structure is established through local subgraph and context analysis, a multi-layer feature aggregation and attention mechanism is introduced in a node graph embedding process, and feature response expression of a key area is enhanced.

The invention relates to the technical field of medical image analysis, computer vision and deep learning, in particular to a texture perception and multi-scale feature adaptive fusion hip joint osteophyte detection method. According to the method, firstly, a bone texture feature extraction module is used for carrying out feature extraction and enhancement on a hip joint X-ray image, a parallel double-branch structure is adopted, gradient and space structure features are extracted through a Sobel operator and pooling operation, and feature maps are fused; then, the fused features are input into a double-backbone network, the first backbone network extracts local fine textures and long-range dependence features by combining convolution, self-attention and a gating mechanism, and the second backbone network extracts multi-scale semantic features through hierarchical grouping and depth separable convolution; and double-trunk output is dynamically weighted and fused through an adaptive fusion module, features are optimized through a multi-scale semantic fusion module, and finally the position and confidence of osteophyte are output.

The invention provides a digital twin three-dimensional model construction method based on a generative large model, and the method comprises the steps: obtaining multi-source monitoring data of a power distribution network, and processing the multi-source monitoring data into a training data set; the method comprises the following steps: mapping multi-source monitoring data into a multi-scale tensor subspace through tensor wavelet structured transformation, adaptively extracting spatial features through a learnable wavelet kernel, and keeping the structural continuity of a physical field in combination with a geometric prior regular term; constructing and training a generative adversarial network through a training data set; inputting and analyzing the physical parameter vector of the target scene, and if the topological similarity score is lower than a preset threshold value, adjusting noise vector regeneration; and if yes, outputting a three-dimensional model tensor and importing the three-dimensional model tensor into a digital twin platform, and driving real-time physical field visualization. According to the method, characteristics of a multi-scale space structure and a nonlinear physical field can be reserved, the physical rationality and generalization ability of the generated model are remarkably improved, and depth identification of topological attributes (such as hole connectivity and surface defects) and local geometric defects of the three-dimensional model is realized.

The invention discloses a wheat organ three-dimensional point cloud segmentation method based on dynamic voxel and category perception. Three-dimensional reconstruction is carried out on a wheat sample based on a three-dimensional Gaussian splash method, and dense point cloud data are obtained; performing data amplification on the dense point cloud data by adopting a dynamic voxel rasterization random sampling method combining dynamic voxel division and a random farthest point sampling strategy, and performing standardized point cloud quantity processing to obtain a standard point cloud data set; and based on the standard point cloud data set, carrying out key organ segmentation by adopting a category perception segmentation network. According to the data enhancement method, key geometric features are maintained while data diversity is expanded, a feature extraction module of the category perception segmentation network solves the problem of ignoring important points during sampling by fusing DCA and a weighted farthest point sampling strategy, and an improved loss function combining a point cloud space structure and information is adopted to improve the accuracy of data enhancement. And the segmentation precision of the wheat complex point cloud data is effectively improved.

The invention provides a training method of a live pig key point detection model and a body size measurement method and device.The training method of the live pig key point detection model comprises the steps that key point labeling is conducted on live pig point cloud data, and multiple key points are obtained; calculating geodesic distances between each point in the live pig point cloud data and the manifold structures corresponding to the plurality of key points, and performing Gaussian smooth mapping on the plurality of geodesic distances to obtain a key point heat map; determining a target loss function based on the key point heat map and the mean square error loss function; and performing regression training on the PointNet + + network by taking the live pig point cloud data as a training sample and the target loss function as a training loss function to obtain a live pig key point detection model. According to the method, the surface morphology and space structure information of the point cloud are fully utilized, multiple features such as the position, the normal vector and the curvature are fused, accurate detection of the semantic key points is achieved, and the live pig key point detection efficiency and accuracy are improved.

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

The invention discloses a rolling bearing vibration signal multi-mode fault classification method. The method comprises the following steps: collecting a vibration time sequence signal of a rolling bearing; the vibration time sequence signals are input into a time sequence branch network and a space branch network in parallel, and the time sequence branch network extracts time sequence dependence characteristics of the signals through a one-dimensional convolutional neural network and a bidirectional gating circulation unit; the spatial branch network converts the vibration time sequence signal into a Markov transform field image, and extracts spatial structure features of the image by using a two-dimensional convolutional neural network and a window Transform-based visual network; performing bidirectional interaction and weighted fusion on the time sequence features and the spatial features through a cross-modal attention mechanism to obtain fusion features; and inputting the fusion features into a classifier, and outputting a fault classification result of the rolling bearing. According to the method, the problems that a traditional single-mode fault diagnosis model is insufficient in adaptability to complex working conditions, multi-mode feature fusion is insufficient, and the generalization ability is weak due to model structure redundancy are solved.

The invention discloses a robust point cloud registration method and system based on global spatial perception and multistage filtering. The method comprises the following steps: respectively obtaining point cloud initial features and spatial structure features of global spatial perception based on point cloud information; inputting the point cloud initial features and the spatial structure features into a transform network for feature enhancement, and obtaining enhanced features; and performing up-sampling processing and multi-stage filtering on the enhanced features to obtain a transformation matrix, and completing point cloud registration. According to the method, global spatial perception and multi-stage filtering are introduced, so that abnormal values can be effectively eliminated while feature matching is completed finally, and the precision and efficiency of point cloud registration are improved.

The invention relates to the technical field of gesture recognition, in particular to an air gesture action intelligent capture and interactive data processing method and system.According to the air gesture action intelligent capture and interactive data processing method and system, significant jumps are marked through timestamps, time sequence deconstruction of action streams is achieved, nonlinear alignment processing of time structures between paragraphs is introduced, and the time sequence deconstruction efficiency is improved. The fragment consistency recognition capability under the conditions of unequal action durations and different rhythms is remarkably improved, spatial topology among three points is modeled by adopting a graph structure, a graph convolutional network is introduced to process dynamic changes of connection relations among different nodes, the continuity and stability of structure judgment can be kept under a shielding or structure deformation scene, and the recognition efficiency is improved. Through a triple constraint mechanism of direction vector included angle amplitude, length ratio and slope fluctuation, the action is subjected to modeling support in three aspects of time dimension, space structure and direction continuity at the same time, and the dynamic stability of gesture recognition and the resolution precision of paragraph judgment are enhanced.

The invention discloses a lodging winter wheat spatial distribution extraction method based on bimodal feature fusion, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: building a double-flow semantic segmentation network DMFCFNet, and training the network through employing a constructed sample data set, inputting the preprocessed remote sensing image and the DSM elevation image into a network in parallel to obtain semantic texture feature information of the remote sensing images with different scales and spatial structure feature information of the DSM elevation image; and performing feature correction and fusion on the obtained semantic texture feature information of the remote sensing image and the spatial structure feature information of the DSM elevation image, and performing segmentation prediction on the fused feature information to realize extraction of the spatial distribution information of the winter wheat lodging region. By correcting and fusing remote sensing image semantic texture features and DSM elevation features and combining multi-scale information aggregation processing, the winter wheat lodging region extraction precision is effectively improved, and the problems of small target missing detection and boundary blur are solved.

The invention discloses an old city boundary intelligent extraction and texture calculation system based on vector topology analysis and semantic segmentation. According to the method, the topological semantic association module provides a multi-dimensional association basis for boundary extraction by integrating spatial structure information of vector topology and functional attribute tags of semantic segmentation. In the coarse extraction stage, the objective space law of the topological features and the subjective function orientation of the semantic tags are mutually verified, so that the misjudgment of'similar forms but inconsistent functions' possibly caused by single dependence on the topological structure or the deviation of'tag coverage but space breakage 'possibly caused by only dependence on the semantic tags is avoided; in the fine correction stage, the module further dynamically calibrates the boundary range through historical-current situation space-time correlation and multi-source data conflict detection, so that the continuity of historical stable elements is reserved, the updating requirement of current situation semantic tags is included, the final boundary better fits the essential characteristics of'active inheritance 'of the old city, and the accuracy of the final boundary is improved. And human experience interference and errors are obviously reduced.

The invention provides a panchromatic sharpening joint optimization method and system based on three-stage progressive fusion. The method comprises three stages of coarse fusion, deblurring detail enhancement and fine fusion. The method comprises the following steps: firstly, performing feature extraction and preliminary fusion on a low-resolution multispectral image and a high-resolution panchromatic image through a dual-path mutual enhancement network; secondly, a multi-layer stacked deblurring module is introduced to perform deep enhancement on fusion features, and the details and definition of the image are effectively improved in combination with partial large kernel convolution, channel mixing and an element-level attention mechanism; and finally, realizing fine optimization of spectrum consistency and a space structure, and outputting a fused image with high spatial resolution and high spectral fidelity. According to the method, the stability and reconstruction quality of panchromatic sharpening under a complex degradation condition are effectively improved through multi-source remote sensing image collaborative modeling and progressive feature fusion, and the method has good practical value and popularization prospect and is superior to a current panchromatic sharpening method based on deep learning.

The invention provides a mine slope risk assessment method and system based on multi-source data space-time fusion, and relates to the technical field of mine monitoring, and the method comprises the steps: obtaining geological and engineering data, and constructing a basic model representing a slope three-dimensional space structure; collecting multi-source heterogeneous monitoring data on a mine slope field, and aligning and fusing the multi-source heterogeneous monitoring data with the basic model in a space-time dimension to generate a continuous dynamic parameter field; and setting a data fusion model, and taking the basic model and the dynamic parameter field as input to obtain a four-dimensional space-time fusion risk field. According to the method, the basic model representing the three-dimensional space structure of the slope is constructed, and a unified space-time bearing base is provided for all heterogeneous monitoring data, so that originally isolated data such as GNSS displacement, deep displacement, blasting vibration and underground water level can be fused in a unified coordinate system; the problem of data splitting in the prior art is fundamentally solved, and integral and integrated expression of slope engineering and geological conditions is formed.

The invention discloses a remote sensing image segmentation method and system based on cross-normal-form feature fusion and alignment, and the method comprises the steps: carrying out the preprocessing of an input remote sensing image, and extracting an initial feature; inputting a cross-normal-form feature fusion and alignment network, and fusing multi-modal and cross-scale remote sensing image structure information through sparse channel enhancement and space alignment and space pixel refining and channel alignment to obtain a first-stage fusion feature; inputting a multi-stage cross-paradigm enhanced feature extraction network, fusing local details and global context information through multi-level information interaction and a dynamic gating mechanism, and gradually extracting a joint feature map of semantic and spatial structure collaborative expression; a final semantic segmentation result is generated through the segmentation head, and composite loss is calculated based on a real label; according to the method, the multi-stage feature extraction network and a cross-normal-form feature alignment mechanism are constructed, local textures, spatial contexts and multi-modal information are effectively fused, and the segmentation performance is enhanced while the calculation efficiency is guaranteed.

The invention discloses a special-shaped shutter fragment splicing drawing method based on space coordinates, and relates to the field of intelligent construction.According to the method, a laser radar scanning device is used for obtaining a window face initial point cloud, and three-dimensional point set representation under a local space coordinate system is established; then executing a multi-level boundary point extraction process including boundary edge identification, mutation point detection and structure segmentation, and constructing a candidate boundary point set Bpre; on the basis, a five-level space structure partitioning strategy is applied, partitioning point distribution rules are set for regions with different geometric characteristics, and finally a window boundary point set B is formed; the point set has high adaptability in the aspects of structural continuity, spatial distribution density, sampling precision and boundary integrity, and the geometric reduction degree of subsequent splicing modeling is remarkably improved; according to the scheme, the problem that a traditional method is difficult to process local complex curvature and structure jump of a special-shaped window is solved, and continuous mapping of structure boundary information to spliced pattern construction is achieved.

The invention discloses a high-end space light illumination control system with scene adaptability, and relates to the technical field of intelligent light control, and the high-end space light illumination control system is characterized in that spatial structure geometric information and surface material distribution are acquired, and an illumination path simulation model is constructed; in combination with a preset illumination demand template, identifying a position with illumination deviation; performing illumination coverage simulation on each position point based on the lamp type and the light emitting angle, calculating an illumination coverage contribution value, and constructing a reference matrix; according to a matrix result, lamp model selection and angle adjustment are carried out, partition control operation is executed, and dynamic optimization adjustment of the brightness, the angle and the color temperature is achieved; closed-loop correction is completed through illumination feedback; according to the invention, the adaptive ability of illumination control to a complex space structure and a dynamic scene is improved, higher control precision and energy efficiency are achieved, and the method is suitable for intelligent illumination systems in scenes such as exhibition display, commercial retail and high-end residence.

The embodiment of the invention provides an action control method, a training method and device of an action control model and equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: extracting a first video feature from a first video, and extracting a first image feature from a first image; obtaining an initial fusion feature according to the first video feature and the first image feature; based on the frequency sensing weight, performing N stages of noise reduction processing on the initial fusion feature through a diffusion neural network to obtain a final fusion feature, N being a positive integer; and generating a second video containing the target subject based on the final fusion feature, the action of the target subject in the second video being controlled based on the action of the reference subject in the first video. The diffusion neural network is converted from the spatial structure feature of the concerned target subject to the action feature of the concerned reference subject at different frequencies, so that the obtained final fusion feature can include more detailed action features.

The invention discloses a manifold perception-based large model navigation method guided by a historical topological graph, and relates to a computer vision technology. The method aims at solving the challenges that in the navigation process, long-distance reasoning experience is insufficient, instruction fragments and dynamic visual observation are difficult to align, and large model reasoning is prone to illusion interference. Firstly, a large model based on an encoder-decoder structure is used for supplementing historical information coding for a visual observation sequence, and therefore global topological information guidance is provided for long-distance reasoning. And secondly, in order to effectively solve the problem that large model reasoning is subjected to illusion interference, significant space-time differences in a visual observation sequence are mined by using a multi-curvature manifold, so that the large model can accurately describe the current environment and make a decision according to a visual reference object in thinking. Besides, in order to strengthen the perception capability of the large model to the space structure and establish a graph self-attention mechanism, the node distance embedded in the constructed historical topological graph is combined with the visual similarity so as to model the space relationship between the nodes.