1402 results about "Augmented reality" patented technology

The invention relates to the technical field of building information models, augmented reality, synchronous localization and map construction, and provides a deep learning-based SLAM-BIM augmented reality cooperative localization method and system, and the method comprises the steps: introducing a Transform time sequence feature extractor and a geometric relation graph, evaluating a dynamic distribution weight through combining with the confidence, achieving the cross-modal closed-loop detection, and obtaining an SLAM-BIM augmented reality cooperative localization result. A lightweight semantic segmentation network and a feature fusion module are utilized, a dense map with consistent geometric semantics is constructed, a space-time error propagation equation is constructed, online calibration is realized by means of BIM scale prior, an incremental fusion algorithm is designed, a global pose is optimized in combination with AR interaction, and the system fuses SLAM visual trajectory features and BIM semantic geometric features through a deep learning technology. According to the method, the problems that traditional SLAM accumulative errors are large and the BIM fusion precision is low are solved, robust positioning and map construction in a complex scene are achieved, the cooperation precision and real-time performance of SLAM and BIM are improved, and the method is suitable for AR scenes such as building construction and operation and maintenance.

The invention discloses a community intelligent monitoring and emergency linkage method and system fusing BIM spatial semantics, and the method comprises the steps: constructing a BIM scene map, and obtaining the attributes and mutual relationships of components and spatial regions in a BIM model; mapping a dynamic target detected in video monitoring into the BIM model, and obtaining spatial semantic information of the dynamic target; based on BIM spatial semantic information of a dynamic target, a target-environment interaction graph is constructed, a graph neural network model is used for training and reasoning, and specific complex events related to spatial contexts are recognized; taking the BIM model as a space-time reference, fusing multi-source heterogeneous data, and reconstructing by adopting a graph-based event association algorithm to form a complete event chain containing an atomic event sequence and an association relationship; and when an emergency event or an event chain is detected to indicate an emergency state, combining BIM preset information and real-time sensor data, dynamically generating an optimal emergency plan, and performing visual commanding and dispatching through a BIM three-dimensional scene and augmented reality.

The invention relates to the technical field of environment detection, and discloses a GIS map-based multi-class environment parameter automatic prediction method, which comprises the following steps: collecting noise intensity, vibration spectrum, sewage turbidity, illumination intensity and PM2.5 concentration in real time through a distributed sensor network, combining social behavior data, carrying out space-time alignment, and generating a multi-dimensional space-time matrix; calculating an energy overlapping degree by adopting a space-time diagram attention network, marking a high-risk collaborative pollution area, and predicting a pollution diffusion path; constructing a self-adaptive prediction model containing physical and behavior driving channels, and dynamically adjusting the weight to optimize the prediction precision; based on the optimization model, reversely deducing a pollution source of an overproof area, matching equipment characteristics and generating a control instruction; and opening an AR (Augmented Reality) interface to verify the treatment effect, and when the virtual-real data deviation exceeds a data deviation threshold, triggering federal learning to update the global model, and generating an environmental protection compliance report. According to the invention, the accurate decision-making efficiency of environmental governance can be improved.

The invention discloses a road bridge design method and system based on a BIM real scene model, and relates to the technical field of constructional engineering information, and the method comprises the steps: a BIM model carries out multi-objective optimization through a quantum annealing algorithm in combination with a bridge-crossing topology library, synchronously optimizes topological connectivity, structural stress and energy consumption indexes, generates a parameterized component, and feeds back the parameterized component to the BIM model; the model of the BIM model is loaded and optimized based on augmented reality equipment, the model is superposed to a reality scene through space anchoring, and the BIM model is corrected in combination with a conflict monitoring engine and a knowledge graph; the corrected BIM model is deployed to edge computer equipment, a dynamic deviation thermodynamic diagram is generated by scanning a construction surface, a lofting robot is driven to execute coordinate dotting, and meanwhile construction error data is transmitted back; according to the method, the problem that a global optimal solution is difficult to find when a traditional optimization algorithm is used for processing a complex multi-target problem is solved by utilizing an advanced quantum computing technology, and the economical efficiency and the structural safety of bridge design are greatly improved.

A high-fidelity three-dimensional reconstruction method for a specular reflection plane comprises the steps of decomposing pixel colors into diffuse reflection and specular reflection components based on a 3D Gaussian sphere, introducing a dynamic reflection ratio parameter and a spherical harmonic function coefficient to respectively represent two reflection characteristics, and simulating light multi-reflection behaviors through weight fusion of cumulative projection and a reflection ratio map. Secondly, in combination with monocular inverse depth calibration, depth smoothing constraint of color gradient weighting and edge mutual exclusion loss, geometric consistency is enhanced, and artifacts are suppressed; a progressive multi-resolution training strategy is further adopted, low resolution is gradually optimized to complete resolution, reflection parameters are activated in stages, 3D Gaussian sphere overgrowth and floating artifacts are inhibited, and efficiency and precision are balanced. According to the method, the reconstruction fidelity of the specular reflection scene is remarkably improved while the real-time rendering advantage of the 3DGS is reserved, and the method is suitable for the high-precision modeling fields of virtual reality, augmented reality and the like.

The invention discloses a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

The invention relates to the technical field of fire fighting systems, in particular to an intelligent cockpit system for fire fighting, which comprises a perception analysis layer, a decision processing layer, a command execution layer and a reinforcement learning closed-loop architecture for mixed reward shaping, and integrates video streams, audio communication and firefighter physiological data containing heart rate variability through a deep multi-modal fusion module. Generating a global fire scene situation of physical constraint verification; through a risk sensitive type three-dimensional fire scene deduction module, a fire extinguishing strategy considering efficiency and safety is generated; through an immersive augmented reality visual interface, in combination with a synchronous positioning and mapping technology, precise navigation in a complex environment and superposed display of situation information containing risk levels are realized; a Q learning algorithm continuous optimization strategy including domain knowledge intermediate process rewards is adopted, closed-loop optimization of perception-analysis-decision-execution-feedback is achieved, and therefore the overall efficiency of fire rescue operation in modern complex disaster scenes is remarkably improved.

The invention relates to the technical field of augmented reality and industrial safety monitoring, in particular to an AR glasses dangerous state dynamic labeling method and system based on multi-source perception, and the method comprises the steps: synchronously collecting data such as a depth image and gas concentration through multi-source equipment such as a depth camera and a gas sensor; extracting rotation invariant point cloud features through an improved dangerous target detection model, and fusing multi-modal data to generate a dynamic danger level; based on VIO, realizing accurate spatial registration of annotation and a real scene, and generating a self-adaptive annotation style according to a danger level; and multi-device labeling pose synchronization is realized through an entropy weighting ICP algorithm, and labeling transparency and size are dynamically optimized in combination with a user fatigue value and environment illumination. According to the method, the comprehensiveness of danger identification and the accuracy of marking are improved, team collaborative operation is supported, the method is suitable for scenes such as electric power inspection and dangerous chemical transportation, and the operation safety and efficiency are guaranteed.

The invention provides a three-dimensional Gaussian digital human generation system and method and electronic equipment, and relates to the technical field of augmented reality, and the system comprises a data obtaining unit which is used for obtaining visual depth data, dynamic behavior data and environmental perception data, and generating point cloud data and multi-modal data according to the visual depth data, the dynamic behavior data and the environmental perception data; the rendering adjustment unit is used for monitoring the fixation area in real time and determining a rendering strategy; according to the real-time computing power parameter, determining a rendering specification; the driving unit is used for performing feature extraction on the multi-modal data to obtain multi-modal features and generating face driving parameters and limb driving parameters of the digital human model; and the modeling unit is used for generating a basic grid of the digital human model according to the point cloud data and the initial digital human model, and performing expression rendering and action rendering on the basic grid according to a rendering strategy and a rendering specification in combination with the face driving parameters and the limb driving parameters to obtain a rendered digital human model. The fluency, authenticity and naturalness of the display effect are improved.

The invention discloses an artificial intelligence assisted makeup and makeup robot collaborative makeup method, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting user face data through a multi-modal sensor, extracting multi-scale features based on improved DenseNet and HRNet networks, and generating a three-dimensional face model; a high-fidelity virtual makeup effect is generated by using a conditional generative adversarial network in combination with user requirements, and real-time comparison and display are performed through an augmented reality interface; the makeup difference is quantified through multi-dimensional feature point mapping, multi-color space analysis and texture comparison, and correction suggestions are dynamically generated; operation is executed through the seven-degree-of-freedom redundant mechanical arm, a hybrid force control strategy and a visual monitoring system are integrated, and safe and accurate execution is ensured. The method solves the problems of uncontrollable traditional makeup effect, low execution precision and lack of dynamic feedback, has the characteristics of high adaptability, real-time response and zero-damage safety, and can be widely applied to the fields of family beauty, film and television makeup and special crowd nursing.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to a visual safety risk early warning system for pseudo-classic architecture mixed structure construction, in particular to the field of safety risk early warning of pseudo-classic architecture construction, through multi-source data fusion and intelligent algorithm cooperation, the construction safety risk management and control capability is remarkably improved, the system collects mechanical and environmental data of key nodes of a high-support formwork body in real time, and the construction safety risk early warning capability is improved. Load distribution is dynamically updated in combination with the building information model, and a high-precision space-time label data stream is generated; predicting a short-term instability risk based on a two-channel neural network, constructing a complex network model to quantify a node failure linkage effect, and generating a visual risk propagation thermodynamic diagram; finally, a grading alarm strategy is triggered through a dynamic threshold value, augmented reality visual warning and equipment linkage control is achieved, the system breaks through the limitation of traditional manual monitoring, full-process closed-loop management of risk perception, prediction, positioning and response is achieved, the occurrence rate of safety accidents is effectively reduced, and the safety and reliability of construction of the complex structure of the pseudo-classic architecture are guaranteed.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention belongs to the technical field of three-dimensional scene modeling, and discloses a dual-branch diffusion three-dimensional scene generation method based on a multi-modal semantic graph, which comprises the following steps of: firstly, receiving multi-modal data such as sketches, texts, automatic completion instructions and scene general knowledge, extracting features and fusing the features into a unified multi-modal semantic graph; utilizing a graph neural network and an attention mechanism to enhance semantic graph features, and optimizing physical constraints through a physical engine; complementing the missing visual modality and graph structure relationship; performing quality scoring on the scene based on semantics, spatial relationships and physical constraints; and finally, respectively generating a spatial layout and a geometric shape through a double-branch diffusion model, and ensuring the coordination of the layout and the shape. The method has the advantages of multi-modal information fusion, physical rationality guarantee, high structure complementation capability, high-quality score optimization and efficient generation process, and is suitable for three-dimensional scene modeling requirements in the fields of virtual reality, augmented reality, robots and the like.

The invention discloses a virtual garment customization method based on 3D modeling, and relates to the technical field of garment customization, and the method comprises the steps: collecting image video data corresponding to the body data of a user, and carrying out the preprocessing of the collected data, and obtaining the basic three-dimensional point cloud data of a human body; carrying out primary human body modeling according to the human body basic three-dimensional point cloud data, then adjusting the model shape based on key parameters input by a user, and carrying out body type matching to realize parameterized human body modeling; secondly, modeling the clothes, predicting clothes deformation by adopting machine learning, simulating cloth dynamics by adopting a physical engine, performing collaborative tracking and matching on clothes and human body dynamic characteristics according to a clothes template selected by a user, and realizing virtual try-on through an AR (Augmented Reality) technology; the customized order confirmation information of the user is obtained, the 2D garment pattern graph is generated and sent to the customized production server, it can be ensured that the user can obtain a high-quality 3D modeling result under different devices and environments, and the user experience can be enhanced through AR try-on.

The invention relates to a visual transformer substation intelligent inspection system and method based on a multi-mode large language model, and belongs to the technical field of power system intelligence. The system obtains image, temperature, vibration and noise data of substation equipment in real time through a multi-modal data acquisition module, and performs preprocessing and fusion. A high-precision three-dimensional semantic model is constructed by using a three-dimensional dynamic modeling module, and the device attributes are automatically labeled by fusing LLM semantic understanding capability. A multi-modal large language model (LLM) engine is combined with cross-modal feature extraction, a dynamic knowledge base and a self-adaptive reasoning unit to realize accurate diagnosis of equipment faults. An augmented reality (AR) interaction module displays the real-time state of equipment through AR glasses and supports natural language interaction. The self-interpretation decision support module generates interpretable fault reports and maintenance suggestions, and the communication and feedback module is responsible for data uploading and remote alarm. According to the method, multi-dimensional perception, dynamic knowledge reasoning and self-adaptive decision support of the equipment state are realized, the intelligent level of substation inspection is remarkably improved, the inspection efficiency is improved by more than 40%, the omission ratio is reduced to less than 1%, and rapid diagnosis of more than 95% of novel faults is supported.

The invention relates to the technical field of high-precision positioning, in particular to an outdoor immersive text and travel hybrid positioning and dynamic rendering optimization method, and the method comprises the steps: obtaining a positioning signal of a mobile terminal in a target service area through a hybrid positioning model; the positioning signals comprise an ultra-wideband ranging signal, a visual inertial odometer signal, a real-time carrier phase difference signal and an inertial navigation signal; processing the positioning signal through a multi-layer signal processing filtering model to obtain coordinate data, and generating a space coordinate; constructing a user movement situation prediction model based on the space coordinates, generating a navigation prediction track and navigation confidence, and generating a preloading strategy; acquiring geographical environment parameters of the target service area, and integrating the space coordinates, the geographical environment parameters, the preloading strategy and the virtual object through a multi-dimensional virtual-real fusion model to generate an augmented reality space scene; and analyzing the user movement track data and the augmented reality space scene through a multi-modal driving model to generate interactive content.

The invention belongs to the technical field of unmanned aerial vehicle technology and intelligent operation and maintenance management, and discloses an unmanned aerial vehicle maintenance task scheduling and resource management platform and method, and the method comprises the steps that a data collection and fusion module obtains and fuses multi-source data; the fault diagnosis and prediction analysis module uses a deep learning model and a CNN-LSTM hybrid model to diagnose faults, predict life and generate a maintenance task list; an optional task dynamic priority evaluation module performs priority ranking on the maintenance task list; the intelligent scheduling decision module uses an improved NSGA-II and VNS mixed algorithm to generate an optimal allocation scheme according to priority and resource constraints; the resource dynamic management and cooperation module realizes dynamic allocation and cooperation of personnel, spare parts, tools and sites, and can integrate AR guidance; and the optional visual monitoring and feedback optimization module displays the state, feeds back data and realizes closed-loop optimization. The maintenance efficiency of the unmanned aerial vehicle can be remarkably improved, the operation cost is reduced, and the flight safety is guaranteed.

Example implementations are directed to recapturing or realigning a room or scene in an artificial reality (XR) environment as a three dimensional (3D) space. To initially capture a room, a user manually annotates, using an XR system, one or more walls around the XR system. The XR system then aligns or localizes itself relative to the room. Once a room is captured and stored, and the user in the future enters the room again, the XR system may not be able to align itself in the room due to, for example, lighting conditions. Rather than require the user to manually recapture every wall captured during the initial room capture, the XR system can select one wall for recapture and, after receiving an annotation or marking of the one wall from the user, use the selection to realign the XR system relative to the room.

The invention provides a multifunctional inspection device detection system based on a distribution network mobile operation terminal, and relates to the technical field of electric power detection, and the multifunctional inspection device detection system comprises a portable main terminal of a dynamic collaborative architecture, a wearable terminal for augmented reality interaction and a distributed heterogeneous sensing cluster, which form a closed-loop data chain through an electric power dedicated low-delay wireless communication network; the portable main terminal is integrated with a heterogeneous computing unit with adaptive computing power distribution, can perform real-time fusion analysis on multi-dimensional sensing data, and outputs a visual result containing fault location and confidence; the wearable terminal superposes fault information to a real scene in a three-dimensional marking form through a virtual-real fusion positioning technology, and supports eye movement and voice collaborative interaction; multi-module collaborative acquisition of the distributed heterogeneous sensing cluster is combined with a nanosecond timestamp synchronization mechanism, so that the limitation of traditional single parameter detection is broken through, rich and synchronous basic data is provided for subsequent analysis, and the comprehensiveness of routing inspection is greatly improved.

A method and computing apparatus are disclosed for allowing a tidying robot to organize objects into non-standard categories and deposit them at non-standard locations that match a user's needs. The tidying robot navigates an environment using cameras to map the type, size, and location of toys, clothing, obstacles, furniture, structural elements, and other objects. The robot comprises a neural network to determine the type, size, and location of objects based on input from a sensing system. An augmented reality view allows user interaction to refine and customize areas within the environment to be tidied, object categories, object home locations, and operational task rules controlling robot operations.

The invention discloses a deep learning-based cold resistance evaluation system for medicago sativa L., and the system comprises a data generation module which is used for generating a phenotypic image and corresponding physiological data of medicago sativa L. under low-temperature stress through a diffusion model embedded with plant low-temperature response physical constraints; the evaluation model module is used for extracting cold resistance characteristics from the image and physiological data by adopting a causal-driven dual-channel adaptive network; and the decision module comprises a hierarchical model distillation unit and a federal reinforcement learning unit, and the hierarchical model distillation unit and the federal reinforcement learning unit realize joint training of model compression and decision strategy optimization through an edge-cloud collaborative architecture, output a cold-resistant decision and realize visualization through an augmented reality interface. The method can effectively solve the core problems of traditional medicago sativa cold resistance assessment in the aspects of data generation, model generalization, decision-making efficiency and the like.

The invention relates to the technical field of artificial intelligence and data analysis processing, and provides a smart home complex scene object analysis method based on multi-modal fusion. The method specifically comprises the steps of performing standardization processing on input data, and extracting multi-scale features of local textures and global semantics; generating a candidate box and a category score by a YOLO model, and adaptively generating a box prompt, a point prompt and a category / text prompt according to the confidence coefficient and the target scale; and inputting the prompt information and the multi-scale features into a mask decoder together, generating a high-precision instance mask in the candidate region, and checking to obtain a final output. According to the method, repeated coding is avoided, the performance of a small object and the performance of a complex boundary target are effectively distinguished, the real-time performance and the resource efficiency are considered at the same time, and the method is suitable for scenes of smart home, indoor navigation, security monitoring, augmented reality and the like.

The invention discloses a large-span truss hoisting and splitting system and method based on digital twinning, and belongs to the technical field of building construction, and the system comprises a physical entity unit, a data acquisition and transmission unit, a digital twinning model unit, a function service and data analysis unit and an application and interaction unit. According to the invention, through real-time sensing of a physical entity state, multi-source data fusion transmission, high-precision digital twinning modeling, intelligent simulation analysis and decision optimization, and fusion of multi-modal man-machine interaction, a virtual control system which is in whole-course synchronous mapping and bidirectional interaction with a physical entity is constructed; according to the method, real-time visual monitoring, trend prediction and risk early warning of the hoisting and splitting process can be achieved, scheme preview and dynamic optimization are supported, virtual-real fusion operation guidance is achieved through the AR technology, and the safety, precision and efficiency of hoisting and splitting operation of the large-span truss are improved.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The invention relates to the technical field of medical image processing and surgical navigation, in particular to a lung cancer intraoperative navigation and positioning method based on three-dimensional reconstruction and a deep neural network, which comprises the following steps of: based on multi-source optical and acoustic fusion, performing fractal wavelet multilayer decomposition on preliminarily purified data acquired in an operation, and performing multi-source optical and acoustic fusion; multi-modal characteristics of optical and acoustic channels and fractal measurement are obtained; using quantum heuristic multi-agent search to find a potential focus or a key area with serious deformation, and performing interpolation completion on a defect or noise part in a nerve radiation field; performing elastic deformation registration on the interpolation result and the lung three-dimensional model, and calculating a nonlinear deformation field and intra-operative tumor prediction coordinate data; and superposing the deformed lung model to an augmented reality environment through external tracking information to display the focus position. And when lung collapse or the traction amplitude exceeds the limit is monitored, searching and registration are repeated for real-time updating, and accurate navigation positioning under dynamic deformation of the lung is realized.

The invention relates to the technical field of auxiliary training, and discloses an AR auxiliary training system for improving the diagnosis and treatment capability of general practitioners on undifferentiated diseases, and an immersive training environment is constructed by adopting an augmented reality technology. The system comprises an environment sensing module, a space positioning module, a case data module, a virtual rendering module, an interactive acquisition module, an intelligent response module, a state control module and an evaluation calculation module. The environment sensing module adopts an RGB-D camera and an inertial measurement unit to collect environment and head movement data of a user; the spatial positioning module determines a virtual patient placement position based on an SLAM algorithm; the virtual rendering module renders a three-dimensional virtual patient in a real environment; the interaction acquisition module supports voice and gesture multi-mode interaction; the intelligent response module generates an intelligent patient answer; the evaluation calculation module carries out comprehensive evaluation from three dimensions. The invention provides a highly immersive and intelligent training environment, and has the advantages of low cost, reusability and high standardization degree.

The invention relates to the technical field of augmented reality, in particular to an AR real-time scene reconstruction and illumination matching system and method based on neural rendering, and the system comprises a real-time scene understanding module, a real-time neural radiation field module and a real-time reflection inference and enhancement module. Comprising a forward network, a feature transformation layer and a backward network, the forward network maps a high-dimensional rendering space to a feature manifold, the feature transformation layer executes mapping from the feature manifold to a rendering manifold, and the backward network performs rendering calculation based on a Riemannian metric driven adaptive ray tracing mechanism; the real-time reflection inference and enhancement module performs material prediction and illumination information calculation based on an illumination dynamic adaptation system on a differential manifold, real-time application of a neural radiation field technology in an AR scene is realized through an innovative differential geometry framework, and a real-time rendering frame rate of 30-60 fps is achieved while a high-quality rendering effect is kept.

The invention relates to the field of electric power systems, in particular to an electric power facility remote intelligent maintenance method and system. The method comprises the following steps: collecting three kinds of heterogeneous monitoring data of vibration spectrum, infrared thermal imaging and partial discharge signals; mapping the data to a three-dimensional feature fusion space, and calculating a mahalanobis distance to generate a fusion feature matrix; extracting spatio-temporal features by using a convolutional long-short-term memory network, and outputting a triple diagnosis result including a fault type, a severity level and an evolution trend; searching a dynamic maintenance scheme in a maintenance strategy knowledge graph based on the diagnosis result; solving an optimal resource scheduling scheme by adopting an improved Hungary algorithm in combination with the geographic topology and the resource state; the maintenance operation is remotely guided through the augmented reality terminal, and real-time verification is carried out; and collecting the maintained data, carrying out residual analysis, and reversely optimizing the knowledge graph. The method realizes full-process intelligent management, improves timeliness and reliability of operation and maintenance of electric power facilities, and is suitable for a remote intelligent maintenance scene of an electric power system.

A system and method for generating and updating a posegraph for multi-user augmented reality experiences. The system receives initial pose data from multiple client devices and generates a posegraph based on this data. When client devices come within proximity of each other, the system detects this and receives relative pose observations from the devices. The system then updates the posegraph based on these observations, assigning confidence values to improve accuracy. This approach enables efficient synchronization of spatial information across devices, allowing for seamless shared AR experiences in large-scale environments without the need for complete map sharing or pre-mapped areas.