341 results about "Spatial computing" patented technology

The invention discloses a multi-agent cooperation system and method based on spatial calculation and multi-modal AI fusion, and the method comprises the steps: collecting all real-time data of a construction site through a multi-modal data collection and fusion unit, and carrying out the processing and integration of all collected data through a multi-modal AI algorithm, and generating a unified semantic association model; a three-dimensional virtual environment of a construction site is constructed through a space calculation unit, real-time synchronous mapping is formed for the construction site, and dynamic changes in the construction process are simulated and predicted; task allocation and behavior planning are carried out on multiple agents through an agent behavior management unit; and the intelligent agents are configured to perform real-time information sharing and task cooperation through a communication protocol through the multi-intelligent-agent cooperation unit. According to the scheme, the problems of difficulty in multi-source data integration, low cross-department cooperation efficiency, insufficient construction dynamic adjustment and the like in intelligent construction in the building industry can be solved.

The invention discloses a high-accuracy threat intelligence assisted network threat tracing method, which comprises the following steps: S1, collecting and preprocessing multi-source network security data, and constructing a time-marked event sequence set; s2, constructing an optimized Transform network model, and processing an attack event sequence by using position coding and time embedding; s3, a black swan optimization algorithm is initialized, and a Transform structure hyper-parameter is dynamically optimized; s4, outputting an attack event semantic vector, and constructing an attack path semantic map; s5, the intelligence information vector is embedded into a Transform hidden space; s6, calculating semantic similarity and dependency intensity, and generating an attack source candidate set and a traceability path; s7, outputting an attack traceability path, a starting point node and an information label, and generating a structured traceability report; and S8, according to the traceability result feedback, updating the black swan algorithm and the Transform model. The method is used for realizing intelligent modeling of multi-source network attack events and high-accuracy traceability analysis of attack source nodes.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention provides a PICC puncture virtual simulation training system and method constructed based on XR. A nursing skill simulation training platform and an intelligent training room based on augmented reality (XR) combine a simulation medical technology, an artificial intelligence large model, XR intelligent hardware, intelligent agent application, space calculation, computer vision and a mainstream technology of Internet of Things in scheme planning and design. According to the PICC puncture virtual simulation training platform, the ground practice, standardization, high virtual simulation and adaptive application of PICC puncture virtual simulation training are realized, and the intelligent development process and teaching concept of PICC puncture teaching are improved. According to the PICC puncture teaching system, intelligent virtual tutors, puncture teaching holographic three-dimensional sharing, virtual reality fusion technologies and the like are provided, an interactive teaching mode can be brought to PICC puncture teaching, the interest of students in teaching and clear operation guidance are improved, the theory and practice are fused, and the clinical cooperation ability is improved.

The invention relates to the technical field of government and enterprise services, and discloses an intelligent adaptation system and method for government and enterprise services, and the method comprises the following steps: carrying out the structural analysis of an original policy text through a deep learning model, including word segmentation, word vector generation, dominant condition extraction and implicit condition reasoning, and finally constructing a policy knowledge graph; the multi-source heterogeneous data is integrated, dynamic enterprise feature vectors are generated through spatial-temporal feature construction, industrial association diagram modeling and spatial-temporal diagram convolution calculation, the enterprise state change gradient is detected, and the abnormal fluctuation risk is recognized; and mapping the policy condition vector and the enterprise feature to a hyperbolic space, calculating a hyperbolic distance and converting the hyperbolic distance into a matching degree, superposing timeliness attenuation, data freshness punishment and state mutation filtering, and outputting a final matching result. According to the invention, a closed-loop system of policy analysis, accurate matching, resource allocation and feedback optimization is finally constructed, the financial resource waste rate is compressed to a low level, and the government affair service efficiency is improved.

A system for providing an extended reality environment that incorporates real-world event data into 3D models representative of the real-world event within the extended reality environment includes an XR processing unit configured to dynamically download 3D hole models of the course, receive real-world live shot data of shots taken on the holes during a golf tournament; map the shot data to the 3D hole models; and cause a spatial computing device to render hole views and immersive views comprising the mapped shot data rendered directly on the 3D hole models in real-time in an interactive extended reality environment. The mapped shot data includes shot trails representative of the shots taken on the golf course, The hole views and immersive views may utilize the same source file.

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention provides a beach big data geographic space intelligent application service method based on a large model Agent, and relates to the technical field of geographic space data artificial intelligence processing and analysis. According to the method, a distributed platform comprising a parallel computing cluster, a spatial database and a large language model is constructed, and standardized storage and metadata management of multi-source remote sensing, vector and attribute data are supported. The system generates a spatio-temporal query instruction through natural language analysis, completes cross-source matching and spatial calculation, outputs an analysis result in a multi-modal form such as a map, a chart and a text, and realizes intelligent and interactive service of geographic data processing.

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on the access permission, receives from the multimodal interface a conversational input and a spatial input from the user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs / APIs), conversational interfaces (text / voice), and spatial computing interfaces (VR / AR / MR / gestural).

The invention belongs to the technical field of industrial operation and maintenance term processing, and provides a big language model training method based on a knowledge graph, and the method comprises the steps: carrying out the cooperation with operation and maintenance personnel in advance to set a fault dictionary, collecting fault instances, carrying out the classification and preprocessing, extracting causal trigger words of the fault instances, and setting a text dependency relationship rule. Splitting and extracting a triple of the fault instance, and importing to form a knowledge graph; mapping the instance attribute into a node feature vector, outputting an embedded representation of a node, and fusing the embedded representation with the multi-modal feature vector to form a fused feature embedded representation; replacing semantic and degree words with fault instances in the knowledge graph to generate positive samples, randomly replacing subjects, objects and causal relationships to generate negative samples, inputting the negative samples into a semantic encoder, mapping the negative samples to the same metric space, and calculating similarity to obtain a comparison loss value to adjust graph convolutional network parameters; and after the fusion feature embedding representation retrieval of the fault instance is carried out and a subgraph is generated, maintenance suggestions are given step by step along a causal chain.

The invention provides a geographic knowledge complex question-answering method based on a space-time agent architecture. The geographic knowledge complex question-answering method comprises the following contents: natural language analysis: accurately understanding intentions of users and information contained in questions; multi-modal geographic entity recognition: processing non-text data contained in user input; generating a WKT format and injecting a prompt; knowledge graph retrieval enhancement; in the spatial computing tool chain arrangement, an LLM generates a problem solving scheme after combining multi-modal information, WKT geometry and related data resources associated with a knowledge graph, tools or functions needing to be called and an execution sequence of the tools or the functions are listed, and the series of tools adopt a model context protocol; in answer generation and response, the method can effectively solve the problems that in the prior art, when complex geographic questions and answers are processed, question input recognition is incomplete, reasoning steps are not accurate, and the answer basis is not real, and more intelligent and reliable question and answer support is provided for the field of natural resource planning and the like.

The invention relates to the technical field of power equipment operation and maintenance and state monitoring, in particular to an optical fiber transformer health state assessment method and system based on feature fusion. According to the invention, sampling signals of a plurality of optical fiber current transformers are collected and preprocessed; constructing a historical data matrix for principal component analysis, establishing a principal component space and calculating a square prediction error control limit value; constructing a simulated fault data set and projecting the simulated fault data set to a principal component space to calculate a square prediction error time sequence; performing wavelet packet decomposition on the square prediction error time sequence to extract a normalized energy feature vector, and training a one-dimensional convolutional neural network model; projecting the real-time sampling signal to a principal component space to judge whether the real-time sampling signal exceeds the limit or not; if yes, the health state level is output through the trained model. According to the invention, accurate grading and real-time online evaluation of the health state of the optical fiber transformer are realized.

The invention discloses a robot redundant attitude planning method and system based on position and obstacle constraints, and the method comprises the following steps: generating a planning path of a robot based on a redundant attitude space and a known robot path position point, the planning path being represented by a position space and an attitude space; constructing an approximate key collision plane; calculating a feasible attitude area based on the obstacle information and the redundant attitude space on the approximate key collision plane; estimating a preferential sampling attitude area; continuously sampling in the position space and the attitude space, acquiring a pose node to expand the random search tree until a target node is reached, and acquiring a target path; and iterating the random search tree extension operation to obtain more target paths, and selecting a target path with the lowest total cost from all the obtained target paths. According to the method, the exploration efficiency of redundant attitude planning can be improved, the integrity and safety of the process are ensured, and the method has relatively high applicability.

System and method for managing information compliance and relevance using autonomous artificial intelligence (AI) agents in data transfer and communication environments. Some embodiments may include a core orchestration engine with multiple autonomous AI agents configured to manage and evaluate the compliance and relevance of information in communication and data transfer environments. The system may use weighted metrics to assess if information and actions comply with regulations and are pertinent to recipients, monitor email and data transfer, ensure regulatory compliance, and enhance information and knowledge sharing within organizations. The system may use semantic embeddings, part-of-speech analysis, and language models to extract and apply regulatory rules efficiently. These features may significantly reduce search space, computational overhead, and manual effort while improving security and accuracy.

Systems and methods for immersive data management in spatial computing are disclosed. A method may include: (1) requesting from an identity and access management service executed in a cloud environment, an access token; (2) receiving, from an identity and access management service, the access token comprising user entitlements to access a plurality of elements in a plurality of scenes; (3) requesting one of the plurality of scenes from a scene filtering service, the request comprising the access token; (4) identifying the user entitlements from the access token; (5) retrieving the requested scene comprising a subset of the elements; (6) adding the elements that the user is entitled to based on the user entitlements to a scene to be displayed; (7) returning the scene to be displayed to the computer program; and (8) displaying the scene to be displayed.

The invention discloses a power load prediction method based on a dynamic expert pool and a load balancing mechanism MoE, and belongs to the field of power load prediction, and the method comprises the following steps: collecting multi-dimensional input data needed by power load prediction, detecting and repairing an abnormal value in the input data, and obtaining a power load prediction result; constructing a time-feature matrix by using the repair data; projecting the time feature matrix into three subspaces of Q, K and V, calculating attention weights among feature dimensions, and obtaining enhanced features through attention weighting; the enhanced features are input into a routing layer of the MoE model, selection probability distribution of experts is calculated, the MoE model adopts a dynamic expert pool and introduces a load balancing mechanism, and a Top-2 expert selection strategy is randomly distributed in the reasoning stage; and when the MoE model is migrated to a new scene, updating the attention weight of the MoE model by adopting a meta-learning strategy. According to the method, complex factors influencing the power load can be comprehensively captured, and the power load prediction precision and the cross-scene adaptive capacity are remarkably improved.

Disclosed are methods, systems, and computer program products that present, by a spatial computing system, an extended reality presentation to a user, wherein the extended reality presentation comprises virtual object(s) generated by a spatial computing system and transmitted to one or both eyes of the user and a physical environment that includes a first physical object and a second physical object and is perceived by the user via light reflected from the physical environment. The extended-reality representation is rendered for an object interaction between the first physical object and the first virtual object or the user. A physics-based effect on the first physical object by the object interaction is determined by using at least a stereoscopic model for the first physical object based at least in part upon the object interaction. The extended reality presentation into an undated extended reality presentation based at least in part upon the physics-based effect.

Various implementations disclosed herein include devices, systems, and methods that convert a mono image to a stereo image pair. For example, a process may obtain an input image including appearance values at pixel positions. The input image corresponds to an appearance of a scene from a first viewpoint. The process may further determine a depth image comprising depth values at original pixel positions that are mapped to corresponding pixel positions of the input image. The process may further generate a first output image corresponding to a second viewpoint of the scene different than the first viewpoint and a second output image corresponding to a third viewpoint of the scene different than the second viewpoint. The process may further enable comfort-based 3D style presets for adjusting comfort parameters for viewing a stereo image pair.

The present application provides an oral treatment table training system and method based on spatial computing technology, which relates to augmented reality technology and is used to solve the problems that existing simulators only support single-person use and only perform training based on oral models, lack full-process doctor-patient communication, and have low simulation authenticity. With the help of spatial computing technology, the virtual-reality integrated head-mounted viewer obtains the positioning data of entities and the dental chair of the treatment table in the real space, and the situation observation server arranges virtual entities and generates a scene file. The simulation computer generates a virtual-reality integrated treatment scene, virtual patients, and treatment tools according to the scene file, and can simulate the whole process of the patient from entry to treatment. And generate force sense information to control the force sense tool in the force sense information and match the pose of the virtual treatment tool with the force feedback tool. The user wears the virtual-reality integrated head-mounted viewer to perform treatment operations. With the help of network synchronization technology, it supports multiple people to watch the operation process of the same treatment table. The present application improves the comprehensive simulation authenticity.

The invention relates to the technical field of clean energy fault positioning, and discloses a fault positioning method and system for abnormal operation of a clean energy station. A signal collecting and processing module of the system collects a multi-source operation state signal in real time, and a smooth signal is reconstructed after high-frequency noise is removed through self-adaptive filtering; the operation state analysis module extracts time-frequency characteristics of the smooth signal, identifies the operation state stage of the station, and generates a fault characteristic spectrum based on preset parameters and real-time frequency data when the station is abnormal; the three-dimensional station modeling module analyzes terrain detection signals in a multi-scale manner, and constructs a three-dimensional model containing equipment space coordinates, topological connection relations and environment characteristics; the fault prediction module maps the fault characteristic spectrum to a three-dimensional model, divides equipment area subspaces, calculates fault probability distribution and generates a fault thermodynamic diagram and a density gradient field; and the optimization decision module initializes particle swarm optimization algorithm parameters, constructs an operation stability objective function, and generates an optimal fault positioning path scheme in combination with equipment health state constraints.

The invention discloses a virtual power plant and resource matching method and system based on cooperation preference in the field of virtual power plant resource matching, and the method comprises the steps: building a photovoltaic, energy storage and air conditioning load multi-mode cooperation preference scoring model, and quantifying the economic effectiveness, equipment loss, user comfort and other multi-dimensional features; a double-tower representation and contrast learning technology is adopted, and resource attributes and service requirements are mapped to a unified semantic space to calculate the matching degree; in combination with an entropy regular optimal transmission algorithm, many-to-many flexible matching is realized on the premise of considering grid power flow safety constraints; and a rolling multi-cycle optimization mechanism is introduced, and a Bayesian update and reinforcement learning dynamic adjustment strategy is adopted, so that the technical problems of insufficient cooperation intention quantification, complex cross-time constraint and difficult security risk control in distributed resource scheduling under high-proportion new energy access can be solved, and the economy and security of virtual power plant scheduling can be effectively improved. And the performance stability of the system under the new energy fluctuation is enhanced.

The invention provides a digital twin enabling air-ground integrated dual power supply network supply-demand game method and system, and belongs to the technical field of intelligent management and control of energy and communication convergence networks. Comprising the following steps: constructing a digital twin system of an energy-communication fusion network, and accessing and mapping dynamic data from an energy network and a communication network in real time; based on the dynamic data, an energy side observation space and a communication side observation space are defined in the digital twin system, and the comprehensive supply and demand imbalance degree of the system is calculated; the energy side intelligent agent and the communication side intelligent agent output control instructions in a continuous action space according to respective observation spaces, and respectively aim at maximizing own long-term accumulated benefits; a final solution of the coordinated game framework is an optimal strategy combination meeting a Nash equilibrium condition; carrying out centralized training and distributed execution on the coordinated game framework by adopting a multi-agent depth deterministic strategy gradient algorithm based on supply and demand perception; and the intelligent agent carries out collaborative decision making based on the global supply-demand imbalance degree and local observation.

The invention discloses a hybrid office efficiency optimization system based on digital twinning and space calculation, and relates to the technical field of computer science and technology, and the system comprises a data collection module which captures the station layout parameters, the personnel movement track, the equipment use state and the environment parameters of an entity office space in real time through an Internet of Things sensor array, and transmits the parameters to a server; the sensor array comprises a positioning base station, a thermal imager, an intelligent electric meter and an acoustic sensor. The hybrid office efficiency optimization system provided by the invention can accurately and comprehensively map the office environment through the real-time data acquisition and multi-source fusion technology, solves the problem of single data acquisition of the traditional system, and improves the efficiency of the office by utilizing the space calculation and three-dimensional modeling technology. According to the system, a three-dimensional space model comprising an interactive station unit, an equipment entity and a personnel virtual avatar is constructed, a basis is provided for dynamic modeling and intelligent optimization, and a multi-objective optimization algorithm and a genetic algorithm are combined, so that the system can be under complex constraint conditions.

The invention discloses a Koopman spatial transformation and mahalanobis distance-based fan health evaluation method, which comprises the following steps of: firstly, acquiring a low-frequency signal and a high-frequency vibration signal of a system, preprocessing data, then, constructing a second-order polynomial basis function by utilizing extended dynamic mode decomposition, and solving a Koopman operator to realize global linearization of a nonlinear system; key modes which are low in attenuation rate and are matched with gear abrasion and bearing fault characteristic frequency are screened through eigenvalue decomposition; real-time data is projected to a modal space, the standardized mahalanobis distance between the real-time data and normal state distribution is calculated, a health index model is constructed in combination with historical data, and health degree quantification and graded early warning are achieved. According to the method, the Koopman theory and the statistical distance are combined, the high-dimensional modeling problem of a nonlinear fan system is solved, accurate mapping of fault severity and service life loss is achieved, and the method is suitable for health management of key parts such as gearboxes and bearings and is expanded to the field of rotary machinery such as water pumps and compressors.

The invention discloses a point cloud map establishing method and device, an unmanned vehicle and a storage medium, and the method comprises the steps: obtaining the point cloud data of a target region and the IMU data of a target vehicle, and determining the neighborhood space of each point in the point cloud data; calculating a covariance matrix of the point cloud in the neighborhood space, and decomposing the covariance matrix to obtain a corresponding characteristic value; calculating the weighted curvature of the corresponding point based on the characteristic value and the point cloud depth and geometric residual error of the corresponding point; and screening the point cloud data based on the weighted curvature, and fusing the obtained reference point cloud data with the IMU data to establish a point cloud map. Therefore, mapping can be realized in a complex scene, especially in an environment including dense forests and bushes, and the mapping efficiency is relatively high.

The invention discloses a heterogeneous AI engine interaction system and method based on a cognitive firewall, and relates to the technical field of artificial intelligence, and the system comprises a cognitive isolation channel which is used for building an isolation communication path between an RAG system and an Agent system, and generating standardized knowledge; the three-layer filtering architecture is used for performing grammar layer verification, semantic layer understanding and pragmatic layer evaluation on the standardized knowledge in sequence and outputting safety knowledge passing verification; the bidirectional value alignment module is used for mapping the safety knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate value conformity, and generating value alignment knowledge according to the value conformity; and the knowledge-instruction conversion engine is used for converting the value alignment knowledge into a structured instruction which can be executed by the Agent system. The method can actively identify, intercept and avoid potential dangerous, misleading, prejudice or ethical inconformity knowledge applications, and greatly reduce negative effects caused by wrong use of knowledge.

The invention relates to the technical field of computer vision, in particular to a complex scene object recognition system based on multi-scale feature fusion, which comprises a feature extraction module, a Riemannian geometric feature pyramid module, a candidate region generation module and an instance recognition and positioning module, and is characterized in that the feature extraction module extracts multi-scale spatial features; the Riemannian geometric feature pyramid module maps spatial features to a Riemannian manifold space, calculates curvature distribution of feature manifolds, dynamically adjusts feature extraction levels and fusion strategies, and realizes geometric consistent fusion of different scale features through geodesic line feature fusion; the candidate region generation module generates an object candidate region based on the fusion features; and the instance identification and positioning module completes final object classification and positioning, introduces a differential geometry theory to construct a Riemannian geometric feature pyramid, realizes geometric representation and adaptive fusion of features, solves the problems of scale change, partial occlusion, background interference and the like in a complex scene, and effectively improves the accuracy and robustness of object identification.

Aspects of the disclosure relate to using machine learning models to automatically prioritize and orchestrate spatial data. A computing system may generate, based on inputting scene data and spatial positioning data into a machine learning model, a prioritized context that indicates physical objects that are significant within a physical environment. The scene data may comprise images of the physical environment and the spatial positioning data may comprise an indication of states of a user in the physical environment. Based on the prioritized context meeting user criteria, generate indications associated with the prioritized context. Based on the scene data, one or more spatial anchors at which to position the one or more indications of the prioritized context within a virtual environment based on the physical environment may be generated. The virtual environment comprising the indications of the prioritized context positioned at the one or more spatial anchors may be generated.

Dynamic allocation of work and data on a spatial compute architecture in which a lead dispatch node dynamically assigns a task of an application to a selected compute tile, and routes a corresponding active message to a local data memory pool of the selected compute tile. The active message may include instructions, data, and / or configuration parameters to enable the selected compute tile to execute the task. If there is insufficient space in the local data memory pool, the lead dispatch node routes the active message to shared memory or to a local data memory pool of another compute tile, and subsequently routes the active message to the local data memory pool of the selected compute tile when space is available. Alternatively, the lead dispatch node may forward the task and the active message to another lead dispatch node.

The invention relates to a converter station equipment operation and maintenance method and device based on space calculation, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring equipment information, target position information and environment information of to-be-analyzed equipment in a to-be-analyzed converter station; pre-processing the equipment information, the target position information and the environment information to obtain pre-processed equipment information, pre-processed position information and pre-processed environment information; performing feature extraction processing and fusion processing on the preprocessed equipment information, the preprocessed position information and the preprocessed environment information to obtain a fusion feature vector corresponding to the to-be-analyzed equipment; inputting the fusion feature vector into a pre-trained equipment operation and maintenance instruction prediction model to obtain a target equipment operation and maintenance instruction corresponding to the to-be-analyzed equipment; and performing corresponding operation and maintenance processing on the to-be-analyzed equipment according to the target equipment operation and maintenance instruction. By adopting the method, the operation and maintenance efficiency of converter station equipment can be improved.