261 results about "Spatial computing" patented technology

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 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.

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.

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 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 the technical field of digital twinning and computer simulation, in particular to a power transmission and transformation project optical cable connection information operation and maintenance system based on a logic model, which comprises a model fusion module for constructing a digital twinning anchor point set by fusing a three-dimensional physical model and a secondary system logic model of a power transmission and transformation project; when the simulation analysis module receives a logic loop alarm, the physical path is reversely traced based on anchor point data, physical field simulation operation is executed, and a physical degradation risk point is output; the augmented reality module realizes spatial registration of a three-dimensional physical model and a live scene through a spatial computing technology, and visually presents logic loop and risk point information. According to the method, the space-time twin anchor points are constructed, the physical field simulation operation is driven, and the diagnosis result and the operation and maintenance instruction are superposed on the live scene through the augmented reality technology, so that the intelligent operation and maintenance closed loop of the optical cable connection information of the power transmission and transformation project is realized, and the troubleshooting efficiency and accuracy are improved.

The invention relates to the technical field of map making, in particular to a method and system for making a three-dimensional high-precision map containing road geometric elements, and the method comprises the steps: extracting original data from an original file through employing a user-defined analysis tool; preprocessing and checking the extracted original data to obtain core element data; and according to the core element data, generating a road three-dimensional model by adopting a space calculation model, and according to the core element data and a road design specification, performing derivative element calculation and generative three-dimensional element supplementation to form a three-dimensional lane-level road electronic map. According to the scheme, the three-dimensional high-precision map containing the road geometric elements can be manufactured, the map manufacturing efficiency is improved, the map precision is enhanced, and the cost is reduced.

The invention relates to the technical field of generalized quantum evidence theory and image processing, in particular to a ship image data target identification method based on the generalized quantum evidence theory, which comprises the following steps: converting synthetic aperture radar ship image data into complex field synthetic aperture radar data, and constructing training data; generating a composite quantum evidence label according to the potential structure; training the quantum evidence neural network to obtain a standard quantum evidence neural network; inputting to-be-recognized complex field synthetic aperture radar data into the standard quantum evidence neural network, and extracting a complex field feature vector of a to-be-recognized sample; normalizing the complex field feature vector into a generalized quantum basic probability amplitude function, and defining a proposition space under a quantum identification framework; and calculating generalized quantum basic probability distribution of the proposition in the proposition space, and executing double judgment to obtain a recognition result of the original synthetic aperture radar ship image data to be recognized. According to the method, the accuracy of ship image data target identification based on the generalized quantum evidence theory can be improved.

The invention discloses a scene imagination retrieval method, device and system, a storage medium and a program product, and the method comprises the following steps: S1, collecting an electroencephalogram signal of a human body when the human body imagines a specific scene; s2, the electroencephalogram signals are preprocessed, and continuous electroencephalogram signal fragments in a short time window are obtained; s3, encoding and fusing the electroencephalogram signal segments to obtain high-dimensional feature vectors, and identifying and encoding retrieval targets in a retrieval target library to obtain a plurality of target feature vectors; s4, performing cross-modal projection on the high-dimensional feature vector and the multiple target feature vectors, and mapping the high-dimensional feature vector and the multiple target feature vectors to a unified semantic space; s5, calculating the similarity between the high-dimensional feature vector and the target feature vector; and S6, performing retrieval target classification based on the similarity, and outputting a retrieval result in the retrieval category with the highest similarity according to the matching confidence. The problems that in retrieval in the prior art, retrieval operation is inconvenient, and implicit retrieval is lacked are solved.

The invention relates to the technical field of unmanned forcible entry robots, in particular to an unmanned forcible entry robot target recognition method and system based on space calculation, and the method comprises the steps: collecting the three-dimensional space data of a forcible entry scene through a multi-mode sensor; constructing a scene digital twinborn model based on space calculation, and performing accurate mapping of a physical space and a digital space; extracting spatial geometric and topological features of the forcible entry target by adopting a spatial feature enhancement algorithm; target classification and pose estimation are completed in combination with a deep learning model of a space attention mechanism; and a real-time space error compensation mechanism is introduced, and a target identification result is dynamically corrected. Compared with the technical problems that in the prior art, target recognition of an unmanned forcible entry robot is likely to be shielded and interfered, the space positioning precision is low, and the unmanned forcible entry robot cannot adapt to complex forcible entry scenes, the method achieves high-precision, anti-interference and dynamic recognition of the forcible entry target by designing a space calculation closed loop, and improves the operation safety and efficiency of the unmanned forcible entry robot.

The invention provides a multi-source domain invariant acoustic feature extraction method and system for an equipment operation state, and belongs to the technical field of equipment maintenance, and the method comprises the steps: constructing a multi-source domain invariant acoustic feature extraction network based on a DANN model, and the network comprises a feature extractor, a classifier, a domain discriminator and a multi-domain acoustic feature class boundary constraint module; the feature extractor extracts high-dimensional features of sound signals, the classifier carries out fault mode recognition, and the domain discriminator realizes domain prediction. The multi-domain constraint module generates an embedding space, and calculates the maximum mean value difference, the local maximum mean value difference and the Euclidean distance among different source domain features. The network constructs a loss function by taking minimization of inter-domain difference and classification loss and maximization of inter-domain distance and domain discrimination loss as targets, and carries out adversarial training through multi-source tagged acoustic data. According to the method, domain invariant features are extracted by using adversarial learning and hidden space constraint alignment of multi-source domain acoustic features, so that the influence of feature offset under a cross-working-condition condition is effectively reduced, and the fault mode recognition accuracy is improved.

The disclosed system utilizes a language classifier to identify the language of a communication transcript. Additionally, the system identifies the intent of the communication transcript using a first natural language processing (NLP) MLM, detects keywords within the communication transcript using a second NLP MLM, and selects target data records to associate with the communication transcript. The selection of target data records includes determining, for each data record from a set of data records, a probability score indicating the likelihood that each data record is associated with the communication transcript. This probability score is determined by a data record prediction MLM. Moreover, the system selects target data records with the highest-ranking probability scores. Finally, the system generates a display report based on the selected target data records and renders the display report on a display of a spatial computing device.

The invention relates to a humanoid robot three-dimensional complex environment navigation method and system, and belongs to the technical field of robot navigation. Comprises: obtaining a random path node; constructing a collision detection model according to the gait cycle of the robot; detecting whether the random path node passes collision detection by using a collision detection model; if not, deleting the random path node, and obtaining the random path node again; otherwise, adding the random path nodes into the random tree; for each node added to the random tree, collision detection and plane size detection at the node are carried out, and when the detection is passed, candidate nodes are added to a global path point sequence; screening the sequence according to the complexity of the current plane to obtain complex region path nodes and flat region path nodes; and corresponding motion instructions are generated according to the operation requirements of the robot on the path nodes in the different areas. According to the method, the dynamic envelope space calculation cost is reduced, and the global path planning accuracy is improved.

The invention provides a spatial heterogeneous computing resource scheduling method and device based on dynamic power consumption budget, a terminal, a medium and a product. The method is applied to task initiating equipment, and comprises the following steps: acquiring a current to-be-executed spatial computing task, and determining a current dynamic spatial heterogeneous computing group; obtaining the current power consumption budget score and equipment capability information of each computing equipment in the computing group; and selecting one or more optimal computing devices from the computing devices by utilizing a multi-objective optimization scheduling algorithm according to the spatial computing task and based on the obtained current power consumption budget score and the device capability information of the computing devices, so that the one or more optimal computing devices execute the spatial computing task. Through power consumption budget scoring and a multi-objective optimization scheduling algorithm, the method effectively prevents a low-power or energy-shortage device from undertaking a high-energy-consumption task, thereby remarkably reducing energy consumption, prolonging the effective service time of the computing device, and improving the stability.

The invention relates to the technical field of cabinet monitoring, and discloses a cabinet aging monitoring system based on a temperature sensor. The system comprises a historical data acquisition module for acquiring historical temperature data of a cabinet in a preset period and generating a historical health data set; extracting temperature characteristic parameters through a characteristic extraction module, and establishing a temperature trend change characteristic matrix; a state space module constructs a multi-dimensional state space according to the matrix, calculates a historical aging event temperature state aggregation degree, and determines a temperature characteristic early warning index set; the real-time risk module extracts real-time temperature characteristic parameters and establishes state vectors, and real-time aging risk correlation factors are calculated and determined through spatial position correlation; and finally, the prediction module combines the data to generate an aging risk probability prediction model, predicts the aging probability of the cabinet and generates a maintenance scheduling suggestion, thereby realizing accurate monitoring and prediction of the aging of the cabinet.

A device may capture real-time images of a product. Capturing may include using an outward-facing sensor on a device. A device may identify the product by using an identification machine learning model. A device may obtain user interaction data corresponding to previous images displayed on the device. A device may identify available offers. A device may identify a set of offers from the available offers using an offer selection machine learning model trained to automatically select offers presentable to users through devices. The offer selection machine learning model may be trained using a dataset of historical interaction data and corresponding offers. A device may present the set of offers through the device. A device may monitor in real-time user interaction with the set of offers. A device may update the offer selection machine learning model according to the real-time user interaction with the set of offers.

The invention discloses a BIM-based multi-dimensional collaborative engineering drawing automatic review method and system, and the method comprises the steps: obtaining BIM model data, and obtaining corresponding review standard provisions; on the basis of the review standard provisions, constructing review rules, and storing the review rules in a rule base; a rule importance level is preset for each review rule, and a parameter threshold value is preset for the review rule related to numerical value verification; analyzing the BIM model data, and constructing a model structure comprising components and association relationships among the components; loading a model structure through a rule engine, calling a digital review rule in a rule base, and carrying out logic judgment and space calculation on components in the model structure, so as to identify a model problem violating the rule; for each identified model problem, carrying out risk level evaluation on the model problem; and generating a structured review report based on the model problem and the corresponding risk level. According to the invention, regularized automatic review of the BIM model is realized.