910 results about "Semantic map" patented technology

The invention belongs to the technical field of digital twinning, and discloses a hydraulic engineering-oriented digital twinning model dynamic construction and risk assessment method, which comprises the steps of dividing a hydraulic engineering into a static basic component area and a dynamic response area, accessing historical hydraulic engineering data, combining BIM and a finite element analysis technology, and carrying out dynamic construction and risk assessment on a digital twinning model. Constructing a structure model of the static basic component area and an agent model of the dynamic response area; combining the structured model with the proxy model to obtain the hydraulic digital twin; state data in the water conservancy digital twinborn body operation process are collected, and a twinborn state sequence is generated; constructing a water conservancy semantic map, and carrying out semantic binding on the water conservancy digital twin, the real-time water conservancy data and the control logic; dynamically updating a twinning state sequence when the node state of the water conservancy semantic map changes by adopting an event-driven strategy; and the response time efficiency and the emergency regulation and control capability of the water conservancy project are further improved.

The invention relates to the technical field of intelligent navigation, in particular to a navigation method, system and equipment based on a multi-modal model, a medium and a product. According to the method, feature extraction and alignment fusion processing are performed on multi-modal data acquired by a robot through a preset multi-modal model to generate unified features, and the task complexity of a target navigation task and the environment type of a target navigation environment are determined according to the unified features, so that the weights of a semantic map and a geometric map are adjusted; and constructing a comprehensive map according to the current observation value, determining a target action sequence according to the current observation value and the target observation value corresponding to the comprehensive map, and controlling the robot to execute the corresponding navigation action so as to improve the navigation efficiency and the navigation accuracy.

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention relates to a robot environment perception method based on single-view three-dimensional scene generation, and the method comprises the steps: collecting a two-dimensional image containing target environment information through employing a monocular camera, generating multi-view information through combining depth estimation, normal prediction and a two-stage semantic guidance diffusion model, and constructing a high-quality three-dimensional scene. And reconstructing three-dimensional point cloud data through the neural radiation field, and performing texture rendering optimization on the point cloud data. A Point Net + + network is used for carrying out semantic analysis on point clouds, a multi-frame time sequence point cloud registration and Kalman filtering tracking method is introduced, modeling is carried out on a dynamic target, and a dynamic semantic map with a motion state is constructed. And finally, structured output environment information is used for robot navigation, path planning and task execution. The problems of high cost, high complexity and insufficient real-time performance and robustness of a three-dimensional scene generation technology in the field of robot environment perception are solved, and the method is high in structuring degree, standard and unified in output format and high in universality and engineering adaptation capacity.

The invention discloses a multi-source knowledge conflict detection and restoration method and system based on a semantic graph, and the method comprises the steps: combining a received user query, multi-source context data and a predefined prompt word template, and inputting the combination into a conflict detection model to generate a structured conflict report; analyzing the conflict report, matching a corresponding repair operator from a predefined repair operator set according to the conflict type, and executing the matched repair operator to dynamically repair the multi-source context data to obtain the repaired context data and generate a decision log; a final response to the user query is generated based on the repaired contextual data. According to the invention, a lightweight detection model based on knowledge distillation is adopted, so that the real-time requirement and the detection precision requirement of online service are met; through a dynamic operator combination framework, repair strategies are automatically selected for different types of conflicts, and the processing efficiency is improved; conflict reports and detailed decision logs are structured, and a transparent and traceable decision process is provided.

The invention discloses a building construction intelligent safety monitoring method based on the Internet of Things, and the method comprises the following steps: obtaining construction environment data, structure state data and operation behavior image data, and carrying out the preprocessing; environment state modeling, local structure strain and behavior recognition and operation scene segmentation are carried out through the edge intelligent processing unit; feature fusion is carried out, and a fusion situation vector is constructed; performing high-frequency anomaly identification and emergency preliminary screening, and generating an edge preliminary early warning result and a high-risk data fragment; constructing a safety evolution trajectory crossing a time window, and fusing historical data to generate a risk semantic map; cloud semantic reasoning operation is executed, and a final risk level judgment result and a corresponding trigger source identifier are generated; and automatically triggering a safety response instruction according to a risk level judgment result. According to the method, the Internet of Things and intelligent semantic analysis are fused, multi-source safety monitoring and self-adaptive response are realized, and the method has the advantages of high real-time performance, global perception and continuous optimization.

The invention relates to the technical field of virtual reality, in particular to an indoor structure reconstruction method based on a panoramic image scene understanding algorithm, and the method comprises the steps: S11, obtaining a plurality of equidistant columnar projection panoramic images of a current indoor scene through a panoramic camera or a panoramic image splicing algorithm; s12, performing semantic segmentation on the collected panoramic image by using SAM, deducing indoor ceiling, floor and wall areas of the panoramic image on the basis of a semantic segmentation result, and generating a multi-channel semantic graph; and S13, inputting the acquired RGB panoramic images and the multi-channel semantic map into a pre-trained panoramic image depth estimation model to obtain a depth map corresponding to each panoramic image. The method is used for automatically generating an indoor structure model, the scheme comprises the core steps of semantic segmentation and layout reasoning, depth estimation and point cloud construction, structure optimization and indoor model reconstruction and the like, and a complete indoor panoramic image scene understanding scheme is formed.

The invention provides an mbse-based system full life cycle management method and system, and relates to the technical field of data synchronization, and the method comprises the steps: obtaining to-be-synchronized data of a source system, constructing a semantic graph structure, generating a mapping strategy based on implicit association between semantic matching learning systems, executing transactional data transmission, and constructing a time sequence traceability tree to track change propagation. And adaptive reconstruction is carried out when a constraint conflict is detected. According to the method, the problem of data synchronization among heterogeneous systems is solved, and the model consistency maintenance capability and the adaptive conflict processing efficiency are improved.

The invention discloses an uncertainty mapping method and device based on selective learnable depreciation, and the method comprises the steps: S1, collecting color images and depth maps at different time points, and projecting the color images and the depth maps to a three-dimensional coordinate system; s2, predicting an evidence vector for each pixel by using an evidence generation network; s3, calculating total evidence intensity based on the evidence vector, and defining credibility and uncertainty based on the total evidence intensity; s4, constructing a noise mask to represent the observation quality, and predicting a damage coefficient by using a selective damage network; s5, the evidence intensity is adjusted in a zooming and depreciation mode; and S6, outputting a final probability, carrying out basic probability distribution of multi-frame fusion observation on the three-dimensional voxel grid, and constructing a semantic map containing uncertainty estimation. According to the method, the conflict rate and uncertainty in the semantic map fusion process can be effectively reduced, and the accuracy and credibility of the map are remarkably improved.

The invention relates to a robot navigation method and system based on a visual language large model and experience memory. The method comprises the following steps: firstly, generating a semantic map with labels by utilizing an SLAM technology through multi-modal environment data sensed by a robot; receiving an instruction, and analyzing the instruction through a large language model to obtain a structured task representation; constructing an experience library based on historical data, and generating an experience package in combination with task representation; planning a path based on a semantic map, a structured representation and an experience package, obtaining an alternative path set, evaluating confidence, determining whether to trigger a clarification action, and outputting an optimal path; and finally, robot navigation is controlled according to the optimal path, and the experience library is updated. Compared with the prior art, the method has the advantages of improving the accuracy and interpretability of autonomous navigation of the robot, improving the navigation success rate of the robot in a complex environment and the like.

The invention discloses a digital human construction method and device based on a heterogeneous emotion semantic graph and long-sequence emotion modeling, and the method comprises the steps: obtaining multi-modal emotion input data of a text, voice and a visual image, extracting features, and constructing a multi-modal emotion feature set with a timestamp; constructing a heterogeneous emotion semantic graph which comprises user entity nodes, modal feature nodes and emotion concept nodes, modeling a semantic association, state transition and conflict suppression relationship through a multi-type edge structure, and introducing a dynamic evolution and conflict discrimination mechanism; performing time sequence modeling on the emotional state sequence by utilizing a local-global double-layer emotional modeling mechanism, and respectively capturing short-time fluctuation and long-time trend; performing cross-modal fusion on the emotional state and the modal features, and decoding the emotional state and the modal features into behavior parameters for controlling expressions, voices and actions of the digital human; and multi-modal emotion expression of the digital human is driven. Compared with the prior art, the emotion recognition accuracy and expression continuity and naturalness can be effectively improved.

The invention provides a passable area reasoning method and system based on a visual language model, and the method comprises the steps: obtaining the multi-modal data of a vehicle and the current position information of the vehicle; analyzing the multi-modal data, and determining visual features and traffic symbol features; performing spatial position coding on the visual object and the traffic symbol elements, and determining aerial view angle coordinate information; performing semantic alignment on the visual features and the traffic symbol features, and determining a shared embedding representation; constructing a traffic semantic map by fusing, sharing and embedding representation based on a graph neural network and bird's-eye view coordinate information of a visual object and a traffic symbol element; and according to the current position information of the vehicle, the traffic semantic map and a preset traffic rule, generating a bird's-eye view semantic map including a passable area, a no-pass area and a semantic association relationship. According to the method and the device, semantic alignment and consistency expression of visual perception and traffic symbol recognition are realized, and further feasible region reasoning of a complex traffic scene is realized.

The invention relates to the technical field of control, in particular to a double-arm robot operation skill learning method and system based on big language model reasoning. The method comprises the following steps: firstly, carrying out context semantic modeling on an input natural language task instruction based on a large language model to generate a task semantic graph; in combination with a semantic entity in the task semantic graph and visual perception data collected by a robot, determining a three-dimensional space position of a target object through a multi-modal matching model, and constructing an environment semantic graph containing object nodes and spatial relation edges; generating an action sequence by using a language model according to the task semantic map and the environment semantic map, and generating a collaborative operation strategy based on the two-arm tail end state and an obstacle map; and finally, collecting feedback data of the sensor in real time when the action sequence is executed. According to the method provided by the invention, the understanding and execution capability of the two-arm robot on the unstructured natural language instruction is remarkably improved.

Methods, systems, and computer storage media for providing a modular cybersecurity platform are described. The modular cybersecurity platform is implemented using a modular cybersecurity engine that operates based on an analytical framework for dynamic data analysis and data management in a data intelligence system. In particular, the analytical framework is based on complementary modular components that are designed to interoperate in the modular cybersecurity engine. The modular cybersecurity engine includes a modular distributed system, a credential detection system, and a credential semantic graph system. The modular cybersecurity engine supports cybersecurity and sensitive data management scenarios that can empower investigators in various investigations, and provide automated flows that are highly scalable and support different types of functionality (e.g., priority embedding pipeline, credential scanning, and credential semantic graph analysis). The utility of the modular cybersecurity engine is demonstrated by its wide-ranging application in addressing complex cybersecurity challenges and sensitive data management tasks.

The invention provides a natural language intelligent analysis and data query instruction generation method based on a large model, which relates to the technical field of natural language processing and comprises the steps of performing semantic analysis by pre-training a large-scale language model, constructing a dynamically enhanced query semantic graph, performing semantic enhancement in combination with an external knowledge graph, and generating a data query instruction. And extracting a structured feature vector and mapping the structured feature vector into a database query syntax tree, and finally optimizing an execution plan based on deep reinforcement learning to generate a standardized query instruction. According to the method, intelligent conversion from natural language query to efficient database instructions is realized, and query precision and execution efficiency are improved.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention discloses a vision-driven multi-mode fusion lightweight semantic map construction method and system. The method comprises the following steps: synchronously acquiring a binocular image pair sequence, IMU data and GNSS data of a target area; based on the acquired multi-modal data, performing multi-sensor joint state estimation through a differential weighted fusion strategy, and outputting camera global pose and scene depth information; based on a current frame and a historical frame in the binocular image pair sequence, combining a camera global pose, extracting geometric prior auxiliary time sequence cross-frame semantic feature fusion through stereoscopic vision, and outputting a two-dimensional semantic segmentation result of the current frame; and back-projecting the two-dimensional semantic segmentation result into a lightweight global three-dimensional semantic map based on camera pose and scene depth information, and carrying out maintenance and updating through a voxelization statistical mechanism. Compared with a traditional dense point cloud map, the method has the light weight effect that the storage space is greatly reduced.

The invention belongs to the technical field of space intelligence, and relates to a dynamic obstacle intervention-oriented space intelligent scene self-reconstruction navigation system, which comprises a dynamic intervention sensing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module, a dynamic obstacle intervention processing module and a dynamic obstacle intervention processing module, the spatial semantic topology reconstruction module is used for automatically updating a spatial semantic map and a topology connection relation according to the dynamic intervention event set; the local topology rapid recombination module is used for carrying out rapid recombination on the intervened and influenced local topology based on a topology incremental learning principle; and the perception and navigation bidirectional consistency optimization module is used for establishing a reverse feedback path between perception and navigation. According to the invention, the real-time response and structure-level self-adaption of the navigation system to environment change can be realized, so that the stability and consistency of navigation decisions can be maintained in changeable scenes. The invention further provides a space intelligent scene self-reconstruction navigation method oriented to dynamic obstacle intervention.

The invention belongs to the field of intelligent planning control, and relates to an intelligent agent instruction execution method and system based on multi-level re-planning. The method comprises the following steps: preliminarily generating a sub-target sequence according to a natural language instruction by utilizing a high-level planning review module based on a large language model, and continuously and dynamically correcting sub-targets in combination with real-time semantic feedback of a current environment; a middle-layer semantic search module is utilized to construct a multi-layer instance semantic map, and ordered search and accurate positioning of all related instances are realized through the multi-layer instance semantic map and common sense reasoning driving; a low-layer action error correction module is used for receiving the RGB image of the current view angle of the intelligent agent in real time, the feasibility of the next preset action is judged, and if potential failure actions are found, the potential failure actions are automatically replaced with safer and more appropriate actions, so that execution failure or collision is avoided. The task success rate and the adaptive capacity of the intelligent agent for executing the natural language instruction in the complex three-dimensional environment can be improved.

The invention relates to the technical field of video analysis, and discloses a linkage alarm method, device and equipment based on video analysis and a storage medium, and the method comprises the steps: carrying out the behavior semantic recognition of a video frame collected by a doorbell, obtaining a visitor behavior mode, coding the video frame and the visitor behavior mode, and obtaining a semantic vector; constructing a door front three-dimensional semantic map according to the video frame and tracking visitors to obtain visitor trajectory data; decoding the semantic vector and the visitor trajectory data to obtain a behavior analysis result; according to the behavior analysis result and the visitor track data, a linkage strategy is selected from a preset action space, an alarm instruction is generated, and the alarm instruction is executed through the doorbell, end-to-end real-time response is achieved, the strict requirement of an actual doorbell application scene for the response speed is met, and the user experience is improved. And the accuracy of doorbell collected video analysis and linkage alarm is improved.

The invention discloses a cross-brand robot rapid access and control method based on RUAPL, and belongs to the technical field of robot control. The method is applied to a multi-robot management platform, and comprises the following steps: receiving an access request sent by at least one target robot; extracting a skill list from the access request to construct a skill semantic map; according to the skill semantic map, performing capability abstraction on the target robot to obtain unified abstract description; performing adaptation conversion based on the RUAPL protocol on the unified abstract description to generate a downlink instruction; and verifying the downlink instruction based on the digital twin sandbox of the target robot, and sending the downlink instruction to the target robot through the control channel under the condition that the verification is passed, so as to configure the target robot. According to the method, rapid access, semantic intercommunication, safety control and unified management of the cross-brand robot in a heterogeneous environment are realized, and the operation and maintenance cost and integration complexity of a multi-brand mixed scene are reduced.

The invention discloses a retrieval method based on a semantic enhanced knowledge graph, which relates to the technical field of information, and comprises the following steps: receiving a natural language query of a user, and carrying out deep analysis on the query, including named entity recognition and linking, relationship extraction and query intention classification; and based on an analysis result, extracting a related local sub-graph from the knowledge graph, generating a query context vector, and generating dynamic semantic embedding for the sub-graph through a query-perceived graph attention network to obtain a dynamic enhanced semantic graph. According to the retrieval method based on the semantic enhancement knowledge graph, the retrieval precision and the recall rate are remarkably improved, the limitation of static knowledge representation is solved through a dynamic semantic enhancement mechanism of query intention perception, so that the local semantic representation of the knowledge graph is highly aligned with the query intention of a specific user; and the ability of understanding and answering complex, fuzzy, ambiguous and multi-hop queries is improved.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

The invention discloses a target guiding navigation method which comprises the following steps: S1, coding an environment image by adopting a multi-teacher distillation pre-trained RADIO framework, extracting an initial visual feature, and splicing the initial visual feature with a target embedded vector to obtain a cross-modal fusion initial input feature; s2, generating causal alignment fusion features through global hybrid factor estimation, residual elimination and anti-fact co-occurrence matrix intervention; and S3, inputting the features into the LSTM, and generating a navigation action strategy through double-commentator conservative value estimation, Gaussian perturbation and semantic post-view playback training in combination with a cross attention fusion semantic graph relationship and time sequence features. According to the method, multi-source prior knowledge is fused to improve visual feature generalization, hybrid interference is separated through a causal de-confusion mechanism, semantic enhancement is combined to reinforce learning training, the problems of category dependence, false correlation, insufficient exploration efficiency and the like of an existing method are solved, and the stability, causal rationality and training effect of a navigation strategy are improved.

The invention provides an artificial intelligence semantic processing system and method oriented to digital media creation, and relates to the technical field of artificial intelligence semantic process.The artificial intelligence semantic processing method comprises the steps that predicate argument relation pairs of language texts are extracted, object space relation pairs of sketch images are extracted at the same time, and a basic semantic unit set is constructed; the integrity and accuracy of cross-modal semantic understanding are ensured, further, semantic units are clustered by using a dynamic routing algorithm, a semantic concept cluster with a clear importance weight is generated, deep mining and structured representation of creation intentions are realized, and the creation intentions are quickly and accurately understood. An initial semantic relation graph is constructed, a graph attention network is used for dynamic reweighting, finally, an enhanced dynamic semantic graph is generated, complex association and a hierarchical structure between semantic concepts are effectively captured, finally, hierarchical analysis is carried out on the semantic graph, and a structured semantic blueprint is output, so that the dynamic semantic graph is obtained. And a reliable semantic processing technology is provided for creation of high-quality digital media contents.

The invention relates to the field of robot control, and discloses an autonomous transfer robot control method, device, equipment and medium in an anti-explosion environment, and the method comprises the steps: collecting the multi-modal environment data of a target anti-explosion environment, carrying out the data correction of the multi-modal environment data, obtaining the multi-modal correction data, and storing the multi-modal correction data in a database; layered redundancy fusion positioning is carried out on the autonomous transfer robot by using the multi-modal correction data to obtain a real-time pose of the autonomous transfer robot, multi-source obstacle detection is carried out on a target anti-explosion environment by using the multi-modal correction data, a dynamic obstacle map is generated, and the real-time pose of the autonomous transfer robot is obtained. And constructing a structured semantic map of the target explosion-proof environment according to the multi-modal correction data and the dynamic obstacle map, and performing path planning and motion control on the autonomous transfer robot according to the real-time pose and the structured semantic map to obtain a target control result. According to the invention, the positioning and navigation stability of the autonomous transfer robot in explosion-proof dangerous environments such as smoke, dust, weak light and strong reflection is improved.

The invention provides a personal question and answer method based on an observation-recording-decision mechanism, which comprises the following steps: an intelligent agent captures RGB images and depth images from all directions through multi-view perception, is used for constructing a 3D scene graph and mapping the 3D scene graph to a 2D semantic map, and meanwhile, the intelligent agent marks each passing position on the 2D semantic map, so that the 3D scene graph is mapped to the 2D semantic map; and dynamically updating the weight of a non-visited position, reducing the selection probability of a boundary point in a marked passing region, based on a 2D semantic map in an observation stage, distinguishing whether a question can be answered or not by an intelligent agent according to an observed RGB picture, if so, directly generating a response, otherwise, navigating to a new region, and repeating the steps until an available or maximum step number is reached, and completing the question answering. According to the method, through construction of a semantic map, weight regulation and control navigation, and fusion design of special VLM analysis and double-criterion decision making, decision making of agent non-redundancy exploration and accurate question and answer is achieved.

The invention discloses an industrial measurement data intelligent analysis report generation method and system based on a large language model, and relates to the technical field of industrial measurement, and the method comprises a multi-source data and abstraction module which carries out the structural processing of original data DAT output by measurement analysis software; and the large language model service and interface module is used for submitting the PCT to a selected large language model LLM through a multi-model adaptation interface to execute semantic reasoning and output a structured text TXT. Structured analysis and semantic association modeling are carried out on original measurement data through multi-source data and an abstract module, when overall alignment deviation or local feature anomaly exists in the original data, a hierarchical dependency relationship between the data can be established through a semantic graph structure, a deviation transmission path is revealed, and the accuracy of the measurement data is improved. The large language model is analyzed in a unified data context, and the utilization depth of measurement data can be effectively improved, so that the accuracy and integrity of report analysis are improved.

The invention discloses a slide generation method and device based on a large language model and a medium, and the method comprises the steps: carrying out semantic analysis processing on slide metadata input by a user, and determining a multi-granularity semantic graph of the slide metadata; performing structured processing on the multi-granularity semantic map through a preset dynamic prompt project to obtain a slide structured outline; and generating a slide based on the slide structured outline and a preset semantic visual mapping rule. According to the method, the slide manufacturing efficiency is improved.