421 results about "Graph optimization" patented technology

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

The invention belongs to the technical field of path planning of unmanned equipment, and relates to a medical rescue unmanned aerial vehicle and robot dog cooperative linkage method, which comprises the following steps: integrating LiDAR point cloud, visual images, IMU inertial data and compensated UWB positioning data into a multi-modal data stream subjected to space-time alignment through time alignment and space alignment technologies; respectively extracting LiDAR and visual feature poses through parallel SLAM (Simultaneous Localization and Mapping) calculation, and carrying out graph optimization and joint optimization by utilizing a GTSAM library, so as to construct a globally optimized pose and a global semantic map; the path planning algorithm provides dynamic channel routing for the unmanned aerial vehicle and the robot dog based on a global semantic map, the feature fusion weight is adjusted by considering the environment illumination intensity and the point cloud density, the reverse iris control mechanism detects whether the robot dog enters a shielding area through a UWB signal intensity attenuation value, the unmanned aerial vehicle is switched to a UWB signal follower, and the UWB signal follower is switched to the unmanned aerial vehicle. And through combination with a Kalman filter, penetration positioning in a shielding area is realized, so that stable execution of a rescue task in a complex environment is ensured.

The invention discloses a cleaning path planning method and a cleaning device, and belongs to the technical field of automatic control, and the method comprises the steps: collecting the point cloud data of a to-be-cleaned region, constructing an initial composite map, setting a perception marking method, dividing the point cloud data into sub-regions, and marking a sudden change region after the sub-regions are matched with historical map features. Locally updating the composite map by using map optimization; constructing a safety buffer zone at the edge of the to-be-cleaned area, setting a buffer adjustment method, performing trajectory prediction on the abrupt change area, and dynamically adjusting a safety buffer distance according to a prediction result; calling a basic cleaning path, generating a layered path, setting a planning adjustment method, updating the layered path according to the abrupt change area in the cleaning process, calculating a pollution index of the to-be-cleaned area, constructing a time sequence prediction model, pre-judging a pollution diffusion trend, and performing dynamic adjustment; and after the to-be-cleaned area is cleaned, decision iteration is carried out to adapt to user habits.

An external rotation 3D lidar device and a simultaneous localization and mapping (SLAM) method comprises a 3D hybrid solid-state lidar device that is driven to rotate by an external motor. The device significantly improves the horizontal field of view of the lidar and can be mounted on a ground robot to comprehensively improve its 360-degree environment sensing capabilities. Error-state Kalman filtering and pose graph optimization are combined and the overall framework is divided into two parts: front-end odometry and back-end loop-closure optimization. Therefore, high-frequency odometry that meets the requirements of the robot can be output in real time and cumulative errors can be eliminated through the back-end loop-closure optimization.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm / model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and / or claimed.

The invention belongs to the technical field of tunnel inspection and auxiliary rescue, and particularly discloses an unmanned aerial vehicle multi-source fusion inspection and auxiliary rescue method based on an urban tunnel, multiple sensors are integrated on an unmanned aerial vehicle platform, and multi-source data are processed through a dynamic weighting factor graph optimization method based on a sliding window. By introducing a Mahalanobis distance consistency test mechanism, dynamically evaluating the consistency between sensor observation data and IMU prediction data, screening out data with high credibility, and endowing each factor with different optimization weights, the better the quality of the sensor observation data is, the higher the factor weight is, so that the robustness of final target estimation is improved, and the target estimation accuracy is improved. And then uniformly converting sensor data at each moment into a global coordinate system by using an SLAM framework, gradually constructing a three-dimensional map of the tunnel, and finally realizing high-precision real-time positioning of the unmanned aerial vehicle and construction of the three-dimensional map of the tunnel for subsequent inspection and auxiliary rescue tasks.

The invention discloses a graph optimization-based UWB / IMU / GNSS multi-unmanned vehicle cooperative positioning method, which comprises a global satellite navigation system GNSS, an ultra wide band (UWB) system, an inertial measurement unit IMU and a robot motion control system, and is characterized in that the UWB system comprises a UWB label and three UWB base stations, the UWB label is deployed on a target unmanned vehicle to be positioned, and the UWB label is deployed on the target unmanned vehicle to be positioned; and the three UWB base stations are respectively arranged on the other three unmanned vehicles as mobile base station unmanned vehicles. The GNSS module and the IMU module are deployed on the unmanned vehicle to serve as a movable UWB base station, and GNSS absolute position information and IMU relative motion information are fused by adopting an extended Kalman filtering algorithm; and according to the determined position of the unmanned vehicle in the movable base station, a distance observation value between the label and the base station is obtained based on a single-side bidirectional distance measurement method, a factor graph model fusing UWB distance measurement constraint and IMU motion constraint is constructed, and a factor graph optimization algorithm is adopted to optimize the position of the target unmanned vehicle. According to the method, the cost of a traditional fixed base station is reduced through the deployment of a mobile base station unmanned vehicle, and the precision and robustness of UWB system positioning in a complex environment are improved in combination with a multi-source sensor data fusion strategy.

The invention discloses a multi-unmanned aerial vehicle cluster cooperative positioning method based on multi-sensor fusion, and the method comprises the following steps: S1, collecting and processing the multi-sensor data of unmanned aerial vehicles, compensating VIO accumulated drift through fusing ultra-wideband ranging information, guaranteeing the time-space consistency of the multi-sensor data through employing a layered time synchronization strategy, and carrying out the collection and processing of the multi-sensor data of the unmanned aerial vehicles; suppressing multipath interference of the UWB signal and eliminating an NLOS error by using adaptive Kalman filtering, and obtaining initial positioning data of the unmanned aerial vehicle; s2, acquiring key frames of multiple unmanned aerial vehicles based on a window sampling method, and performing key frame optimization and outlier processing to balance noise suppression and real-time performance; and S3, constructing a factor graph model of the key frame data based on a graph optimization theory, and obtaining optimized cluster position information by solving a nonlinear least square optimization problem. The method effectively improves the accuracy and stability of multi-unmanned aerial vehicle cluster cooperative positioning, and can be applied to the fields of unmanned aerial vehicle logistics distribution, environment monitoring and the like.

The invention relates to the technical field of block chains, and discloses a block chain and privacy computing collaborative verification system, which comprises a data fragmentation module, a privacy fusion module, a collaborative verification module, a consensus correction module and a fragmentation graph optimization module. The data fragmentation module constructs a multi-stage data collaborative topology through a preprocessing node and a dynamic fragmentation protocol; the privacy fusion module realizes privacy protection by using a ciphertext analysis unit to switch links in a multi-state manner and a zero-knowledge verification unit; a collaborative verification module generates a reference verification strategy through a consensus modeling node multi-dimensional fusion model, and a state optimization unit dynamically aggregates and sorts fragmentation states; the consensus correction module corrects the fragmentation state through an isolation verification unit and a delay compensation unit and calibrates ciphertext parameters; and the fragment atlas optimization module constructs a stability evaluation network monitoring synchronization rate and performs closed-loop adjustment on a verification strategy. The system realizes dynamic fragmentation, privacy fusion and intelligent verification, and improves the efficiency, security and stability of block chain data processing.

The invention discloses a large language model low-delay reasoning method based on dynamic reasoning graph optimization, and provides a low-delay reasoning method based on dynamic reasoning graph optimization. Constructing a template inference graph which can be rewritten and replayed, and establishing a template library according to input shape vectors; during reasoning, a template is matched with a distance threshold value, and only the attention / feedforward sub-graph is locally recaptured when the distance threshold value exceeds the threshold value; executing a forward execution graph, injecting a key value cache page pointer, numerical value precision and an adapter identifier, and performing playback; dividing the pre-filling and decoding sub-graphs to implement graph-level scheduling; switching the key operator when the key operator operates between a standard / fast kernel and different precisions; page-level backspacing of speculative branches is achieved through a shadow page table and reference counting, and batch and template selection is adjusted in a self-adaptive mode based on online indexes; compared with the prior art, the method has the advantages that the recapture and start overhead is reduced, tail delay and jitter are inhibited, and the hardware utilization rate and the service stability are improved.

The invention discloses a robot vision-inertia SLAM method and device and a medium, and belongs to the technical field of computer vision and robot navigation. The method comprises the following steps: synchronously acquiring images and inertial data through a robot binocular camera and an IMU, performing feature enhancement on an original image in combination with a pre-trained deep learning model, introducing an adaptive brightness compensation mechanism and designing an image light supplementing module based on a generative adversarial network (GAN), recovering low-illumination image details, and improving the image quality of a low-illumination area; an entropy-based adaptive dynamic interference rejection algorithm is provided, and dynamic interference feature points are rejected in combination with IMU (Inertial Measurement Unit) data; a low-rank approximate improved graph optimization algorithm is adopted, and global map construction and pose optimization are accelerated; and through entropy-based nonlinear dynamic smoothing coefficient adjustment, the track stability is improved. According to the method, the positioning precision and robustness in a low-light environment are improved, the calculation efficiency is improved, and dynamic interference is effectively resisted.

The invention discloses an intelligent storage multi-AGV scheduling method and system, and relates to the technical field of storage scheduling. A graph optimization algorithm is combined with a multi-target scheduling tradeoff factor model to solve a task path, output a task allocation and path planning scheme, load the task allocation and path planning scheme to a time axis, and perform time sequence simulation on the path; potential congestion points or resource conflict areas in the paths are identified, part of AGV paths are optimized according to identification results, time passing pressure indexes and actual AGV operation states are used as input, scheduling verification is performed through multi-round offline simulation, a multi-AGV scheduling holographic data set is integrated, and a task state driven multi-AGV closed-loop scheduling scheme is constructed. According to the scheduling method, a multi-dimensional scheduling model fusing tasks, paths and resource information is constructed, and graph optimization, dynamic simulation and closed-loop control are combined, so that efficient collaboration and real-time response of multiple AGV systems are realized.

The invention discloses a network security intrusion detection system and method fusing a graph neural network, and the method comprises the steps: collecting multi-source flow data in a network operation process, constructing an induction topological graph through employing a graph convolution modeling technology, and revealing a deep incidence relation between network nodes; a hidden attack mode is identified by adopting a multi-layer graph propagation and resonance enhancement technology, and high-quality node embedding representation is generated through a graph attention mechanism and abnormal resonance amplification; an attack behavior map is extracted by combining depth map learning and a map pooling technology, and a self-adaptive protection strategy is generated through map inversion mapping and dynamic topological transformation; an interlaced detection sequence is constructed by adopting a multi-layer detection rule decomposition and graph optimization sorting technology, and a self-adaptive response signal is generated through active defense prediction and time difference bottleneck analysis, so that intelligent detection, accurate analysis and self-adaptive protection of network intrusion behaviors are realized, and the intelligent level and protection effect of network security protection are improved.

The invention discloses a multi-point cloud view angle mechanical arm grabbing system based on deep learning. The system comprises the following steps that S1, point cloud data of multiple view angles are obtained through a depth camera according to a graph optimization algorithm; s2, performing high-precision splicing on the point cloud data by adopting a deep learning method to generate a complete object three-dimensional model; s3, calculating an optimal grabbing pose of the object by utilizing a GraspNet grabbing estimation network; s4, the estimated grabbing pose information is transmitted to a robotic arm through an ROS service communication mechanism; s5, MoveIt! The function package plans and optimizes the grabbing path of the robotic arm; and S6, subscribing the joint state information, and driving the robotic arm to complete the actual grabbing action. According to the method, the multi-view point cloud splicing technology and deep learning are combined, the point cloud registration precision is remarkably improved, and therefore the grabbing precision and stability of the robotic arm are improved. Through communication between the ROS and the Windows system, the point cloud data are processed at the Windows end, and the grabbing pose is output, so that the problem of system instability caused by insufficient computing power of edge equipment is effectively solved. According to the method, the flexibility and usability of the system are further enhanced, so that the system can adapt to various complex grabbing scenes.

The invention relates to the technical field of CAD drawing optimization, and discloses a CAD drawing optimization method based on parameterized design. The method comprises the steps that a design constraint condition set is received, and geometric constraint parameters and engineering constraint parameters in the design constraint condition set are extracted; generating an initial geometric topological structure according to the geometric constraint parameters, and performing parameterized mapping on the structure in combination with the engineering constraint parameters to form a parameterized geometric model; identifying key feature nodes in the model, dynamically associating the key feature nodes with engineering constraint parameters, and constructing a parameter-driven relation network; and iteratively optimizing the parameterized geometric model through the network to generate an optimized geometric model meeting a design constraint condition set. According to the method, close association of geometry and engineering constraints is achieved, repeated design modification is reduced, efficiency and accuracy are improved, the design standardization degree can be improved, collaborative design is facilitated, and the method is suitable for CAD drawing design of complex products.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

The invention discloses a high-precision point cloud map construction method based on pose map optimization, and the method comprises the steps: preliminarily detecting the observation reliability of each frame of GNSS, and screening credible data; a priori factor is constructed for initial position GNSS signal quality, and map coordinate anchoring is completed; estimating a relative pose between adjacent frames, and constructing an inter-frame constraint edge; scanning context global descriptors are introduced to realize efficient screening and accurate matching of loopback candidate frames; further evaluating the quality of the GNSS by using a loopback detection result and the pose of the laser odometer, setting a GNSS factor weight, and constructing a GNSS factor edge; constructing a complete pose graph model, performing overall optimization on all pose nodes by using a nonlinear optimization method, and correcting accumulative errors; and converting the laser point cloud into a global coordinate system, and splicing to generate a global consistent high-precision point cloud map. According to the invention, the global positioning capability of the high-quality GNSS data and the local geometric precision of the laser point cloud can be effectively fused, and the high-precision point cloud map can be continuously and stably constructed.

The invention relates to the technical field of network optimization, and discloses an AI-driven real-time network optimization algorithm, which comprises the following steps: data optimization: preprocessing network state data by using a generative adversarial network, extracting optimization features and generating enhanced data; topology modeling: modeling network topology by adopting a graph neural network, and updating node features; resource optimization: dynamically adjusting calculation, storage and bandwidth resources by using deep reinforcement learning; global scheduling: optimizing resource allocation based on a multi-objective optimization method; secondary optimization: combining a Lagrangian relaxation method and a graph optimization technology to further optimize a scheduling strategy; and adaptive learning: improving the adaptive capacity of the system in a dynamic environment through self-supervised learning and meta-learning. Through a matrix operation technology based on a regular equation and in cooperation with an efficient data preprocessing process, the effect of improving the solving speed of the regression model is achieved, the problem that calculation is slow on a big data set in a traditional method is solved, and rapid and accurate regression analysis is achieved.

The invention relates to the technical field of deep learning, in particular to a PyTorch framework-based compatibility expansion system, which comprises a cross-framework model converter, a heterogeneous hardware abstraction layer, a hybrid computational graph execution engine, an intelligent distributed trainer and a self-adaptive optimizer, the cross-frame model converter converts input model formats of different frames into PyTorch executable formats, the heterogeneous hardware abstraction layer supports rear ends of various hardware and automatically selects an optimal calculation path, and the hybrid calculation graph execution engine fuses dynamic graph flexibility and static subgraph optimization and supports dynamic control function execution flow. An intelligent distributed trainer automatically selects a parallel scheme and is compatible with multi-protocol communication, a self-adaptive optimizer performs dynamic optimization based on hardware characteristics and a PyTorch tensor, a cross-frame model converter supports model conversion of multiple deep learning frames, the cost of migration among different frames by a user is reduced, and the universality of the PyTorch frame is improved.

The invention discloses an energy storage income maximization method based on a strategy gradient algorithm, and the method comprises the following steps: collecting energy storage equipment state parameters, power market real-time price data and load prediction data, and carrying out the preprocessing; feature extraction is carried out, charging and discharging strategy parameters are initialized, and an initial charging and discharging strategy is generated; according to the initial charging and discharging strategy, generating a charging and discharging action of a cost cycle, executing the charging and discharging operation, and generating charging and discharging feedback information; a reward signal is calculated, and the reward signal, the energy storage state feature vector and the charging and discharging action are used for updating charging and discharging strategy parameters; performing feedback optimization on future cycle income by using a time sequence dependency relationship; and performing lower-layer autonomous strategy optimization and upper-layer equipment coordination, and generating a globally optimal cooperative charging and discharging strategy. The strategy gradient and graph optimization method is fused, energy storage cooperative control is achieved, and the method has the advantages of being high in income, high in adaptability and stable in strategy.

The invention relates to the technical field of smart park integrated management, and discloses a smart park comprehensive management and control system and method based on multi-modal data analysis, and the method comprises the steps: obtaining a park multi-dimensional real-time data flow, and extracting a current situation feature vector and a personnel distribution thermodynamic diagram; analyzing the historical emergency event data sequence by using a space-time convolutional neural network, and outputting an event type identification vector; and generating a dynamic evacuation path planning and resource scheduling scheme based on a graph optimization algorithm, constructing a minimum cost flow optimization problem taking the weighted sum of time cost and waiting time as a target function, and solving to obtain an optimal people flow allocation scheme. The problem that emergency response cannot adapt to dynamic environment changes is solved. Experiential knowledge cannot be effectively inherited and utilized. And the emergency response system is slow in response and improper in resource allocation.

The invention provides a multi-modal basic model convolution operation optimization framework method suitable for a GPU / DCU. The method is used for solving the technical problems that the optimization process of an existing convolution operator optimization method is time-consuming and cross-layer operator fusion is difficult to capture. The method comprises the following steps: receiving a convolution operator through a parameterized input interface; collecting shape features of each convolution operator in the structured parameter set by using a shape analyzer to realize convolution feature analysis; judging whether batch normalization and nonlinear activation function fusion calculation is started or not; a heuristic optimizer dynamically selects a calculation graph optimization strategy based on convolution parameter characteristics; a calculation task is adapted to a physical architecture of the GPU / DCU, so that hardware resources are utilized to the maximum extent; a convolution calculation kernel function oriented to DCU / GPU architecture optimization is generated based on the convolution parameter features; kernel function assembly line execution is achieved through a DCU / GPU hardware task queue; gradient synchronization among multiple computing units is realized by adopting atomic operation at an equipment end. According to the method, the execution efficiency of the convolution operation in the multi-modal basic model can be remarkably improved.

The invention discloses a knowledge graph construction and application method based on the insurance industry, and belongs to the technical field of industry knowledge graphs. Comprising the following steps: S1, data collection and arrangement; collecting multi-source data of the insurance industry, and sorting the data; s2, knowledge modeling: defining an entity, determining a relationship, and performing attribute setting; s3, constructing a map: performing entity recognition and disambiguation, performing relation extraction, and storing a map database; s4, map optimization: adding semantic information, optimizing a relationship and creating an index; and S5, applying the atlas. According to the invention, multi-source data of the insurance industry is collected, and more comprehensive and accurate information support is provided for insurance business; according to the technical scheme, multiple application methods of the knowledge graph are provided, insurance business links such as risk assessment and management, customer portrait and precision marketing, intelligent customer service and knowledge questions and answers, product design and optimization, claim settlement management and the like are covered, the practical value of the knowledge graph is realized, and the efficiency and effect of each business link can be effectively improved.

The invention discloses an artificial intelligence customer service system based on natural language processing, and particularly relates to the technical field of natural language processing, the artificial intelligence customer service system comprises an input processing module, an intention collaboration module, an intention optimization module and an intention dynamic decision module, the intent collaborative module is used for calculating a context entropy value based on historical N rounds of dialogue feature distribution and a time decay weight, the intent collaborative module is used for outputting an emotion intensity signal through a multi-mode emotion recognition model according to input information and generating an intent correction vector in combination with a preset intent-emotion mapping matrix, and the intent optimization module is used for constructing and optimizing a collaborative loss function so as to improve the intent correction vector. And the intention dynamic decision-making module is used for performing intention classification on the input of the user based on the optimized model parameters and performing dynamic adjustment according to context entropy and emotion analysis, and the intention dynamic decision-making module is used for adjusting and making a decision on the response strategy of the system based on the output of the input processing, intention collaboration and optimization module and according to the context of the current dialogue.

The invention provides an operator processing method and device based on MindSpore, and relates to the technical field of data processing.The method comprises the steps that graph nodes with explicit calculation semantics in a deep learning model are extracted and are abstracted into an independent operator type set in a unified mode; constructing a minimum executable network structure to verify whether the minimum executable network structure can be successfully identified and generate intermediate representation in a MindSore framework, further calling a graph compiler to judge whether back-end graph optimization can be completed, loading to a running environment to execute verification if the back-end graph optimization can be completed, and calculating an error based on forward output and gradient return results, and if the error is lower than the set tolerance, identifying the operator as a function effective operator. According to the method and the device, the problem that the MindSore framework supports the operator incompletely or is insufficient in performance in the large model training and reasoning process can be solved, and the function effectiveness and the execution consistency of the target operator in the stages of composition, compilation and operation are guaranteed.

The invention discloses a high-precision robust robot detection method and system based on constraint pose graph optimization, and the method comprises the steps: collecting point cloud data of a detection object in multiple poses, and introducing a standard sphere and a standard ball rod in the collection process for constructing a graph constraint; a robust constraint pose map optimization algorithm framework SYMM-RCPGO is constructed; substituting the point cloud data into SYMM-RCPGO to construct an attitude graph and a constraint attitude graph to obtain a target function; iteratively solving the SYMM-RCPGO in the SE3N by combining the objective function according to the graph constraint, and using a dynamic constraint boundary algorithm to relax the tightness of the constraint to obtain the three-dimensional point cloud data of the detection object with a complete appearance; and registering the measured three-dimensional point cloud of the detection object with the designed CAD model, and accurately calculating the pose matrix of the detection object to complete the positioning of the detection object. According to the method, the SYMM distance function with a higher D-optimal measurement upper limit, the robust Welsch function and the constraint graph are utilized, so that the method is superior to a traditional PGO in the aspects of accuracy and robustness.

The invention discloses a building plane element recognition method based on a graph neural network and a convolutional neural network, and belongs to the technical field of building plane element recognition. The identification method comprises the following steps: inputting a building plane pixel image, and carrying out preprocessing through image layer screening and information extraction to obtain a binary pixel image; vectorizing the binarized pixel image to obtain a vectorized image; performing image segmentation on the vectorized image; using the segmented vectorized image to construct a regional adjacency graph; performing regional adjacency graph optimization on the regional adjacency graph through graph analysis and regional merging by using a graph neural network, and identifying a room edge; calculating the area of the room; using the convolutional neural network image recognition model to recognize articles in the room; and performing room function prediction according to an identification result. According to the method, the problem of shape blurring caused by convolution in a traditional method is avoided. The graph neural network model training effect is improved, and the classification accuracy is improved. The method is suitable for various planar graph styles and is high in universality.

The invention discloses a high-altitude track AGV accurate positioning and map building method and system. The method comprises the following steps: collecting data through multiple sensors; constructing a fusion model, and dynamically distributing fusion weights of laser and visual SLAM based on visual image information entropy and laser point cloud geometric feature distribution; semantic features such as track planes and edge lines are extracted and matched with the prior model; and jointly inputting the weight fusion pose, the UWB constraint and the semantic constraint into a back-end graph for optimization, solving an optimal pose and updating the map. The system comprises corresponding modules. Through cooperation of dynamic weight distribution and semantic auxiliary optimization, the problems of low AGV positioning precision and poor robustness in a high-altitude orbit environment are solved, dynamic closed-loop updating of a map can be realized, and the automation and intelligence level of an intelligent detection system is remarkably improved.

The invention discloses a distributed power supply load model establishment method and system, and relates to the technical field of load model establishment, after an establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.