151 results about "Global topology" patented technology

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention discloses a manifold perception-based large model navigation method guided by a historical topological graph, and relates to a computer vision technology. The method aims at solving the challenges that in the navigation process, long-distance reasoning experience is insufficient, instruction fragments and dynamic visual observation are difficult to align, and large model reasoning is prone to illusion interference. Firstly, a large model based on an encoder-decoder structure is used for supplementing historical information coding for a visual observation sequence, and therefore global topological information guidance is provided for long-distance reasoning. And secondly, in order to effectively solve the problem that large model reasoning is subjected to illusion interference, significant space-time differences in a visual observation sequence are mined by using a multi-curvature manifold, so that the large model can accurately describe the current environment and make a decision according to a visual reference object in thinking. Besides, in order to strengthen the perception capability of the large model to the space structure and establish a graph self-attention mechanism, the node distance embedded in the constructed historical topological graph is combined with the visual similarity so as to model the space relationship between the nodes.

The invention discloses an intelligent data quality repairing method based on dynamic rule evolution, which belongs to the technical field of data quality management, and comprises the following steps: constructing a knowledge graph based on physical storage structure information and service logic of structured data; performing deep learning on the knowledge graph by using a graph neural network, and dynamically generating a global topology view based on a learning result; in combination with a historical damage mode and a global topology view, an optimal structured data scanning path is generated by utilizing reinforcement learning, and association anomalies of abnormal partitions in an optimal path scanning result are identified based on a graph neural network; the method comprises the following steps: constructing a multi-modal association sub-graph based on association anomaly, repairing structural defects in the multi-modal association sub-graph through a correct physical structure reversely deduced by a graph neural network, and carrying out credibility scoring on a repairing result to form a structured data management closed loop. The method can adapt to continuously evolved data modes and novel anomalies, continuously improves the robustness and autonomy of the system to deal with complex data problems, and reduces the long-term operation and maintenance cost.

The invention relates to the technical field of 3D scanning, and discloses a real-time alignment highlight curved surface metallographic 3D scanning method and system, and the method comprises the steps: extracting normal vector features from a multi-view point cloud, calculating an approximate geodesic distance, and constructing a normal vector geodesic distance correlation graph; constructing a constraint propagation intensity matrix through a geodesic distance inverse ratio and a normal vector included angle exponential attenuation function, and enabling the constraint propagation intensity matrix to act on a multi-view point cloud registration residual error to generate a closed-loop error energy field; constructing a graph cut network node at the gradient extreme value of the energy field, and executing a minimum cut algorithm under geodesic constraint by taking the integral value of the energy field as the side capacity to complete global point cloud fine registration; according to the method, the local geometry and the global topology are combined through the normal vector geodesic association diagram, the problem that the registration accumulative error breaks out at the closed loop position due to the fact that the highlight curved surface metallographic surface lacks texture features is solved, and real-time accurate alignment of the highlight curved surface metallographic workpiece multi-view point cloud on an automatic detection production line is achieved.

The invention discloses an underground mine pipeline type point cloud completion method based on axial slicing, which relates to the technical field of point cloud data processing, is used for performing data completion on pipeline type point clouds, and comprises the steps of axially slicing preprocessed pipeline point cloud data, performing dimension reduction on three-dimensional point clouds to two-dimensional plane processing, and calculating related geometric parameters of pipelines. The method comprises the following steps: extracting a pipeline center line based on improved DBSCAN clustering and least square circle fitting, constructing a skeleton model under geometric topology constraints, combining local geometric features and global topology constraints, realizing geometric reconstruction of a missing region through rotating surface generation and a local interpolation algorithm, and realizing robust repair of the missing region. According to the method, a convex hull and an RANSAC method are combined to extract a pipeline center line and geometric parameters, pipeline point cloud data complementation is realized through a continuous rotation correction method, and the pipeline point cloud complementation quality is remarkably improved.

The invention provides a power distribution network topological structure automatic identification and optimization reconstruction method and system, and relates to the technical field of power systems, and the method comprises the steps: obtaining power distribution network information, and generating an initial topological graph; setting heterogeneous sensing nodes to form a sensing domain, and identifying a topological association relationship by an edge computing unit; generating a global topological feature matrix; dynamic response characteristics and load characteristics are extracted, and association strength is calculated; and establishing a Bayesian network analysis causal relationship, calculating an intervention effect value and generating a candidate reconstruction scheme. Accurate identification and intelligent reconstruction of the topological structure of the power distribution network are realized, and the reliability and the operation efficiency of the system are improved.

The invention discloses a novel power system-oriented power distribution network topology optimization configuration method and system. The method comprises the steps of obtaining current power grid topology data of a target power distribution network and supply and demand state data of a period of time in the future; the power grid topological data comprises a global topological graph and a current topological structure; the supply and demand state data comprises power supply data and load data; performing iterative optimization on the topological structure of the target power distribution network by adopting an optimization algorithm based on a particle swarm to obtain a new topological structure; wherein in the iterative optimization process, a fitness function is constructed by taking minimization of power grid network loss and node voltage deviation as targets, a population is initialized according to a current topological structure, the fitness of particles is calculated according to the supply and demand state and the fitness function, and particle position updating is performed according to the global topological graph under radial constraints.

The invention relates to the technical field of display, and discloses an LED screen correction coefficient adjusting method and system, and the method comprises the steps: collecting the original brightness and chromaticity data of each LED unit; constructing a global topological connection relation graph; calculating an initial correction coefficient; the corrected residual error of the boundary pixels of the adjacent units is analyzed; establishing a consistency constraint equation set aiming at minimizing the difference between the boundary brightness and the chromaticity; and performing joint solution to obtain a global optimization correction matrix and writing the matrix into a driving module. The system comprises a data acquisition module, a topology construction module, an initial correction module, a residual analysis module, a constraint construction module, a global optimization module and a coefficient writing module, and integrates a temperature drift compensation and visual verification mechanism. Through cross-unit collaborative optimization, seamless fusion of splicing areas is realized, and brightness uniformity, chromaticity consistency and long-term operation stability are improved.

The invention relates to the technical field of artificial intelligence, in particular to a knowledge distillation acceleration method and system for a multi-modal large model, and the method comprises the steps: obtaining multi-modal input data; feature mapping of topology maintenance is executed, topological representation of a multi-modal feature space is constructed, and a feature mapping module is designed based on a residual module; a topological hierarchical attention mechanism is applied, and multi-head topological attention fusion is realized through sensing of a local topological attention unit and a global topological structure; topology-driven multi-task knowledge distillation is executed, five cross-modal distillation tasks are designed, and distillation loss is calculated based on topological similarity measurement; a topology-aware model acceleration technology is adopted, adaptive quantization is performed based on feature topology importance, neural network structure search for topology maintenance is executed, a knowledge distillation process is guided through a topology principle, a topological structure relationship of a feature space is maintained, and multi-modal knowledge is effectively transmitted. Experiments show that compared with an original large model, the method has the advantage that the size of the model is reduced by 80-90%.

The invention discloses a wiring diagram recognition method and device based on deep learning and electronic equipment, and the method comprises the steps: carrying out the correction and mask processing of an initial wiring diagram, obtaining a first wiring diagram, and fusing mask parameters and mask skeleton parameters; extracting circuit elements in the first wiring diagram based on fusion mask parameters and mask skeleton parameters, and classifying the circuit elements to obtain a first element set of the first wiring diagram; performing port connection line pairing processing and topological optimization processing on the first element set, and determining a second element set and a corresponding second wiring diagram; according to the second element set, performing multi-dimensional verification and global topological structuring processing on the second wiring diagram to obtain a topological graph data model; and determining a JSON file, a visual topological graph, a VF2 check report and a Neo4j graph database of the wiring diagram according to the structured data in the topological graph data model. According to the invention, a more accurate wiring diagram can be identified.

The invention discloses an action sports scoring method and device based on big data analysis, and relates to the technical field of computer vision, and the method comprises the following steps: S1, constructing a human body posture tensor manifold; s2, generating aligned action track characteristics; s3, generating symmetrical positive definite manifold features; s4, generating deep manifold distribution parameters; s5, quantifying global distribution deviation characteristics; s6, calculating a residual vector based on the deep manifold distribution parameters and the standard action distribution parameters, inputting the residual vector into the improved AGCN model for processing, constructing an adaptive topology based on the residual vector, and performing aggregation analysis to obtain a joint-level physical angle error after multi-scale space-time convolution and manifold enhancement; and S7, outputting a comprehensive score vector. According to the method, the limitation that a traditional method only depends on local geometric features and ignores global topological structure constraints and statistical distribution priori is overcome, and an efficient solution is provided for intelligent scoring of sports actions.

The invention provides a power distribution network topology construction splicing method based on recursive grouping and a graph attention network. The method comprises the following steps: acquiring electrical attribute information of node equipment in a power grid by using a monitoring sensor; recursive grouping is carried out, nodes and branches in the feeder line of the power distribution network are identified by using the constructed impedance matrix and distance matrix, an initial topological graph is constructed, and a node feature matrix and an initial adjacent matrix are obtained; a GATv2 graph attention network is introduced, dynamic attention calculation is performed by using the node feature matrix and the initial adjacent matrix, and a final topological graph of the feeder is generated; a landmark matching method and Pearson's correlation coefficient calculation are adopted, and boundary points of each independent topology are identified and matched; performing accurate alignment on the topological graph generated by each feeder line by using a graph embedding synchronization technology, and finally performing global topology processing; and carrying out topology verification on the constructed global topological graph. Construction and efficient splicing of line topologies in different areas of the power distribution network are achieved, and efficient operation and intelligent development of a modern power distribution network are supported.

The application provides a distributed remote sensing interpretation vector post-processing method based on a grid DCEL topology. The method comprises: performing grid slicing on global grid data to obtain grid data of a plurality of grid units; constructing a bidirectional edge connection table topology based on the grid data of each grid unit respectively; performing elimination processing, thinning processing and smoothing processing on the topology of each grid unit respectively; merging the topology of each grid unit to obtain a global topology; and exporting the global topology as vector data. In this way, a high-performance grid topology engine based on a distributed architecture can be provided, a DCEL topology structure can be constructed from grid data, and distributed computing can be supported through a topology level merging algorithm; the grid is used to construct the topology, thereby reducing the steps of grid vectorization, and the grid-based topology construction method can naturally support distributed computing.

The invention relates to the technical field of data processing, and provides a data task scheduling method and system and a computer readable storage medium. The method comprises the following steps: constructing an initial task graph model according to a task definition; dynamically receiving a topology change instruction to perform online adjustment on the current task graph model; performing data consistency verification on the adjusted task graph model; after verification is passed, effective switching of the adjusted new task graph model is achieved, and the task being executed in the system is not interrupted in the effective switching process; task scheduling and resource allocation are carried out based on the new task graph model and the heterogeneous resource state after the new task graph model takes effect, and a target execution task adaptive to the heterogeneous environment is determined; and executing the target execution task in the heterogeneous environment. A dynamic topology maintenance mechanism is introduced, a topology change instruction is received and responded in real time, and finally seamless effective switching of a new model is realized, so that the problem of service interruption caused by static topology stiffness of a traditional system is solved, and the flexible adjustment capability of coping with global topology is improved.

A distributed inference communication method of a super-large-scale graph neural network comprises the following steps: dividing a global topology to be processed into a plurality of local sub-domains matched with the number of GPUs, and determining a local master node and a boundary master node allocated to each GPU; calculating coloring priorities of nodes, and performing parallel graph coloring based on the coloring priorities to construct a global independent set sequence; mapping the global independent set sequence to the local sub-domains of the GPUs to generate a local task slice sequence, which comprises boundary node slices and internal node slices, evaluating communication load and calculation load of the slices, and taking the internal node slices as calculation fillers to be executed in the communication waiting period of the boundary node slices; and each GPU executes an inference task by using a multi-stream asynchronous pipeline constructed locally according to the local task slice sequence. The method can improve the throughput of distributed inference.

The invention provides a spatial-temporal feature fusion-based power distribution network topology dynamic reconstruction method. The method comprises the steps of obtaining first time sequence data, second time sequence data and a line operation state; generating a first spatial-temporal characteristic parameter based on the first time sequence data and the line operation state; generating a second spatial-temporal characteristic parameter based on the second time sequence data and the line operation state; generating a local initial topology reconstruction strategy based on the first spatial-temporal characteristic parameter and the second spatial-temporal characteristic parameter; performing neighborhood correction on the initial topology reconstruction strategy to obtain a first topology reconstruction strategy; performing consistency judgment on the first topology reconstruction strategy to generate a global topology reconstruction strategy; and based on the global topology reconstruction strategy, generating a corresponding switching operation instruction and outputting the switching operation instruction to an instruction execution end of the power distribution network. According to the method provided by the invention, effective fusion of the spatial-temporal characteristics of the power supply side and the load side in the urban-rural junction power distribution network is realized, the operation flexibility and efficiency of the power distribution network are improved, and waste of electric power resources is reduced.

The invention relates to the technical field of computer cluster communication, in particular to a set communication method suitable for a core particle equipment cluster with unified bus interconnection, which comprises the following steps of: acquiring hierarchical topology and link information of the core particle equipment cluster, and constructing a global topology information base according to the acquired hierarchical topology and link information; optimizing the core size level primitive, the node level primitive and the super node level primitive; a link monitoring unit is deployed to monitor a link state index in real time, an alarm is triggered when a link fault is detected, an optimal backup link is screened based on a global topology information base, a communication path is updated, and a dynamic resource scheduling algorithm is adopted to allocate computing power and communication resources; and dynamically adjusting a communication path and bandwidth allocation according to a set communication task requirement and a link dynamic parameter. The method is suitable for a three-level link architecture of a core particle equipment cluster, can improve the communication efficiency and the bandwidth utilization rate, enhances the cluster reliability and the resource utilization rate, and effectively meets the requirements of high computing power and high communication intensity of large model training.

The invention belongs to the technical field of intelligent scheduling, and particularly relates to a heterogeneous cloud container cluster scheduling model training method and scheduling method based on topology awareness, and the training method comprises the steps: obtaining a cluster topological graph; obtaining a state vector time sequence of the nodes based on the node operation data, wherein each state vector comprises a performance index representing bottom layer resource contention; obtaining a queue state matrix; processing the state vector time sequence by using a feature extractor to obtain a node initial embedding vector; processing the initial embedded vectors of all the nodes by using a graph attention network to obtain a cluster global topology vector; obtaining a joint observation state based on the cluster global topology vector and the queue state matrix; inputting the joint observation state to the Actor-Critic network to generate a scheduling strategy; the accumulated rewards are maximized through a near-end strategy optimization algorithm, and network parameters are updated; and connecting the feature extractor, the graph attention network and the decision network to form a scheduling model. And the topological relation between the nodes is globally sensed, the interference attribute of the micro-architecture is captured, and accurate scheduling is realized.

The application relates to an outdoor geographical semantic retrieval enhanced navigation and search method and system, which comprises the following steps: receiving a natural language instruction, and extracting navigation target semantic elements and search target semantic elements; under the constraint of a structured index, performing beam search, and outputting the latitude and longitude and boundary information of the entity; decomposing the natural language instruction into a subtask sequence, and under the constraint of a predefined executable action and condition space, generating a structured behavior tree; calling a routing engine, generating a global topology path and a waypoint sequence based on the nearest reachable boundary point of the entity; based on the scheduling of the structured behavior tree, performing real-time navigation along the waypoint sequence; after reaching the vicinity of the target, searching for the target related to the search target semantic elements and performing clustering positioning under the constraint of a polygon boundary, obtaining a final target point and converging navigation. Compared with the prior art, the application has the advantages of high positioning accuracy and wide application scenarios.

This invention provides a stylizable and deformation-resistant QR code encoding and recognition method based on topological coding, comprising: converting the binary string to be encoded into natural numbers using a bijective function and recursively decomposing it to generate a topological tree structure; determining the spatial allocation ratio based on the tile traces of each node in the topological tree; performing inward shrinking processing on the polygonal region carrying the QR code, recursively dividing it into non-overlapping closed sub-regions according to the ratio, mapping the hierarchical structure of the topological tree to a QR code with a nested structure and stylizing it; scanning the QR code image and performing preprocessing, extracting the boundary contours of the closed regions and constructing a global topological tree, enumerating multiple candidate topological trees and decoding them one by one, and filtering out the valid binary information as the recognition result after CRC verification. This invention achieves the deformation resistance of the QR code through the mapping of the topological tree structure and nested regions, and improves its visual effect and application flexibility through stylization processing.

The invention relates to the field of artificial intelligence, and particularly provides a 3D model scoring method and system based on a neural network model, a terminal and a medium, and the method comprises the steps: employing a pre-trained neural network model to generate standard fusion features for a 3D model uploaded by a teacher user, and employing a local geometric feature extraction model to extract local geometric features, extracting global topological relation characteristics by using a global topological relation modeling model, and performing weighted fusion on the local geometric characteristics and the global topological relation characteristics by using an attention mechanism to obtain fusion characteristics; using a pre-trained neural network model to generate a to-be-scored fusion feature for the 3D model uploaded by the student user; comparing the fusion feature to be scored with a standard fusion feature, and obtaining a 3D model score based on a pre-configured scoring rule according to a comparison result; and feeding back the 3D model score of the student user to the teacher user. According to the invention, two-channel feature extraction of CNN and GNN is combined, so that the scoring efficiency and accuracy are improved.

The invention discloses a layered planning method for indoor autonomous exploration of a mobile robot based on dynamic prediction, and the method comprises the steps: S1, dividing an exploration environment where the robot is located into a plurality of local subspaces, and marking the state of each subspace as an unexplored state, an explored state, an explored state or a semi-explored state according to the data of a sensor and a semantic detection result; s2, establishing a global topological graph connected with each subspace, and planning a global access path by using a traveling salesman problem model; s3, integrating a pedestrian trajectory prediction model in the selected current subspace, and evaluating and selecting a local viewpoint capable of maximizing information gain and minimizing dynamic collision risk based on a prediction result; the method has the beneficial effects that a semi-exploration state and a self-adaptive revisit mechanism are introduced, and when the door is possibly opened, the check is turned back, so that the dead angle of a map is eliminated, and the exploration completeness is improved; pedestrian trajectory prediction is incorporated into viewpoint scoring, and people deadlock is avoided.

This invention discloses a gene regulation inference method guided by topological data analysis and gene network embedding. It combines TDA and GNN to enhance the inference capability of gene regulation networks. By capturing the topological structure of the gene regulation network graph through TDA features, the model's ability to model gene expression is enhanced. The TDA features and GAT embedding representations are effectively integrated through gating fusion. This fusion mechanism enables the model to adaptively adjust node embeddings based on global topological characteristics, which not only improves the accuracy of gene interaction representation but may also enhance the accuracy of regulatory relationship prediction. The traditional GAT architecture is extended through a four-layer graph attention mechanism. Each layer uses residual connections to alleviate the gradient vanishing problem and improve training stability. In addition, independent multilayer perceptron branches are designed for transcription factors and target gene embeddings. This deep architecture can achieve more expressive feature transformations and capture subtle patterns in gene regulation networks.

The application relates to the technical field of power grids and discloses a power grid operation and maintenance multi-task planning method and system based on space-time characteristics. The method comprises the following steps: based on equipment time sequence dynamic characteristics and equipment space correlation characteristics, performing power grid operation monitoring tasks and equipment state monitoring tasks to obtain fault detection statistics and fault positioning statistics; performing a risk research and judgment process on the fault detection statistics and the fault positioning statistics and time sequence qualitative trend characteristics to obtain an operation risk assessment result of a target power grid; inputting the equipment space correlation characteristics into each graph neural network for learning to obtain global topology aggregation characteristics of the target power grid and node space characteristics corresponding to each equipment; and inputting the global topology aggregation characteristics and each node space characteristic into a corresponding strategy network for processing to output an operation and maintenance coordination planning strategy of the target power grid. The application not only improves the overall execution efficiency of power grid operation and maintenance work, but also reduces the comprehensive cost of operation and maintenance scheduling.

The invention relates to the technical field of server cluster operation and maintenance risk assessment, and discloses an AI multi-modal feature fusion-based server fault intelligent diagnosis and logic self-healing system, which comprises the following steps of: obtaining a multi-modal telemetry data stream containing a utilization rate, a memory allocation gradient and connection relation complexity; extracting an architecture design benchmark constraint to construct a running state multi-dimensional benchmark model; calculating a topology deviation value of a real-time state vector deviating from the model, decoupling steady-state deviation and dynamic disturbance by utilizing characteristic decomposition logic, and positioning a logic abnormal node; determining a compensation parameter set based on a consistency objective function minimization rule, and driving correlation node parameters to converge to design constraints; according to the method, node weights are coordinated through a global topology correction mechanism, secondary logic oscillation caused by local repair is avoided, and cluster operation continuity is enhanced.

The invention discloses a method for configuring an equipment management rule through a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: extracting a multi-modal biological feature, calculating an emotion weight, and generating an emotion enhanced semantic structure through emotion mapping; inputting the emotion enhanced semantic structure into a device management knowledge graph, and generating executable rule logic through a graph neural network model; converting executable rule logic into a device instruction set through a dual-path rule code, executing emotion constraint to verify the device instruction set, and outputting a verification rule packet; executing the verification rule packet, collecting feedback data through rule monitoring, executing target parameter adjustment, generating a rule version updating record and outputting a device response log; neighbor node information is aggregated by using graph convolution operation, and a dynamic rule fusing a global topological relation is generated. Knowledge-driven multi-hop reasoning is realized, so that rule logic can be accurately associated with cross-equipment and cross-scene complex constraints.

The invention provides a microorganism-drug association prediction method based on multi-scale adaptive graph representation learning. The method comprises the following steps: constructing a microorganism-drug heterograph comprehensive feature matrix; processing the matrix through a spectrum kernel attention mechanism, generating a spectrum kernel feature matrix in a spectrum space by using a Chebyshev polynomial, and explicitly fusing graph topology information in combination with a multi-head attention mechanism to capture a global topology dependency relationship and obtain global feature representation; the matrix is processed based on a structure perception adaptive neighborhood sampling mechanism, a neighborhood sampling strategy is dynamically predicted and adjusted according to structural features such as degrees and centrality of nodes, local neighborhood information is obtained in a mixed sampling mode, and local feature representation is obtained; and fusing global and local feature representations, and calculating a potential association score between the microorganisms and the drugs through a prediction module. According to the method, the global structure dependence and the local structure heterogeneity of the graph can be effectively combined, and the accuracy and the robustness of microorganism-drug association prediction are remarkably improved.

The embodiment of the invention provides a vectorized map coding method and system, electronic equipment and a medium, in the method, a map is coded into a structured form containing a topological relation between a lane section and a target node, and geometric information and semantic information are endowed, so that a basis is established for multi-scale feature fusion. Then, lane line global features are generated through the first graph structure data. Construction of node features focuses on detail mining of local scales, and it is ensured that microscopic geometric and semantic features of lane lines are not missed; and the global topological relation of the vectorized map can be accurately captured through the construction of the global features of the lane lines. And finally, carrying out fusion processing on the node features of the local scale and the global features of the lane line of the global scale, and through cross-scale feature association, enabling the features of each target node to not only reserve geometric details of the lane section where the target node is located, but also integrate global topology semantics of the whole vectorized map. And the accuracy of track prediction in the automatic driving system is obviously improved.

The invention discloses a gas extraction pipe network coupling characteristic global inversion real-time resolving method, which relates to the technical field of mine gas extraction safety monitoring and comprises the following steps of: constructing a closed-loop resolving system of a coupling characteristic penetrating through a whole process by taking coupling association of gas concentration and gas physical parameters as underlying logic; after coupling feature pre-extraction, coupling type global topology modeling, steady-state coupling control equation set construction and sparse measurement point data preprocessing, a multi-target weighted inversion target function is constructed, pipe network global parameter inversion is achieved through a coupling self-adaptive numerical iteration algorithm with double-threshold convergence judgment, then coupling type calculation reliability quantitative verification is conducted, and a multi-target weighted inversion target function is obtained. And performing working condition self-adaptive coupling correction on the low-precision section to form a closed-loop logic of coupling inversion-verification-correction. According to the method, high-precision global inversion under sparse monitoring can be realized, the investment of monitoring equipment is reduced, the sensing precision of the operation state of the pipe network and the safety management and control level are improved, and the method is suitable for state monitoring and regulation and control of various mine gas extraction pipe networks.