297 results about "Octree" patented technology

The invention discloses an interactive large-screen visualization system and method based on multi-dimensional data real-time rendering, relates to the technical field of image rendering, and aims to solve the bottleneck problems of a traditional system in the aspects of data throughput efficiency, rendering delay and interactive response. The method comprises the following steps: dividing space blocks by adopting a non-uniform octree, dynamically adjusting the size of the blocks according to data density, and combining a ZSTD + Hoffman hybrid compression algorithm; a CPU-GPU three-level asynchronous processing assembly line is constructed, and task priorities are dynamically adjusted through a fixation point weight algorithm; the master node dynamically allocates LOD parameters, and the slave nodes achieve special-shaped large-screen sub-pixel-level splicing based on a spherical coordinate system model; a Q-learning algorithm is adopted to predict a user operation sequence, and dual-channel bandwidth adaptive allocation is realized in combination with an ASTP protocol. The system comprises a dynamic partitioning module, a pipeline scheduling module, a multi-node cooperation module, an intelligent preloading module and an exception handling module. Real-time interaction visualization of a large amount of data can be realized, the rendering efficiency is improved, and hardware resource occupation is reduced.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention provides a part surface efficient three-dimensional reconstruction method based on adaptive octree sampling, and belongs to the field of surface reconstruction. The part surface efficient three-dimensional reconstruction method based on self-adaptive octree sampling comprises the following steps of performing multi-stage filtering and denoising on collected point cloud data; octree nodes are adaptively divided according to local point cloud density and curvature features, the curvature of each node is calculated, feature region marking is carried out, voxel downsampling processing is carried out on non-feature regions, curvature weighted random sampling processing is carried out on feature regions (such as cracks and recesses), and high resolution of the regions is reserved; a multi-resolution space grid is generated based on octree hierarchy, a hierarchical symbol distance field is constructed, and the Poisson surface reconstruction process is accelerated. The method can be used in the fields of surface topography analysis, process optimization, quality control and the like, is particularly suitable for scenes requiring efficient and accurate surface topography detection, and has a wide application prospect.

The invention discloses a self-adaptive octree three-dimensional magnetic method inversion method fused with abnormal region recognition, and belongs to the technical field of three-dimensional magnetic method data inversion processing. Comprising the steps that initial inversion is rapidly completed on a coarse grid, the overall contour of an underground structure is obtained, a fuzzy c-means clustering algorithm is introduced, and a target area with significant magnetic anomaly is automatically extracted from a coarse solution; and constructing a multi-level octree grid in the extracted abnormal region to realize fine subdivision of the complex geological boundary, and further executing high-precision inversion by using a fine model as a new starting point. The adaptive octree grid has the characteristics of high efficiency, small grid number and small data fitting difference. The process of coarse inversion, intelligent identification, local refinement and fine inversion can be circularly executed as required, and computing resources are dynamically allocated. Compared with a traditional global fine grid scheme, the inversion process has the advantages that the inversion precision is maintained and even improved, and meanwhile, the number of grid units is remarkably reduced.

The invention provides a multi-modal data fusion method and device based on dynamic Gaussian modeling and a vehicle. According to one embodiment of the invention, the multi-modal data fusion method based on dynamic Gaussian modeling comprises the following steps: obtaining continuous multi-frame multi-modal data, obtaining 3D voxel features and multi-view image features through the multi-modal data, constructing a dynamic Gaussian parameterized model based on the 3D voxel features, and fusing the dynamic Gaussian parameterized model and the multi-view image features through the dynamic Gaussian parameterized model. Generating a multi-view-angle Gaussian feature by using a dynamic Gaussian parameterized model and the multi-view-angle image feature, and projecting the multi-view-angle Gaussian feature to a dynamic resolution BEV space to obtain a BEV feature, and obtaining a 3D voxel feature by adopting dynamic voxelization, and the dynamic voxelization is based on octree sparse voxel coding, and the resolution is adaptively adjusted according to the scene complexity. According to the invention, the resource utilization rate can be optimized and calculated while the sensing precision is ensured.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention discloses an urban district regulation construction management operation and maintenance method and system based on three-dimensional digital twinning, and the method comprises the steps: collecting multi-source data, including geographic raster data, BIM model data and mobile phone signaling data, of an urban district, and achieving the coordinate alignment of the multi-source data through an LM algorithm; the multi-source data generates feature data composed of coding tables defining urban features through feature engineering, and an octree is constructed based on the feature data; defining leaf nodes of the octree as voxels, and applying multi-scale convolution to each voxel to obtain super voxels with space-time labels; constructing a constraint network based on the super voxels and a construction, management or operation and maintenance specification constraint library; and judging the risk area of the city district by calculating the space-time constraint intensity of the constraint edge, and outputting a corresponding risk area list. According to the invention, the problems of multi-source data integration, real-time dynamic monitoring and risk prediction in the planning, construction, management, operation and maintenance processes of the urban district are solved.

The invention relates to the technical field of geological analysis, and discloses a method for fusing a three-dimensional geological model and a WebGIS (Web Geographic Information System), aiming at solving the problems of poor data intercommunity, limited spatial analysis capability, difficulty in real-time updating and low model loading efficiency of the existing method, and the scheme mainly comprises the following steps: extracting geological model data, and carrying out standardized format processing; lightweight processing is carried out, and geological attributes and model nodes are bound one by one; the format is converted into a binary format embedded with a metadata label, and data integrity verification and automatic error correction are carried out in the conversion process; constructing a non-uniform adaptive octree spatial index, a multi-level attribute data architecture and a real-time updating mechanism of attribute data; after the geological model and the terrain are precisely registered and fused, a dynamic loading mechanism, a high-performance rendering mechanism and a geological model real-time updating mechanism are constructed, and a three-dimensional geological analysis tool is integrated. According to the method, efficient loading, real-time rendering, dynamic updating and spatial analysis of the three-dimensional geologic model in the WebGIS are realized.

The invention relates to an OpenGL (Open Graphics Library)-based large-scale three-dimensional model object quick pickup method. The method comprises the following steps: converting an interactive operation of a user into a three-dimensional space structure; a CPU constructs octree space acceleration structures based on a bounding box of an object in a large-scale three-dimensional model, and leaf nodes, intersecting with a pickup ray or a pickup view pyramid, of an axis alignment bounding box in each octree space acceleration structure are screened out to serve as candidate leaf nodes; collecting triangles contained in all candidate leaf nodes, and constructing a candidate triangle set; dynamically allocating thread groups and threads of the GPU based on the number of candidate triangles in the candidate triangle set; and performing highlight display on the candidate triangles intersected with the pickup ray or the pickup view cone, and feeding back IDs of the corresponding candidate triangles. According to the method, the data transmission quantity and the video memory access pressure are remarkably reduced, the pickup response time is greatly shortened in a large-scale three-dimensional model scene, and the requirements of real-time interaction and visual operation can be met.

The invention relates to the technical field of three-dimensional models, in particular to a drawing method for making a three-dimensional model based on three-dimensional laser point clouds, which comprises the following steps of: sequentially reading point cloud data files of the three-dimensional laser point clouds, inserting coordinate values of each point into an octree structure, and when octree node buffer areas in a memory are full, drawing a three-dimensional laser point cloud into the octree structure; and writing the node data in the buffer area into a hard disk, and traversing the hard disk octree node set from bottom to top. According to the method, the three-dimensional laser point cloud data is inserted step by step, and the hierarchical aggregated octree structure is utilized, so that the point cloud data processing efficiency is effectively improved, and the memory occupation pressure is reduced; representative points in an octree structure are fused with an average normal to construct a continuous function field, a grid model is adaptively generated in a multi-detail-level mode, and the detail retaining capacity and drawing precision of the model are improved. Furthermore, accurate identification and positioning of topological features are realized by adopting calculation of a cell complex sequence and a topological feature noise rank.

The invention discloses a rapid local repair method for a self-intersecting triangular mesh. According to the method, local processing and rational number calculation are combined, aiming at an input low-quality triangular mesh, firstly, the mesh quality is optimized through a preprocessing step, intersection areas are positioned, then the mesh is partitioned based on octree division and connected component analysis, and the intersection areas and non-intersection areas are separated. In each intersection area, a shape-preserving boundary three-dimensional grid optimization method based on rational numbers is adopted to ensure the robustness of intersection calculation, and surface grids are extracted after successful conversion into floating-point number representation. And finally, seamlessly splicing the repaired local grid and the non-intersecting grid, and improving the overall grid quality through an edge re-gridding method based on curvature self-adaption. According to the method, the non-intersecting high-quality grid can be quickly generated, the method is suitable for downstream applications such as finite element analysis, meanwhile, the calculation overhead is remarkably reduced, and the algorithm robustness is improved.

A system and method for encoding and decoding the geometry of 3D point clouds using octree-based data structures are disclosed. The method involves encoding and decoding bitstreams containing octree structure information and vertex data, including the presence and position of vertices on cuboid edges corresponding to leaf nodes. The decoding process determines triangles connecting vertices within each cuboid, which are voxelized to reconstruct the 3D point cloud. To enhance voxelization accuracy, triangles may be extended along one or more sides based on a sampling distance parameter (dsampldsampl) or adaptive halo parameters. The encoding process utilizes similar principles to encode the octree structure and vertex information, supporting geometry reconstruction with high fidelity. The system employs the Möller-Trumbore algorithm and barycentric coordinate calculations with constraints based on dsampldsampl for voxelization. Extensions may include fixed or adaptive parameters encoded within the bitstream.

The invention provides a public space dynamic risk network assessment method fusing spatio-temporal characteristics, which relates to the technical field of network assessment, and comprises the steps of acquiring basic data, adaptively dividing grid units by adopting an octree algorithm, establishing a connection relation graph, calculating a risk propagation probability by utilizing a graph neural network, and calculating a risk propagation path by applying an improved A * algorithm. And finally outputting risk level distribution, propagation path risk change and prevention and control deployment suggestions. According to the invention, refined quantitative evaluation of public space risks is realized, and risk early warning accuracy and prevention and control measure pertinence are improved.

The invention discloses an adaptive edge detection iterative denoising point cloud reconstruction method and system. A regularization parameter and a kernel bandwidth are automatically set through point cloud normalization and local covariance noise estimation; an implicit curved surface is fitted based on a parameterized Gaussian field, and initial curved surface construction without a normal direction is realized. Edge detection is carried out by utilizing Voronoi covariance measurement, and the edge detection is continuously mapped into a projection weight, so that feature details of the point cloud are reserved in an acute angle region, and smooth denoising is realized in a flat region. The regularization intensity is dynamically adjusted through residual trend analysis to enhance detail representation, and a triangular mesh is extracted and output in combination with octree discretization and contour surface extraction. The method has unsupervised adaptivity, has outstanding performance in the aspects of edge fidelity, noise robustness and complex structure adaptability, can effectively process point cloud data with high noise, missing normal direction or irregular structure, and is suitable for the fields of engineering survey, reverse modeling, cultural relic digitization and the like.

A method for encoding a three-dimensional (3D) point cloud into a bitstream, includes: obtaining octree information including an octree structure of a volume, wherein the octree structure includes a plurality of cuboids as leaf nodes of the octree structure; providing a threshold range; determining vertex positions Vj on edges of each cuboid as vertex information, wherein the vertex position Vj on the respective edge is determined by points of the point cloud in a distance to the respective edge smaller than the threshold range; and encoding the octree information and the vertex information into a bitstream; wherein the threshold range is dependent on the sizes of the cuboids.

The invention discloses a rendering method, which comprises the following steps: constructing an octree, and storing a to-be-rendered object in a to-be-rendered three-dimensional scene in a child node of the octree to obtain a space management octree; dividing the view cone into different view cone levels; traversing the space management octree, and determining a first intersection part between the space of the child node and the view cone and a view cone level to which the first intersection part belongs; distributing the first intersection part to different rendering queues according to the view cone hierarchy to which the first intersection part belongs; shielding and removing the first intersection parts in the different rendering queues to obtain current visible objects to be rendered; and rendering the current visible to-be-rendered object. According to the method, the octree is constructed to manage the three-dimensional scene space, the view cone levels are divided, the rendering queues are distributed, the view cone removing and multi-level shielding removing technologies are combined, the visible objects are accurately judged, the rendering load is effectively reduced, and the method has the advantages that the rendering performance is improved, space management is optimized, and the complex scene processing efficiency is improved.

The invention relates to the field of CIMs, and discloses a method and system for automatically degrading a CIM6-level three-dimensional model to CIM5 based on a semantic rule engine, and the method comprises the steps: obtaining CIM6-level three-dimensional model data, and constructing a scene hierarchical tree based on the three-dimensional model data; identifying the category, the attribute, the service label and the importance constant of each object in the scene hierarchical tree; calculating geometric complexity, semantic importance and topological dependency of each object, and calculating a comprehensive weight; matching the interval of the comprehensive weight and executing a corresponding processing operation based on the matched interval; performing grid merging and texture baking operation on the reserved object; performing grid instantiation on the proxy object after proxy replacement; collision holes in the model are detected through an octree in combination with a GJK algorithm to complete topological consistency verification, a lightweight CIM5-level model data packet is generated, and a difference report is output. According to the method, the consistency of the key semantic information and the topology is kept while the data volume of the CIM6 model is reduced.

The invention relates to an interstellar medium radiation transfer calculation method and device based on ray tracing. The method comprises the following steps: discretizing a three-dimensional space model of an interstellar medium by using a Cartesian grid to obtain a plurality of grid units, and emitting a plurality of uniformly distributed light rays at the center of each grid unit to construct a discretized radiation transfer equation; for each of the light rays, screening out the grid units through which the light rays penetrate by adopting an octree algorithm; calculating the length of a path where each light ray intersects with the grid units when the light ray passes through the grid units by adopting a flat plate method; and solving a discretized radiation transfer equation according to the path length to obtain a parameter equation of the interstellar medium. By adopting the method, the calculation amount of the ray tracing numerical integration in the radiation transfer equation can be reduced.

In one implementation, a method of decoding point cloud data is presented, comprising: decoding features representing voxel attributes in an octree structure, wherein a decoded feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed; determining an attribute probability of the current voxel based on the decoded feature for the current voxel; decoding attribute information of voxels in the octree structure, wherein the attribute information for the current voxel is decoded based on the attribute probability for the current voxel; and reconstructing the point cloud based on the attribute information of voxels in the octree structure. On the encoder side, the features are extracted and encoded into the bitstream.

The invention discloses a dual-path three-dimensional point cloud compression reconstruction method fusing multi-scale geometric features. The method comprises the following steps: extracting representative skeleton anchor points from original point clouds by adopting farthest point sampling; predicting an octree node occupancy state by using an anchor encoder, and performing entropy coding to generate a skeleton bit stream; a skeleton condition feature encoder is introduced, probability distribution of local features is predicted by using skeleton context priori, cross-path context association is constructed, and a feature bit stream is generated; in the decoding stage, the sparse skeleton point set is restored from the skeleton bit stream, local features are recovered from the feature bit stream in combination with the skeleton structure context, fine-grained reconstruction is carried out on the point cloud through a geometric fusion point cloud up-sampling module, and finally the high-fidelity point cloud is obtained. According to the invention, the point cloud compression effect and reconstruction quality can be improved. The method can be widely applied to the point cloud field.

The invention belongs to the field of unmanned aerial vehicle (UAS) traffic management (UTM) and computer science, and particularly relates to a method and system for airspace modeling and path planning of a logistics unmanned aerial vehicle, and the method comprises the steps: S1, receiving multi-source data, and constructing a hierarchical mixed data model; s2, coding the control constraint data and the dynamic environment data into semantic attributes of nodes in a two-dimensional quadtree layer and / or voxels in a three-dimensional octree layer so as to generate a semantic airspace model containing computable rules; s3, based on the semantic airspace model, executing a hierarchical path planning algorithm to determine a flight path from the starting point to the ending point; and S4, combining the refined paths, and outputting a four-dimensional flight path containing a timestamp. The airspace rule is internalized into the model attribute, the real-time performance, safety and compliance of path planning are remarkably improved, the airspace resource utilization efficiency is improved, and key technical support is provided for large-scale urban unmanned aerial vehicle logistics operation.

A G-PCC coder may determine an occupancy of a reference child node in a reference node, wherein the reference node is in a reference frame of point cloud data used for inter prediction of a current node in a current frame of the point cloud data. The G-PCC coder may further determine a context for decoding a current occupancy bit of a current child node of the current node based on the occupancy of the reference child node, and arithmetic decode the current occupancy bit using the context.

The invention discloses a low-altitude path planning method and system based on a dynamic adaptive graph neural network, and belongs to the technical field of artificial intelligence and robot autonomous navigation. Aiming at the problems of large calculation amount of a fixed grid map, slow dynamic response of a static octree, failure of a traditional heuristic function on a non-uniform graph and the like, the invention provides the following scheme: constructing a resolution self-adaptive non-uniform graph by using the octree; a Morton code is adopted to realize rapid positioning of a dynamic obstacle and millisecond-level updating of a map; an attention mechanism is introduced into a graph attention network (GAT), weights are adaptively allocated to different neighbor nodes, features are aggregated, and a high-precision heuristic cost estimation value is output; and finally, integrating the estimated value into an algorithm to perform efficient path search. According to the method, the memory occupation and the calculation complexity are remarkably reduced, and the planning efficiency and the path safety in a dynamic environment are improved.

In one implementation, a method of decoding point cloud data is presented, comprising: decoding features representing voxel attributes in an octree structure, wherein a decoded feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed; determining an attribute probability of the current voxel based on the decoded feature for the current voxel; decoding attribute information of voxels in the octree structure, wherein the attribute information for the current voxel is decoded based on the attribute probability for the current voxel; and reconstructing the point cloud based on the attribute information of voxels in the octree structure. On the encoder side, the features are extracted and encoded into the bitstream.

The invention relates to the technical field of intelligent numerical control machining, in particular to a Tri-dexel model efficient cutting simulation method based on octree acceleration, and the method comprises the steps: firstly selecting a three-dimensional model needing Tri-dexel processing; performing grid division on the three-dimensional model to obtain a plurality of triangular grid units, and generating a Dexcel ray according to the three-dimensional model; triangular patches in the triangular mesh are stored in an octree form; grouping the Dexcel rays according to the depth of the octree, and searching patches possibly intersected with the Dexcel rays in the octree by using the Dexcel rays for intersection; performing de-duplication processing on the repeated intersection points; the intersection points of the same Dexel ray are connected in pairs, and finally a Tri-dexel model is generated; and then the Tri-dexel model of the workpiece cut by the tool can be obtained through the one-dimensional Boolean operation of the Dexcel line segment between the workpiece and the Tri-dexel model of the tool. Through verification, the Tri-dexel efficiency of the complex three-dimensional model in machining simulation is improved, and the calculation amount and the storage space of machining simulation can be effectively reduced when a complex three-dimensional scene in numerical control machining simulation is coped with.

The invention discloses a point cloud compression method based on multi-dimensional feature fusion, electronic equipment and a medium, and aims to effectively reduce the compression bit rate while maintaining the precision of a space structure. According to the method, space, channel and topology redundant information is fused under a unified framework for the first time, and the method is suitable for efficient point cloud compression of various three-dimensional scenes such as automatic driving and virtual reality. Meanwhile, a local graph convolution Mama module is introduced, and the limitation that a point cloud topological structure is difficult to capture in a traditional method is effectively solved. Then, the octree nodes are divided into two dimensions of space and channel through a space-channel coupling grouping module, so that gradual feature fusion is realized, and the context information prediction precision is improved; experimental results show that the compression efficiency is remarkably improved on a plurality of radar and voxel human body model data sets.

The invention discloses a millimeter wave radar point cloud compression method based on heterogeneous representation and an implicit neural network. Firstly, point cloud fine geometric features are extracted through a point-based compression network, global semantics are aggregated through a voxel-based compression network, and point-voxel heterogeneous joint representation and preliminary compression are realized. Secondly, constructing an adaptive octree to code a compressed data space structure to generate a partitioned bit stream, and performing statistical compression on the voxel-level high-dimensional features by using context-aware entropy coding to generate a feature bit stream; then, joint transmission is performed on the partition bit stream, the feature bit stream and the neural network weight for implicit representation. And finally, decoding and recovering the coarse-grained point cloud at a receiving end, inputting the coarse-grained point cloud and a neural network weight into a point-based synthesis network based on implicit neural representation, and finally reconstructing a high-fidelity point cloud through continuous surface modeling and detail compensation. According to the method, the compression efficiency and the reconstruction quality of the millimeter wave radar point cloud under the low code rate are remarkably improved.

The invention discloses a steel truss extraction method based on point cloud segmentation and feature analysis, and belongs to the technical field of three-dimensional point cloud processing. The method mainly comprises the following steps: firstly, carrying out multi-scale filtering denoising on an original point cloud and constructing an octree index; local geometric features such as normal vectors, curvatures and first-order plane fitting errors of all the points are calculated; based on the adaptively selected seed points, carrying out region growth by combining a normal vector included angle, a curvature difference and an adaptive edge criterion to realize preliminary segmentation; and finally, a coarse-to-fine multi-layer cascade classifier is adopted to screen the segmented areas, and the steel truss point cloud is accurately extracted through geometric dimension filtering, shape feature screening and multi-dimensional feature matching in sequence. The method does not need pre-training, has the advantages of strong anti-interference capability, high extraction precision and high calculation efficiency, and is suitable for engineering scenes of bridge detection, building surveying and mapping and the like.

The invention discloses a GIS (Geographic Information System) data dynamic updating optimization algorithm based on three-dimensional digital twinning, which comprises the following steps of: S1, acquiring GIS data, preprocessing and generating a three-dimensional observation data set with a unified structure; s2, dividing a target area by adopting an octree structure, and constructing a three-dimensional digital twin model and a spatial index system; s3, constructing a spatial topological graph model based on the spatial adjacency relation and the semantic tag; s4, according to a topological graph node structure, constructing an improved hierarchical Bayesian network model, calculating an update posterior probability, and generating an update priority sequence; s5, performing difference comparison based on the updating priority sequence, and executing incremental updating; and S6, the prior distribution of the Bayesian network nodes is dynamically corrected, the topological graph updating probability is adjusted, and loop optimization is carried out until convergence. According to the invention, real-time, efficient and accurate updating of the GIS data is realized.