148 results about "Spatial geometry" patented technology

The invention provides a finite element analysis method and system for geometric design of a flow regulating valve, and relates to the technical field of data processing.The method comprises the steps that 1, spatial geometric feature analysis is conducted on each data point in a discrete contour coordinate dataset, the principal curvature value of each data point on the sealing surface of a metal diaphragm is solved, and the principal curvature value of each data point is calculated; determining a point with the maximum average curvature value on the sealing surface of the metal diaphragm as a core morphology reference point; step 2, establishing a local coordinate system by taking the core morphology reference point as an original point, screening four sealing state points from the discrete contour coordinate data set, solving a corresponding space plane equation, and calculating a space intersection line between the space plane and the sealing surface of the metal diaphragm; according to the method, through accurate positioning of the core reference point, definition of the analysis sub-domain, grid division, mechanical solution, leakage rate calculation and parameter optimization, the dynamic particulate matter problem is effectively improved, and the sealing performance and the micro flow adjusting precision are improved.

The invention discloses an intelligent control method of a numerical control dotting and ruling machine for mold machining, and particularly relates to the field of numerical control machining control. The method comprises the following steps: establishing a three-dimensional point cloud model containing surface microscopic curvature distribution and a local stress-strain relationship by acquiring a space geometric model and material physical parameters of a target mold workpiece; performing grid division on the point cloud model, extracting curvature features and local elastic modulus of each grid unit, and constructing a parameter matrix; in combination with the initial track of the preset tool path, modulating by adopting a self-adaptive control algorithm to generate a corrected path; in the machining process, a control vector is constructed based on the corrected path and the dotting depth and the scribing force which are fed back in real time, an execution mechanism is driven to conduct closed-loop control, and dynamic compensation of each dotting and scribing action is achieved; the method can effectively solve the problems of uneven dotting depth, lineation deformation and quick tool abrasion in the machining process of complex curved surfaces or high-strength materials, and the machining precision and stability are remarkably improved.

The invention relates to a collision detection processing method and device, equipment and a medium. The method comprises the following steps: in response to a collision detection event triggered in a game scene, determining a first time window corresponding to the event based on a first thread; on the basis of a first timestamp corresponding to the first time window, acquiring spatial geometric information corresponding to all colliders contained in a detection range corresponding to the collision detection event; the second thread performs collision detection on each collision body according to the spatial geometric information, and returns a corresponding collision detection result to the first thread; and in response to a trigger event that the first time window reaches the second time stamp, the first thread determines whether collision occurs between the colliders based on the collision detection result. According to the method and the device, the collision detection calculation task is separated to the independent threads to be executed at the first timestamp, and the collision detection result is uniformly processed at the second timestamp, so that the accuracy and the stability of collision judgment logic are ensured while the game running efficiency is remarkably improved.

The invention relates to the technical field of defect detection imaging, and discloses a high-precision three-dimensional imaging method and system based on continuous magnetic flux leakage data. Collecting surface magnetic flux leakage signals of a measured target to form an initial signal set, and extracting feature vectors by using a convolutional neural network model; separating three-dimensional space data, and obtaining initial three-dimensional position coordinates of the defect through an iterative mapping algorithm; mining spatial geometric features, determining potential geometric boundaries, and fusing data to construct a refined position model; extracting a detection subset, correcting a shape error, constructing a space reconstruction grid, filling holes, and obtaining a complete three-dimensional shape representation; and through precision calibration and signal matching verification, a high-precision three-dimensional image is output. According to the method, the defect positioning and shape reconstruction precision can be effectively improved, the imaging integrity and accuracy are guaranteed, the problems of fuzzy positioning and shape distortion of traditional magnetic flux leakage imaging are solved, and the defect detection requirements of a complex detected target are met.

The invention provides a ground laser positioning mountainous area multi-machine lifting cooperative control method and system, and relates to the technical field of cooperative control, and the method comprises the steps: 1, calculating a first real-time pose of an aircraft through a laser positioning signal transmitted by a laser positioning base station; according to the image data of the three optical identification points, calculating spatial three-dimensional coordinates of the three optical identification points to define a spatial geometric curved surface patch, and decomposing the curved surface patch into a strong signal manifold and a weak signal homotopy group; 2, taking the optical identification point of the central point of the upper edge of the front end surface of the load structure as a spatial reference origin, and connecting the two optical identification points of the left vertex and the right vertex of the lower edge of the rear end surface to form a first spatial vector and a second spatial vector; through laser positioning signal partitioning, load pose multi-source fusion optimization and dynamic compensation control of the Lyapunov potential function, the method adapts to signal shielding and attenuation scenes of mountainous complex terrains, inhibits load swing, reduces the risk of obstacle scraping and collision, and improves the safety of multi-machine lifting.

In various examples, optical depth estimation for interior space monitoring systems and applications is disclosed. Absolute 3D depth estimates from monocular image data may be generated using a machine learning model using 3D geometry priors and a joint learning framework that combines depth estimation with object segmentation. Three-dimensional geometry priors provide surface-level information that enriches the model's understanding of the relevant spatial geometry and resolves scale ambiguity in monocular depth estimation within automotive in-cabin environments. The model may include a common (e.g., shared) encoder stage that outputs features extracted from an optical image sensor feed to separate decoder stages that include a depth estimation decoder and a segmentation decoder. Joint learning for depth estimation and segmentation tasks during training achieves a more nuanced understanding of the in-cabin environment, leading to significantly improved depth accuracy.

The invention relates to the field of engineering management, and particularly discloses a quality management system and method for engineering construction, and the method comprises the steps: firstly carrying out the preprocessing of a BIM model, and constructing a reference frame integrating visual features and spatial geometric information; when a problem image shot by a mobile terminal at a construction site is received, a computer vision and space calculation technology is introduced, a two-dimensional field image is analyzed into an accurate pose of a shooting camera in a three-dimensional BIM space, and sight line tracing is automatically performed in a BIM digital model based on the accurate pose. And intelligently inferring and retrieving candidate components which are highly matched with the perspective of the problem. And finally, on-site unstructured problem data and the unique identifier of the BIM component are firmly bound through lightweight interaction confirmation with the user, and a standardized structured problem record is obtained. Therefore, the problems of low manual positioning efficiency and unstable association can be solved, and the whole-process intelligent closed loop of quality problem data from acquisition to structured storage is realized.

This invention discloses a graph theory-based method and system for analyzing the connection, expansion, and instability of coal and rock fractures. The method includes: acquiring acoustic data through microseismic monitoring, performing location calculations and source mechanism inversion, and extracting core parameters of coal and rock fractures; comprehensively considering the geometric dimensions and directional characteristics of fractures, calculating the spatial geometric relationships between fractures, defining four topological relationships, and calculating the fracture penetration probability index (BCI); constructing a fracture network topological model based on graph theory, with fractures as graph nodes and geometric information allocated to edges, defining node types and edge directions; acquiring spatiotemporal topological attribute parameters of the fracture network, quantitatively analyzing its stage-specific time-varying trends with coal and rock deformation and the dynamic expansion characteristics of fracture network spatial propagation, identifying key modes of abrupt instability and key stages of fracture spatial expansion; and proposing a graph neural network-based fracture surface development morphology reconstruction method, providing a means to visualize the cross-scale evolution and spatial development morphology of fracture connections, expansion, and instability.

This invention discloses a 3D scene segmentation and interaction method based on 3D Gaussian, comprising: Step 1, instance discovery: inputting 3D Gaussian scene data, dividing the 3D Gaussian points into structurally coherent instance-level Gaussian groups to obtain refined instances; Step 2, instance and scene semantic assignment: selecting representative viewpoint images of refined instances and inputting them into a visual-language model to generate semantic description labels for the instances; filtering instance pairs in the scene through spatial geometric relationships, and clarifying the spatial relationship description of instance pairs through a large model, constructing a static scene graph that integrates geometric proximity relationships and instance semantic relationships, forming a structured description of all instances; Step 3, natural language-driven instance localization and interaction: receiving user input commands, combining the instance semantic description, the static scene graph, and the real-time viewpoint direction relationship during the query to perform multi-dimensional matching, determining the target instance ID, and executing interactive operations.

This invention relates to a multi-level semantic-driven visual language navigation method, belonging to the field of computer vision language navigation. The invention constructs a perception-planning-control end-to-end collaborative structure. The method involves: First, in the front-end perception stage, combining target-oriented semantic masks and spatial geometric features, a semantic enhancement potential field is constructed through morphological contour projection and a dynamic fusion mechanism to extract local candidate waypoints. Second, in the high-level planning stage, a spatiotemporal topology map is dynamically maintained, and a graph-aware cross-modal attention mechanism is used for global path planning. Finally, in the low-level action control stage, a multi-level escape mechanism is introduced, achieving proactive resolution of microscopic physical deadlocks through physical fault tolerance, semantic redirection based on near-field area ratio, and negative feedback topology pruning. This invention effectively overcomes the defects of blind exploration and significantly improves the navigation success rate and system robustness in complex continuous environments.

The application discloses a method and apparatus for spatial data processing, and relates to the technical field of data processing. One specific implementation mode of the method comprises: acquiring at least two types of spatial data sets; constructing spatial indexes for the spatial data sets; constructing spatial geometric groups in accordance with the spatial indexes and the at least two types of spatial data sets; placing the spatial geometric groups into a message queue, and subjecting the spatial geometric groups to a spatial correlation calculation by idle threads, which are threads for processing spatial data in the spatial data sets. The implementation mode can effectively balance calculation resources, reduce consumption of the calculation resources, and meanwhile improve a processing efficiency of spatial data.

The application provides a tetrahedron classification method and system based on a geological body surface grid, and relates to the technical field of geological modeling. The method comprises the following steps: acquiring closed surface grid data of a plurality of geological bodies; performing tetrahedron subdivision on the closed surface grid to obtain corresponding tetrahedron grids; performing category division on the subdivided tetrahedrons based on a zoning strategy and a priority strategy according to spatial geometric information and attribute labels of the geological bodies to obtain tetrahedron classification information; identifying contact surfaces in the generated tetrahedron grids, and generating a contact surface category matrix based on the boundary relationship between the plurality of geological bodies; and outputting a tetrahedron grid data structure containing the tetrahedron classification information and the contact surface category matrix. The application can significantly reduce manual operation, improve classification accuracy and simulation efficiency, and is suitable for complex geological numerical simulation tasks.

The application provides a class-level 6D pose estimation method based on geometric perception key points, and relates to the technical field of pose estimation. On the basis of fusing RGB image and point cloud features, a dynamic key point proposal module is designed to generate class-consistent and geometric-adaptive key points to enhance the adaptability to intra-class deformation and weak texture targets. Secondly, a spatial geometry attention module is introduced to model the spatial structure relationship between key points to optimize the key point distribution. Finally, through the geometric perception reconstruction and scale estimation module, point cloud reconstruction and scale regression are simultaneously realized under the condition of no CAD model prior, and end-to-end optimization is carried out by using multi-task loss. The experimental results on REAL275, CAMERA25 and HouseCat6D datasets show that the method of the application is superior to existing methods in IoU and pose accuracy indexes, and has stronger generalization and robustness in real complex scenes.

The application discloses a kind of deep development tomographic SAR imaging method based on Taylor linearization and classification prior feedback, obtains multiple preprocessed two-dimensional single view complex image data;Constructing city area-oriented tomographic SAR imaging model;ADMM solving framework and augmented Lagrangian function are constructed, and original variable, auxiliary variable and multiplier variable are iteratively updated;After iterative optimization, high-precision city three-dimensional point cloud imaging result is output, and three-dimensional reconstruction of city scene is realized.The application breaks through the limitation that single criterion is difficult to remove high-amplitude false target, effectively suppresses false target caused by non-stationary noise using spatial continuity prior, significantly improves the accuracy of obtaining target height information;By jointly optimizing the Taylor approximation of physical model, the prior guidance of semantic classification and the continuity constraint of spatial geometry, not only the super-resolution ability is improved, but also the smoothness and accuracy of the ground object elevation are ensured while preserving the details of complex structure.

The application provides a picture rendering method and device, computer equipment and a storage medium. Based on the spatial geometric parameters and appearance parameters of a Gaussian point, the equivalent maximum radius of each Gaussian point in a screen space is determined, and accurate bounding box information is generated accordingly. Compared with the approximate method of a traditional axis-aligned bounding box, the application can more accurately represent the actual coverage range of the Gaussian point on the screen, improve the accuracy and picture detail fidelity of the rasterization process, reduce the number of irrelevant tiles divided into the bounding box, and effectively improve the rasterization efficiency.

The present application relates to the technical field of structural mechanics analysis, in particular to a spreader saddle stress nephogram generation method and system, comprising the following steps: based on the spreader saddle regional cable segment force measurement and spatial geometric information, constructing the initial stress of the saddle body grid, obtaining the stress concentration rate distribution through homogenization and normalization, extracting the gradient mutation grid to form a sequence of demarcation points, and generating the saddle area nephogram in the HSV space mapping according to the non-uniform color scale. The present application can adaptively identify the area with the most severe stress change by introducing the analysis of stress concentration rate and its spatial gradient, and no longer relies on the fixed color scale division method, but uses the high gradient position as a dynamic anchor point to construct a non-uniform color scale sequence. The processing logic tilts the color scale resolution according to the real stress response of the structure, making the color transition in the stress concentration area more fine, and the stress peak and gradient form are clearly presented. At the same time, a wider color scale interval is used in the stress gentle area for general expression.

The application relates to the field of embodied intelligence and autonomous navigation, in particular to a wheelchair navigation method and device, equipment and medium, wherein the method comprises the following steps: acquiring a current environment image, a navigation instruction and a historical environment image of a wheelchair; extracting an environment structure feature of the current environment image, and determining whether a target environment is an indoor environment or an outdoor environment based on the environment structure feature; when the target environment is the indoor environment, generating a bird's-eye view feature map according to the current environment image, and combining the navigation instruction to navigate; when the target environment is the outdoor environment, predicting a three-dimensional point cloud, a relative pose and a driving trajectory based on the current environment image and the historical environment image, and combining the navigation instruction to navigate. Therefore, the problems of poor unified representation capability of perception, mapping and decision, insufficient spatial geometric expression and real-time performance, and lack of continuous environment updating and memory capability in the related art are solved.

The application discloses a kind of non-rigid space mapping-based multi-camera multi-target tracking methods without calibration, it is related to computer vision and intelligent video analysis technical field.The method includes: obtaining the position of target bounding box in each camera and the corresponding appearance feature representation;Extract the effective area that can be used for geometric modeling, generate binary effective area mask;Get dense displacement field and spatial correspondence confidence;Fixed step is constructed on the source camera image Sampling grid, the grid point is screened, and the high-confidence control point set from source view to target view is formed;According to the control point set, establish the cross-camera space mapping relationship under non-calibration condition;The spatial geometric similarity of target bounding box in different cameras is calculated, and the cross-camera target association is carried out by combining the appearance feature similarity, to realize the multi-camera multi-target tracking under non-calibration condition.The method can establish the cross-camera space mapping relationship, realize stable multi-camera multi-target tracking.

The present application relates to the technical field of three-dimensional packing, and more particularly to a three-dimensional packing method based on a graph neural network, a medium and an equipment, which solves the constraint perception defects of relying only on the spatial geometric state by constructing a heterogeneous relationship graph containing multiple complex constraints such as mutual exclusion, pressure bearing and temperature control, and generating relationship perception embedding vectors and global graph embedding vectors by using a graph neural network coding; the deep fusion of space and constraint information is realized by simultaneously fusing container space occupation, global constraint profiles and personalized constraint features of goods through decision state representation; the latest constraint topology and space conditions are ensured for each packing action by cyclically executing the packing action and dynamically updating the state, so that a complete packing scheme meeting multiple constraints is finally output, and the compliance and space utilization of the packing scheme are improved.

The present application belongs to the field of computer vision and artificial intelligence, and particularly relates to a four-dimensional dynamic scene representation and reconstruction method, system and device based on structural equivalence prior, aiming at solving the problems of poor spatio-temporal consistency, large calculation cost and low long sequence reconstruction accuracy in dynamic scene reconstruction. The present application comprises: decomposing the input four-dimensional voxel sequence into multiple two-dimensional feature planes containing spatial projection and spatio-temporal coupling; introducing structural equivalence prior to fuse the plane features, generating unified and continuous spatio-temporal latent space representation by establishing the internal correlation between spatial geometric gradient and time evolution; finally, using a decoding module containing residual connection to decode the latent space representation, and recovering the high-fidelity four-dimensional dynamic scene layer by layer. The present application effectively improves the consistency and accuracy of dynamic scene reconstruction by establishing an explicit spatio-temporal coupling relationship, while taking into account the calculation efficiency, and has broad application prospects in the fields of autonomous driving, augmented reality, etc.

The application discloses a kind of prediction methods of building three-dimensional vibration response, comprising the following steps: step one, the dynamic stiffness matrix of the column, beam, floor and shear wall four components in the frame-shear wall structure of building is obtained, according to spatial geometric coordinate relationship is assembled, obtains the dynamic stiffness matrix [D] of building G ;Step two, according to external force load vector [F] G And the dynamic stiffness matrix [D] G Of building, the displacement response vector [X] G Of building is obtained;Step three, according to the displacement response vector [X] G Of building obtained, according to building displacement response, the strategy of installing damping support in the bottom of building is used to carry out damping. According to the technical scheme provided by the application, the strategy of installing damping support in the bottom of building can be used to carry out damping according to building displacement response.

This application relates to a high-fidelity real-time rendering method, apparatus, device, and storage medium for dynamic digital humans. It includes receiving multimodal driving signals, mapping them to expression and pose parameters, inputting a parameterized human model to generate standard spatial geometry and skinning weights, and constructing a three-dimensional Gaussian sputtering field to deform to the target pose to obtain dynamic geometric data; arranging sparse virtual cameras in the target pose space, and simultaneously rendering and generating a sparse reference view set containing color and depth within a single rendering call; determining dense virtual viewpoints based on the display device's viewpoint parameters, and generating depth range textures for each dense viewpoint through downsampling depth reprojection and point sputtering techniques; determining ray step intervals using the depth range textures, sampling along the rays and fusing signed distance values ​​truncated from multiple viewpoints to generate surface position textures; projecting the surface positions onto sparse view sampled colors, fusing to generate a dense view color image, and adapting it for output to different display devices.

An image encoding / decoding method, a bitstream transmission method, and a computer-readable recording medium having a bitstream stored thereon are provided. An image decoding method according to the present disclosure may be an image decoding method performed by an image decoding device, and includes the steps of: configuring candidates for a spatial geometric partition mode (SGPM), each of which includes a prediction mode for an intra prediction mode of each partition partitioned by a partition mode of the SGPM; and predicting the current block based on any one of the candidates, in which a prediction mode of a first candidate among the candidates is determined as information indicating an intra block copy prediction mode of the reference position.

The present application belongs to the technical field of space geometry measurement, and particularly relates to a virtual multi-view six-degree-of-freedom pose measurement method, medium and system. The pose information of a cooperative target located in a combined field of view is obtained through coaxial cameras and a prism; a virtual camera is formed by rotating the prism; a coordinate system is established; target rotation representation is performed; a first rotation matrix is estimated; a virtual camera visual axis and a visual axis included angle are determined; a second rotation matrix is determined; a rotation error cost function is established; the rotation matrix and the rotation error cost function are represented by a quaternion; multi-rotation information fusion is performed; an exit point and an exit light vector of the prism are determined; feature point coordinates of the cooperative target are represented; determined object-side residuals are determined; an object-side residual cost function is constructed; and translation information fusion is performed. Compared with the prior art, the present application solves the problem that existing six-degree-of-freedom pose measurement devices are susceptible to noise, and the method proposed in the present scheme effectively improves measurement accuracy, adaptability and stability.

The application discloses a kind of complex geological conditions under multi-metal ore blending unit automatic division method, comprising the following steps;Step 1: collecting rock powder test data;Step 2: the rock powder test data collected is preprocessed;Step 3: based on space geometry, find out blast pile borehole boundary point by means of-shape method, form peripheral boundary polygon, and-shape method is used to approach blast pile boundary;Step 4: the blast pile borehole boundary point is expanded outward;Step 5: by means of Voronoi diagram algorithm, blast hole is divided into tessellation polygon on the level, and the range including influence area is formed;Step 6: fine ore blending unit solution division and evaluation, the inverse ratio of the square of the distance of ore block and each ore blending center is weighted, the attribution proportion of each ore block to different unit is calculated, and ore blending unit division is realized.The application realizes the whole process automation from rock powder test data acquisition processing, boundary extraction, unit division to resource quantity estimation.

The invention discloses a spatial geometric model display method and system based on advanced mathematics, which can realize full-automatic and high-fidelity conversion from an original function expression to a three-dimensional rotating curved surface by constructing an advanced mathematics symbol analysis method. A dynamic sampling strategy based on a function change rate and a quaternion space rotation mechanism are adopted, and geometric accuracy and calculation stability of complex functions, especially piecewise functions and implicit functions under any rotation axis are guaranteed; real-time rendering and mathematical formula synchronous deduction are combined, an intuitive mapping relation from algebraic expression to spatial form is established, and the understanding depth of students on the rotating curved surface generation principle is remarkably enhanced; interactive operation and a multi-level teaching auxiliary function are introduced, the sense of participation and teaching applicability of the demonstration process are improved, the method is suitable for university mathematics public courses and engineering professional basic teaching scenes, and the structural defects of an existing visualization means in the aspects of semantic comprehension, adaptation flexibility and teaching fusion degree are effectively overcome.

The invention discloses an action recognition method based on a double-geometry guidance time-frequency decoupling graph convolutional network, relates to the technical field of human body behavior recognition, and aims to jointly enhance the spatial geometry and time dynamic representation capability of human body actions and overcome the limitation of the prior art. The core of the method comprises a double-geometry refining attitude guidance module and a time-frequency decoupling dynamic coding module, the double-geometry refining attitude guidance module innovatively fuses Euclidean space diagram convolution and Riemannian manifold diagram convolution, and the modeling ability and analysis efficiency of the model for a high-order cooperative relationship in complex skeleton geometry are significantly improved. Therefore, the spatial dependency can be understood more deeply; the time-frequency decoupling dynamic coding module ingeniously fuses capture of time-domain transient response and extraction of a frequency-domain rhythm mode, especially through introduction of an innovative rhythm-KAN module, complementary modeling of short-time dynamic and long-time rhythm is achieved, and the ability of depicting diversified human body dynamic is comprehensively enhanced.

The invention provides intelligent surveying equipment and method for traditional wading heritage facilities, and relates to the technical field of heritage facility surveying and protection. The method comprises the following steps: deploying multi-source sensing equipment in a target facility area, collecting data including a space geometric structure, a surface image and wall humidity, and performing fusion processing to form a unified fusion data set; inputting the fused data set into a pre-trained hidden danger recognition model, and comprehensively analyzing multi-dimensional features to recognize hidden dangers such as cracks and leakage; constructing a facility reconstruction three-dimensional model based on the hidden danger distribution characteristics and the fusion data, wherein the model simultaneously comprises a geometric structure, seepage information and a hidden danger space position; and determining a structure risk level and a potential instability position by combining the model. According to the technical scheme, the data integrity and the defect identification precision can be improved, the risk assessment is more reliable and refined, and the intelligent level and the decision support capability of the wading heritage facility exploration can be effectively improved.

The invention discloses a self-calibration surface normal field reconstruction method and device based on asynchronous event streams. The method comprises the following steps: controlling an illumination unit to actively scan to generate a time-varying illumination field, and acquiring an event stream through a monocular asynchronous vision sensor; taking the ratio of the ambient light to the surface albedo as a fourth component to construct a joint state vector containing a three-dimensional surface normal vector, deducing the orthogonal constraint of the joint state vector and a four-dimensional differential observation vector, and realizing mathematical decoupling of ambient light interference; representing a time-varying illumination track as weighted superposition of discrete anchor points by adopting a parameterized basis function with a local support characteristic, and establishing a corresponding relation between an asynchronous event and global illumination; eliminating generalized bas-relief transformation fuzziness by utilizing rigid constraint provided by preset spatial geometric configuration of the lighting unit; and the surface normal field and illumination parameters are solved at the same time through global joint iterative optimization. According to the method, precise illumination calibration is not needed, and low-data-bandwidth and high-dynamic-range surface reconstruction can be achieved under the non-darkroom condition.

The invention relates to the technical field of map generation, in particular to a topographic map intelligent labeling and generating method based on a knowledge graph, and the method comprises the steps: obtaining data in real time; constructing a knowledge graph; generating a to-be-labeled element; carrying out preliminary annotation integration; carrying out low-confidence correction; dynamically updating the annotation; generating a primary topographic map; adjusting the historical duration; and generating a final plate. According to the method, a knowledge graph structure is introduced, spatial geometry, texture and spectral features of image data are fused with semantic features in text terrain description information, a feature vector library and hierarchical attribute relationship is constructed, and a labeling confidence calculation and dynamic updating mechanism is introduced in a labeling stage; and the spatial distribution and feature difference degree of the low-confidence region is corrected, so that the problems of inaccurate terrain change capture and limited labeling precision caused by single data source processing, static knowledge representation and insufficient utilization of multi-temporal data are effectively solved.