115 results about "Spatial intelligence" patented technology

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention provides a spatial intelligent three-dimensional modeling method based on a two-dimensional structure design sketch image, and the method comprises the steps: carrying out the preprocessing of an input two-dimensional structure design sketch image, and obtaining a preprocessed design sketch image, extracting two-dimensional structure design features from the preprocessed design sketch image, and encoding the two-dimensional structure design features to generate structured tensor representation; deducing space components, function partitions and constraint logic based on structured tensor representation to construct a sketch semantic graph, nodes of the sketch semantic graph representing physical structure units, and edges of the sketch semantic graph representing connection relations and stress constraints among the physical structure units; determining a node embedding vector corresponding to each node in the sketch semantic graph so as to perform three-dimensional space coding on the node embedding vector to obtain a node potential vector, and generating a geometric reasoning sequence during three-dimensional structure geometric reasoning according to a mechanical logic definition on the basis of edges in the sketch semantic graph, and performing three-dimensional structure geometric reasoning based on the node potential vectors and the geometric reasoning sequence to generate a three-dimensional structure model.

The invention provides an urban space intelligent processing method based on multi-modal fusion, and the method comprises the steps: taking multi-modal data as input, and constructing a unified data stream processing and feature alignment mechanism; a physical space is used as a core framework, and the multi-modal data is converted into a space behavior graph with space-time position semantics; constructing an entity attribute-relation type-influence weight ternary interaction model on the basis of an interaction layer of the spatial behavior map, and analyzing a human, object and environment ternary interaction relation in the city and the park based on the ternary interaction model; designing a space intelligent engine with time sequence modeling and dynamic prediction capabilities; and constructing a task processing system. According to the invention, through deep combination of multi-modal fusion and a space intelligent technology, full-link upgrading of urban space from data perception to intelligent decision is realized, and powerful technical support is provided for fine management, efficient operation and safety guarantee of complex space scenes.

The invention provides a beach big data geographic space intelligent application service method based on a large model Agent, and relates to the technical field of geographic space data artificial intelligence processing and analysis. According to the method, a distributed platform comprising a parallel computing cluster, a spatial database and a large language model is constructed, and standardized storage and metadata management of multi-source remote sensing, vector and attribute data are supported. The system generates a spatio-temporal query instruction through natural language analysis, completes cross-source matching and spatial calculation, outputs an analysis result in a multi-modal form such as a map, a chart and a text, and realizes intelligent and interactive service of geographic data processing.

The invention relates to the technical field, in particular to a multi-mode classroom teaching optimization system integrating space intelligent management and somatosensory annotation. According to the technical scheme, the system comprises a space intelligent management module which dynamically adjusts the environment through a genetic algorithm to enable the comfort level index to be optimal, a somatosensory annotation interaction module which is used for recognizing gesture tracks of teachers and students and automatically controlling teaching equipment according to the intention of a user, and a multi-modal data fusion module which is used for carrying out data fusion on the teaching equipment according to the intent of the user. The module improves the accuracy of data analysis and the robustness of system interaction, and the optimization scheduling and resource allocation module optimizes teaching resource allocation based on a Lagrange multiplier method and intelligently schedules resources. Through intelligent environment management, multi-modal data fusion, high-precision gesture recognition and an optimization algorithm, the intelligent level of classroom teaching is improved, and more advanced technical support is provided for future intelligent education.

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

The invention relates to the technical field of robot perception and data generation technologies, in particular to a first-view-angle drilling method and device based on memory enhancement and a storage medium, and the method comprises the steps: extracting a plurality of target frames from a historical video stream stored in a space intelligent machine, obtaining a plurality of memory elements based on the plurality of target frames, performing three-dimensional reconstruction on each target frame, constructing a target world model, planning a first visual angle track of the target robot in the target world model, performing imaging simulation on the target robot, performing real-time rendering on a first visual angle video of the target robot, and performing real-time rendering on a second visual angle video of the target robot based on the same time axis and action script as the first visual angle video. A third-person video corresponding to the space intelligent machine is generated, and a drilling result is output; according to the method, the long-term memory data of the space intelligent machine is ingeniously used, high-quality drilling data can be quickly generated, the reliability of the first-view video is improved, and a reliable basis is provided for training and testing of a robot algorithm.

The invention provides a space intelligent non-planar reflection generation method based on surface normal guidance. Extracting multi-layer visual representation from an original RGB image containing a non-planar reflection medium scene, and identifying a non-planar reflection area by combining brightness distribution of the original RGB image and a spatial symmetry relation; analyzing non-linear deformation features of textures in the non-planar reflection area, and predicting pixel-level three-dimensional surface normal vectors and confidence distribution in combination with multi-layer visual representation to construct a surface normal graph; determining a line-of-sight vector based on a camera imaging model, and executing geometrical optical mapping operation on the line-of-sight vector, the surface normal graph and the confidence distribution to construct a reflection offset field; extracting an intermediate semantic feature representation corresponding to the non-planar reflection region, and generating a pre-distortion reflection feature representation according to the reflection offset field and the intermediate semantic feature representation; and injecting the pre-distortion reflection feature representation, the multi-layer visual representation and the initial reflection area mask into a generative model for image reconstruction, and generating a non-planar reflection image.

The invention discloses a space chain reasoning-based space intelligent multi-modal space understanding and planning method, relates to the field of space intelligence, solves the problem that the existing space intelligent multi-modal reliability and interpretability are insufficient, and comprises the following steps: S1, obtaining multi-modal space data; s2, constructing a spatial semantic state diagram; s3, task nodes are extracted, the spatial semantic state diagram is traversed, a logical reasoning list is constructed, and a reasoning track is obtained; s4, performing simulation operation on the motion subject model in the space world model based on the reasoning track, recording a simulation position and a simulation operation state, and optimizing the reasoning track to obtain an implementation track; s5, constructing a space cost function, and dynamically optimizing the implementation trajectory according to the space cost function; s6, the reasoning track is corrected; according to the method, the reliability and interpretability of space intelligence are effectively improved in a space chain reasoning mode.

A comprehensive spatial AI platform for neural 3D reconstruction and built environment analysis integrates foundation models, deep learning methods, and spatial reasoning capabilities to provide expert knowledge of the physical world. The platform combines symbolic AI and machine learning to facilitate 3D semantics for insurance, real estate, construction, robotics, and other business applications. The system processes captured images, videos, or point clouds through neural networks with low compute requirements, incorporating device-agnostic advanced spatial intelligence that delivers geometric, semantic, and relational data. The platform includes proprietary training innovations, comprehensive measurement and semantic understanding, external sensor integration, human-in-the-loop quality assurance, and API integration for programmatic access. Advanced spatial reasoning capabilities enable property damage assessments, construction progress tracking, robotics navigation, and real-time applications including room dimension validation and automated repair estimates, supporting enterprise workflows across various industry segments while enabling productivity improvements and new value-added spatial AI use cases.

The invention provides a traffic risk processing method and system based on a spatial intelligent scene, and relates to the technical field of traffic management. The method comprises the following steps: a cloud sends an inspection strategy to inspection equipment, the inspection equipment acquires multi-source heterogeneous data of a to-be-detected area according to the inspection strategy, constructs a digital twin initial model of the to-be-detected area according to the multi-source heterogeneous data, and uploads the digital twin initial model and the multi-source heterogeneous data to the cloud; and the cloud constructs a digital twin target model of the to-be-detected area according to the digital twin initial model and the multi-source heterogeneous data, determines a traffic risk level of the to-be-detected area according to the digital twin target model of the to-be-detected area, and issues a risk processing strategy matched with the traffic risk level to the inspection equipment and the risk processing equipment. The invention aims to improve the problems that the traffic condition monitoring is not comprehensive, the risk early warning is lagged, the emergency disposal efficiency is low, and the overall traffic safety level is influenced in the existing method.

The invention relates to the technical field of data processing, in particular to a space intelligent reasoning method and system, and the method comprises the steps: generating a plurality of initial space reasoning tasks corresponding to a to-be-executed task instruction of a robot according to a pre-constructed space intelligent machine, and determining a first type of reasoning tasks and a second type of reasoning tasks from the initial space reasoning tasks, obtaining target space environment information corresponding to the first type of reasoning tasks and the second type of reasoning tasks, and obtaining structured data corresponding to the first type of reasoning tasks and the second type of reasoning tasks through a deep learning model, performing calculation according to the structured data corresponding to each first type of reasoning task by using a target calculation program corresponding to each first type of reasoning task in the sandbox simulator to obtain an accurate space prediction result; through the combination of the deep learning model and the sandbox simulator, the calculation accuracy can be improved, and the accuracy and reliability of overall space intelligent reasoning are enhanced.

The invention discloses a multi-dimensional network space security intelligent analysis and early warning platform, which relates to the technical field of network security intelligent analysis and early warning, and comprises the following steps: a multi-source network data acquisition module acquires multi-level data and encrypts and gathers an association identifier; the data preprocessing and standardization module filters invalid data to unify a format, and encrypts sensitive information to generate a verification abstract; the multi-dimensional feature extraction module extracts multiple types of features and maps the features to the same vector space; the intelligent security analysis module analyzes threats in combination with a deep learning model and a rule engine; the grading early warning and response module triggers early warning according to grades; the distributed data storage and traceability module adopts a hybrid architecture to ensure that data cannot be tampered and traced; and the visual interaction and management module presents the state in multiple dimensions. According to the platform, multi-source data are integrated to realize multi-dimensional collaborative analysis, and an early warning threshold is dynamically adjusted to avoid false alarm and missing alarm; strategy dynamic optimization, compliance adaptation and full-process tracing are realized, and the network security protection capability is comprehensively enhanced.

The invention relates to the technical field of computer data processing and artificial intelligence, in particular to a spatial intelligent data expression design and definition mode highly suitable for a large model, comprising: receiving multi-modal observation data as input; a probability deduction mechanism based on a generative large model; defining a probabilistic feature for each of the atomized spatial entities; constructing a geometric shape of the atomized space entity by adopting a semantic primitive; and generating a spatial entity objectification expression model containing the probabilistic features and the semantic primitives. According to the method, grid patches with high lexical element consumption are abandoned, and the geometric morphology is constructed by adopting the semantic primitives, so that the spatial data can be understood, generated and stored by a large model at extremely low lexical element cost.

The invention provides a spatial intelligent visual physical process inference method based on an implicit physical large model, and belongs to the field of spatial intelligent and artificial intelligence modeling calculation. The problems of low prediction precision and lack of physical consistency for complex physical scenes in the prior art are solved. The method comprises the following specific steps: acquiring multi-modal data of an environment, and representing the multi-modal data in a unified coordinate system; preprocessing the multi-modal data, designing a geometric coding model, a dynamic prediction model and an energy conservation constraint, introducing a space-time attention mechanism, predicting the motion state of an object according to the multi-modal data, and obtaining prediction data of the motion state of the object; acquiring real observation data of object motion, comparing the difference between the prediction data and the real observation data, and performing correction and weight updating on the prediction data of the model through a self-adaptive residual term; according to the method, visual information and implicit physical law modeling are fused, and the self-supervised physical consistency constraint is utilized, so that automatic learning and prediction of a potential physical process are realized.

The invention relates to a ship power system high-degree-of-freedom digital twin interaction method based on space intelligence. The method comprises the steps that S1, a power system digital twin driven by multi-source data is constructed; s2, building a high-fidelity three-dimensional model of ship power system space perception; s3, multi-degree-of-freedom dynamic space virtual-real interaction design is carried out; and S4, performing three-dimensional interaction driving and resource optimization management. Compared with a traditional power system two-dimensional monitoring means, the ship power system digital twinborn space intelligent interaction method system is based on multi-level high-fidelity three-dimensional modeling and digital twinborn construction, the system global situation mastering and equipment local state accurate sensing capacity is improved, and the ship power system digital twinborn space intelligent interaction method system has the advantages of being high in practicability and easy to popularize. The multi-degree-of-freedom dynamic interaction and three-dimensional visual virtual-real mapping capability of the system based on multi-source data driving is enhanced, and a three-dimensional interaction technical architecture with a digital twinborn body as a core is provided for operation and maintenance of a ship power system.

The invention relates to the field of space intelligence, and provides a cross-scene space intelligence semantic topology generation method and system, and the method comprises the steps: introducing a transition region modeling mechanism with a transition node as a core in a cross-scene space semantic topology construction process, regions which are mutated at the same time in geometric structures, semantic attributes and topological scales among different space scenes serve as independent objects to be recognized, expanded and processed, and the problems of semantic fracture, topological distortion and space incoherence caused by simple splicing only in the scene boundary level in the prior art are solved. Transition nodes are subjected to structure expansion based on the topological connection relation, a transition area reflecting the communication relation of different space scenes is constructed, and semantic transition consistency constraint and topological structure deformation compensation are implemented in the transition area. In addition, in combination with a multi-scale topology adaptive adjustment mechanism, the cross-scene semantic topology can dynamically adjust the node granularity and the topology level under different spatial scales.

The invention belongs to the technical field of space intelligent modeling, and relates to a space intelligent world modeling method based on a global NURBS parameter domain. Consistent mapping and management are carried out on the multi-Patch geometric objects in a global NURBS parameter domain; generating and optimizing a control point matrix and a weight matrix for geometric expression based on a global NURBS parameter domain; realizing automatic smooth transition of the multi-Patch geometry in the splicing area based on a continuity keeping mechanism of curvature and normal constraint; executing NURBS curved surface subdivision operation according to the curvature change rate and the geometric error threshold value; and performing global solution on the control point matrixes of all the Patches, and outputting a space intelligent world model which is continuous and differentiable in a global range and has consistent parameters. According to the method, the continuity and controllability of geometric modeling are remarkably improved. The invention further provides a spatial intelligent world modeling system based on the global NURBS parameter domain.

A comprehensive spatial AI platform for neural 3D reconstruction and built environment analysis integrates foundation models, deep learning methods, and spatial reasoning capabilities to provide expert knowledge of the physical world. The platform combines symbolic AI and machine learning to facilitate 3D semantics for insurance, real estate, construction, robotics, and other business applications. The system processes captured images, videos, or point clouds through neural networks with low compute requirements, incorporating device-agnostic advanced spatial intelligence that delivers geometric, semantic, and relational data. The platform includes proprietary training innovations, comprehensive measurement and semantic understanding, external sensor integration, human-in-the-loop quality assurance, and API integration for programmatic access. Advanced spatial reasoning capabilities enable property damage assessments, construction progress tracking, robotics navigation, and real-time applications including room dimension validation and automated repair estimates, supporting enterprise workflows across various industry segments while enabling productivity improvements and new value-added spatial AI use cases.

The invention relates to a visual language model training method and system, and the method comprises the steps: providing a multi-modal geometric training data set which comprises a plurality of data units, each data unit comprises digital image data for encoding a geometric figure, a section of text data for describing a question to the geometric figure and a standardized answer encoded in a markup language format supporting machine analysis; generating one or more candidate answers according to the text data and the digital image data in the geometric training data set by applying a reinforcement learning framework and taking the visual language model as a strategy; for each generated candidate answer, a reward function module is applied to allocate a reward value. According to the method, generalized space intelligent improvement is realized, the effectiveness and robustness of training are improved, and the model can be encouraged to explore different problem solving paths and find a correct reasoning chain.

The invention relates to the technical field of intelligent sensing systems, in particular to a scene difference information storage method and system, and the method comprises the steps: recognizing a plurality of target objects from a scene image set according to the scene image set collected by a space intelligent machine and the obtained space structure dynamic information, and carrying out the storage of the scene difference information for any target object; on the basis of the spatial structure dynamic information, a preset difference recognition algorithm is adopted for target objects to recognize difference information of the target objects in multiple dimensions, the difference information of each target object in different dimensions is input into a preset world model, and a target difference feature vector corresponding to the scene image set is output, the target difference feature vector corresponding to the scene image set and preset original scene data are stored in the space intelligent machine in an associated mode; the complex scene is subjected to difference analysis and storage through the high-dimensional spatial data, real-time and accurate scene difference analysis can be realized, and efficient query and reasoning of subsequent tasks are facilitated.

The invention provides a building information model and city information model cross-scale consistency three-dimensional modeling method based on space intelligence. The method comprises the following steps: extracting a three-dimensional geometric patch set from an original building information model, constructing an initial modeling object and a component space diagram, and calculating a transformation operator through footprint matching to realize automatic alignment; the user-defined family attributes are analyzed to execute cross-scale semantic mapping, semantic classification information is generated, and a building shell model is generated through geometric generalization processing; opening features are extracted and mapped to the shell model, three-dimensional topology reconstruction is executed in combination with the component space diagram, and city information model target components with topology closing performance are generated. And finally, performing incremental updating based on the alignment deviation. According to the method, the problems of inaccurate geographic alignment, geometric degradation and semantic loss in cross-scale model fusion are solved, and the logic leakproofness and data maintenance efficiency of urban digital twin base modeling are effectively improved.

The invention discloses a spatial intelligent scene early warning method and system based on three-layer cooperative constraint, relates to the field of spatial modeling, and solves the problem that the spatial intelligent scene early warning method is poor in early warning effect. S2, establishing a scene three-dimensional coordinate system and a model three-dimensional coordinate system according to an acquisition result, performing interactive analysis on the scene three-dimensional coordinate system and the model three-dimensional coordinate system, and obtaining regional laser point division data according to an analysis result; s3, performing scene intelligent analysis on the target early-warning area according to the early-warning area real-time interaction model, issuing space intelligent early warning according to an analysis result, and establishing an early-warning area real-time interaction model on the basis of the model edge sampling points. According to the invention, the accuracy of a space intelligent early warning result can be improved.

The application discloses a kind of spatial intelligence three-dimensional reconstruction method and system based on cross-scale topological consistency, it is related to spatial intelligence field, solve the problem that existing spatial intelligence three-dimensional reconstruction technology exists three-dimensional reconstruction effect is not good, including steps S1: acquisition area plan image is carried out to modeling region space marking, obtain modeling region plan image, to target modeling region preliminary creation three-dimensional model, obtain target region preliminary three-dimensional model, step S2: target region preliminary three-dimensional model is verified with target modeling region scene geometry topological, according to the topological element repair of target region preliminary three-dimensional model according to verification result, obtain target region repair three-dimensional model, step S3: according to the real-time color data of target modeling region corresponding to target region preliminary three-dimensional model is carried out real-time color restoration, obtain spatial region three-dimensional reconstruction model, the application improves the structural integrity and geometric accuracy of three-dimensional model under complex scene.

This invention provides a CIM model generation method and system based on spatial intelligence and visual knowledge fingerprints, belonging to the field of smart city digital twins; it solves the problem of low efficiency in building texture generation; specifically as follows: edge detection is performed on the top view of the original CIM model, and the plot boundaries are synthesized with the Canny edge map to generate the building layout; planning indicators are extracted from the GIS data of the target plot, and a white model is generated using a modeling engine based on the building layout; the white model is automatically UV unwrapped, and a semantic segmentation map is output based on constraint rules and construction generation logic, and then encoded based on the visual features parsed from the target plot CIM model to generate texture maps; the generated texture maps are remapped back to the surface of the CIM3 level white model to obtain a generative reconstruction model; this invention realizes the continuous regeneration of CIM data features based on spatial intelligence, and solves the contradiction between privacy and utility by resolving spatial logic reasoning and generation.

The invention relates to the technical field of power station intelligent monitoring, and discloses a power station health state abnormity AI monitoring method based on large model data analysis. The method comprises the following steps: synchronously acquiring multi-modal operation data and environment background information of a power station, constructing a power station global operation situation topological graph reflecting a dynamic association relationship between equipment, and extracting multi-granularity abnormal symptoms from the topological graph; and analyzing historical fault cases by using the large model, and constructing and dynamically updating an exception knowledge base containing fault evolution modes and strategies. And performing matching reasoning on the real-time abnormal symptom and the knowledge base mode to generate a multi-layer abnormal decision flow. The decision flow is fused and optimized through a collaborative analysis engine, a monitoring conclusion is formed, a power station health state portrait and a corresponding operation and maintenance decision cluster are output, a complete number sequence physical model is intelligently constructed for the space of an intelligent power station, and accurate perception and intelligent diagnosis of overall and relevance abnormity of the power station are achieved.

The application discloses a mineral prediction method based on spatial intelligence, relates to the technical field of spatial intelligence, and comprises the following steps: identifying the spatial relationship of a spatial entity element list, constructing a topological connected graph, extracting connected structure attributes, executing semantic relationship reasoning, and generating an object-level spatial intelligent scene graph; based on the object-level spatial intelligent scene graph, constructing an anisotropic cost field, calculating a minimum cost channel distance, mapping into a channel weight expression, and generating a channel weight layer; performing spatial alignment and multi-modal spatial correlation feature fusion on a multi-source mineralization evidence data set, the object-level spatial intelligent scene graph and the channel weight layer, executing mineral inference through a mineral prediction machine learning model, and generating a mineral prediction layer. The application realizes dynamic modeling of a mineralization path, and improves prediction accuracy and engineering practicability.

The present invention provides a space intelligent flying robot in a microgravity-normal air environment, belonging to the field of flying robots. To achieve the goal of enabling the space intelligent flying robot in a microgravity environment to have independent control over position, posture, output force, and torque. The robot comprises a carrier main frame, an outer shell, a computing system disposed within the carrier main frame, a visual navigation system disposed in front of and behind the carrier main frame, a laser ranging module embedded in the outer shell, a power supply system disposed on the left and right sides of the carrier main frame, a power system embedded in the outer shell and within the carrier main frame, a gripping system disposed below the carrier main frame, and an interactive system disposed in front of and above the main frame. While realizing basic functions, the present invention also makes innovative designs in the robot architecture, optimizing the robot's performance in manipulation, control, perception, and real-time performance, and has high engineering application value and good engineering application prospects.

The invention discloses a scene multi-dimensional reconstruction method fusing data operation and space intelligence, and relates to the technical field of space-time semantic reconstruction, and the method comprises the steps: fusing a panoramic image, business data and a spatial point cloud, and constructing a semantic enhancement three-dimensional point cloud and a business process knowledge graph; and marking a space operation area corresponding to the service node in the point cloud under the guidance of the map. And coding the node timestamps and the service states into time-varying attribute vectors of the spatial points, and reconstructing to generate a dynamic three-dimensional space-time voxel field. And a knowledge graph topology is embedded in the voxel field to form a space-time semantic field, and multi-dimensional subdivision is performed to obtain associated slices of space, time and business dimensions, so that a scene multi-dimensional reconstruction model is constructed. According to the method, unified representation of service logic, physical space and time evolution is realized, and multidimensional analysis and insight based on a world model are supported.