98 results about "Spatial intelligence" patented technology

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 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 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 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 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 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.

The invention discloses a space intelligent three-dimensional reconstruction method and system based on cross-scale topological consistency, relates to the field of space intelligence, and solves the problem that the existing space intelligent three-dimensional reconstruction technology is poor in three-dimensional reconstruction effect. S2, performing scene geometric topology verification on the initial three-dimensional model of the target area and the target modeling area, performing topology element repair on the initial three-dimensional model of the target area according to a verification result, and performing three-dimensional model construction on the initial three-dimensional model of the target area to obtain an initial three-dimensional model of the target area; s3, performing real-time color restoration on the initial three-dimensional model of the target area according to the real-time color data corresponding to the target modeling area to obtain a three-dimensional reconstruction model of the space area, and improving the structural integrity and geometric accuracy of the three-dimensional model in a complex scene.

The utility model discloses a splicing type three-view building structure which comprises a display frame and a plurality of display square blocks, and a clamping block structure and a clamping groove structure are arranged on the two adjacent side edges of the display frame respectively. Crisscrossed dividing strips are arranged in the display frame and divide the interior of the display frame into N rows and N columns of square frame units, and N is larger than or equal to 3; the display square blocks are detachably placed in the square frame units; the clamping block structure comprises a plurality of clamping blocks, the clamping groove structure comprises a clamping groove strip arranged on one side of the display frame, and a plurality of clamping grooves in one-to-one correspondence with the clamping blocks of the clamping block structure are formed in the clamping groove strip in the length direction of the clamping groove strip; according to the utility model, manifestation forms of an object under different visual angles can be effectively displayed, so that children can simultaneously observe three-dimensional figures and projection views of three views, the children can be helped to establish conversion thinking between a two-dimensional plane and a three-dimensional space, and the development of early-stage space intelligence of the children is facilitated.

The application discloses a kind of based on spatial intelligence's active fault three-dimensional model automatic construction method, belong to geological exploration and earthquake engineering technical field;Method includes: collection data, analysis and extract minimum complete subdirectory;Through adaptive threshold hierarchical clustering combined with improved RANSAC algorithm, the exclusive small earthquake cluster of each fault is automatically identified;Based on local weighted regression, three-dimensional automatic slice is made to small earthquake cluster, and each profile fault interpretation line is fitted by moving least square method;Finally, active fault three-dimensional fine model is constructed;Test model rationality and output model file;The application realizes the automation, quantification of fault modeling whole process, can be fused multi-source data and realize multi-element constraint modeling, adapt complex fault zone modeling, and the fine three-dimensional model constructed can provide key data support for fault present-day deformation inversion, three-dimensional potential source research, earthquake geological disaster assessment, with higher engineering application value.

The application discloses a space intelligent multi-modal space understanding and planning method based on space chain reasoning, relates to the field of space intelligence, and solves the problem of insufficient reliability and interpretability of the existing space intelligent multi-modal, comprising the following steps: S1, acquiring multi-modal space data; S2, constructing a space semantic state graph; S3, extracting a task node, traversing the space semantic state graph, constructing a logical reasoning list, and obtaining a reasoning track; S4, simulating the operation of a motion subject model in a space world model based on the reasoning track, recording the simulated position and the simulated operation state, optimizing the reasoning track, and obtaining an implementation track; S5, constructing a space cost function, and dynamically optimizing the implementation track according to the space cost function; and S6, correcting the reasoning track; the application effectively improves the reliability and interpretability of space intelligence through the space chain reasoning mode.

The present application relates to the field of space intelligence and real-time graphics rendering, and provides a space intelligence panoramic Gauss block streaming rendering method and system, on the one hand, by structuring the space scene and using Gauss parameters for explicit expression, each rendering unit has clear spatial position, direction information and energy distribution attribute, and the explainability and traceability of the rendering process are improved; on the other hand, by using cross-block energy conservation constraint and geometric continuity correction mechanism, the problems of brightness drift between blocks, boundary fault and topological discontinuity in traditional parallel rendering are effectively eliminated. At the same time, by integrating rendering, splicing and performance feedback mechanism into a dynamic closed-loop system, the performance state of the execution body and the rendering quality index are collected in real time in each frame rendering process, and the block granularity, sampling density and illumination weight and other parameters are adaptively adjusted, so that the millisecond-level streaming output and stable continuous panoramic rendering effect are realized.

The invention provides a CIM model generation method and system based on spatial intelligence and visual knowledge fingerprints, and belongs to the field of smart city digital twinning. The problem of low building texture generation efficiency is solved; the method specifically comprises the following steps: carrying out edge detection on a top view of an original CIM model, and synthesizing a plot boundary and a Canny edge graph to generate a building layout; extracting planning indexes from the GIS data of the target plot, and generating a white model by using a modeling engine according to the building layout; performing automatic UV expansion on the white mold, outputting a semantic segmentation map based on a constraint rule and a construction generation logic, and performing coding based on visual features analyzed in a target plot CIM model to generate a texture map; remapping the generated texture map back to the surface of the CIM3-level white mold to obtain a generative reconstruction model; according to the method, feature continuity regeneration based on space intelligence of the CIM data is realized, and space logic reasoning and generation are solved, so that the contradiction between privacy and utility is solved.

This application provides a spatial intelligent non-planar reflection generation method based on surface normal guidance. Multi-layer visual representations are extracted from the original RGB image of a scene containing a non-planar reflective medium. The non-planar reflective regions are identified by combining the brightness distribution and spatial symmetry of the original RGB image. The nonlinear deformation features of the texture in the non-planar reflective regions are analyzed, and pixel-level 3D surface normal vectors and confidence distributions are predicted using the multi-layer visual representations to construct a surface normal map. A gaze vector is determined based on a camera imaging model, and geometric optics mapping operations are performed on the gaze vector, surface normal map, and confidence distribution to construct a reflection offset field. Intermediate semantic feature representations corresponding to the non-planar reflective regions are extracted, and a pre-distorted reflection feature representation is generated based on the reflection offset field and the intermediate semantic feature representation. The pre-distorted reflection feature representation, multi-layer visual representation, and initial reflective region mask are injected into the generation model for image reconstruction, generating a non-planar reflective image.

The present application relates to model information processing technical field, especially a kind of equipment inspection method and system based on space intelligence, its method is provided with to generate inspection queue associated chain, when generating associated chain, periodically collect several operating data or several fault data of each equipment, and record according to collection period;According to the connection relationship of each equipment, the basic associated chain corresponding to each equipment is formed. Through the grouping of equipment, the associated equipment is synchronously inspected, which avoids the problem that the traditional inspection method cannot determine the equipment failure caused by system problems, at the same time, the equipment is associated by using merging semantics, which avoids the problem of missing equipment operation or equipment failure caused by subjective judgment, thereby effectively determining the mapping relationship between equipment entity and digital model system, at the same time, by using the above method, the inspection queue of equipment can be adjusted through the connection between equipment systems, thereby effectively improving the accuracy of building equipment inspection.