1256 results about "Tree based" patented technology

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention discloses a large model reasoning method and system based on tree diagram and knowledge graph retrieval enhancement, and the method comprises the steps: segmenting a document into text blocks, recognizing the entities, relationships and factual descriptions of the text blocks, and forming a knowledge graph of the text blocks; constructing a factual summary of the documents based on the knowledge graph of the text blocks, and performing clustering recursion on all the documents by using a Gaussian mixture model to generate a similarity tree structure graph; the method comprises the following steps: obtaining a user problem, selecting a reasoning path from a similarity tree structure diagram through a large language model debate iteration method, obtaining a corresponding document cluster, pruning the cluster based on the user problem and a confidence score of a document, selecting a plurality of text blocks from the pruned document, and finally obtaining a knowledge graph based on the selected text blocks. And generating a reasoning result. The invention relates to the technical field of natural language processing, can realize retrieval enhancement based on a tree diagram and a knowledge graph, and accurately queries related information when a large model answers a complex multi-hop question, so that the reasoning accuracy is effectively ensured.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention provides a sparse matrix distributed solution method, computer equipment and a storage medium, which are used for simulation of a circuit and an electromagnetic field, and the method comprises the following steps: generating a super node based on an elimination tree of a matrix generated in a simulation process; constructing an assembly tree according to the elimination tree and the super nodes; constructing a wavefront matrix according to the rows and columns of the matrix corresponding to the super nodes; traversing the assembly tree layer by layer, and carrying out parallel calculation on complete assembly blocks and incomplete assembly blocks of the wavefront matrix on the same layer of the assembly tree; according to the incomplete assembly blocks of each layer, the complete assembly blocks of the next layer of the assembly tree are updated until the last layer of the assembly tree is reached; and ending the traversal of the assembly tree, and solving an equation LUx = b by using the complete assembly blocks of all the wavefront matrixes. According to the scheme, the solving speed can be greatly improved, memory occupation is reduced, and simulation of a circuit and an electromagnetic field is facilitated.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

The invention relates to an optimization method and device for large-scale graph data visual rendering and a storage medium, and the method comprises the steps: obtaining to-be-rendered graph data, constructing an R-tree spatial index according to the to-be-rendered graph data, and constructing a self-adaptive fragmentation model based on an R-tree data structure; monitoring interaction data of a user in real time, performing viewport prediction according to the interaction data, and determining a to-be-loaded sub-graph according to a prediction result and the adaptive fragmentation model; the method comprises the following steps: acquiring system data of client equipment, analyzing the performance of the client equipment according to the system data, and determining a rendering strategy according to a performance analysis result; and performing off-screen rendering on the to-be-loaded sub-graph according to the rendering strategy, and displaying an off-screen rendering result on the client device after detecting that the user completes the corresponding operation. According to the method, the rendering granularity is dynamically adjusted through the adaptive fragmentation model, and the rendering quality and performance consumption are balanced; the user interaction intention is pre-judged through a viewport prediction algorithm, and window smooth switching is achieved in combination with off-screen rendering.

The invention provides a distributed network data integrity verification method and device based on a block chain. The method comprises the steps that original data is divided into m data blocks, each data block comprises n data units, the initial hash value of each data block is calculated, and the initial hash value is uploaded to a block chain network; uploading the data block to be verified to the block chain network, and recalculating the hash value of the data block; preliminarily verifying the integrity of each data block according to the determined verification parameter and a preset threshold value, and judging whether the data block meets a preliminary integrity condition or not; for the data blocks meeting the preliminary integrity condition, constructing a ternary tree according to the hash value of each data unit; judging whether the data in the data block meets a complete condition or not; and when all the segmented data blocks meet a complete integrity condition, judging that the original data is complete. The problems that a traditional data integrity verification method is low in efficiency and prone to being attacked are effectively solved.

To automatically generate an FTA real-time dynamic tree based on a fault knowledge base.SOLUTION: In step 1, an FTA logic model is newly constructed by a method for generating an FTA real-time dynamic tree based on a fault knowledge base. In step 2, a fault knowledge base model is selected based on a logical device. In step 3, a fault library is constructed based on the FTA logic model to generate a logic rule for an FTA model, and the fault knowledge base model is processed to perform decomposition-mapping into an FTA logic fault tree model. In step 4, the fault diagnosis tool for the event node is set. In step 5, a formation of an FTA logic fault tree is completed. In step 6, the FTA logic model is published, and the FTA logic model is instantiated. In step 7, a scheduling task is configured. In step 8, the configured scheduling task performs a periodic fault diagnosis and issues a fault alarm.SELECTED DRAWING: Figure 1

The invention relates to the technical field of power systems, in particular to a power transmission line tree obstacle hidden danger detection method and system considering the wind deflection influence and a medium, and the method comprises the steps: carrying out the tree growth prediction according to an original multi-source data set, and obtaining a tree growth prediction result; performing three-dimensional windage yaw effect analysis based on the tree growth prediction result and the original multi-source data set to obtain a tree windage yaw quantification result; respectively calculating the risk time of the tree in the vertical direction, the horizontal direction and the inclined direction according to the tree windage yaw quantification result; evaluating a comprehensive risk index between the tree and the power transmission line according to the risk time to obtain a tree barrier hidden danger degree; and taking minimization of the total pruning amount and maximization of the safety margin as optimization objectives, generating a tree pruning strategy based on the hazard degree of the tree obstacle hidden danger, and pruning the risk trees of the power transmission line according to the tree pruning strategy. According to the method, tree growth prediction and windage yaw effect quantification are carried out by fusing multi-source data, and the power transmission line fault risk caused by tree obstacles is effectively reduced.

The invention relates to the technical field of electronic digital data processing, in particular to an operation state monitoring method and system for injection molding equipment, and the method comprises the steps: collecting a multi-dimensional operation parameter time sequence of the injection molding equipment at different moments, collecting parameters of the same dimension into data sets, and constructing an isolated tree based on each data set; after sorting tree nodes of the isolated tree according to a data average value, distributing a tree node sequence number for each item of data; the combination of the dimension parameters at the same moment is regarded as a data point, the distribution relation of any two data points on the isolated tree is compared, the same-group degree of the data points is calculated, and the data points with the same-group degree higher than a set threshold value are classified into the same group; calculating the abnormal degree of the data points; and if the abnormal degree exceeds a set threshold value, determining that the data point is an abnormal data point and giving an alarm at the same time. The problem that the accuracy and reliability of state detection are not high is solved.

The invention relates to an input data quality evaluation method for photoetching process window analysis. The method comprises the following steps: S1, obtaining standardized data; s2, adopting an RANSAC regression algorithm to obtain a robust regression residual analysis confidence coefficient; s3, obtaining the confidence of the isolation tree based on the abnormal score of the isolation tree; s4, adopting a local outlier factor method to obtain local outlier factor confidence; s5, integrating the isolation tree confidence coefficient and the local outlier factor confidence coefficient to obtain an unsupervised confidence coefficient; s6, obtaining a Gaussian confidence coefficient; and S7, integrating the robust regression residual analysis confidence coefficient, the unsupervised confidence coefficient and the Gaussian confidence coefficient to obtain a comprehensive confidence coefficient, and obtaining an input data quality evaluation result for photoetching process window analysis. Compared with the prior art, the method has the advantages of improving the input data quality of photoetching process window analysis and the like.

The invention discloses an operation and maintenance fault processing method and device based on a large model and a knowledge graph, equipment and a medium, and relates to the field of operation and maintenance fault diagnosis processing, and the method comprises the steps: constructing an operation and maintenance fault processing domain ontology, and inputting historical operation and maintenance fault information and the operation and maintenance fault processing domain ontology into a preset large model to obtain a target triple, obtaining an operation and maintenance fault processing knowledge graph based on the target triple; performing dependency syntactic analysis on the historical operation and maintenance fault information to obtain a dependency syntactic tree, processing the dependency syntactic tree based on the operation and maintenance fault processing knowledge graph to obtain a first dependency syntactic tree, and obtaining a fault processing question and answer template library by using the first dependency syntactic tree; determining a second dependency syntax tree based on the operation and maintenance fault information to be processed, determining a question and answer template corresponding to the second dependency syntax tree from a question and answer template library, and instantiating the question and answer template to obtain a semantic query graph; and determining a target sub-graph corresponding to the semantic query graph from the knowledge graph, and obtaining an operation and maintenance fault processing result based on the target sub-graph.

The invention discloses a policy service recommendation method and system based on a large language model, and relates to the field of policy information, and the method comprises the steps: analyzing a policy file, extracting policy terms with a numbering structure, building a unique identifier, and forming a term node set; identifying explicit or implicit reference behaviors in terms, constructing a reference path, and generating a reference directed graph; constructing a clause mapping tree based on the target clauses; mapping tree nodes are extracted to form structured prompt information, and the structured prompt information is embedded into large language model input; generating a policy report paragraph containing clause reference content; and outputting the structured policy text with clause reference chain closure and recommending the structured policy text to the user. According to the method, the clear reference context combined with the user information can be provided for the large language model, the large language model is guided to generate a text with a reasonable structure and complete terms for recommendation, and the problem of inaccurate generation when an existing model processes a complex reference structure is solved.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention provides a building instantiation segmentation method and device, a terminal and a storage medium, and relates to the technical field of three-dimensional point cloud processing, and the method comprises the steps: carrying out the plane segmentation of a building point cloud of a target building, and extracting a potential vertical plane point cluster from a building plane point cluster obtained through the segmentation; mapping all three-dimensional point clouds in the potential vertical plane point cluster to a two-dimensional feature space to obtain a two-dimensional point set, and processing the two-dimensional point set by using a preset dual-threshold judgment mechanism to extract a building vertical structure point cluster; generating a building distance map based on the building vertical structure point clusters, and generating instance anchor points on the distance map; extracting a multi-level contour in the distance map based on the instance anchor points, constructing a tree-shaped hierarchical relationship corresponding to the multi-level contour, and constructing a mark matrix based on the tree-shaped hierarchical relationship; and building two-dimensional instantiation segmentation is carried out on the distance map based on the mark matrix. Through the unsupervised hierarchical segmentation framework, the dependence on the annotated data can be eliminated, and the segmentation precision is improved.

The embodiment of the invention provides an air-sea cross-domain communication method and device. The method comprises the following steps: fusing a multi-system communication protocol into a cross-domain communication network protocol through a water surface wireless communication multi-band access technology, and carrying out adaptive networking; the method comprises the following steps: performing dictionary table mapping on original information sent by underwater subsurface buoy sensor equipment to obtain a translated character sequence, constructing a Huffman tree according to the occurrence frequency of each character in the translated character sequence, and generating a Huffman code corresponding to each character according to the Huffman tree; the method comprises the following steps: monitoring channel noise and a channel error rate of an underwater acoustic channel in real time, determining a real-time channel state, determining a channel coding parameter adjustment strategy matched with the real-time channel state through an adaptive algorithm, and dynamically adjusting a coding mode and coding parameters of the channel according to the channel coding parameter adjustment strategy. Uploading the Huffman code to a superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters; according to the invention, the air-sea cross-domain communication efficiency can be effectively improved.

The invention discloses an automatic driving model and method based on end-to-end joint optimization, and particularly relates to the field of visual automatic driving, and the method comprises the following steps: processing an input image and a point cloud, and constructing instance features; static and dynamic interaction scene representations based on sparse instances are constructed through query and attention mechanisms; constructing a parallel prediction-planning module based on the tree structure; extracting time sequence information of a historical track to construct a probability space; and modeling and predicting a time sequence dependency relationship of the trajectory through a gating loop unit, and screening an optimal future trajectory by using a confidence score. According to the method, the dynamic feasibility and safety of the planned trajectory can be improved, meanwhile, the calculation overhead is reduced, and an efficient and robust solution is provided for actual landing of the automatic driving technology.

The invention provides a social network hierarchical visualization method and system based on a structure entropy and a coding tree, and the method comprises the steps: obtaining graph data, building the coding tree based on the graph data with the minimized structure entropy, and enabling the coding tree to be used for capturing a hierarchical community structure of a graph; constructing a minimum entropy priority queue, adaptively selecting non-leaf nodes for division by adopting the minimum entropy priority queue, and distributing a current structure entropy value as a weight for a cross-community edge; force-oriented layout based on structure entropy and layout entropy is used, attraction is adjusted based on edge weight, standard repulsive force is kept, and a force model is established; solving a layout entropy maximization target for the force model to calculate a layout result; and for the calculated layout result, outputting and rendering a layout result with a clear community separation effect as a final visualization result.

The invention relates to the technical field of data processing, in particular to an efficient transmission method for electric energy meter acquired data, which comprises the following steps: acquiring multi-dimensional feature data of an electric energy meter at each moment, and obtaining multi-dimensional real-time feature data at the current moment and a multi-dimensional historical feature data sequence within a preset historical time period; using an isolated forest algorithm to randomly select sample points to form at least one sample subset, for any sample subset, obtaining the abnormal degree of each sample point in any sample subset under each feature, obtaining a segmentation condition under each feature according to the abnormal degree, and generating an isolated tree; obtaining a normal weight of each path in the isolated tree according to fluctuation features of sample points in a sample subset corresponding to the isolated tree and differences between the sample points and the multi-dimensional real-time feature data; and the final score is obtained by using the normal weight of each path in each isolated tree, and the adjustment frequency is obtained according to the final score, so that the interference of non-system abnormal data such as noise on frequency adjustment is effectively reduced.

The invention discloses an intelligent substation configuration file automatic modeling method and system, and the method comprises the steps: receiving multi-manufacturer and multi-format configuration files, extracting the structural features of the configuration files, and carrying out the data cleaning of data records in the configuration files; constructing a configuration file analytic tree based on the structural features, and extracting the incidence relation between the key nodes; performing data standardization on the data records; key features of the data records are extracted; constructing a feature association graph according to the key features, the key nodes and the association relationship; identifying in the feature association graph to obtain a target feature, and obtaining a corresponding difference data set; training a machine learning model based on the difference data set and the feature association graph until the training frequency reaches a preset frequency threshold; performing hyper-parameter adjustment and model integration on the trained machine learning model to obtain an optimized machine learning model; and deploying the optimized machine learning model to a production environment to generate a target substation configuration file.

Techniques are described herein that are capable of performing AI-based entity maliciousness analysis using embedding and sampling. A representative sample of data associated with an entity is selected by comparing embeddings that represent the data. A potentially anomalous data point is identified in at least a portion of the data based on a proximity of a node, which corresponds to the potentially anomalous data point, in a tree to a root node of the tree. A statistically anomalous data point is identified in representative sample data points, which define the representative sample, as a result of the statistically anomalous data point indicating an unexpected occurrence of an event. An AI model is triggered to determine whether the entity exhibits malicious behavior by providing an AI prompt, including the representative sample and a description of the potentially anomalous data point and the statistically anomalous data point, to the AI model.

The invention discloses a DOM-based element identification method and device, and the method comprises the steps: S1, filtering an obtained webpage original HTML according to a set rule to obtain a simplified HTML, and constructing a preliminary DOM tree according to the simplified HTML; s2, performing element type conversion, information fusion and icon information interpretation on webpage elements in the preliminary DOM tree to obtain an extracted DOM tree; and S3, inputting the extracted DOM tree as a part of cue words into a multi-modal large language model MLLM to obtain recognized elements. On the basis of analyzing an original HTML (Hypertext Markup Language), necessary information is reserved, picture description information is supplemented, data integrity is ensured, the MLLM can better understand a webpage and predict operation through the simplified DOM tree, and the analysis efficiency is improved while the use cost is reduced.

The invention discloses a multi-model structure perception parallel strategy search method for large model post-training. The method comprises the following steps: firstly, analyzing a network structure of each model for deep learning, evaluating a model calculation graph, constructing calculation cost, communication cost and video memory cost of each model, and pre-distributing calculation power for each model; secondly, according to the model calculation graph and the calculation power allocated to each model, prefillamp is adopted for the inference model; and carrying out code separation deployment, and distributing different data parallel scales for the prefill stage and the code stage. And finally, according to the model calculation graph, the calculation cost and the video memory cost, in combination with the data parallel scale, a tree-based multi-model parallel strategy search method is adopted, and a distributed parallel strategy of each model in the post-training process is searched. According to the method, the parallel strategy with the shortest overall end-to-end time consumption is searched for all the models in parallel, so that distributed training is carried out, and the training cost is reduced.

The invention provides a human resource management interaction method, system and device and a medium, and relates to the technical field of human resource management.The method comprises the steps that firstly, organizational structure management, electronic contract signing and employee management functions are unified in a single interface through full-process integrated operation, the time cost of multi-module switching is reduced, one-stop processing is achieved, and the management efficiency is improved; the processing efficiency of the human resource business is improved; secondly, realizing dynamic organization architecture management based on a tree diagram structure, allowing real-time adjustment of a department and post hierarchical relationship, generating a version snapshot with a timestamp, ensuring real-time performance and traceability of data, and avoiding errors caused by a traditional static architecture; furthermore, the electronic contract voucher is directly associated with the employee archive, so that the consistency of contract data, the organization structure and the employee state is ensured, and the data security is enhanced; and finally, related configuration is executed by using the current organization structure version snapshot and the electronic contract certificate, and real-time linkage of organization structure change and employee adjustment is realized.

The present invention discloses a method, device, and medium for identifying software component analysis, which relates to the technical field of software technology. Specifically, it includes: obtaining all files in the software to be identified to obtain a first data set; extracting meta-file data to obtain a second data set, and obtaining a first open-source file set through first feature matching; performing deletion processing on the first data set to obtain a third data set, calculating the minimum hash signature through the program abstract syntax tree and the corresponding sub-syntax tree to generate a second feature set, and obtaining a second open-source file set through third feature matching; performing deletion processing on the third data set to obtain a fourth data set, obtaining an updated syntax tree based on the abstract syntax tree and the fourth data set and constructing a control flow diagram, and obtaining a third open-source file set through fourth feature matching between the third feature set and a preset reference database; merging the first open-source file set, the second open-source file set, and the second open-source file set to obtain the total open-source component set in the software to be identified.

The invention relates to the technical field of network information, in particular to a training method and device based on online tree search, equipment and a medium, and the method comprises the steps: carrying out the initialization processing of given prompt information based on entropy guide tree search, and generating a guide tree root; selecting a bifurcation point of a guide tree root according to the entropy value, and performing extension processing on the bifurcation point of the guide tree to obtain a tree structure; and calculating a node value in the tree structure by using a Monte Carlo method, calculating a reward signal based on the node value in the tree structure, and strengthening the tree search strategy model. The exploration diversity is enhanced through tree search, the learning efficiency is improved through process supervision, a closed-loop optimization system is formed, the capacity of a large language model on complex reasoning tasks such as mathematics and programming is remarkably improved, and the method has wide application value.

The invention discloses an SIND network construction method and system based on KD-Tree multi-source feature fusion, and the method comprises the steps: obtaining individual QSM data, mapping the individual QSM data to a preset standardized template space to obtain a standardized brain image, and segmenting the brain image into different brain regions; encoding the QSM data of each brain region into multi-dimensional vector data, and constructing a KD tree of each brain region based on a variance-sensitive dynamic division strategy and median segmentation; calculating KL divergence of any two brain regions based on a KD tree, and determining an SIND value between the two brain regions according to the KL divergence; and carrying out dimension reduction analysis on the SIND value by adopting diffusion mapping, and determining the magnetic sensitive gradient characteristics of the brain region. According to the method, efficient data processing, multi-source feature fusion, KL divergence measurement and diffusion mapping analysis of the KD Tree are combined, the magnetic sensitive gradient features of the brain regions can be accurately analyzed, the structure and function relation between the brain regions is disclosed, and a powerful tool is provided for brain science research.

Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

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