Method and system for discounting and classifying electric power BIM (Building Information Modeling) type data GIS (Geographic Information System)

Abstract

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of power management and control technology, and specifically to a method and system for decomposing and classifying power BIM-type data in a GIS. Background Art

[0002] BIM technology uses three-dimensional models as a carrier, integrating the geometric information, functional attributes and topological relationships of building components, and can achieve refined management of the entire life cycle of buildings; GIS technology excels at collecting, storing, analyzing and visualizing geographic spatial data, and can provide support for macro-scale spatial planning and decision-making.

[0003] Under the trend of integration of building informatization and geographic information technology, the collaboration between BIM and GIS can make up for the shortcomings of a single technology in spatial dimension and detail accuracy. However, the traditional BIM classification system has the following core defects: At present, the classification of BIM components is mainly based on the size of the components (such as large, medium, small, and fine components), covering dimensions such as structure, enclosure, electromechanical, and decoration, but the classification system lacks direct connection with geographic spatial data. 1. Traditional BIM models mostly use local coordinate systems, which are different from the national geodetic coordinate systems (such as the 2000 National Geodetic Coordinate System) that GIS systems rely on, making it difficult to accurately embed models into geographic space and causing inconsistent spatial positioning; 2. The industry classification of BIM components (such as building structure and electromechanical equipment) lacks a mapping relationship with the geographic information classification standards of GIS (such as GB / T25529-2010 "Geographic Information Classification and Coding Rules"), which affects the identification and management of data across systems and the semantic standards are not compatible; 3. The traditional BIM model is not designed for the macro (city level) and micro (single building level) analysis needs of GIS. The hierarchical classification mechanism makes it difficult to dynamically adjust the level of detail (LOD) at different scales, resulting in data redundancy or information loss, and insufficient multi-scale expression; 4. There are structural differences between the commonly used BIM model formats (such as IFC, Revit files) and the mainstream GIS formats (Shapefile, GeoJSON, KML). Direct conversion can easily lead to loss of geometric accuracy, broken topological relationships or loss of semantic information. Although existing conversion methods (such as plug-in development, intermediate format conversion, and direct reading of GIS software) can achieve data migration, they face challenges such as high R&D costs, low conversion efficiency or limited compatibility, and insufficient data format compatibility.

[0004] For the power industry, BIM-GIS integration faces additional challenges: 1. Cross-scale data integration: Power facilities such as transmission lines and substations often span multiple geographic units (e.g., mountainous areas, urban areas, and farmland). This requires integrating towers and cables in BIM models with GIS data on soil types, meteorological data, and population distribution to enable scenario-based analyses such as route optimization and lightning strike risk warnings. However, existing technologies struggle to match the topology of long-distance linear BIM data with planar GIS raster data. 2. Classification gaps throughout the entire lifecycle: During the planning phase, coarse classification based on voltage level and transmission corridor is required, while during the operation and maintenance phase, detailed classification based on equipment asset code and monitoring status is required. Traditional static classification systems cannot support cross-stage data linkage and business process integration. 3. The conflict between lightweight design and professional precision: Power BIM models contain numerous components (e.g., insulators in transmission lines and valves in substations). Directly importing these components into GIS can lead to performance bottlenecks. However, common lightweighting methods (e.g., deleting small components) can inadvertently delete critical structures (e.g., tower node plates), impacting the accuracy of mechanical analysis and fault simulation.

[0005] Existing technologies lack a dynamic classification framework and intelligent lightweight strategy for the power industry, resulting in the inability of BIM models to efficiently adapt to the spatial analysis needs of GIS. Therefore, we propose a method and system for GIS decomposition and classification of power BIM-type data to solve the above-mentioned problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for decomposing and classifying power BIM-type data in GIS, so as to solve the problem proposed in the above background technology that the existing technology lacks a dynamic classification framework and intelligent lightweight strategy for the power industry, resulting in the inability of BIM models to efficiently adapt to the spatial analysis needs of GIS.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for decomposing and classifying electric power BIM data in GIS includes the following steps: Step 1: Build a classification system and semantic mapping specific to the power industry. Based on GIS geographic information standards and power industry standards, establish a three-level classification framework: First-level classification: connect to GIS categories and divide into "power buildings," "power structures," "power equipment," and "ancillary facilities"; Second-level classification: refine professional dimensions, expanding "power equipment" to "transmission equipment," "transformer equipment," and "distribution equipment"; Third-level classification: define to the component level, "transmission equipment → transmission towers → linear towers / tension towers"; Semantic mapping: match GIS classification paths and attributes for each BIM component; Step 2: Unify the spatial coordinate datum and topological relationship, convert the BIM local coordinate system to the GIS National Geodetic Coordinate System 2000, align the vertical datum with the 1985 National Elevation Datum, and ensure that the positioning error is ≤ 0.1 meter; establish the spatial logic between power components, match the GIS topological rules, and use GIS tools to verify the spatial consistency with terrain and road elements; Step 3: Multi-scale classification and dynamic LOD model generation. The classification hierarchy is from macro to micro. At the macro scale, components are aggregated according to "voltage level-functional area," substations are simplified into "high-voltage nodes," and transmission lines are displayed as corridors, hiding details to improve GIS loading efficiency. At the micro scale, classification is refined according to the "equipment-component" hierarchy, from substation to equipment layer to main transformer, supporting component-by-component attribute query and association with real-time monitoring data. Dynamic LOD classification adapts to different scales, automatically switching model details according to the GIS scale, compressing the number of faces to less than 10% at small scales and retaining key structures and high-definition textures at large scales. Step 4: Data format conversion and intelligent lightweight processing. Format adaptation: Use IFC / FME tools to convert BIM models into CityGML, 3DTiles, and GIS-compatible formats, and map attribute data to GeoJSON queryable fields. A lightweight strategy uses lossless compression for key components, while geometric simplification and lossy compression are applied to secondary components. Repeated components are stored only once and loaded in blocks according to the geographic grid, reducing data redundancy and improving loading speed. Step 5: Classification verification and dynamic update mechanism: Compare the BIM model with the GIS base map to ensure that the spatial location and semantic classification are correct. Generate incremental update packages through BIM model change detection, and automatically trigger real-time adjustment of GIS classification attributes in combination with IoT data to achieve full life cycle data synchronization.

[0008] Preferably, the data format conversion and intelligent lightweight processing in step 4 also includes encrypting the BIM data containing sensitive parameters to ensure the security of the data during format conversion and transmission.

[0009] Preferably, in the classification verification and dynamic update mechanism of step five, blockchain technology is used to store the classification results and update records, and the legality of the BIM model changes is automatically verified through smart contracts to ensure the non-tamperability of data updates.

[0010] Based on the above-mentioned method for decomposing and classifying power BIM-type data in GIS, a system for the method for decomposing and classifying power BIM-type data in GIS will also be provided, including a classification system construction module, a spatial benchmark unification module, a multi-scale model generation module, a data conversion and lightweight module, a verification and update module, and a system integration and interaction module. The classification system construction module includes a standard mapping unit and a semantic attribute management unit. The standard mapping unit is used to interface the built-in GIS geographic information standard with the power industry standard and support customized three-level classification framework configuration; the semantic attribute management unit is used to automatically match GIS classification paths for power BIM components and support two-way mapping and extension of geographic spatial attributes and power professional attributes.

[0011] Preferably, the spatial reference unification module includes a coordinate conversion engine unit and a topology verification tool unit. The coordinate conversion engine unit is used to integrate a seven-parameter conversion algorithm to realize automatic conversion between the BIM local coordinate system and the GIS National 2000 geodetic coordinate system, align the vertical reference with the 1985 National Elevation Reference, and control the positioning error within ±0.1 meters; the topology verification tool unit is used to automatically detect the topological relationship between power components based on the GIS spatial analysis engine and generate topology repair suggestions.

[0012] Preferably, the multi-scale model generation module includes a macro aggregation unit, a micro refinement unit and a dynamic LOD engine unit. The macro aggregation unit is used to batch aggregate power facilities according to "voltage level-functional area" to generate a simplified geometric model to support overlay analysis with GIS urban planning data; the micro refinement unit is used to generate a fine model that supports component-by-component query based on the BIM model hierarchical structure and associate it with real-time monitoring data; the dynamic LOD engine unit is used to automatically switch the model detail level according to the GIS map scale, call the pre-generated LOD version, and achieve a dynamic balance between rendering efficiency and detail accuracy.

[0013] Preferably, the data conversion and lightweight module includes a format conversion middleware unit, a structure hierarchical processing unit and an instantiation and block management unit. The format conversion middleware unit is used to support bidirectional conversion between BIM native format and GIS compatible format, and retain geometry, attributes and topological relationships based on the IFC standard; the structure hierarchical processing unit is used to use lossless compression for key components and geometric simplification and lossy compression for secondary components; the instantiation and block management unit is used to automatically identify duplicate components, reduce data redundancy through instance references, and block large models according to geographic grids to achieve "on-demand calling" when GIS loading.

[0014] Preferably, the verification and update module includes an accuracy verification unit and a dynamic update interface unit. The accuracy verification unit is used to provide a visual comparison tool, automatically detect the spatial position deviation and semantic classification accuracy between the BIM model and the GIS base map, and generate a verification report; the dynamic update interface unit is used to connect the BIM collaboration platform and the Internet of Things monitoring system, capture model change events in real time, generate incremental update packages, and support sensor data to trigger real-time updates of GIS classification attributes.

[0015] Preferably, the system integration and interaction module includes a user interface unit and an API interface unit. The user interface unit provides a graphical operation interface, supports classification rule configuration, model preview, and visualization of spatial analysis results; the API interface unit is used to open data import / export, classification query and spatial analysis interfaces, supports seamless integration with the power planning platform and operation and maintenance management system, and realizes data flow across business systems.

[0016] Preferably, the multi-scale model generation module in claim 5 further includes an industry scenario adaptation unit, and the industry scenario adaptation unit is used to automatically call preset classification rules and LOD strategies according to different power business scenarios.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This application builds an industry-specific classification system, breaks through the cross-system semantic fault, establishes a three-level classification framework of "power buildings / structures / equipment / ancillary facilities", accurately connects GIS geographic information standards with power industry standards, realizes automatic mapping of BIM components and GIS classification, reduces manual configuration errors, supports cross-domain correlation analysis of "location-geology-meteorology", and improves the efficiency of lightning risk assessment; 2. Unify spatial benchmarks and topologies to improve data spatial accuracy. A seven-parameter algorithm is used to achieve high-precision conversion from the BIM coordinate system to the National 2000 Geodetic Coordinate System, resolving the "spatial drift" problem of traditional models. Automatically verify topology conflicts based on the GIS engine, reducing manual verification time and assisting in optimizing power grid planning paths. 3. Dynamic adaptation of multi-scale models supports full-scenario business analysis. Macro-scale components are aggregated by "voltage level-functional area" to adapt to city-level power grid planning. Micro-scale classification is refined by "equipment-component," linking real-time monitoring data to shorten fault location time. Dynamic LOD technology automatically switches model details based on scale, balancing visualization effects and system performance. 4. Intelligent data processing balances efficiency and accuracy, achieving lossless conversion between BIM and GIS formats through IFC / FME. Key components are losslessly compressed, and secondary components are hierarchically simplified, compressing data while retaining key structures. Instancing and segmentation technologies reduce data redundancy, accelerating the rendering of large data models. Full lifecycle data management ensures trustworthiness and dynamic updates. Automated verification mechanisms enable comprehensive testing of spatial location and classification accuracy, replacing traditional manual spot checks. Blockchain evidence storage ensures data cannot be tampered with, and smart contracts automatically verify the legitimacy of changes. Integration with BIM and IoT systems enables minute-level responses to "design change-GIS synchronization," shortening troubleshooting times. 5. System integration and interaction optimization strengthen business collaboration. The graphical interface lowers the operating threshold, and non-professionals can quickly configure classification rules. The API interface opens up data links for power planning, operation and maintenance systems, improving business collaboration efficiency. Built-in preset rules for transmission / transformation / distribution scenarios adapt to the in-depth analysis needs of different businesses and promote the digital transformation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the overall steps of the present invention; Figure 2 This is a block diagram of the overall system connection modules of the present invention; Figure 3 A block diagram of a module unit for building the classification system of the present invention; Figure 4 This is a block diagram of the spatial reference unified module unit of the present invention; Figure 5 Generate a block diagram of module units for the multi-scale model of the present invention; Figure 6 This is a block diagram of the data conversion and lightweight module unit of the present invention; Figure 7 This is a block diagram of the verification and update module unit of the present invention; Figure 8 This is a block diagram of the system integration and interaction module unit of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Please see the attached Figure 1-8 As shown, a GIS decomposition and classification method for power BIM data includes the following steps: Step 1: Construct a classification system and semantic mapping exclusive to the power industry. Based on GIS geographic information standards and power industry standards, establish a three-level classification framework: First-level classification: connect to GIS categories and divide them into "power buildings", "power structures", "power equipment", and "ancillary facilities"; Second-level classification: refine professional dimensions, and expand "power equipment" to "transmission equipment", "transformer equipment", and "distribution equipment"; Third-level classification: define to the component level, "transmission equipment → transmission tower → linear tower / tension tower"; Semantic mapping: match GIS classification paths and attributes for each BIM component; Integrate the power industry standard (DL / T1927-2018) with GI S geographic information standard (GB / T25529-2010) is used for hierarchical mapping to establish a three-level classification framework of "power buildings / structures / equipment / ancillary facilities", realizing GIS semantic alignment of BIM components from macro functions ("power equipment") to micro types ("transmission poles → linear towers"), binding dual attributes to each BIM component, and in terms of geographic spatial attributes through latitude and longitude, altitude, and soil type (connecting to GIS raster data); in terms of power professional attributes, voltage level, wind resistance level, and equipment status (derived from BIM design parameters and operation and maintenance data), forming a cross-domain association model of "spatial position-structural characteristics-operation status".

[0021] Step 2: Unify spatial coordinate datums and topological relationships, converting the BIM local coordinate system to the GIS National Geodetic Coordinate System 2000, aligning the vertical datum with the 1985 National Elevation Datum, and ensuring a positioning error of ≤0.1 meter. Establish spatial logic between power components, match GIS topological rules, and use GIS tools to verify spatial consistency with terrain and road elements. A seven-parameter conversion algorithm is used to convert the BIM local coordinate system (such as the building coordinate system) to the GIS National Geodetic Coordinate System 2000 (CGCS2000), aligning the vertical datum with the 1985 National Elevation Datum, and controlling the positioning error within ±0.1 meter to ensure the absolute positioning accuracy of the model in GIS geographic space (for example, the deviation between the substation and satellite image overlay is less than 0.05 meters). The GIS spatial analysis engine automatically verifies the topological relationships between BIM components (such as the "tower-to-conductor" connection) and the spatial logic with geographic elements (such as roads and water systems) (such as the "substation-to-road" adjacency relationship). Topological repair rules are then generated to achieve consistent conversion of the BIM model from "building-level local topology" to "city-level global topology."

[0022] Step 3: Multi-scale classification and dynamic LOD model generation. The classification hierarchy from macro to micro. At the macro scale, components are aggregated according to "voltage level-functional area", such as abstracting substations into "high-voltage nodes" and simplifying transmission lines into "power corridors". The number of model faces is compressed to less than 10% of the original data, hiding details to improve GIS loading efficiency and support macro-decision-making such as power supply radius analysis and path optimization in power grid planning. At the micro scale, the classification is refined according to the "equipment-component" level, from substation → equipment layer → main transformer. Component-by-component attribute query and association with real-time monitoring data are supported, achieving second-level fault location and component-by-component maintenance management. Dynamic LOD classification adapts to different scales and automatically switches model details according to the GIS scale. At small scales, the LOD0 model is called to display only the outline and core attributes. At large scales, the LOD3 fine model is loaded to retain key structures such as bolts and sensors. This reduces data transmission volume by 70% while ensuring analysis accuracy (mechanical calculation error ≤ 5%). Step 4: Data format conversion and intelligent lightweight processing. Format adaptation: Use IFC / FME tools to convert BIM models into CityGML, 3DTiles, and GIS-compatible formats. Attribute data is mapped to GeoJSON queryable fields, preserving geometric topology and semantic properties to resolve surface deformation and data loss issues caused by traditional conversion. A lightweight strategy uses lossless compression for key components, while geometric simplification and lossy compression are used for secondary components. While reducing data volume by 70%, the key structure retention rate reaches 100%. Duplicate components are stored only once and loaded in blocks according to geographic grids (1km×1km), reducing data redundancy and increasing loading speed. BIM data containing sensitive parameters is encrypted and transmitted and stored using the AES-256 encryption algorithm to ensure data security during format conversion and transmission. Step 5: Classification verification and dynamic update mechanism, compare the BIM model with the GIS base map to ensure the correct spatial location and semantic classification, generate incremental update packages through BIM model change detection, automatically trigger real-time adjustment of GIS classification attributes in combination with IoT data, realize full life cycle data synchronization, use blockchain technology to store classification results and update records, and automatically verify the legitimacy of BIM model changes through smart contracts to ensure the non-tamperability of data updates.

[0023] This application realizes the leap from "simple data superposition" to "deep fusion analysis" of power BIM and GIS through technological innovation of work processes. Macro-scale models support the optimization of power grid layout and reduce investment waste caused by path conflicts; precise coordinates and topological relationships assist component prefabrication and on-site installation, shortening construction period; micro-scale data and real-time monitoring are linked to improve equipment health management and reduce operation and maintenance costs; a BIM-GIS fusion methodology exclusive to the power industry is established to provide a replicable technical paradigm for the digital transformation of new power systems.

[0024] Please see the attached Figure 2-8 As shown, according to the above embodiment, a system for the decomposition and classification method of power BIM-type data GIS will also be provided, including a classification system construction module, a spatial benchmark unification module, a multi-scale model generation module, a data conversion and lightweight module, a verification and update module and a system integration and interaction module. The classification system construction module includes a standard mapping unit and a semantic attribute management unit. The standard mapping unit has built-in GIS geographic information standard (GB / T25529-2010) and power industry standard (DL / T1927-2018) interfaces, supporting users to define a three-level classification framework of "power buildings / structures / equipment / ancillary facilities", realizing A hierarchical mapping from macro functions (e.g., "power equipment") to micro components (e.g., "transmission tower → linear tower") resolves the semantic gap between traditional BIM classification and GIS. The semantic attribute management unit automatically matches GIS classification paths for power BIM components (e.g., "main transformer → infrastructure - power facilities - substation equipment") and establishes a bidirectional association between geospatial attributes (latitude and longitude, soil type) and power-specific attributes (voltage level, wind resistance level), forming a cross-domain data link of "spatial location - structural characteristics - operating status." For example, tower coordinates are automatically associated with soil corrosion levels in GIS raster data, assisting in operation and maintenance risk assessment. The spatial reference unification module includes a coordinate conversion engine unit and a topology verification tool unit. The coordinate conversion engine unit integrates a seven-parameter conversion algorithm to realize the automatic conversion of the BIM local coordinate system (such as the building coordinate system) to the GIS National 2000 Geodetic Coordinate System. The vertical reference is aligned with the 1985 National Elevation Datum, and the positioning error is controlled within ±0.1 meters, ensuring that the superposition deviation between the substation model and the satellite image is less than 0.05 meters, meeting the millimeter-level accuracy requirements of geographic positioning for power grid planning. The topology verification tool unit is based on the GIS spatial analysis engine and automatically detects topological logic such as the "tower-conductor" connection relationship and the "substation-road" adjacent relationship, generating a visual conflict report and repair suggestions (such as adjusting the tower position to avoid the prohibited construction zone), shortening the traditional manual verification cycle and realizing the process upgrade from "manual error correction" to "intelligent verification-automatic repair". The multi-scale model generation module includes a macro aggregation unit, a micro refinement unit, a dynamic LOD engine unit and an industry scenario adaptation unit. The macro aggregation unit performs batch abstraction of power facilities according to "voltage level-functional area" (such as simplifying transmission lines into "power corridors") to generate a macro model adapted to urban planning, with compressed model faces and improved loading speed; the micro refinement unit generates a fine model that supports component-by-component query based on the BIM hierarchical structure (such as "substation → equipment layer → main transformer"), and associates real-time oil temperature and load data to meet the second-level fault location requirements; the dynamic LOD engine unit automatically switches model details according to the GIS scale (calling the LOD0 outline model at a small scale and loading the LOD3 fine model at a large scale), reducing the amount of data transmission; the industry scenario adaptation unit has built-in preset rules for transmission planning, substation operation and maintenance, etc. For example, the transmission scenario automatically hides hardware details and focuses on corridor path analysis, and the substation scenario automatically associates monitoring data to generate a three-dimensional model with status tags, realizing intelligent adaptation of "one model for multiple scenarios"; The data conversion and lightweight module includes a format conversion middleware unit, a structure hierarchical processing unit and an instantiation and block management unit. The format conversion middleware unit is based on the IFC standard and realizes the bidirectional conversion between BIM native formats such as Revit and Bentley and CityGML and 3DTiles, retains geometric topology (such as pipeline connection relationship) and semantic attributes (such as equipment parameters), reduces surface deformation error, and supports professional applications such as hydraulic simulation and energy consumption analysis; the structure hierarchical processing unit uses ZIP lossless compression for key components (such as tower node plates), and geometrically simplifies and lossily compresses secondary components (such as ladders) (vertex accuracy 0.01 meter), ensuring 100% retention of key structures while compressing the data volume by 70%; The instantiation and block management unit identifies repeated components (such as insulators) and stores only one instance, and loads them in blocks according to 1km×1km geographic grids, reducing data redundancy by 60%. The rendering speed of a 50km line model is increased by 3-5 times. The integrated AES-256 encryption algorithm encrypts the transmission and storage of BIM data containing geographic coordinates and equipment operating parameters, meeting the data security level protection requirements of the power industry and preventing the leakage of sensitive information. The verification and update module includes an accuracy verification unit and a dynamic update interface unit. The accuracy verification unit uses a visual comparison tool to fully detect the spatial position and classification accuracy of the model, generate an automated verification report, and replace the traditional manual inspection with a sampling rate of 30%. Blockchain evidence storage technology verifies classification results and update records. Make tamper-proof records, and the smart contract automatically verifies the compliance of the approval process of design changes to ensure the credibility of the data throughout its life cycle; the dynamic update interface unit connects to the BIM collaboration platform and the Internet of Things system, captures model change events (such as equipment replacement) and sensor data (such as wind speed exceeding the threshold) in real time, and generates incremental update packages containing only changed components, achieving a 5-minute response to "design change-GIS synchronization"; sensor data triggers dynamic classification rules (such as soil moisture exceeding the standard marks the tower foundation as "high corrosion risk"), and links the operation and maintenance system to automatically dispatch maintenance work orders, shortening the fault handling time; the system integration and interaction module includes a user interface unit and an API interface unit. The user interface unit provides a graphical operation interface and supports classification rules Configuration, model preview, and visualization of spatial analysis results. The user interface unit provides a visual classification rule configuration interface, supporting non-professionals to define a three-level classification framework and map component attributes by dragging and dropping. The model preview function displays the classification effect in real time, lowering the technical threshold; the spatial analysis results are visualized in the form of heat maps, three-dimensional topology maps, etc.; the API interface unit is used to open data import / export, classification query and spatial analysis interfaces, supporting seamless integration with the power planning platform and operation and maintenance management system, and realizing data flow across business systems. For example, the planning platform calls the macro model to analyze the power supply radius, and the operation and maintenance system obtains micro component data for fault diagnosis, promoting the data connection of the entire process of "planning-design-operation and maintenance", and improving business collaboration efficiency.

[0025] Through modular design and technology integration, this application has built a power BIM-GIS fusion solution with "unified standards, spatial accuracy, scenario adaptation, data security, and intelligent management". It automates the entire process from classification mapping to format conversion, reducing manual intervention. Technologies such as dynamic LOD and incremental updates shorten the analysis and operation and maintenance cycle. Encryption algorithms and blockchain technology meet the protection requirements of power critical information infrastructure. Preset industry rules and dynamic adaptation mechanisms support rapid switching of multiple business scenarios such as transmission, transformation, and distribution.

[0026] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0027] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A GIS decomposition and classification method for power BIM data, characterized by: The following steps are involved: Step 1: Build a classification system and semantic mapping specific to the power industry. Based on GIS geographic information standards and power industry standards, establish a three-level classification framework: First-level classification: align with GIS categories and divide into "power buildings," "power structures," "power equipment," and "ancillary facilities." Second-level classification: refine professional dimensions, expanding "power equipment" to "transmission equipment," "transformer equipment," and "distribution equipment." Third-level classification: define down to the component level, "transmission equipment → transmission towers → linear towers / tension towers." Semantic mapping: match GIS classification paths and attributes to each BIM component. Step 2: Unify the spatial coordinate datum and topological relationship, convert the BIM local coordinate system to the GIS National Geodetic Coordinate System 2000, align the vertical datum with the 1985 National Elevation Datum, and ensure that the positioning error is ≤ 0.1 meter; establish the spatial logic between power components, match the GIS topological rules, and use GIS tools to verify the spatial consistency with terrain and road elements; Step 3: Multi-scale classification and dynamic LOD model generation. The classification hierarchy progresses from macro to micro. At the macro scale, components are aggregated by "voltage level-functional area," simplifying substations into "high-voltage nodes." Transmission lines are displayed as corridors, hiding details to improve GIS loading efficiency. At the micro scale, classification is refined by the "equipment-component" hierarchy, from substation to equipment layer to main transformer. Component-by-component attribute queries are supported and linked to real-time monitoring data. Dynamic LOD classification adapts to different scales, automatically switching model details based on the GIS scale. Small scales compress the number of faces to less than 10%, while large scales retain key structures and high-definition textures. Step 4: Data format conversion and intelligent lightweight processing. Format adaptation: Use IFC / FME tools to convert BIM models into CityGML, 3DTiles, and GIS-compatible formats, and map attribute data to GeoJSON queryable fields. A lightweight strategy uses lossless compression for key components, while geometric simplification and lossy compression are applied to secondary components. Repeated components are stored only once and loaded in blocks according to the geographic grid, reducing data redundancy and improving loading speed. Step 5: Classification verification and dynamic update mechanism: Compare the BIM model with the GIS base map to ensure that the spatial location and semantic classification are correct. Generate incremental update packages through BIM model change detection, and automatically trigger real-time adjustment of GIS classification attributes in combination with IoT data to achieve full life cycle data synchronization.

2. The method for decomposing and classifying electric power BIM data in GIS according to claim 1 is characterized by: The data format conversion and intelligent lightweight processing in step 4 also include encrypting the BIM data containing sensitive parameters to ensure the security of the data during format conversion and transmission.

3. The method for decomposing and classifying electric power BIM data in GIS according to claim 1 is characterized by: In the classification verification and dynamic update mechanism of step five, blockchain technology is used to store the classification results and update records, and the legality of the BIM model changes is automatically verified through smart contracts to ensure the non-tamperability of data updates.

4. A system for the power BIM data GIS decomposition and classification method as claimed in claim 1, characterized in that: It includes a classification system construction module, a spatial benchmark unification module, a multi-scale model generation module, a data conversion and lightweight module, a verification and update module, and a system integration and interaction module. The classification system construction module includes a standard mapping unit and a semantic attribute management unit. The standard mapping unit is used to interface the built-in GIS geographic information standard with the power industry standard and support customized three-level classification framework configuration; the semantic attribute management unit is used to automatically match GIS classification paths for power BIM components and support two-way mapping and extension of geographic spatial attributes and power professional attributes.

5. The system for decomposing and classifying electric power BIM data in GIS according to claim 4 is characterized in that: The spatial reference unification module includes a coordinate conversion engine unit and a topology verification tool unit. The coordinate conversion engine unit is used to integrate a seven-parameter conversion algorithm to realize the automatic conversion of the BIM local coordinate system and the GIS National 2000 geodetic coordinate system, align the vertical reference with the 1985 National Elevation Reference, and control the positioning error within ±0.1 meters; the topology verification tool unit is used to automatically detect the topological relationship between power components based on the GIS spatial analysis engine and generate topology repair suggestions.

6. The system for decomposing and classifying electric power BIM data in GIS according to claim 4 is characterized by: The multi-scale model generation module includes a macro-aggregation unit, a micro-refinement unit and a dynamic LOD engine unit. The macro-aggregation unit is used to batch aggregate power facilities according to "voltage level-functional area" to generate a simplified geometric model, supporting overlay analysis with GIS urban planning data; the micro-refinement unit is used to generate a detailed model that supports component-by-component query based on the BIM model hierarchy and associates it with real-time monitoring data; the dynamic LOD engine unit is used to automatically switch the model detail level according to the GIS map scale, call the pre-generated LOD version, and achieve a dynamic balance between rendering efficiency and detail accuracy.

7. The system for decomposing and classifying power BIM data in GIS according to claim 4 is characterized by: The data conversion and lightweight module includes a format conversion middleware unit, a structure hierarchical processing unit, and an instantiation and block management unit. The format conversion middleware unit is used to support bidirectional conversion between BIM native format and GIS compatible format, and retain geometry, attributes and topological relationships based on the IFC standard; the structure hierarchical processing unit is used to use lossless compression for key components and geometric simplification and lossy compression for secondary components; the instantiation and block management unit is used to automatically identify duplicate components, reduce data redundancy through instance references, and block large models according to geographic grids to achieve "on-demand call" when loading GIS.

8. The system for decomposing and classifying electric power BIM data in GIS according to claim 4 is characterized by: The verification and update module includes an accuracy verification unit and a dynamic update interface unit. The accuracy verification unit is used to provide a visual comparison tool to automatically detect the spatial position deviation and semantic classification accuracy between the BIM model and the GIS base map, and generate a verification report; The dynamic update interface unit is used to connect the BIM collaboration platform and the Internet of Things monitoring system, capture model change events in real time, generate incremental update packages, and support sensor data to trigger real-time updates of GIS classification attributes.

9. The system for decomposing and classifying electric power BIM data in GIS according to claim 4 is characterized in that: The system integration and interaction module includes a user interface unit and an API interface unit. The user interface unit provides a graphical operation interface, supports classification rule configuration, model preview, and visualization of spatial analysis results; the API interface unit is used to open data import / export, classification query and spatial analysis interfaces, supports seamless integration with the power planning platform and operation and maintenance management system, and realizes data flow across business systems.

10. The system for decomposing and classifying electric power BIM data in GIS according to claim 4 is characterized in that: The multi-scale model generation module in claim 5 also includes an industry scenario adaptation unit, which is used to automatically call preset classification rules and LOD strategies according to different power business scenarios.

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