Power communication equipment three-dimensional modeling method and system based on parameterized rule base

Abstract

The invention provides an electric power communication equipment three-dimensional modeling method and system based on a parameterized rule base, and relates to the technical field of digital modeling of electric power communication equipment, and the method comprises the steps: constructing the parameterized rule base comprising a basic sub-base, an equipment exclusive sub-base, a compliance verification sub-base and an optimization rule sub-base; collecting and preprocessing multi-source data; performing layered three-dimensional modeling of the base layer, the facility layer and the data layer based on the rule base; carrying out lightweight optimization on the model by adopting grid simplification, texture compression and LOD grading technologies; and finally, carrying out geometric accuracy, compliance and performance verification on the model. The system correspondingly comprises a parameterized rule base module, a data acquisition and preprocessing module, a layered modeling module, a model optimization module, a model verification module and the like. Through parameterization rule driving, the modeling efficiency is improved by 50%-85%, the rendering performance that the mobile terminal is larger than or equal to 30 FPS and the AR terminal is larger than or equal to 60 FPS is ensured while the precision is ensured, and the problems of precision and light weight balance, model compliance and multi-scene adaptation are solved.

Description

Technical Field

[0001] This invention relates to the field of digital modeling technology for power communication equipment, and specifically to a method and system for three-dimensional modeling of power communication equipment based on a parametric rule base. Background Technology

[0002] Power communication line equipment and facilities are a core component of power communication systems, undertaking the transmission tasks of power dispatching, production management, and other services. Their operational status directly affects the reliability and security of the power communication system. With the expansion of power communication networks, the wide distribution of equipment and the complexity of operating environments place higher demands on the accuracy, efficiency, and adaptability of equipment modeling.

[0003] Existing modeling methods for power communication equipment suffer from the following main problems: First, low modeling efficiency. Traditional manual modeling relies on human intervention, and the conversion cycle from 2D drawings to 3D models is long, with manual modeling of a single device taking an average of 3-5 days, making it difficult to adapt to the needs of large-scale equipment modeling. Second, difficulty in balancing accuracy and lightweight design. High-precision modeling often results in massive data volumes, affecting the rendering performance of mobile devices, AR devices, and other terminals. On the other hand, excessive lightweight design can cause model distortion, such as mobile rendering frame rates generally being below 25 FPS and AR glasses below 40 FPS, failing to meet real-time interaction requirements. Third, poor model compliance. The lack of unified industry standards leads to inconsistent models built by different modelers, making it difficult to meet the needs of full lifecycle management. Fourth, insufficient fusion of multi-source data. Design data, field data, and business data are fragmented, making it difficult for the model to reflect the actual operating status of the equipment. Fifth, insufficient adaptability. Existing models are mostly designed for single application scenarios, making it difficult to meet the needs of multiple scenarios such as AR operation and maintenance, remote assistance, and virtual training.

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for 3D modeling of power communication equipment based on a parametric rule base. The method constructs a parametric rule base by extracting equipment geometric parameters, physical attribute parameters, connection relationship rules, compliance verification rules, and lightweight optimization rules. The collected data undergoes standardized preprocessing, followed by basic layer modeling, facility layer modeling, and data layer modeling. Based on the optimization rules in the parametric rule base, mesh simplification, texture compression, Level of Detail (LOD) grading, and parametric dynamic adjustment techniques are employed to perform lightweight optimization of the 3D model. Finally, based on the compliance verification rules in the parametric rule base, the optimized 3D model undergoes geometric accuracy verification, compliance verification, and performance verification. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for three-dimensional modeling of power communication equipment based on a parametric rule base, which solves the problem of balancing three-dimensional modeling accuracy and lightweight design, increases the adaptability of the model to different scenarios, and improves the efficiency of three-dimensional modeling.

[0006] The technical solution adopted in this invention is to provide a method and system for three-dimensional modeling of power communication equipment based on a parameterized rule base, comprising the following steps: Step S1: Construct a parameterized rule base: Review power communication industry standards, equipment design specifications, and operation and maintenance requirements; extract equipment geometric parameters, physical attribute parameters, connection relationship rules, compliance verification rules, and lightweight optimization rules; and establish a structured parameterized rule base. The parameterized rule base includes a basic rule sub-base, a device-specific rule sub-base, a compliance verification sub-base, and an optimization rule sub-base. Step S2, Multi-source data acquisition and preprocessing: Collect design drawings, CAD files, GIS data, field scanning data and sensor data of power communication equipment; perform format conversion, noise reduction, coordinate registration and data fusion processing on the collected data to generate standardized modeling data sources; Step S3, Layered 3D Modeling: Based on the parametric rule base, a layered modeling strategy is adopted to construct a 3D model of the equipment. The layered modeling includes basic layer modeling, facility layer modeling and data layer modeling. Among them, basic layer modeling realizes the 3D reconstruction of terrain, landforms and buildings, facility layer modeling constructs a fine geometric model of power communication equipment, and data layer modeling realizes the binding of equipment business data with the 3D model. Step S4, Model Optimization: Based on the optimization rules in the parametric rule library, the 3D model is optimized by using mesh simplification, texture compression, LOD grading and parametric dynamic adjustment techniques to ensure that the model meets the rendering performance requirements of different terminal devices. Step S5, Model Validation: Based on the compliance verification rules in the parameterized rule base, the optimized 3D model is subjected to geometric accuracy verification, compliance verification, and performance verification. After the verification is passed, a standardized 3D model asset is generated.

[0007] Preferably, in step S1, the basic rule sub-library includes general geometric constraint rules, physical attribute mapping rules, and data interaction rules. The device-specific rule sub-library is divided into cable rules, external equipment rules, maintenance equipment rules, and underground facility rules according to equipment type. Cable rules include flexible deformation constraints and sag calculation rules; external equipment rules include assembly relationship rules and port identification rules; and underground facility rules include GIS coordinate mapping rules and concealed works modeling rules. Compliance verification rules include geometric error thresholds, industry standard adaptation rules, and data integrity verification rules. Optimization rules include mesh simplification thresholds, texture compression format rules, and LOD level switching conditions, supporting synchronization with the power communication industry standard database via API interface to ensure rule timeliness. Interaction technology supports AR device interaction methods such as gesture recognition, voice control, and gaze operation. The model must support spatial surround display, air zoom / rotation, and other interaction logic to ensure compatibility with the platform's human-computer interaction functions.

[0008] Preferably, in step S2, data acquisition employs a combination of manual measurement, laser scanning, UAV aerial photography, and multi-view photogrammetry. Laser scanning is used to acquire high-precision point cloud data of the equipment, while multi-view photogrammetry supplements the equipment's texture details. Data preprocessing includes using statistical filtering algorithms to remove point cloud noise, unifying the coordinate systems of multi-source data through coordinate transformation technology, and integrating design data with on-site measured data using a data fusion algorithm. The data fusion algorithm employs a deep learning-based point cloud-image fusion algorithm to improve the data integrity in occluded areas.

[0009] Preferably, in step S3, the basic layer modeling integrates the BIM model with the GIS data in IFC format to achieve accurate reconstruction of terrain undulations and building shapes. The facility layer modeling employs a combination of parametric modeling and reverse engineering. For regular structural equipment, models are quickly generated by defining parameters; for complex-shaped equipment, polygonal modeling and surface modeling techniques are combined to construct the shape. The data layer modeling uses data visualization technology to dynamically correlate real-time equipment operating parameters, historical fault records, and the 3D model, supporting gesture interaction, voice query, and tag pop-up functions. Specifically, the slope error of the basic layer modeling is ≤1°, and the building outline matching degree is ≥95%. The data layer modeling adds a real-time business data synchronization mechanism, supporting millisecond-level data linkage with SCADA and GIS systems via Kafka message queues.

[0010] Preferably, in step S4, mesh simplification uses the EdgeQuadric algorithm to streamline non-feature region patches, achieving a compression rate of no less than 60%. Texture compression uses the BC7 format or the Draco algorithm to compress the 4K texture map volume to less than 25% of its original volume. LOD grading sets three levels of precision based on the distance between the model and the viewpoint, corresponding to macro-planning scenarios, operation and maintenance inspection scenarios, and maintenance training scenarios, ensuring a rendering frame rate of ≥30FPS on mobile devices and ≥60FPS on AR glasses.

[0011] Preferably, in step S5, geometric accuracy verification uses a comparison between on-site measurement data and model data to ensure that the geometric error of the model in the macro-planning scenario is ≤5cm, ≤1cm in the operation and maintenance inspection scenario, and ≤1mm in the maintenance training scenario. Compliance verification verifies the consistency of model parameters with power communication industry standards by calling industry standard rules from the compliance verification sub-library. Performance verification tests the model's loading speed, rendering frame rate, and resource utilization by simulating a multi-terminal operating environment. Specifically, performance verification includes performance stability testing under strong electromagnetic interference (electric field strength ≤100V / m) and temperature conditions ranging from -20℃ to 55℃.

[0012] A 3D modeling system for power communication equipment based on a parameterized rule base includes: Parametric rule base module: Used to store and manage various rules required for modeling power communication equipment, including basic rule sub-library, equipment-specific rule sub-library, compliance verification sub-library and optimization rule sub-library, supporting the addition, modification, deletion and query of rules; Data acquisition and preprocessing module: includes a data acquisition unit and a data preprocessing unit. The data acquisition unit is used to acquire design drawings, CAD files, GIS data, on-site scanning data and sensor data. The data preprocessing unit is used to perform format conversion, noise reduction, coordinate registration and data fusion processing on the acquired data. Layered modeling module: includes basic layer modeling unit, facility layer modeling unit and data layer modeling unit. The basic layer modeling unit is used to realize the three-dimensional reconstruction of terrain, landform and buildings. The facility layer modeling unit is used to build a fine geometric model of power communication equipment. The data layer modeling unit is used to realize the binding of equipment business data and three-dimensional model. Model optimization module: Used to perform lightweight optimization of 3D models based on optimization rules in the parametric rule base, employing techniques such as mesh simplification, texture compression, LOD grading, and parametric dynamic adjustment. Model Validation Module: Used to perform geometric accuracy verification, compliance verification, and performance verification on the optimized 3D model according to compliance verification rules, and generate a verification report; Application Integration Module: This module integrates validated 3D models with AR operation and maintenance platforms, remote assistance systems, and training systems, supporting multi-terminal adaptation and business applications of the models.

[0013] Preferably, the parameterized rule base module uses a distributed database to store rule data, supporting rule version control and batch updates; the data acquisition unit is equipped with a laser scanner, drone, high-definition camera and BeiDou positioning device, supporting data acquisition in multiple indoor and outdoor scenarios; the model optimization module has a built-in GPU acceleration unit to improve model optimization processing efficiency. Modeling needs to adapt to the positioning requirements of multiple scenarios such as BeiDou positioning, QR code positioning + inertial navigation, and SLAM spatial mapping, and performs outdoor coarse positioning, indoor fine positioning and centimeter-level registration respectively, with the model coordinate system and positioning data linked in real time.

[0014] Preferably, the application integration module supports output in common formats such as glTF2.0 and USDZ, is compatible with development engines such as Unity and Unreal Engine, and can be adapted to multiple terminals such as AR glasses, mobile devices, and web browsers to meet the needs of application scenarios such as AR-assisted inspection, virtual-real fusion training, and intelligent fault diagnosis.

[0015] Preferably, it also includes a security protection module, which uses national cryptographic algorithms SM2 / SM3 / SM4 to encrypt the transmission and storage of modeling data. It enables different users to access the model data in a hierarchical manner through access control, ensuring data security and privacy. The data transmission is combined with the platform's network communication design, which specifies that the model data must support multiple network transmissions such as Wi-Fi / 4G / 5G, adapt to the DMZ zone secure exchange mechanism, and support model preloading and data synchronization in offline mode.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention drives the modeling process through a parametric rule base, enabling rapid generation and batch creation of equipment models. The modeling time for a single device is reduced by 50%-85% compared to traditional manual modeling, significantly lowering the modeling threshold. Non-professional modelers can complete model building after a short training. 2. Based on the optimization rules in the parameterized rule base, this invention uses techniques such as mesh simplification, texture compression, and LOD grading to achieve lightweight model while ensuring that the model accuracy meets industry standards, thus ensuring smooth operation on multiple terminal devices. 3. The parameterized rule base in this invention integrates power communication industry standards and design specifications, and ensures that the model meets industry requirements through a compliance verification mechanism, avoiding inconsistencies caused by manual modeling, and providing standardized digital assets for the full life cycle management of equipment; 4. This invention achieves the organic integration of design data, field data and business data, so that the three-dimensional model not only reflects the geometric shape of the equipment, but also presents the equipment's operating status in real time, providing comprehensive data support for operation and maintenance decisions; 5. The model in this invention supports multiple common output formats, is compatible with multiple application scenarios such as AR operation and maintenance, remote assistance, and virtual training, and can be adapted to multiple terminal devices such as AR glasses, mobile terminals, and web browsers, thereby enhancing the reusability of the model. 6. This invention ensures data security through encryption using national cryptographic algorithms and access control. The parameterized rule base supports dynamic updates, and the system modules adopt a loosely coupled design, allowing for the expansion of new modeling functions and application scenarios according to business needs. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the three-dimensional modeling method for power communication equipment based on a parameterized rule base of the present invention; Figure 2 This is a schematic diagram of the parameterized rule base structure of the present invention; Figure 3 This is a schematic diagram of the layered three-dimensional modeling structure of the present invention; Figure 4 This is a schematic diagram of the LOD hierarchy of the model of this invention; Figure 5 This is a flowchart of the three-dimensional modeling system for power communication equipment based on a parameterized rule base of the present invention. Detailed Implementation

[0018] To better understand the purpose, system architecture, and functional implementation of this embodiment, the embodiments and features in the embodiments of this application can be combined with each other without conflict. The exemplary embodiments disclosed in this application will be described below with reference to the accompanying drawings, which include specific technical details disclosed in this embodiment to aid understanding; however, these details should be considered exemplary rather than restrictive.

[0019] like Figure 1 As shown, a 3D modeling method for power communication equipment based on a parameterized rule base consists of steps S110 to S150, including: S110, Parametric rule base construction, industry standard sorting, geometric, physical and compliance rule extraction and different sub-library division; S120, Multi-source data acquisition and preprocessing, performs preprocessing operations such as format conversion, noise reduction, coordinate registration and data fusion on laser scanning, UAV aerial photography, manual measurement and design data acquisition; S130, layered 3D modeling, basic layer modeling integrates BIM+GIS, facility layer modeling uses parametric and polygon modeling, and data layer modeling binds business data. S140, Model optimization, including mesh optimization, texture compression, LOD grading, and dynamic parameter adjustment; S150, Model Validation: Perform geometric accuracy verification, compliance verification, and performance verification, and process the verification results.

[0020] Specifically, in step S110, relevant standards in the power communication industry, such as DL / T547-2021 "Operation and Maintenance Procedures for Power Communication Optical Cables" and Q / GDW11642-2016 "Power Equipment Coding Standards," as well as equipment design specifications and operation and maintenance business requirements, are reviewed to extract the core rules and parameters required for modeling, and a structured parametric rule base is established. The parametric rule base specifically includes: The basic rules sub-library includes general geometric constraint rules, physical property mapping rules, and data interaction rules, along with their data formats and interface standards. Geometric constraint rules cover dimensional tolerances and proportional relationships. Physical property mapping rules include material parameters and mechanical properties. Data interaction rules include data formats and interface standards. Equipment-specific rule sub-library: categorized by equipment type, cable rules include flexible deformation constraints, sag calculation rules, and core count and sheath material association rules; external line equipment rules include assembly relationship rules, port identification rules, and component connection constraints; maintenance equipment rules include tool adaptation rules and operating space constraints. The compliance verification sub-library includes geometric error thresholds, industry standard adaptation rules, and data integrity verification rules. Geometric error thresholds address the accuracy requirements of different application scenarios. Industry standard adaptation rules include device coding and interface specifications. Data integrity verification rules include attribute field integrity and the validity of associated data. Optimization rule sub-library: mesh simplification threshold, texture compression format rules, LOD level switching conditions, and model lightweighting evaluation metrics.

[0021] Specifically, in step S120, a multi-method collaborative data acquisition approach is adopted to ensure the comprehensiveness and accuracy of the modeling data: Design data acquisition: Collect static data such as equipment design drawings, CAD files, GIS spatial data, and equipment ledgers; On-site data acquisition: High-precision point cloud data of the equipment is obtained by using a laser scanner, and large-scale terrain and equipment distribution data are obtained by using drone aerial photography. Multi-view photogrammetry is used to supplement the texture details of the equipment, and manual measurement is combined to obtain accurate size data of occluded areas or key nodes. Business data acquisition: Collect dynamic data such as equipment sensor data (e.g., temperature, vibration), historical maintenance records, and fault data.

[0022] Preprocess the collected data: Format conversion: Converts data from different formats such as CAD, GIS, and point cloud into a standardized format; Data cleaning: Statistical filtering algorithms are used to remove noise points from point cloud data, and outlier detection and repair algorithms are used to handle missing data; Coordinate registration: Using BeiDou positioning data and inertial measurement unit data, the coordinate system of multi-source data is unified to ensure data spatial consistency; Data fusion: The data fusion algorithm is used to integrate design data, field data and business data to generate a standardized modeling data source.

[0023] Specifically, in step S130, a three-dimensional model of the device is constructed based on a parameterized rule base and a hierarchical modeling strategy, achieving comprehensive modeling from the basic environment to device details and business data: Basic layer modeling: By integrating BIM models with GIS data in IFC format, a three-dimensional model of infrastructure such as terrain, landforms, and buildings is reconstructed to ensure the accurate restoration of the environment in which the equipment is located and to provide a spatial reference for subsequent equipment positioning; Facility-level modeling: Differentiated modeling methods are adopted for different types of power communication equipment. For regular structural equipment, including fiber optic junction boxes and fiber distribution boxes, models are quickly generated based on equipment-specific rules in the parametric rule library by defining key parameters such as size, number of ports, and material. For complex shaped equipment, including fiber optic splice boxes and flexible fiber optic cables, polygon modeling, surface modeling, and physics engine technology are combined to simulate the geometric shape and flexible deformation of the equipment, ensuring that the model closely matches the actual equipment. Data layer modeling: Bind business data such as real-time operating parameters, historical maintenance records, fault information and operation guides of the equipment to the 3D model, realize the dynamic 3D display of business data through data visualization technology, and support querying equipment information through gesture recognition, voice control and other methods.

[0024] Specifically, in step S140, the 3D model is optimized based on the optimization rules in the parameterized rule base to balance model accuracy and runtime performance. Mesh simplification: The EdgeQuadric algorithm is used to simplify the polygons in non-feature regions of the model, reducing the number of polygons while preserving key details, with a compression rate of no less than 60%. Texture compression: The BC7 texture compression format or Draco algorithm is used to compress the device texture map, reducing the size of the 4K texture map to less than 25% of the original size, thereby reducing the video memory usage. LOD (Level of Detail) grading: Three levels of precision are set based on the distance between the model and the viewpoint: L0 is for macro-planning scenarios, with a geometric error ≤5cm, suitable for regional layout display; L1 is for operation and maintenance inspection scenarios, with a geometric error ≤1cm, suitable for equipment-level operation and maintenance; L2 is for maintenance training scenarios, with a geometric error ≤1mm, suitable for component-level maintenance guidance. Dynamic LOD switching ensures a rendering frame rate ≥30FPS on mobile devices and ≥60FPS on AR glasses. Parameterized dynamic adjustment: Based on the device's operating status and application scenario requirements, the model's level of detail display and data loading priority are dynamically adjusted through parameterized rules to improve interactive response speed.

[0025] Specifically, in step S150, the optimized 3D model is fully verified according to the compliance verification rules in the parameterized rule base: Geometric accuracy verification: The geometric error of the model is verified by comparing the actual measurement data with the model data in three scenarios: macro planning, operation and maintenance inspection, and maintenance training, to ensure that the error meets the threshold requirements set by the rule base. Compliance verification: Call the industry standard rules in the compliance verification sub-library to verify whether the model's device code, interface specifications, material parameters, etc. comply with the power communication industry standards, and ensure the interoperability and universality of the model; Performance verification: Simulate the multi-terminal operating environment of AR glasses, mobile devices, web browsers, etc., and test the model's loading speed, rendering frame rate, resource utilization and other performance indicators to ensure that it meets the operating requirements of different application scenarios; Verification result processing: Standardize and encapsulate models that pass verification to generate model assets in common formats such as glTF2.0 and USDZ; for models that fail verification, return to the corresponding step S for modification and optimization based on the verification report.

[0026] Accordingly, the present invention also provides a 3D modeling system for power communication equipment based on a parametric rule base, comprising: The parameterized rule base module stores and manages various rules required for modeling power communication equipment, including a basic rule sub-library, a device-specific rule sub-library, a compliance verification sub-library, and an optimization rule sub-library. It uses a distributed database to store rule data and supports adding, modifying, deleting, querying, and version control of rules. The rule content can be dynamically adjusted according to industry standard updates and changes in business needs.

[0027] Data Acquisition and Preprocessing Module: This module includes a data acquisition unit and a data preprocessing unit. The data acquisition unit is equipped with a laser scanner, drone, high-definition camera, BeiDou positioning equipment, and inertial measurement unit, supporting multi-source acquisition of design data, field data, and business data. The data preprocessing unit incorporates format conversion tools, noise reduction algorithms, coordinate registration tools, and data fusion algorithms to achieve standardized processing of the acquired data.

[0028] The layered modeling module includes a basic layer modeling unit, a facility layer modeling unit, and a data layer modeling unit. The basic layer modeling unit supports the integrated reconstruction of BIM and GIS data; the facility layer modeling unit integrates parametric modeling, polygon modeling, surface modeling, and physics engine technologies to achieve detailed modeling of different types of equipment; the data layer modeling unit supports the binding and visualization of business data with 3D models, and provides functional interfaces such as gesture interaction and voice query.

[0029] Model optimization module: Includes built-in mesh simplification tools, texture compression tools, LOD hierarchical management tools, and a parametric dynamic adjustment engine. Based on optimization rules in a parametric rule base, it automatically performs lightweight model optimization and supports manual adjustment of optimization parameters to adapt to specific scenario requirements. The module is equipped with a GPU acceleration unit to improve model optimization processing efficiency.

[0030] The model validation module includes a geometric accuracy validation unit, a compliance validation unit, and a performance validation unit. The geometric accuracy validation unit automatically compares field data with model data; the compliance validation unit calls upon industry standard rule bases for compliance verification; and the performance validation unit simulates a multi-terminal operating environment to test model performance. The module generates detailed validation reports, supporting the traceability of validation results and guidance for model optimization.

[0031] Application Integration Module: Supports seamless integration of validated 3D models with AR operation and maintenance platforms, remote assistance systems, and training systems. Provides common output formats such as glTF2.0 and USDZ, is compatible with development engines such as Unity and Unreal Engine, and can be adapted to multiple terminals including AR glasses, mobile devices, and web browsers, meeting the needs of application scenarios such as AR-assisted inspection, virtual-real fusion training, and intelligent fault diagnosis.

[0032] Security protection module: The modeling data is encrypted and transmitted and stored using the national cryptographic algorithms SM2 / SM3 / SM4 to prevent the leakage of sensitive information; the role-based access control (RBAC) mechanism enables different users to access the model data in a hierarchical manner, ensuring data security and privacy.

[0033] Example 1 This invention uses a 3D model of a power communication optical cable and junction box as an example to illustrate the implementation process of this invention: Step S110: Construct a parameterized rule base. This involves reviewing industry standards such as DL / T547-2021 "Operation and Maintenance Regulations for Power Communication Optical Cables" and Q / GDW11642-2016 "Power Equipment Coding Standard," and combining these with the design specifications and operation and maintenance requirements of optical cables and junction boxes to construct a parameterized rule base. Figure 2 As shown: Basic rule sub-library: Defines the optical cable geometric tolerance as ±0.1mm, the junction box material parameters as metal with a reflectivity of 0.8 and a roughness of 0.1, and the data exchange format as IGES; Equipment-specific rule sub-library: Cable rules include optical cable sag calculation rules based on temperature, tension parameters, core count and sheath diameter correlation rules; External equipment rules include junction box assembly relationship rules including component connection tolerance ±0.5mm, and port identification rules in which port number corresponds one-to-one with core count; Compliance verification sub-library: Set the geometric error for operation and maintenance inspection scenarios to ≤1cm, the error for maintenance training scenarios to ≤1mm, and the equipment coding to comply with the Q / GDW11642-2016 standard; Optimized rule sub-library: Mesh simplification compression rate ≥65%, texture compression adopts BC7 format, LOD level switching distance thresholds include L0 / L1 switching distance of 5m and L1 / L2 switching distance of 1m.

[0034] Step S120: Multi-source data acquisition and preprocessing: Design data acquisition: Collect CAD drawings, GIS coordinate data, and equipment ledgers including core count, sheath material, and port quantity of optical cables and junction boxes; On-site data acquisition: High-precision point cloud data of the junction box was obtained using a laser scanner, and photos of the surface texture of the optical cable and junction box were taken by multi-view photogrammetry. The coordinates of key nodes of the optical cable laying path in the trench were measured manually. Data preprocessing: Convert CAD data to IGES format, remove point cloud noise through statistical filtering, complete coordinate registration using BeiDou positioning data, and integrate design data with field data to generate a standardized modeling data source.

[0035] Step S130, Layered 3D modeling as follows Figure 3 As shown: Basic layer modeling: Integrating GIS data and BIM models to reconstruct the 3D model of the terrain, trenches and surrounding buildings in the optical cable laying area; Facility layer modeling: Based on a parametric rule base, a 3D model of the optical cable is generated by defining parameters such as the number of optical cores and the diameter of the sheath. The physics engine is used to simulate the flexible deformation of the optical cable. Polygon modeling and surface modeling techniques are used to construct the junction box model to ensure that the port position and assembly relationship meet the rule requirements. Data layer modeling: Bind real-time temperature data of optical cables, maintenance records of junction boxes, fault history and other business data to the 3D model, and support obtaining equipment information through gesture zoom and voice query.

[0036] Step S140, Model Optimization: Mesh simplification: The EdgeQuadric algorithm is used to simplify the non-critical area patches of the optical cable, achieving a compression rate of 68%. Texture compression: The 4K texture map of the junction box was compressed using the BC7 format, reducing the size from 16MB to 4MB; LOD (Location-Oriented Displacement) grading: L0 level geometric error ≤ 5cm, L1 level geometric error ≤ 1cm, and L2 level geometric error ≤ 1mm are set to achieve dynamic switching between different scenarios, such as... Figure 4 As shown; Parametric dynamic adjustment: The level of detail displayed in the model is dynamically adjusted based on the distance between the inspection personnel and the equipment, thereby improving the speed of interactive response.

[0037] Step S150, Model Validation: Geometric accuracy verification: Key dimensions such as the port spacing of the optical cable junction box and the diameter of the optical cable were measured in the field and compared with the model data. The error was ≤0.8mm, which meets the requirements of maintenance training scenarios. Compliance verification: Verify that the device coding conforms to the Q / GDW11642-2016 standard and the interface specification conforms to the DL / T547-2021 requirements; Performance verification: On the AR glasses, the model loading time is ≤2s and the rendering frame rate is ≥60FPS, which meets the requirements of operation and maintenance scenarios. Generate model assets: After successful verification, generate 3D model assets of optical cables and junction boxes in glTF2.0 format.

[0038] Example 2 like Figure 5As shown, this embodiment of the invention provides a 3D modeling system for power communication equipment based on a parameterized rule base. First, raw scene data is acquired through scanning, drones, or manual methods. The acquired data is then cleaned and preprocessed to form a standardized dataset suitable for modeling. Throughout the data acquisition and processing process, national cryptographic algorithms and role-based access control are introduced to ensure data security and access compliance. Subsequently, the preprocessed data is input into the parameterized rule base module. This module uniformly manages and stores the rules involved in modeling, optimization, and output, providing rule support and parameter constraints for subsequent layered modeling and model optimization. Based on this, layered modeling is performed according to the rule base constraints, constructing basic, facility, and data layer models respectively to achieve a structured representation of complex scenes. After modeling is completed, the model undergoes network simplification, texture compression, and multi-level detail optimization to improve rendering efficiency and terminal adaptability. The optimized model then enters the accuracy, compliance, and performance verification stage. If the verification results do not meet the requirements, the model returns to the optimization stage for iterative adjustments; if the verification passes, the model is encapsulated in a standardized format and integrated into applications, supporting multiple output formats such as glTF and USDZ, and multi-terminal adaptation. Ultimately, the completed model is deployed to AR, mobile, and PC terminals, achieving a complete closed-loop process from data collection, modeling, optimization, and verification to application release.

[0039] The parameterized rule base module uses a distributed MySQL database to store rule data, and includes a basic rule sub-library, a device-specific rule sub-library, a compliance verification sub-library, and an optimization rule sub-library. It provides a web-based management interface, supporting the addition, modification, deletion, and version control of rules, and can synchronize rule content with the power communication industry standard database via API.

[0040] The data acquisition and preprocessing module is equipped with a FARO laser scanner, a DJI Phantom 4 RTK drone, a high-definition industrial camera, and a Beidou dual-mode positioning device. The data preprocessing unit has built-in format conversion tools, statistical filtering algorithms, coordinate registration tools, and deep learning-based data fusion algorithms, which can automatically complete the standardization processing of the acquired data.

[0041] The basic layer modeling unit in the layered modeling module supports the integration of BIM models and GIS data in IFC format. The facility layer modeling unit integrates the Unity3D engine, Blender modeling tools, and PhysX physics engine to achieve parametric modeling and complex shape modeling. The data layer modeling unit supports database interfaces such as MySQL and MongoDB, can bind real-time device running data, and provides interactive methods such as gesture recognition based on LeapMotion and voice control based on iFlytek API.

[0042] The model optimization module includes MeshLab mesh simplification tools, NVIDIA Texture Tools texture compression tools, and custom LOD level management tools. It is equipped with an NVIDIA RTX 3090 GPU acceleration unit, which improves the model optimization processing speed by more than 3 times compared to traditional methods. It also supports manual adjustment of optimization parameters to adapt to special scene requirements.

[0043] In the model verification module, the geometric accuracy verification unit uses a laser rangefinder to collect data on-site and automatically compares it with the model data. The compliance verification unit has a built-in power and communication industry standard rule library, which supports a combination of automatic verification and manual review. The performance verification unit simulates multiple terminal environments such as HoloLens2AR glasses, Huawei Mate40 mobile phone and Chrome browser to generate performance test reports.

[0044] The application integration module supports output formats such as glTF2.0, USDZ, and FBX, and is compatible with development platforms such as Unity, Unreal Engine, and WebXR. It has been integrated with the power communication AR operation and maintenance platform, remote assistance system, and virtual training system, enabling applications such as AR-assisted inspection, remote fault diagnosis, and virtual operation training.

[0045] The security protection module uses the national cryptographic algorithm SM2 for identity authentication, SM4 for encrypted data transmission and storage, and SM3 for data integrity verification. Based on the RBAC mechanism, different roles such as administrators, modelers, and maintenance personnel are set up and different data access permissions are assigned to ensure data security.

[0046] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for three-dimensional modeling of power communication equipment based on a parameterized rule base, characterized by, The method comprises the following steps: S1, constructing a parameterized rule base comprising basic rules, device-specific rules, compliance verification rules, and optimization rules; S2, multi-source data acquisition and preprocessing to generate standardized modeling data sources; S3, hierarchical three-dimensional modeling, including modeling of the basic layer, the facility layer, and the data layer; S4, optimization of the model based on the optimization rules in the parameterized rule base; S5, geometric accuracy verification, compliance verification, and performance verification of the optimized three-dimensional model.

2. The method of claim 1 wherein, In step S1, the parameterized rule base comprises a basic rule sub-base, a device-specific rule sub-base, a compliance verification sub-base, and an optimization rule sub-base, wherein each sub-base is used to constrain general modeling rules, modeling rules for different types of power communication devices, model compliance requirements, and model lightweight optimization strategies.

3. The method of claim 1 wherein, In step S2, the multi-source data acquisition comprises at least two of manual measurement, laser scanning, unmanned aerial vehicle aerial photography, and multi-view photogrammetry.

4. The method of claim 1 wherein, In step S3, the hierarchical three-dimensional modeling comprises: basic layer modeling for three-dimensional reconstruction of terrain, topography, and buildings; facility layer modeling for constructing parameterized geometric models of power communication devices; data layer modeling for binding and displaying device operation data, operation and maintenance data, or business data with the three-dimensional model.

5. The method of claim 1 wherein, In step S4, the model optimization comprises at least one of the following methods: mesh simplification, texture compression, level of detail (LOD) grading, and dynamic adjustment based on parameterized rules to make the model meet the rendering performance requirements of different terminals.

6. The method of claim 1 wherein, In step S5, the model verification comprises geometric accuracy verification, compliance verification, and performance verification, and the model is returned to the model optimization step for iterative adjustment when the verification fails.

7. A parametric rule-based power communications equipment 3-D modeling system, characterized by, It comprises: a parameterized rule base module for storing and managing modeling rules; a data acquisition and preprocessing module for acquiring and standardizing multi-source modeling data; a hierarchical modeling module for constructing a hierarchical three-dimensional model of the device based on the rule base; a model optimization module for lightweight processing of the three-dimensional model according to optimization rules; a model verification module for verifying the model according to compliance verification rules; an application integration module for outputting and deploying the three-dimensional model that passes the verification.

8. The system of claim 7, wherein, The data acquisition and preprocessing module is equipped with a laser scanner, a drone, and a Beidou positioning device; the model optimization module has a built-in GPU acceleration unit; the system supports multi-scenario positioning requirements based on Beidou positioning, two-dimensional code positioning + inertial navigation, and SLAM space mapping, and the model coordinate system is linked in real time with the positioning data.

9. The system of claim 7, wherein, The application integration module supports glTF2.0 and USDZ format output, is compatible with Unity and UnrealEngine development engines, and can be adapted to AR glasses, mobile devices, and web browsers.

10. The system of claim 7, wherein, It also comprises a security protection module that uses the national cryptographic algorithms SM2, SM3, and SM4 to encrypt and transmit and store modeling data, and realizes hierarchical access through a role-based access control (RBAC) mechanism, supports Wi-Fi, 4G, and 5G multi-network transmission, and model preloading in offline mode.

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