Deep Learning and GIM Standard-Based Intelligent 3D Modeling Algorithm for Substations with Distributed Collaborative Design System

INTRODUCTION: Contemporary substation design methodologies encounter fundamental limitations in achieving optimal geometric precision and collaborative efficiency, particularly when addressing the integration of heterogeneous data sources while maintaining strict adherence to Geographic Information Modeling (GIM) standards. Existing computational approaches demonstrate significant deficiencies characterized by prolonged processing durations and constrained accuracy levels, thereby necessitating the development of innovative solutions that leverage cutting-edge artificial intelligence techniques to overcome these systematic challenges. OBJECTIVES: This investigation aims to develop and validate a comprehensive intelligent 3D modeling algorithm specifically designed for electrical substation applications that seamlessly integrates advanced deep learning methodologies with rigorous GIM standard compliance and sophisticated distributed collaborative design functionalities, while simultaneously achieving substantial improvements in geometric accuracy and computational efficiency compared to conventional design paradigms. METHODS: The proposed algorithmic framework employs sophisticated hierarchical neural network architectures that incorporate multi-scale convolutional feature extraction mechanisms and adversarial generative training protocols. The comprehensive system architecture integrates four critical components: intelligent data acquisition and preprocessing modules, advanced deep learning computational engines, automated GIM standard compliance verification systems, and distributed collaborative design platforms. Experimental validation was conducted using an extensive dataset encompassing 12,847 technical engineering drawings, 1,156 high-resolution point cloud segments, and 3,428 photogrammetric image collections, with comprehensive field testing involving up to 32 concurrent collaborative users across diverse operational scenarios. RESULTS: The developed intelligent modeling algorithm achieved exceptional geometric accuracy of 96.8% compared to 87.3% demonstrated by traditional methodologies, representing a substantial 9.5 percentage point improvement in modeling precision. Computational efficiency demonstrated remarkable optimization with processing time reduced by 94%, decreasing from the conventional range of 180-240 minutes to an unprecedented 12.4 minutes per complete substation model. Extensive field validation trials confirmed seamless collaborative scalability with negligible performance degradation under multi-user operational conditions, while maintaining GIM standard compliance exceeding 99.2% across all tested configurations and operational scenarios. CONCLUSION: The developed intelligent 3D modeling system establishes a new technological paradigm for substation design applications, delivering exceptional improvements in both geometric accuracy and computational efficiency while maintaining stringent GIM compliance requirements. The framework's robust integration capabilities enable seamless deployment within existing power system management infrastructures without necessitating extensive modifications to established operational workflows, thereby providing a comprehensive foundation for next-generation collaborative engineering design platforms in critical infrastructure applications.