Intelligent Power Grid Design Review via Active Perception-Enabled Multimodal Large Language Models
This addresses the need for safer and more efficient power grid engineering design review, though it is incremental as it builds on existing MLLM methods with a novel prompt-driven approach.
The paper tackles the problem of automated review of ultra-high-resolution power grid design drawings, which current systems struggle with due to computational demands and lack of semantic understanding, and proposes a three-stage MLLM-based framework that improves defect discovery accuracy and reliability in review judgments.
The intelligent review of power grid engineering design drawings is crucial for power system safety. However, current automated systems struggle with ultra-high-resolution drawings due to high computational demands, information loss, and a lack of holistic semantic understanding for design error identification. This paper proposes a novel three-stage framework for intelligent power grid drawing review, driven by pre-trained Multimodal Large Language Models (MLLMs) through advanced prompt engineering. Mimicking the human expert review process, the first stage leverages an MLLM for global semantic understanding to intelligently propose domain-specific semantic regions from a low-resolution overview. The second stage then performs high-resolution, fine-grained recognition within these proposed regions, acquiring detailed information with associated confidence scores. In the final stage, a comprehensive decision-making module integrates these confidence-aware results to accurately diagnose design errors and provide a reliability assessment. Preliminary results on real-world power grid drawings demonstrate our approach significantly enhances MLLM's ability to grasp macroscopic semantic information and pinpoint design errors, showing improved defect discovery accuracy and greater reliability in review judgments compared to traditional passive MLLM inference. This research offers a novel, prompt-driven paradigm for intelligent and reliable power grid drawing review.