Optimizing the Power Grid's Two-Ticket System Based on High-Voltage Line Inspection Results Using DenseNet Model

As China's power grid continues to expand and grow more complex, ensuring its safe and stable operation has become increasingly critical. While smart inspection technologies provide real-time monitoring, the traditional "two-ticket" system in power grid operation and maintenance suffers from inefficiencies, high error rates, and management difficulties. This paper presents an optimized approach for the two-ticket system by leveraging smart inspection results. Focusing on high-voltage power line defect detection, a deep learning method is used. The SegNet network accurately identifies defect locations, DenseNet’s residual structure improves feature learning, and the SENet attention mechanism emphasizes defect features. By integrating these insights into the two-ticket system, this method automates ticket generation, approval, and circulation, thereby improving efficiency, reducing errors, and ensuring the grid’s stability.