Research on AI-Based Photovoltaic Construction Management

With the global energy structure transitioning towards cleanliness, the installed capacity of photovoltaic power generation continues to expand. However, traditional construction management models face bottlenecks such as low quality inspection efficiency, unreasonable resource scheduling, and frequent safety accidents. This paper addresses the pain points of photovoltaic construction management by constructing an intelligent management system that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Deep Q-Networks (DQN). Empirical research on a photovoltaic project in Guizhou shows that the defect detection accuracy of components based on ResNet-50 transfer learning reaches 98 %, with detection efficiency improved by five times compared to manual methods; the DQN dynamic scheduling model increases equipment utilization during peak construction periods to 89 %; and a fivelevel early warning system achieves a rapid response to extreme risks in 42 seconds, enhancing accident handling efficiency by 50 times. This research provides a quantifiable intelligent solution for photovoltaic engineering and has significant practical value in promoting the construction of clean energy infrastructure.