Abstract: With the continuous improvement of the ecological environment, the contradiction between bird population activities and power grid safe operation has become increasingly prominent. Traditional static prevention measures based on physical isolation have limited protection areas and are vulnerable to environmental erosion. Moreover, birds are prone to adaptive defects. By integrating deep learning recognition algorithms with laser, loud sound, and ultrasonic waves, non- invasive multi-modal dispersal technologies, a “perception - decision - execution - optimization” full-process closed- loop prevention system has been constructed. This system achieves precise millisecond-level identification and targeted intervention of affected birds, demonstrating significant fault voltage reduction effects and operational cost advantages in complex power grid scenarios.