A digital twin modeling method for comprehensive service management in universities was proposed, targeting their digital transformation. By combining generative artificial intelligence algorithms with digital twins, data from various aspects of university management were aggregated, including fire safety, energy consumption, and underground piping. A GenAI risk assessment model was proposed to achieve comprehensive monitoring and intelligent scheduling of campus resources. The proposed method accurately represents the complex and diverse comprehensive service entities in universities, predicts potential risks effectively, and helps improve the efficiency and quality of comprehensive service management
