This research proposes a dynamic risk management framework based on ontology, aiming to reveal and manage the relationship between dynamic risks in the field through the identification and comparison of crowd ontology and risk ontology. The research introduces the concept and implementation of "BIM Fences," integrating computer vision and digital twin technology to enhance the dynamic risk management and maintenance of buildings. The BIM Fence system comprises three core components: context awareness, digital twin, and risk management. Context awareness utilizes computer vision technology to identify the location of crowd flow in real-time, establishing a dynamic database of people’s locations. The digital twin then combines this crowd flow data with the BIM model to create a real-time digital twin model. Finally, the risk management module defines risk zones on the digital twin platform and determines whether risks occur based on collision detection between crowd flow and risk zones, thereby providing corresponding risk warnings. This research validated the effectiveness of the BIM Fence The experimental results showed that the system could accurately record the entry of experimental subjects into the risk zones, provided that the experimental subjects maintained an appropriate distance, initially demonstrating the high reliability of the system. In summary, this research not only proposes the concept of BIM Fence but also demonstrates its potential in dynamic risk management of building operation through actual system development and experimental verification, providing a new direction for the digital transformation of the building operation industry.