Research on the Chain Risk Identification of Large-Scale Skiing Events

Large-scale skiing events are commonly conducted in complex outdoor terrains and weather conditions, where adverse weather conditions can significantly impede the smooth progress of these events. The occurrence of adverse weather conditions increases the risk of domino chains caused by meteorological disasters during large-scale skiing events. To promptly identify the cascading risks caused by adverse weather, this paper proposes a solution to promptly identify cascading risks caused by adverse weather by integrating information related to adverse weather risks of skiing events and constructing a domain knowledge ontology. The paper analyzes and identifies the risk transmission paths of adverse weather, and constructs the risk identification rule base of related event chains to enable global risk chain reasoning driven by spatiotemporal semantics. The proposed approach is demonstrated through the skiing events of the 2022 Beijing Winter Olympics, from which the study correctly identifies the risk events caused by adverse weather encountered in multiple skiing events through reasoning. The results show the accuracy and feasibility of the event chain inference model.