This study addresses the challenges of managing energy consumption and flight state variation in unmanned aerial vehicles (UAVs) operating in time-varying wind fields by proposing a real-time path replanning strategy based on an event-triggered mechanism (ETM). The approach first employs an improved particle swarm optimization (IPSO) algorithm for path planning, which assesses the similarity between paths under varying wind conditions to quantify the impact of wind field changes on the current path. Additionally, a Kalman filter (KF) is applied to predict the flight state of a UAV, evaluating the effect of wind variations on state prediction accuracy by comparing actual and predicted states. Based on these insights, an ETM is established to update the path only when wind field changes exceed a specified threshold. Simulation results demonstrate that the proposed strategy significantly reduces energy consumption and computational load, while enhancing the adaptability in complex wind environments.