基于AIS数据的桥区水域船舶非安全行为检测OA

The detection of unsafe ship behaviors in bridge waterways is crucial for ship collision avoidance.To enhance navigation safety,this study proposes a novel model that integrates a heuristic algorithm with a neural network for behavior detection.First,this study conducts an in-depth analysis of AIS data from consecutive bridge zones in the Wuhan section of the Yangtze River.In accordance with maritime regulations,four types of unsafe ship behaviors are defined:overspeeding,turning,crossing,and anchoring.Then,through an examination of multi-dimensional features,such as ship position,speed,and heading,a thorough analysis of navigation patterns and rules is conducted.This analysis yields specific decision criteria for the unsafe behaviors,thus establishing a specialized database for ship behaviors in bridge waterways.Finally,an improved PSO-LSTM model is developed to mitigate the negative impact of random parameter initialization in conventional LSTM networks on detection accuracy.Experimental results indicate that the proposed model exhibits clear superiority over SVM,BP,LSTM,and improved PSO-BP models in both visualization and evaluation metrics,achieving high detection precision for unsafe ship behaviors in bridge waterways and thus providing reliable decision-making support for maritime authorities.