基于自动识别系统大数据的船舶施工轨迹识别与预测

针对疏浚监控管理工作很难全天覆盖所有船舶、无法做到实时监控的问题,分析某绞吸挖泥船的AIS(自动识别系统)高频数据,包括疏浚船舶动态的航行轨迹、速度、航向等数据。对船舶施工轨迹辨识和预测进行研究,提出利用DBSCAN聚类算法粗略识别出施工区域,利用LOF(局部异常因子)算法去除航行轨迹中非施工状态下的轨迹,并利用时间序列ARIMA模型对船舶施工轨迹进行预测。结果表明,DBSCAN聚类算法结合LOF算法进行施工轨迹辨识方法合理可行,ARIMA模型进行施工轨迹预测的方法具有精确度高、实时性、易实现的特点。

Identification and prediction of ship construction path based on AIS big data

In view of the problem that the dredging supervision is difficult to cover all ships all day and cannot achieve real-time monitoring,we analyze the high frequency data of AIS(automatic identification system)of a cutter suction dredger,including the dynamic longitude,latitude,speed,heading direction,etc.,and study the identification and prediction of ship construction trajectory by using the DBSCAN(density-based spatial clustering of applications with noise)clustering algorithm to roughly identify the construction area,using the LOF(local outlier factor)algorithm to remove the non-construction trajectory in the trajectory,and using the time series ARIMA model to predict the ship construction trajectory.The results show that DBSCAN clustering algorithm combined with the LOF algorithm is reasonable and feasible,and ARIMA(autoregressive integrated moving average)model is characterized by high accuracy,real-time and easy realization.