现代统计学理论的船舶交通数据深度挖掘

Deep mining of ship traffic data based on modern statistical theory

船舶交通变化十分复杂,通过对其数据进行挖掘,可以了解船舶交通变化态势,便于制定相应的船舶交通管控措施。为了更好掌握船舶交通变化态势,提出基于现代统计学理论的船舶交通数据深度挖掘方法。首先采集大量的船舶交通数据,并对数据进行一些预处理,然后采用现代统计理论中的灰色模型和支持向量机分别对船舶交通数据进行挖掘和建模,最后对两者挖掘结果进行组合,并采用实例分析了船舶交通数据深度挖掘效果。结果表明,本文方法的船舶交通数据挖掘精度要高于其他方法,能够更好拟合船舶交通变化态势,在实际应用中价值更高。

Ship traffic change is very complex.Through data mining,we can understand the situation of ship traffic change,which is convenient to develop the corresponding ship traffic control measures.In order to better grasp the situation of ship traffic change,we put forward the modern statistical theory of ship traffic data depth mining method.Firstly,a large number of ship traffic data are collected and preprocessed.Then,the gray model and support vector machine in modern statistical theory are used to mine and model the ship traffic data respectively.Finally,the mining results of the two are combined,and the effect of ship traffic data deep mining is analyzed by an example.The results show that the accuracy of ship traffic data mining of this method is higher than that of other methods,which can better fit the situation of ship traffic change,and has higher value in practical application.