基于大数据分析的船舶交通特征统计分析

Statistical analysis of ship traffic characteristics based on big data analysis

为了降低现阶段船舶交通特征统计分析数据的离散度,提高统计分析质量,提出了基于大数据分析的船舶交通特征统计分析方法。根据数据挖掘结果进行数据初步处理,获取船舶交通数据极值以及样本差和方差,根据当前船舶交通数据流进行数据卡方拟合和分布拟合,获取拟合曲线和曲线函数,根据拟合曲线的实际特征值确定当船舶交通流理论特征值,并在理论特征值中添加边界条件,利用边界矩阵获取实际特征数据集合,实现船舶交通特征统计。仿真实验数据结果表明,应用设计方法统计的船舶交通特征值,极差数据降低了29%,标准差降低了35%,可以有效降低数据离散。

In order to reduce the dispersion of statistical analysis data of ship traffic characteristics and improve the quality of statistical analysis.The design proposes a statistical analysis method of ship traffic characteristics based on big data analysis.By data mining,the current vessel traffic flow data processing,to obtain extremum and the poor samples and variance of vessel traffic data,based on the current vessel traffic data stream,chi-square distribution fitting and data fitting,get fitting curve and curve function,according to the actual characteristic values of the fitted curve,to determine the characteristic value as the ship vessel traffic flow theory,the theory of characteristic value,add boundary conditions,boundary matrix is used to collect the actual characteristics of the data collection,realize the characteristics of vessel traffic statistics.The results of simulation experiment show that the range data and standard deviation are reduced by 29%and 35%respectively,which can effectively reduce the data dispersion.