由于人工记录、手动测量等方式存在信息不及时、不准确以及局限性的问题，无法获取到实时、全面的航行数据，降低了大数据异常属性划分结果的有效性，因此提出物联网环境下船舶航行大数据异常属性划分方法。在物联网环境下利用离散度函数，加权处理船舶航行大数据属性特征。通过密度选择法，确定船舶航行大数据异常属性划分的初始聚类中心。利用属性加权快速聚类算法，结合离散度函数与初始聚类中心，完成船舶航行大数据异常属性划分。实验证明，所提出方法可有效划分船舶航行大数据异常属性。在不同大数据规模下，该方法异常属性划分的加速比均较大，即异常属性划分速度较快。
Due to the problems of untimely, inaccurate, and limited information in manual recording and measurement methods, real-time and comprehensive navigation data cannot be obtained, which reduces the effectiveness of big data anomaly attribute classification results. Therefore, a method for dividing ship navigation large data anomaly attributes in the internet of things environment is proposed. In the context of the internet of things, the discrete degree function is used to weight and process the attribute features of ship navigation big data. Determine the initial clustering center for dividing abnormal attributes of ship navigation big data through density selection method. Using attribute weighted fast clustering algorithm, combined with discrete degree function and initial clustering center, complete abnormal attribute division of ship navigation big data. The experiment proves that the proposed method can effectively classify the abnormal attributes of ship navigation big data. Under different big data scales, this method has a relatively high acceleration ratio for abnormal attribute division, which means that the speed of abnormal attribute division is faster.
2023,45(24): 204-207 收稿日期：2023-07-01
DOI：10.3404/j.issn.1672-7649.2023.24.039
分类号：TP371
作者简介：朱慧珍(1987-),女,讲师,研究方向为人工智能及数据挖掘
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