船舶在进行智能化管理和航行时，需依据可靠的自主巡航数据完成，将大量的传感器数据和监测信息作为输入，以便系统能够作出正确的决策。然而，这些数据可能存在冗余信息干扰，影响着智能决策系统的可靠性，因此研究决策树算法在船舶自主巡航数据消冗中的应用。采用滤波、插值以及混合式时序数据生成的方式，进行船舶自主巡航数据的时序处理，生成规范的船舶自主巡航时序数据；依据处理后的数据生成决策树，划分船舶自主巡航数据类别；通过计算同类间数据相似度，并设计消除器，实现船舶自主巡航数据消冗处理，获取没有冗余的巡航数据。测试结果显示，该算法的数据时序处理效果较好，可以完成不同数据类别之间的划分，同时能够计算同类数据之间的相似度，最大空间缩减比为27.8%。
Intelligent management and navigation of ships need to be completed according to reliable autonomous cruise data, and a large number of sensor data and monitoring information are used as input, so that the system can make correct decisions. However, these data may have redundant information interference, which affects the reliability of intelligent decision-making system. Therefore, the application of decision tree algorithm in ship autonomous cruise data redundancy is studied. Filtering, interpolation and hybrid time series data generation are used to process the time series of ship autonomous cruise data and generate standardized time series data of ship autonomous cruise. According to the processed data, a decision tree is generated to classify the autonomous cruise data of the ship; By calculating the data similarity between the same kind and designing the eliminator, the ship autonomous cruise data can be eliminated and the cruise data without redundancy can be obtained. The test results show that the algorithm has a good effect on data time series processing, which can divide different data categories and calculate the similarity between similar data, and the maximum space reduction ratio is 27.8%.
2024,46(12): 157-161 收稿日期：2024-02-28
DOI：10.3404/j.issn.1672-7649.2024.12.027
分类号：TP301
基金项目：湖北省教育厅科技处科研计划项目（B2018273）
作者简介：生力军(1983-),男,硕士,副教授,研究方向为计算机信息技术
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