船舶虚拟驾驶是训练船舶驾驶效果的主要途径，为准确了解船舶虚拟驾驶效果，研究基于数据挖掘的船舶虚拟驾驶效果评价方法。依照系统性、层次性等原则，构建初始船舶虚拟驾驶效果评价指标体系；采用数据挖掘技术中的聚类算法筛选初始指标体系内的指标，清除重复指标，得到最终使用的船舶虚拟驾驶效果评价指标体系；采集指标体系中评价指标的相关数据，同时对所采集数据实施归一化与标准化等预处理。采用数据挖掘技术中的神经网络，构建基于径向基神经网络的船舶虚拟驾驶效果评价模型，将评价指标数据作为模型输入，得到船舶虚拟驾驶效果评价结果。实验结果显示该方法所构建的评价指标体系具有较高科学性，能够准确评价船舶虚拟驾驶效果。
Ship virtual driving is the main way to train the ship driving effect. In order to understand the ship virtual driving effect accurately, the evaluation method of ship virtual driving effect based on data mining is studied. According to the principles of systematization and hierarchy, the evaluation index system of initial ship virtual driving effect is established. The clustering algorithm in data mining technology is used to screen the indexes in the initial index system, eliminate the duplicate indexes, and obtain the final evaluation index system of ship virtual driving effect. Collect the relevant data of the evaluation indicators, and carry out normalization and standardization of the collected data. The neural network of data mining technology is used to construct the evaluation model of ship virtual driving effect based on radial basis function neural network. The evaluation index data is used as the input of the model to obtain the evaluation results of ship virtual driving effect. The experimental results show that the evaluation index system constructed by this method is highly scientific and can accurately evaluate the effect of ship virtual driving.
2022,44(18): 181-184 收稿日期：2022-03-29
DOI：10.3404/j.issn.1672-7649.2022.18.038
分类号：TP183
基金项目：辽宁省科技厅支持计划项目(2020JH2/10700007)
作者简介：马大勇(1979-),男,硕士,副教授,主要从事数字媒体研究
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