为避免船舶信息风险引发重大船舶航行事故，研究基于组合优化算法的船舶信息风险评估方法。选取通信、环境、管理、人为这4个方面因素共17个指标，构建船舶信息风险评估指标体系，将其作为径向基函数（RBF）神经网络输入层输入数据，经隐含层映射操作后，通过输出层输出评估到的船舶信息风险等级，采用结合模糊C均值聚类算法和遗传算法的组合优化算法，合理选取RBF神经网络隐含层中心向量并寻优获取最佳隐含层基函数宽度和权值向量，提升船舶信息风险评估效果。实验结果表明：该方法可有效评估多艘船舶的信息风险，并可依据评估结果获取何种因素导致船舶信息风险，提出针对性指导建议。
In order to avoid major ship navigation accidents caused by ship information risks, a ship information risk assessment method based on combinatorial optimization algorithms is studied. Selecting a total of 17 indicators from four aspects of communication, environment, management, and human factors, a ship information risk assessment index system is constructed. It is used as input data for the radial basis function (RBF) neural network input layer, and after hidden layer mapping operation, the evaluated ship information risk level is output through the output layer. A combination optimization algorithm combining fuzzy C-means clustering algorithm and genetic algorithm is adopted, Reasonably selecting the center vector of the hidden layer in the RBF neural network and optimizing it to obtain the optimal width and weight vector of the hidden layer basis function, in order to improve the effectiveness of ship information risk assessment. The experimental results show that this method can effectively evaluate the information risk of multiple ships, and based on the evaluation results, identify the factors that cause ship information risk and provide targeted guidance suggestions.
2024,46(5): 163-166 收稿日期：2023-12-08
DOI：10.3404/j.issn.1672-7649.2024.05.030
分类号：U698
作者简介：朱国君(1975-),男,硕士,研究方向为航运管理、水路运输及海商法
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