不同潮流时段船舶靠泊作业风险贝叶斯决策模型

为采取有效决策行为降低船舶靠泊作业风险,分析船舶靠泊作业流程,结合专家的知识对任务节点进行筛选,得到船舶靠泊作业过程图。通过对因素间可靠度进行检验,构建贝叶斯网络结构,建立船舶靠泊作业风险决策模型。结合油船靠泊作业相关数据,获取各节点条件概率,将贝叶斯推理、决策等分析方法应用到模型中,得出一定风险状态下的风险决策结论。将潮流分为6个时段,根据每个时段的证据推理结果,识别出各时段船舶靠泊作业中的关键环节,为相关部门提供参考。

Bayesian decision making model for ship berthing operation risk in different tide periods

To reduce the ship berthing operation risk by taking effective decision making behaviors, the ship berthing operation process is obtained by analyzing the ship berthing operation flow and combining the knowledge of experts to screen the task nodes. By testing the reliability between factors, the Bayesian network structure is constructed, and the risk decision making model of the ship berthing operation is established. Combined with the data related to the berthing operation of oil tankers, the conditional probability of each node is obtained, the Bayesian reasoning and decision making analysis methods are applied to the model, and the risk decision making conclusion under a certain risk state is obtained. The tide is divided into six periods. According to the evidence reasoning results of each tide period, the key tasks in the ship berthing operation of each tide period are identified, which provides reference for relevant departments.