远程控制船舶搁浅风险耦合要素与关键路径识别

范存龙; 邱煜辉; 尹传忠; 席永涛; 胡甚平; 韩冰

doi:10.13637/j.issn.1009-6094.2025.082010.13637/j.issn.1009-6094.2025.0820

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远程控制船舶搁浅风险耦合要素与关键路径识别

安全评价安全工程 | 更新时间：2026-04-17

- 远程控制船舶搁浅风险耦合要素与关键路径识别
- 远程控制船舶搁浅风险耦合要素与关键路径识别
- 安全与环境学报 2026年26卷第4期页码：1235-1244
- 作者机构：
  
  1. 上海海事大学交通运输学院
  2. 上海海事大学商船学院
  3. 上海船舶运输科学研究所有限公司
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年项目(52301419);国家自然科学基金面上项目(52272353,52372416)
- DOI：10.13637/j.issn.1009-6094.2025.082010.13637/j.issn.1009-6094.2025.0820
  中图分类号： U698
- 纸质出版：2026
- 稿件说明：
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[1]范存龙,邱煜辉,尹传忠,等.远程控制船舶搁浅风险耦合要素与关键路径识别[J].安全与环境学报,2026,26(04):1235-1244.
[1]范存龙,邱煜辉,尹传忠,等.远程控制船舶搁浅风险耦合要素与关键路径识别[J].安全与环境学报,2026,26(04):1235-1244. DOI： 10.13637/j.issn.1009-6094.2025.082010.13637/j.issn.1009-6094.2025.0820.

DOI：10.13637/j.issn.1009-6094.2025.0820.

摘要

为了评估远程控制船舶在港口水域航行过程中的搁浅风险，有必要明确该类型船舶风险耦合效应中的关键要素和关键风险耦合路径问题。结合深圳西部港区的98起船舶搁浅事故报告数据，引入N-K模型构建远程控制船舶5种风险因素类型及其组合下的风险耦合效应模型；通过区间数排序、配对秩检验和多维度敏感性分析，分析远程控制船舶的搁浅风险耦合效应的响应程度，以及子样本之间的差异。结果表明：在单一风险源下，人员、船舶和技术是较为关键的风险因素类型，但单独作用的重要性在多因素耦合情景下有所降低；“船舶-技术”是最重要的关键风险耦合路径；多因素复杂耦合在不同时段的敏感性均存在差异；在复杂耦合情景下，远程控制人员是关键性防御环节。

Abstract

While intelligent shipping is an inevitable trend

the coupling mechanisms of grounding risks for remotely controlled vessels are still not well understood. To address this gap

a multi-dimensional sensitivity analysis is conducted to identify critical factors contributing to the risk coupling effects associated with smart vessel grounding. The analysis focuses on 98 grounding accident reports from the western waters of Shenzhen Port

categorizing them into sub-samples based on the time of occurrence to align with the respective shifts of the chief

second

and third officers. Risk coupling evaluation is performed using the 24Model and N-K model to quantify the coupling effects across five types of risk factors: Human-related

Organization-related

Ship-related

Environment-related

and Technology-related

as well as their various combinations. Paired rank tests

interval number ranking

and sensitivity analysis are conducted to identify key risk factor types

assess the influence of the number of combined risk factors

determine key risk coupling pathways

and analyze differences among subsamples. The number of accidents is incorporated as a new input parameter for the N-K model

and risk coupling values are recalculated accordingly. A sensitivity analysis is then performed to determine the relative significance of each factor in relation to the overall risk coupling effects. The results indicate that the risk coupling effects among the chief

second

and third officers during their work shifts show considerable similarity across various combinations of risk factor types when remotely controlled vessels operate in port waters. However

the sensitivity of the risk coupling values varies across the three work shifts under multi-factor complex coupling scenarios. The risk coupling values exhibit significant sensitivity to increases in the number of accidents. Sensitivity analysis confirms that the impact of changes in the number of accidents associated with different risk factor types on risk coupling effects is characterized by nonlinearity. While Human-related

Ship-related

and Technology-related factors are crucial when considered in isolation

their individual significance diminishes in comparison to scenarios involving multiple risk factor combinations. Specifically

individual Ship-related or Technology-related factors demonstrate limited importance; however

their combined influence significantly enhances the sensitivity of risk coupling values

thereby revealing the key risk coupling pathways. In complex coupling scenarios

it is essential to mitigate Human-related risks to prevent the amplification of the risk coupling effect.

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