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作 者:胡甚平[1] 黎法明[2] 席永涛[1] 吴建军[1]
机构地区:[1]上海海事大学商船学院,上海201306 [2]广东交通职业技术学院海事学院,广州510800
出 处:《应用基础与工程科学学报》2015年第2期409-419,共11页Journal of Basic Science and Engineering
基 金:上海海事大学校基金项目(20120057);广东省交通运输厅科技项目(201202004);国家自然科学基金项目(51279099)
摘 要:海上交通运输业是公认的高风险行业之一,本文引入人工智能方法,对其系统风险演化机理与多因素耦合作用研究.首先,建立海上交通系统风险云模型,阐明基于3个不安全因素的海上交通系统风险成因耦合机理;其次,采用云模型对海上交通系统风险成因的耦合作用和影响程度进行仿真;再次,结合中国沿海水上交通事故的案例样本信息,运用云模型不确定推理船员、船舶和环境等不安全因素对海上交通系统风险的影响;最后,利用综合云对3因素之间的耦合作用进行升云分析,定量化揭示风险成因的影响程度和作用机理.应用算例表明,不安全因素中任意两个因素的耦合对船舶海上交通系统风险影响的程度并不相同,多因素相互耦合是海上交通系统风险形成的结构性机理.现阶段而言,环境不安全状态的风险成影响地位比船员的不安全行为大.Maritime transportation is recognized as one of the high-risk sectors, and artificial intelligence approach was introduced to study formation mechanism and multi-factor coupling relationship on marine traffic system(MTS) risk.First, the cloud-model-based MTS risk was put forward and risk causal factors coupling mechanism was presented based on three unsafe segments in MTS risk. Second, effectiveness of coupling and influence from risk causes factors was quantitatively analyzed using comprehensive cloud model. Third, by using the historical accident sample on coastal MTS, the three elements influencing MTS risk was simulated separately by means of the cloud model reasoning, including unsafe behaviors of crew ( UBC ) , unsafe state of ships (USS)and unsafe conditions for environment (UCE)involved MTS causal segments.Furthermore, the coupling mode was quantitative analysis between the three causal segments on MTS, and the influence degree and effect mechanism from the risk causal segments was concluded, using mentioned comprehensive cloud model. The illustrative example shows that,influence degree of the risk coupling elements varied from any two of the three unsafe elements.UCE is one of the key segments in the MTS risk construct elements, and the risk caused by UCE is also higher than one caused by UBC.
关 键 词:水上交通 系统仿真 风险评估 耦合机理 云模型 综合云
分 类 号:X951[环境科学与工程—安全科学]
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