基于事故链情境的铁路潜在风险关联推理研究
发布时间：2018-11-07 15:31
【摘要】：为科学合理、精准地对铁路潜在关键风险源进行定位管控,针对复杂事故链情境下的潜在风险关联推理问题,提出基于事故链情境本体建模的知识推理方法。首先在分析事故链案例情境要素的基础上,解析事故致因机理;其次构建基于本体的事故风险源知识模型,并建立事故风险源知识库;之后基于由知识关联分析获取的风险源关联推理规则,采用Apriori算法对隐患库进行关联分析,并将所得关联规则重新导入事故风险源知识库,以实现知识库的自学习与自更正;最后通过以地点为关键索引的事故链实例分析,验证方法的合理性和有效性,为精细化铁路风险预控提供参考。
[Abstract]:In order to locate and control railway potential key risk sources scientifically and accurately, a knowledge reasoning method based on situational ontology modeling of accident chain is proposed to solve the problem of potential risk association reasoning in complex accident chain. Firstly, on the basis of analyzing the situational factors of accident chain cases, the mechanism of accident cause is analyzed, secondly, the knowledge model of accident risk source based on ontology is constructed, and the knowledge base of accident risk source is established. Then, based on the inference rules of risk source association obtained from knowledge association analysis, the hidden trouble database is analyzed by Apriori algorithm, and the association rules are reimported into the accident risk source knowledge base to realize the self-learning and self-correction of the knowledge base. Finally, the rationality and validity of the method are verified by an example of the accident chain with the key index of location, which provides a reference for the precise railway risk pre-control.
【作者单位】： 北京交通大学经济管理学院;
【基金】：国家自然科学基金(51278030) 北京交通大学科研基金(2017YJS080)
【分类号】：U298
本文编号：2316787
[Abstract]:In order to locate and control railway potential key risk sources scientifically and accurately, a knowledge reasoning method based on situational ontology modeling of accident chain is proposed to solve the problem of potential risk association reasoning in complex accident chain. Firstly, on the basis of analyzing the situational factors of accident chain cases, the mechanism of accident cause is analyzed, secondly, the knowledge model of accident risk source based on ontology is constructed, and the knowledge base of accident risk source is established. Then, based on the inference rules of risk source association obtained from knowledge association analysis, the hidden trouble database is analyzed by Apriori algorithm, and the association rules are reimported into the accident risk source knowledge base to realize the self-learning and self-correction of the knowledge base. Finally, the rationality and validity of the method are verified by an example of the accident chain with the key index of location, which provides a reference for the precise railway risk pre-control.
【作者单位】： 北京交通大学经济管理学院;
【基金】：国家自然科学基金(51278030) 北京交通大学科研基金(2017YJS080)
【分类号】：U298
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