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刘鹏, 赵慧含, 仰彦妍, 景江波, 魏卉子, 丁恩杰. 基于OWL的瓦斯爆炸事故语义本体构建及推理[J]. 煤炭科学技术, 2018, (8).
引用本文: 刘鹏, 赵慧含, 仰彦妍, 景江波, 魏卉子, 丁恩杰. 基于OWL的瓦斯爆炸事故语义本体构建及推理[J]. 煤炭科学技术, 2018, (8).
LIU Peng, ZHAO Huihan, YANG Yanyan, JING Jiangbo, WEI Huizi, DING Enjie. Construction and reasoning of semantic ontology for gas explosion accident based on OWL[J]. COAL SCIENCE AND TECHNOLOGY, 2018, (8).
Citation: LIU Peng, ZHAO Huihan, YANG Yanyan, JING Jiangbo, WEI Huizi, DING Enjie. Construction and reasoning of semantic ontology for gas explosion accident based on OWL[J]. COAL SCIENCE AND TECHNOLOGY, 2018, (8).

基于OWL的瓦斯爆炸事故语义本体构建及推理

Construction and reasoning of semantic ontology for gas explosion accident based on OWL

  • 摘要: 基于矿山安全环境分析和事故危险源理论,利用本体语言OWL构建了矿山危险源本体,并结合与瓦斯爆炸事故的内在联系,构建了瓦斯爆炸事故树本体。继而通过设计本体推理工具的自定义规则,实现了从根源危险源到状态危险源的推理,同时根据瓦斯爆炸事故树本体实现了顶上事件发生概率的计算。提出的矿山安全领域本体构建及推理方法,可用于瓦斯爆炸事故预警的辅助决策。试验结果表明,构建的矿山危险源本体可实现从根源危险源到状态危险源的正确推理,并可根据瓦斯爆炸事故树本体自下而上计算顶上事件发生概率,验证了利用OWL本体推理辅助预测瓦斯爆炸事故的有效性。

     

    Abstract: Based on mine safety environment anlalysis and danger source theory,coal mine danger source ontology by OWL is constructed.Exploring the intrinsic relation between mine danger source and gas explosion accident,we construct gas explosion fault tree.Moreover,by designing reasoning rules,we realize the reasoning process from the root danger source ontology to the status danger source ontology.Finally,from bottom to top,based on the basic events status,the probability of the top accident event is calculated according to the constructed gas explosion fault tree ontology.The proposed construction and reasoning methods of mine accident ontology can be used for assistant decision-making on gas explosion accident warning.The experimental results show that the constructed mine accident ontology can correctly implement the reasoning from the root danger source to the status danger source.Meanwhile,the probability of top accident event of the gas explosion fault tree ontology is exactly calculated.These results verify the effectiveness of OWL ontology technology in predicting coal mine gas explosion accidents.

     

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