从非结构化的事故报告到职业风险管理的混合决策支持系统:共识融合方法
Rajan Kumar Gangadhari1, Meysam Rabiee2, Vivek Khanzode1
1Operations and Supply Chain Management, Indian Institute of Management, Mumbai 400087, India.
Journal of safety research
|June 10, 2024
概括
本研究引入了一种混合机器学习方法,用于分析石油行业的非结构化事故报告,改善风险因素优先级,减少分析时间,以提高安全性.
科学领域:
- 石油行业的安全 石油行业的安全
- 职业健康和安全问题 职业健康和安全问题
- 数据科学应用数据科学应用
背景情况:
- 石油行业的工作场所事故对人员,财产和环境构成重大风险.
- 事故报告主要是非结构化的文本,这使得传统的分析方法耗时且依赖于专业知识.
- 需要先进的方法来分析这些被忽视的文本数据,以获得更好的安全洞察力.
研究的目的:
- 开发基于机器学习的决策支持系统,用于分析非结构化事故数据.
- 创建一种混合方法,将文本挖掘和团体决策结合起来,以优先考虑风险因素.
- 克服传统事故数据分析技术的局限性.
主要方法:
- 一种混合方法,将改进的文本挖掘与公正的集团决策框架相结合.
- 使用上下文词嵌入模型和术语频率来提取风险因素集群.
- 采用最好-最坏的方法来结合客观 (文本挖掘) 和主观 (专家意见) 的权重来确定风险优先级.
主要成果:
- 从非结构化事故报告中确定了五个关键风险因素集群,涵盖了32多个子因素.
- 成功结合客观和主观权重来优先考虑已识别的风险因素.
- 使用印度石油工业事故数据证明了框架的适用性.
结论:
- 拟议的框架有效地分析石油行业事故的非结构化文本数据.
- 这种方法可以显著减少分析时间,提高风险因素分类和优先级的准确性.
- 该框架可适应各种行业的事故数据分析,以改进安全协议.
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