在环境因素的合下,对多种类型的安全事件进行风险识别和评估
Qian Liu1, Junqiao Li2, Zhixin Jin1,3
1College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong, 030600, China.
Scientific reports
|February 16, 2026
概括
本研究引入了一种用于识别和评估工业安全事件风险的新模型. 它整合了理论和数据,以更好地了解多种因素如何导致安全事件.
科学领域:
- 工业安全 工业安全 工业安全
- 风险管理 风险管理
- 数据科学数据科学数据科学
背景情况:
- 高风险的工业环境产生了大量的传感器数据用于安全研究.
- 目前的数据驱动方法无法捕捉影响安全事件 (SE) 的因素之间的复杂,非线性关系.
- 需要一个系统的框架来解释多个因素对不同SE类型的共同贡献.
研究的目的:
- 为多种类型的SE风险识别和评估提出理论数据综合模型.
- 阐明由环境因素的非线性合驱动的SE的链式进化过程.
- 建立一个系统的框架,以理解层次因素的关联和跨尺度的相互作用.
主要方法:
- 从跨规模的出现视角开发了一个理论框架.
- 构建了一个数据驱动的风险识别和评估模型 (RIAM).
- 利用在线学习来量化可测环境因素 (MEF) 对 SEs 的贡献.
主要成果:
- 拟议的模型有效地捕捉了多因素合产生的累积风险影响.
- 在SE识别中提高准确性.
- 在风险评估中提高了模型的解释性.
结论:
- 综合模型为复杂工业环境中的SE预测和预防提供了一种新的方法.
- 从跨规模的新兴视角来理解SE的方法论基础.
- 强调非线性因子合在工业安全中的重要性.
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