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Updated: May 21, 2025

08:05
Design and Analysis for Fall Detection System Simplification
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整合机器学习和一个大型语言模型,构建一个域知识图,以减少从高处落事故的风险
Zhipeng Zhou1, Xinhui Yu1, Joseph Jonathan Magoua1
1Department of Management Science and Engineering, College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Accident; analysis and prevention
|March 21, 2025
概括
对高空落事故的新知识图表有助于设计更好的防摔系统 (FPS). 该系统对事故数据进行了结构化,以改善高空工作场景的安全规划和决策.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 在工程领域的人工智能.
- 知识管理系统知识管理系统
背景情况:
- 从高处摔倒 (FFH) 事故是导致工作场所伤害和死亡的主要原因.
- 有效的防摔系统 (FPS) 对高空工作 (WAH) 的安全至关重要.
- 目前的FPS设计和选择面临着由于缺乏实用,结构化数据的挑战.
研究的目的:
- 开发一个专门的知识图 (FFH-KG) 对高空落事故.
- 支持设计和选择有效的防摔系统 (FPS).
- 为改善WAH环境中的安全规划和决策提供经验洞察力.
主要方法:
- 一种混合自然语言处理方法,结合手动提取,实体识别,文本细分和基于规则的关系提取.
- 开发一个由专家定义的本体学 (图表层) 来为知识图奠定基础.
- 整合机器学习和大型语言模型来进行文本挖掘,分类落类型,完善WAH场景,并确定落原因.
主要成果:
- 构建一个拥有2,200个实体和4,820个关系的综合FFH-KG.
- 该FFH-KG是建立在防摔设备标准和事故调查报告的基础上.
- 通过三个案例研究验证FFH-KG的检索性能,证明其实际适用性.
结论:
- 对于FPS设计人员和安全专业人员来说,FFH-KG是一个有价值的决策支持系统.
- 它通过提供对FFH事故的结构化,经验性的见解来加强对策的开发.
- 该研究推动了跨行业的智能安全工程和管理,以提高WAH安全.
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