数据采矿实施方法,以确定地下硬煤采矿职业事故的根本原因和预防措施
Bilal Altındiş1, Fatih Bayram2
1TTK Turkish Hard Coal Corporation, Türkiye.
Safety and health at work
|December 19, 2024
概括
数据挖掘确定了导致采矿事故的关键因素,包括工人的经验和轮班时间. 这一分析为改善职业安全和防止未来在地下煤矿中受伤提供了实际见解.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
- 职业安全与健康问题 职业安全与健康问题
背景情况:
- 采矿业务产生了大量的生产力和安全数据.
- 将这些数据转化为可操作的信息对于企业的成功至关重要.
- 数据挖掘提供了强大的工具,可以从复杂的数据集中提取有价值的见解.
研究的目的:
- 分析来自地下硬煤矿 (2010-2021) 的职业事故数据.
- 用数据挖掘技术识别伤害的根本原因.
- 为事故预防提供实际建议.
主要方法:
- 利用跨越多年的受伤事故数据.
- 通过WEKA程序使用数据挖掘算法,包括k-Nearest Neighbors和支持向量机.
- 应用关联规则挖掘 (先验,预测先验) 来确定事故因果关系.
主要成果:
- k-最近的邻居和支持向量机器表现出强大的预测性能.
- 协会规则挖掘确定了工人的经验,班次时间和作为主要事故司机的位置.
- 月度生产,工资和事故原因也显示出显著的影响.
结论:
- 这些发现与采矿企业的运营现实一致.
- 该研究为评估和减轻职业事故提供了实际建议.
- 数据驱动的洞察力可以增强安全协议并降低受伤率.
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