地下矿山中的地缘危险识别:一个移动应用程序
Pedro Lopez1, Nathalie Risso1, Angelina Anani1
1Department of Mining and Geological Engineering, University of Arizona, Tucson, AZ 8572, USA.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
一个新的移动应用程序,HUMApp,使用人工智能来检测地下矿山中危险的屋顶倒塌,提高安全性. 这种危险识别工具达到90%的准确性,有助于采矿业务的积极风险管理.
科学领域:
- 地质技术工程 地质技术工程
- 采矿领域的人工智能
背景情况:
- 矿业存在重大地质技术危险,对工人的安全构成风险.
- 有效的危险识别对于减轻地下采矿环境中的风险至关重要.
研究的目的:
- 引入地下矿山危险识别应用程序 (HUMApp),以提高矿山安全.
- 开发一个移动应用程序,有效地识别地质技术危险,特别是屋顶塌.
主要方法:
- 开发一个使用卷积神经网络 (CNN) 的移动应用程序 (HUMApp).
- 数据集准备,CNN模型培训和严格的绩效评估.
- 用于危险识别的专家评估对应用程序的验证.
主要成果:
- 在检测地质技术危险方面,HUMApp实现了90%的准确性.
- 回忆和F1得分也达到了90%,表明了高可靠性.
- 在HUMApp的评估和专家评估之间发现了显著的一致性.
结论:
- 在识别关键的地质技术危险方面,HUMApp表现出高可靠性和有效性.
- 该应用程序提供了一个可扩展的解决方案,用于在各种地下采矿环境中主动进行风险管理.
- HUMApp有可能在采矿行业内显著提高安全协议.
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