在地下矿山中预测货运卡车的行程时间
Victor Simon1,2, Robert Pellerin1,2, Michel Gamache1,2,3
1Department of Mathematics and Industrial Engineering, Polytechnique Montreal, 2500 Chemin de Polytechnique, H3T 1J4 Montreal, QC Canada.
Mining, metallurgy & exploration
|August 11, 2025
概括
本研究引入了一种机器学习模型,仅使用信标数据来预测地下运输卡车的行程时间,从而改善没有GPS的运营规划和生产力. 该系统在各种路线上显著减少了预测错误.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 准确的运输卡车 (HT) 旅行时间 (TT) 预测对于地下矿山运营规划,开采率预测和生产力提升至关重要.
- 传统的方法通常依赖于GPS,在地下环境中无法使用,限制了预测能力.
- 基于信标的定位系统提供了跟踪的替代方案,但尚未用于预测性TT分析.
研究的目的:
- 开发和验证用于预测HTTT的机器学习方法,仅使用信标检测数据来预测HTTT.
- 消除对传统遥测系统的依赖,以便在地下矿山中进行TT预测.
- 证明信标数据在提高矿山运营规划和生产率方面的有效性.
主要方法:
- 一种新的机器学习方法,结合了三个路线分割策略:全路线,短段和主要段预测.
- 整合高斯混合模型,长期短期记忆网络,以及用于预测的堆叠组合.
- 使用来自两个不同的地下矿山环境的数据进行验证.
主要成果:
- 拟议的方法在预测HTTT方面显著超过了行业基准.
- 在上升路线上预测误差降低了34%,在下降路线上降低了18%.
- 对于自动驾驶运输卡车来说,已经证明了更高的精度,突出了先进的应用潜力.
结论:
- 信号发射器检测数据可以有效地用于在地下矿山中准确的HTTT预测.
- 开发的机器学习模型为传统遥测提供了一个可行的替代方案,增强了运营规划.
- 这项研究释放了信标系统用于预测分析的潜力,支持改善矿山管理和生产力.
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