融合真实与模拟数据，用深度学习预测露天矿运输车队产能
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data
- 结合真实运营数据与模拟故障场景训练模型
- LSTM模型中位绝对误差为15.1%，优于XGBoost的14.3%
- 可为矿山调度提供抗扰动的实时决策支持
准确预测露天矿短期运输车队产能至关重要，因天气波动、设备故障和人员变动带来显著不确定性。本文提出一种深度学习框架，融合高分辨率降雨数据、车队运行遥测数据与合成生成的机械故障场景，使模型能捕捉高影响性突发故障。评估两种架构：XGBoost回归器中位绝对误差（MedAE）为14.3%，长短期记忆网络（LSTM）为15.1%。SHAP值分析表明累积降雨量、历史载重趋势和模拟故障频率是主要预测因子。整合模拟故障数据与排班特征显著降低预测波动。未来工作将加入维修调度指标（如平均无故障时间、平均修复时间）、详细人力资源数据（操作员缺勤率、班组效率）、爆破计划等，进一步提升预测鲁棒性与适应性。该混合建模方法为动态不确定环境下主动、数据驱动的车队管理提供了综合决策支持工具。
原文摘要 · Abstract (English)
Accurate short-term forecasting of hauling-fleet capacity is crucial in open-pit mining, where weather fluctuations, mechanical breakdowns, and variable crew availability introduce significant operational uncertainties. We propose a deep-learning framework that blends real-world operational records (high-resolution rainfall measurements, fleet performance telemetry) with synthetically generated mechanical-breakdown scenarios to enable the model to capture fluctuating high-impact failure events. We evaluate two architectures: an XGBoost regressor achieving a median absolute error (MedAE) of 14.3 per cent and a Long Short-Term Memory network with a MedAE of 15.1 per cent. Shapley Additive exPlanations (SHAP) value analyses identify cumulative rainfall, historical payload trends, and simulated breakdown frequencies as dominant predictors. Integration of simulated breakdown data and shift-planning features notably reduces prediction volatility. Future work will further integrate maintenance-scheduling indicators (Mean Time Between Failures, Mean Time to Repair), detailed human resource data (operator absenteeism, crew efficiency metrics), blast event scheduling, and other operational constraints to enhance forecast robustness and adaptability. This hybrid modelling approach offers a comprehensive decision-support tool for proactive, data-driven fleet management under dynamically uncertain conditions.
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