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基于速度场的露天矿卡车多路段行程时间组合预测模型

田凤亮 王忠鑫 孙效玉 辛凤阳 宋波 王金金 曾祥玉 周浩 赵明

田凤亮,王忠鑫,孙效玉,等. 基于速度场的露天矿卡车多路段行程时间组合预测模型[J]. 工矿自动化,2022,48(6):95-99, 146.  doi: 10.13272/j.issn.1671-251x.17916
引用本文: 田凤亮,王忠鑫,孙效玉,等. 基于速度场的露天矿卡车多路段行程时间组合预测模型[J]. 工矿自动化,2022,48(6):95-99, 146.  doi: 10.13272/j.issn.1671-251x.17916
TIAN Fengliang, WANG Zhongxin, SUN Xiaoyu, et al. Combined prediction model of truck multi-section travel time in open-pit mine based on velocity field[J]. Journal of Mine Automation,2022,48(6):95-99, 146.  doi: 10.13272/j.issn.1671-251x.17916
Citation: TIAN Fengliang, WANG Zhongxin, SUN Xiaoyu, et al. Combined prediction model of truck multi-section travel time in open-pit mine based on velocity field[J]. Journal of Mine Automation,2022,48(6):95-99, 146.  doi: 10.13272/j.issn.1671-251x.17916

基于速度场的露天矿卡车多路段行程时间组合预测模型

doi: 10.13272/j.issn.1671-251x.17916
基金项目: 中煤科工集团沈阳设计研究院有限公司科技创新项目(NK023-2021);中国煤炭科工集团有限公司科技创新创业资金专项重点项目资助(2020-ZD002)。
详细信息
    作者简介:

    田凤亮(1992—),男,内蒙古赤峰人,工程师,博士,主要从事露天矿卡车智能调度研究与应用工作,E-mail:fliangtian@foxmail.com

    通讯作者:

    王忠鑫(1984—),男,内蒙古赤峰人,教授级高级工程师,博士研究生,主要从事煤炭露天开采理论、核心装备及其智能化技术研究工作,E-mail:wzx_syy@qq.com

  • 中图分类号: TD824

Combined prediction model of truck multi-section travel time in open-pit mine based on velocity field

  • 摘要: 受限于露天矿道路的复杂性,现有的卡车行程时间预测方法在实际部署中存在困难,导致卡车优化调度系统只实现调度而非优化。提出了一种基于速度场的露天矿卡车多路段行程时间组合预测模型。将露天矿道路划分为多个路段,采用随机森林算法构建单元预测模型,预测卡车在每一路段的行驶时间,再对各单元预测模型预测值累加,得出卡车在复合路段上的行程时间预测值。为提高预测精度,将卡车平均速度作为行程时间影响因素,根据已采集的卡车速度信息构建速度场,求取路段上所有点卡车速度的平均值,将其近似为卡车在该路段的平均速度并输入单元预测模型。以伊敏露天矿卡车调度系统中的卡车行程信息为基础数据,训练得到组合预测模型,并对该模型进行预测精度与实时性实验,结果表明:基于速度场的露天矿卡车多路段行程时间组合预测模型对于复合路段上的卡车行程时间具有较高的预测精度,平均绝对误差百分比为4.81%,较基于随机森林算法的单一预测模型降低2%以上;组合预测模型运算时间不超过1 s,可实现卡车行程时间实时预测。

     

  • 图  1  露天矿卡车多路段行程时间组合预测模型原理

    Figure  1.  Principle of combined prediction model of multi section travel time of truck in open-pit mine

    图  2  露天矿卡车速度分布

    Figure  2.  Speed distribution of truck in open-pit mine

    图  3  露天矿卡车速度场

    Figure  3.  Truck speed field in open-pit mine

    图  4  组合预测模型的预测误差分布

    Figure  4.  Prediction error distribution of the combined prediction model

    图  5  文献[10]中模型的预测误差分布

    Figure  5.  Prediction error distribution of the model in reference [10]

    图  6  组合预测模型运算时间

    Figure  6.  Operation time of the combined prediction model

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出版历程
  • 收稿日期:  2022-04-01
  • 修回日期:  2022-06-16
  • 网络出版日期:  2022-06-28

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