基于数据挖掘的露天矿道路质量研究
Research on open-pit mine road quality based on data mining
孙欣武 1张瑞新 1孙健东 2邢丹 1马赛赛 1王妍1
作者信息
- 1. 华北科技学院 计算机学院,河北 三河 065201
- 2. 华北科技学院 矿山安全学院,河北 三河 065201
- 折叠
摘要
探讨了露天矿山道路质量评价方法;通过深度挖掘露天矿山运输环节矿卡运行数据,设计道路提取算法,分析了竖直加速度和高程数据,将 2 种评价指标高度H标准差和竖直加速度均方根RMSVA分别进行研究,并验证了二者的一致性和有效性;将评价结果划分为良好、一般、差 3 个级别,指出了不同级别道路对应的道路问题并给出修护建议.结果表明:2 种指标在坡度-4%～4%范围内均可有效评价露天矿山道路质量,有助于提高矿山道路维护效率和质量.
Abstract
This article explores the evaluation method of road quality in open-pit mines,through deep mining of mine truck operation data in transportation links of open-pit mines,designs road extraction algorithms,analyzes the vertical acceleration and elevation data,and investigating the two evaluation metrics:the standard deviation of the altitude H and the root-mean-square of the vertical acceleration RMSVA,respectively.We verify the consistency and validity of the two.The evaluation results are categorized into three levels:good,fair and poor,and the road problems corresponding to the different levels are pointed out and suggestions for repair are given.The results show that the two indexes are effective in evaluating the quality of open-pit mine roads in the range of slope-4%to 4%,which helps to improve the efficiency and quality of mine road maintenance.
关键词
道路质量/RMSVA/露天煤矿/智能化/数据挖掘Key words
road quality/RMSVA/open-pit coal mine/intelligent/data mining引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据