安全智能采矿:大数据时代的若干探索与挑战
Safe and intelligent mining:Some explorations and challenges in the era of big data
董陇军 1王剑 1王加闯 1王宏伟1
作者信息
- 1. School of Resources and Safety Engineering,Central South University,Changsha 410083,China
- 折叠
摘要
为进一步完善采矿学学科体系,针对传统采矿学理论与方法上存在的不足,立足于学科建设高度,积极推进采矿学学科理论的研究与课程内容体系的基本建设,本文开展了安全智能采矿学的建立研究.首先,根据采矿学发展进程以及国内外智能采矿的发展现状,提出了安全智能采矿学的概念,并分析了其学科内涵与学科属性.其次,从学科建设角度提出安全智能采矿学学科基础与学科任务,并构建了学科内容体系.最后,本文提出了安全智能采矿学面临的十大挑战,讨论了安全智能采矿学的未来研究任务与应用前景.安全智能采矿学的创建,是大数据时代下信息化采矿工程学科发展的必然趋势,可为安全智能采矿学学科的发展注入生机与活力.
Abstract
To improve discipline systems and address shortcomings of traditional mining theory,this work defines the safe and intelligent mining using the advanced theory and technology in the era of big data.It gives some explorations and challenges for the mining discipline theory and the basic construction of the course content system.Firstly,according to the development process of mining science and the situation of intelligent mining around the world,it presents the concept of safe and intelligent mining,and analyzes the connotations and attributes of the discipline.Secondly,it gives the discipline basis and tasks of safe and intelligent mining from the construction of discipline,and structures the discipline content system.Finally,the work introduces the challenges and discusses the research tasks and application prospects of safe and intelligent mining in terms of mining technology,equipment,methods,data and information platforms,and environment in the future.The construction of safe and intelligent mining is a trend that is unavoidable as the field of information-based mining engineering develops in the age of big data.This trend injects vitality and vigor into the field's development while enhancing the environmental protection of mines.
关键词
采矿学/采矿工程/安全智能采矿学/学科建设/内容体系Key words
mining environment/safe and intelligent mining/discipline construction/sensor system/content system引用本文复制引用
基金项目
National Key R&D Program of China(2021YFC2900500)
Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(52161135301)
出版年
2023
NETL
NSTL
维普
万方数据