中国科技核心期刊
美国化学文摘社（CAS）数据库
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日本科学技术振兴机构数据库(JST)
三山岛金矿开采环境生成与预测研究
- 赵龙|李海龙
- 作者单位：
- 山东黄金矿业（莱州）有限公司三山岛金矿|山东黄金矿业（莱州）有限公司三山岛金矿
基金项目：
- 国家自然科学基金项目（51974043）
详细信息：
- 作者简介：
- 赵龙（1988—），男，辽宁辽阳人，工程师，从事金属矿山采矿技术研究等相关工作；山东省莱州市三山岛镇，山东黄金矿业（莱州）有限公司三山岛金矿，261442；E-mail:165162428@qq.com
- 通讯作者：
- isnull
- PDF下载
Research on the generation and prediction of the mining environment in the Sanshandao Gold Mine
- English Author：
- Sanshandao Gold Mine,Shandong Gold Mining Industry(Laizhou)Co.,Ltd.|Sanshandao Gold Mine,Shandong Gold Mining Industry(Laizhou)Co.,Ltd.
- Unit：
- 摘要
- 在线预览
- 参考文献
摘要:
为了保障开采设备在采场内安全作业，需要实时获取采场的应力、应变、位移等采场物理环境数据。针对三山岛金矿地下规模化无人采矿技术，开展了试验采场环境生成方法研究。采用三维激光扫描仪扫描得到试验采场空间形态的点云数据与彩色图像信息，处理后得到试验采场的空间环境；采用采场环境感知装置连接多类型光纤光栅传感器，完成集群试验采场内物理环境数据的实时采集和存储，进而得到作业采场范围内的开采环境。通过分析已回采采场物理环境数据变化情况，建立能够快速预测临近采场物理环境变化的预测模型，对开采装备的安全运行和试验采场的安全生产具有指导作用。
关键词:
金属矿山；深部开采；无人采矿；环境生成；预测模型
Abstract:
To ensure the safe operation of mining equipment in the stope,it is necessary to obtain the physical environment data of the stope such as stress,strain,and displacement in the stope in real-time.The study of the generation method of the test stope environment is carried out for the scaled-up unmanned underground mining technology in Sanshandao Gold Mine.A three-dimensional laser scanner is used to scan and obtain the point cloud data and colored image information of the spatial form of the test stope.After processing,the space environment of the test stope is obtained;the stope environment perception device is used to connect multiple types of fiber grating sensors to complete the cluster of real-time collection and storage of physical environment data within the test stope,and then the mining environment within the operating stope is obtained.Based on the change of physical environment data of the mined stope,a prediction model that can quickly predict the change of the physical environment of the adjacent stope is established,which can play a guiding role in the safe operation of mining equipment and the safe production of the test stope.
Keywords:
metal mine;deep mining;unmanned mining;environment generation;prediction model