摘要
煤矿井下掘进作业是煤炭开采过程中的关键环节,其自动化管理水平直接影响生产效率和安全性。针对传统掘进管理系统存在的监控盲区与数据集成度低以及响应速度慢等问题,设计开发了基于物联网和人工智能技术的新型掘进自动化管理系统。通过构建“云-网-端”三层协同架构,实现了掘进过程的分布式控制与智能决策。采用多源传感网络优化设计、自适应控制算法改进以及边缘计算等技术,显著提升了系统的监测精度与控制响应速度以及故障诊断能力。半实物仿真实验结果表明,优化后的系统在传感器精度、网络延时以及控制响应等关键指标上均取得显著提升,故障诊断准确率达到90%以上。
The underground tunneling operation of coal mines is a key link in the coal mining process,and its automation management level directly affects production efficiency and safety.In view of the problems of monitoring blind spots,low data integration,and slow response speed in the traditional tunneling management system,a new type of tunneling automation management system based on the Internet of Things(IoT)and artificial intelligence technology has been designed and developed.By constructing a three-layer collaborative architecture of"cloud-network-terminal",the distributed control and intelligent decision-making of the tunneling process have been realized.By using multi-source sensing network optimization design and adaptive control algorithm improvement,as well as edge computing technology,the monitoring accuracy,control response speed,and fault diagnosis ability of the system have been significantly improved.The results of the semi-physical simulation experiment show that the optimized system has achieved significant improvements in key indicators such as sensor accuracy,network delay,and control response,and the accuracy of fault diagnosis reached more than 90%.
作者
仉冰
史禄
ZHANG Bing;SHI Lu(Shandong New Mine Zhaogang Energy Co.,Ltd.,Dezhou,Shandong 251113,China)
出处
《自动化应用》
2025年第15期118-121,共4页
Automation Application
关键词
煤矿掘进
自动化管理
物联网
人工智能
实时监控
优化设计
coal mine tunneling
automation management
Internet of Things
artificial intelligence
real-time monitoring
optimization design
