摘要
为解决煤矿瓦斯异常涌出风险预警过程中过度依赖系统模型,不能动态实时修正预测模型,预测、预报精准度和可靠度不高的难题,基于动态数据驱动技术,搭建瓦斯异常涌出风险预警系统架构,探讨动态数据驱动的瓦斯涌出监测曲线拟合、动态预警模型选择和修正、预警系统研发等关键性技术,开发基于动态数据驱动的瓦斯异常涌出风险预警系统软件。结果表明:动态数据驱动技术在煤矿瓦斯异常涌出风险预警方面具有强大的信息处理和问题求解能力,可实现仿真系统与实际系统间的动态响应和控制功能,并实时反馈修正,使预测结果更加精确、可靠,设计研发的预警系统可在矿井受瓦斯异常涌出威胁时发出可靠的预警信号。
In order to solve the problems of excessive dependence on the system model in the early warning process of gas abnormal emission risk in coal mine,unable to dynamically modify the prediction model in real time,and the accuracy and reliability of prediction and forecasting are not high,the framework of an early warning system of gas abnormal emission risk was built based on the dynamic data-driven technology.The key technologies of the fitting of gas emission monitoring curve,the selection and correction of dynamic early warning model and the development of early warning system based on dynamic data-driven were discussed,and an early warning system software of gas abnormal emission risk based on dynamic data-driven was developed.The results showed that the dynamic data-driven technology had strong capabilities of information processing and problem solving for the early warning of abnormal gas emission risk in coal mine.It could realize the dynamic response and control function between the simulation system and the actual system,and feed back and correct in real time,so as to make the prediction results more accurate and reliable.The designed and developed early warning system could send the reliable early warning signal for the mine threatened by the abnormal gas emission.
作者
张巨峰
施式亮
邵淑珍
游波
吴芳华
张立志
ZHANG Jufeng;SHI Shiliang;SHAO Shuzhen;YOU Bo;WU Fanghua;ZHANG Lizhi(School of Resource & Environment and Safety Engineering,Hunan University of Science and Technology,Xiangtan Hunan 411201,China;School of Energy Engineering,Longdong University,Qingyang Gansu 745000,China)
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2022年第2期100-105,共6页
Journal of Safety Science and Technology
基金
国家自然科学基金项目(51974120,51774135,51974119)
甘肃省自然科学基金项目(21JR11RM049)
甘肃省高等学校创新能力提升项目(2019B-154)。
关键词
数据驱动
瓦斯
异常涌出
风险预警
预警系统
data-driven
gas
abnormal emission
risk early warning
early warning system
作者简介
张巨峰,博士研究生,教授,主要研究方向为煤矿灾害预防与控制;通信作者:施式亮,博士,教授,主要研究方向为煤矿灾害预防与控制、系统安全评价与预测、安全系统工程等。
