基于风网的自学习动态监测系统分析

煤矿井下巷道风速是随时变化的,主要规律是一种围绕某一平均值的上下起伏的平稳随机过程,其表现为平均风速和脉动风速,风速传感器最大限度地反映了井下主要巷道风速信息。我们将井下风速传感器与通风解算技术相结合,对全矿井的风网进行实时计算,从而得到了全矿井较准确的实时分风量分布状况。系统能够将风速传感器采集到的实时风速转换为巷道的实时风量,根据月风量统计结果进行巷道阻力系数的自动调整;系统采用相关分析技术,测定煤矿井下数据之间相关关系和规律,并据此建立预分析测模型,进而进行风量的预测和控制;系统具有自我学习功能,通过不断修正模型参数,将实时井下探测数据用于分析和预测,为安全管理提供有效指导。

Analysis on self-learning dynamic monitoring system based on ventilation network

The wind speed in underground tunnel of mine varies at any time,the main rule is a stationary random process with up and down motion around an average value,and it shows as average wind speed and pulsation wind speed.The wind speed sensor can reflect the wind speed information of main underground tunnel to the greatest degree.By combining the underground wind speed sensor and ventilation calculating technology,the real-time calculation of ventilation network in whole mine was conducted to obtain the more accurate distribution of real-time wind rate.The system could convert the measured real-time wind speed by wind speed sensor into the real-time wind rate of tunnel,and automatically adjust the resistance coefficient of tunnel based on the statistical results of monthly wind rate.The relation analysis technology was applied in the system to examine the relationship and rule of underground data,and establish prediction model to conduct the prediction and control of wind rate.The system owned the function of self-learning,by amending the parameters in the model,the real-time underground monitoring data could be used to analysis and prediction,which can provide effective instruction for safety management.