College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China.
Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China.
Sensors (Basel). 2022 Mar 8;22(6):2096. doi: 10.3390/s22062096.
The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd., Shanghai, China). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction.
通风参数的准确可靠监测是智能通风系统的关键。为了实现气流的可视化，必须使用少量传感器解决气流重建问题。在本研究中，提出了一个新的概念，称为独立切割集，它依赖于基础图的结构，以确定传感器的最小数量和位置。我们在金美公司（中国上海）拥有的一个煤矿中评估了其有效性。我们的结果表明，需要设置风速传感器的隧道不到 30%，就可以重建所有隧道的良好气流（有些矿山的隧道超过 200 个）。结果表明，该算法是可行的。使用该算法重建的通风网络的空气体积与实际空气体积相同。该算法为流量重建提供了理论支持。
