Cheng Hengyu, Zhu Jinsong, Wang Sining, Yan Ke, Wang Haojie
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221006, China.
China Academy of Safety Science and Technology, Beijing 100012, China.
Sensors (Basel). 2024 May 27;24(11):3463. doi: 10.3390/s24113463.
As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing.
作为影响自动喷水灭火系统灭火精度的主要外部因素之一，对随机风进行分析研究很有必要。然而，在实际应用中，关于随机风对喷头灭火落点影响的研究较少。针对这一问题，本文构建了一种新型随机风采集系统，提出了一种在随机风影响下基于随机森林（RF）预测射流轨迹落点的方法，并与常用的预测模型支持向量机（SVM）进行了比较。本文方法将50m预测结果在x方向的误差从2.11m降至1.53m，y方向的误差从0.64m降至0.6m，总平均绝对误差（MAE）从31.3降至23.5。同时，预测随机风影响下不同距离处射流轨迹的落点，以证明所提方法在实际应用中的可行性。实验结果表明，本文提出的系统和方法能有效改善随机风对射流轨迹落点的影响。综上所述，本文提出的图像采集系统和误差预测方法在灭火方面有诸多潜在应用。
