根据改进的算法,研究棉花机的火灾检测
Zhai Shi1, Fangwei Wu1, Changjie Han1
1College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
棉花采摘工因燃烧而面临着火灾风险. 这项研究引入了一种改进的多传感器数据融合系统,该系统与BP神经网络相结合,并通过混合灰狼-粒子群算法进行优化,以实现准确的实时火灾检测.
科学领域:
- 农业工程 农业工程
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 棉花采摘者在复杂的环境中工作,隐藏的火灾构成重大风险.
- 传统的火灾检测方法对于棉花收获的动态条件是不够的.
- 棉花的物理和操作特性增加了在采摘过程中燃烧的可能性.
研究的目的:
- 设计一个改进的多传感器数据融合算法,用于提高棉花采摘机的火灾检测.
- 开发一个强大的火灾检测系统,利用红外温度和CO传感器.
- 提出和验证一个优化的反向传播 (BP) 神经网络模型,用于准确的火灾预测.
主要方法:
- 开发一个棉花火灾检测系统,将红外温度和CO传感器与上部计算机集成在一起.
- 使用混合灰狼优化器和粒子群优化 (MGWO-PSO) 算法优化的新型BP神经网络模型的实现.
- MGWO-PSO算法包含一个突变运算符,以提高可搜索性和PSO原则,以有效优化BP网络.
主要成果:
- 优化BP神经网络的MGWO-PSO实现了0.96929.0的相关系数 (R).
- 该系统的预测准确率为96.10%,预测错误率低至3.9%.
- 准确的早期预警率达到了96.07%,虚假报警和遗漏率低于5%.
结论:
- 拟议的多传感器数据融合和优化的BP神经网络为实时棉花采摘者火灾检测提供了有效的方法.
- 该系统提供及时警告,大大提高了现场作战期间的安全性.
- 这项研究提出了一种新的方法,可以准确地检测棉花采摘机中的火灾,减轻与燃烧相关的风险.
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