基于气体传感器和随机森林的毒害气体浓度检测方法研究

Study of Toxic Gases Concentration Detecting Method Based on Gas Sensors and Random Forest

研究了一种基于气体传感器和随机森林分析模型的毒害气体浓度检测方法,在温度25℃、相对湿度80%、标准大气压的条件下,实验模拟检测氨气、甲苯、硫化氢三种毒害气体泄漏并记录检测数据,采用非线性随机共振模型解析检测数据特征值,采用随机森林模型对检测特征值进行气体类型预测。研究结果表明,所提出的方法具有较好的预报精度。本文所提出的方法可以有效的解析检测传感器的信号特征向量,依托随机森林模型对于毒害气体泄漏浓度的判断更加精准,将在实验室危化品泄漏监测领域具有广阔的前景。

Toxic gas concentration detecting method based on gas sensors and random forest analysis model was studied.The detection system for hazardous gas was constructed.Under the conditions of temperature 25℃,relative humidity 80%and standard atmospheric pressure,the experimental simulation of ammonia gas,toluene,hydrogen sulfide in different concentrations was conducted,and non-linear stochastic resonance model was used to extract the eigen values of measurement data.Results indicated that the proposed method presented good prediction accuracy.The method proposed in this paper is promising in the field of toxic gases leaky monitoring.