摘要
随着全球变暖日益严重,精准检测CO_(2)浓度具有重要的研究意义。可调谐半导体激光吸收光谱技术(TDLAS)具有高灵敏度、高分辨率等特点,被广泛应用于气体检测领域。为进一步提升TDLAS气体检测技术的检测精度,本文提出一种基于深度学习的原位激光二氧化碳检测系统。该系统采用BP神经网络算法反演CO_(2)浓度,补偿了温度压强对气体浓度反演的影响,提升了检测精度;采用无线通信模块,通过MQTT协议将检测的CO_(2)数据上传至OneNET云平台,实现了CO_(2)浓度的原位检测。经测试,该系统可以快速、稳定的处理数据,并且适配于其他气体检测系统。
With the increasing global warming,accurate detection of CO_(2) concentration is of great research importance.Tunable semiconductor laser absorption spectroscopy(TDLAS)technology is widely used in the field of gas detection because of its high sensitivity and high resolution.To further improve the detection accuracy of TDLAS gas detection technology,an in situ laser CO_(2) detection system based on deep learning is proposed in this paper.The system adopts BP neural network algorithm to invert CO_(2) concentration,which compensates the influence of temperature and pressure on gas concentration inversion and improves the detection accuracy;it adopts wireless communication module and uploads the detected CO_(2) data to OneNET cloud platform through MQTT protocol to realize the in-situ detection of CO_(2) concentration.The system has been tested to process data quickly and stably,and is adaptable to other gas detection systems.
作者
王彪
杨子腾
卞广雨
王冠懿
赵奕飞
薛金波
程林祥
WANG Biao;YANG Ziteng;BIAN Guangyu;WANG Guanyi;ZHAO Yifei;XUE Jinbo;CHENG Linxiang(Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;Jilin University,Changchun 130015,China;Jilin Agricultural University,Changchun 130118,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《激光杂志》
CAS
北大核心
2023年第6期48-52,共5页
Laser Journal
基金
吉林省科技发展计划重点科技研发项目(No.20220203016SF)
中国科学院大学生创新实践训练计划项目(No.2022008090)。
