为准确获取隧道环境信息、〖ＪＰ＋１〗综合评判隧道空气质量，设计并开发了一套基于物联网和云计算的公路隧道环境感知系统。将有害气体、温度气压、能见度和风速风向等高精度传感器在隧道内集成部署，利用STM32微处理器实现环境信息的实时监测，应用4G移动通信网络进行数据的远程传输，最终实现隧道环境监测数据的云端存储与实时查看。与各专用检测设备相比，本系统CO、NO2、能见度和风速采集数据平均绝对百分比误差分别为3.36%、1.73%、5.43%和2.06%。实测结果表明,本系统集成度高、布设简便、运行稳定，弥补了便携式隧道监测设备采集内容单一、数据精度不足和传统固定式隧道监测设施结构复杂、布设分散的技术缺陷。系统的长期在线运行，可为解析隧道内空气质量在交通影响下的变化规律、动态调节通风设施运行提供实测数据支撑。
To acquire the accurate tunnel environment information
and comprehensively evaluate the tunnel air quality, a series of highway tunnel
environment perception system (TEPS) based on the Internet of Things and cloud
computing is designed and developed. Highprecision sensors measuring
pollutant concentration, temperature and pressure, visibility, and wind speed
and direction are arranged and integrated in a tunnel. An STM32 microprocessor
is adopted for realtime monitoring of environmental
data, and a 4G mobile communication network is used for remote data
transmission, realizing the cloud storage and realtime
display of the tunnel environment monitoring data. Compared with various
special detectors, the mean absolute percentage errors of CO and NO2 concentrations, visibility, and wind speed of the proposed system
are 3.36% and 1.73%, 5.43%, and 2.06%, respectively. Results demonstrate that
the TEPS is highly integrated, can be easily deployed, and provide stable
operation. These performance characteristics overcome the shortcomings of
portable monitoring devices, such as single collection content and insufficient
data accuracy, and those of the traditional fixed tunnel monitoring systems,
such as complex structure and scattered layout. By operating the TEPS online
for a long time, suitable data can be measured to support the exploration of
the variations in air quality in the tunnel caused by traffic and dynamical
adjustment of the ventilation systems.
