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隧道建设(中英文) ›› 2021, Vol. 41 ›› Issue (9): 1502-1508.DOI: 10.3973/j.issn.2096-4498.2021.09.008

• 研究与探索 • 上一篇    下一篇

公路隧道环境感知系统的设计与实现

钱超1 2, 邓木生1, 李虎雄1, 陈建勋2, *, 王辰瑶1   

  1. 1. 长安大学电子与控制工程学院, 陕西 西安 710064 2. 长安大学公路学院, 陕西 西安 710064
  • 出版日期:2021-09-20 发布日期:2021-10-01
  • 作者简介:钱超(1984—),男,江苏新沂人,2013年毕业于长安大学,交通信息工程及控制专业,博士后,副教授,现主要从事公路隧道安全运营方面的教学与研究工作。E-mail: qianchao@chd.edu.cn。* 通信作者: 陈建勋, E-mail: chenjx1969@chd.edu.cn。
  • 基金资助:
    国家重点研发计划项目(2018YFB1600100); 陕西省自然科学基金项目(2019JQ-264); 大学生创新创业训练计划项目(202010710387, 202110710034)

Design and Implementation of an Environment Perception System for Highway Tunnels 

QIAN Chao1, 2, DENG Musheng1, LI Huxiong1, CHEN Jianxun2, *, WANG Chenyao1   

  1. (1. School of Electronic and Control Engineering, Chang′an University, Xian 710064, Shaanxi, China; 2. School of Highway, Chang′an University, Xi′an 710064, Shaanxi, China)

  • Online:2021-09-20 Published:2021-10-01

摘要: 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CONO2、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%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。

关键词: 公路隧道, 环境感知, 空气质量, 通风控制, 物联网, 云计算

Abstract: 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. Highprecision 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.

Key words: highway tunnel, environment perception, air quality; ventilation control, Internet of Things, cloud computing

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