基于多种交互方式的分布式空气质量监测系统设计与实现
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摘要
针对目前空气质量监测只反映一个城市的总体水平、监测指标不全面、交互方式不佳等问题,设计并实现了一个基于多种交互方式的分布式空气质量监测系统,以实现对空气质量的实时高效监测。该系统包含了数据采集子系统、数据管理子系统和数据处理子系统。为了让数据采集子系统更准确地采集空气质量数据,分区域部署了多个终端监测设备组成一个分布式系统实现多站点监测。在数据管理子系统中,使用云平台进行监测数据的存取管理,实现海量数据的便捷、实时、可靠存取。在数据处理子系统中,采用多种交互方式供用户获取和分析空气质量数据。交互方式包括数据的云端交互、数据的移动端交互和数据的三维可视化交互,使得用户不仅能更便捷地查询空气质量还能及时地收到污染指标超标报警和空气质量预测信息。该系统在实际应中能够全面地监测各区域空气质量,并且交互功能丰富,有效弥补了目前空气质量监测的不足。
In view of only reflecting the overall level of a city,monitoring incomplete indicators,having poor interactions and other issues regarding current air quality monitoring,a new air quality monitoring distributed system based on multiple interactions is designed and implemented to monitor air quality real-timely and efficiently. The system includes the data acquisition subsystem,the data management subsystem,and the data processing subsystem. In order to make the data acquisition subsystem collect air quality data more accurately,air quality at different locations is monitored with multiple monitoring devices deployed in different areas. In the data management subsystem,the cloud platform is used to implement access management of monitoring data,which achieves convenient,real-time and reliable access of mass data. In the data processing subsystem,multiple interactions are used for users to obtain and analyze air quality data. The interactions include data interaction of cloud,data interaction of mobile terminal,and data interaction of 3 D visualization,so that users can not only query air quality more easily,but also receive pollution warning and air quality forecast information in time. In the process of actual application,the system is able to monitor air quality in different areas comprehensively and has abundant interactions,which compensates for the current defects of air quality monitoring effectively.
In view of only reflecting the overall level of a city,monitoring incomplete indicators,having poor interactions and other issues regarding current air quality monitoring,a new air quality monitoring distributed system based on multiple interactions is designed and implemented to monitor air quality real-timely and efficiently. The system includes the data acquisition subsystem,the data management subsystem,and the data processing subsystem. In order to make the data acquisition subsystem collect air quality data more accurately,air quality at different locations is monitored with multiple monitoring devices deployed in different areas. In the data management subsystem,the cloud platform is used to implement access management of monitoring data,which achieves convenient,real-time and reliable access of mass data. In the data processing subsystem,multiple interactions are used for users to obtain and analyze air quality data. The interactions include data interaction of cloud,data interaction of mobile terminal,and data interaction of 3 D visualization,so that users can not only query air quality more easily,but also receive pollution warning and air quality forecast information in time. In the process of actual application,the system is able to monitor air quality in different areas comprehensively and has abundant interactions,which compensates for the current defects of air quality monitoring effectively.
引文
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[15]WANG J,NIU T,WANG R.Research and application of an air quality early warning system based on a modified least squares support vector machine and a cloud model[J].International Journal of Environmental Research&Public Health,2017,14(3):249.
[2]环境保护部,国家质量监督检验检疫总局.GB3095-2012环境空气质量标准[S].北京:中国环境科学出版社,2012.Ministry of Environmental Protection,State Administration of Quality Supervision,Inspection and Quarantine.GB3095-2012 Ambient air quality standards[S].Beijing:China Environmental Science Press,2012.
[3]闫涛.基于物联网的空气质量监测系统设计与应用技术研究[D].济南:山东大学,2016.YAN T.Design and application technology research of air quality monitoring system based on internet of things[D].Ji’nan:Shandong University,2016.
[4]沈佳辉,骆懿,陆家明.基于WSN的环境质量监测系统设计与实现[J].通信技术,2015,48(9):1087-1091..SHEN J H,LUO Y,LU J M.Design and implementation of environmental quality monitoring system based on WSN[J].Communication Technology,2015,48(9):1087-1091.
[5]胡敦利,李亮,高杨,等.基于MSP430的空气环境质量监测系统设计[J].高技术通讯,2015(3):279-285.HU D L,LI L,GAO Y,et al.Design of air environment quality monitoring system based on MSP430[J].High Technology Letters,2015(3):279-285.
[6]赫高进,易天洋,薛晨曦,等.基于内存空间数据库的空气质量实时监测系统[J].计算机研究与发展,2015(s1):149-153.HAO G J,YI T X,XUE CH X,et al.Air quality real-time monitoring system based on memory space database[J].Computer Research and Development,2015(s1):149-153.
[7]钟艺华.基于物联网的空气质量监测终端应用的设计与开发[D].北京:北京邮电大学,2014.ZHONG Y H.Design and development of air quality monitoring terminal application based on internet of things[D].Beijing:Beijing University of Posts and Telecommunications,2014.
[8]李庆,张娅.远程空气质量监测系统设计[J].自动化与仪表,2011,26(6):54-56.LI Q,ZHANG Y.Design of remote air quality monitoring system[J].Automation and Instrumentation,2011,26(6):54-56.
[9]李苗苗.区域空气污染扩散数据可视化技术的研究[D].济南:山东大学,2011.LI M M.Study on visualization technology of regional air pollution diffusion data[D].Ji’nan:Shandong University,2011.
[10]胡亚,朱军,林珲,等.基于大气污染扩散模型的空气污染多维动态可视化表达研究[J].高技术通讯,2013,23(7):728-734.HU Y,ZHU J,LIN H,et al.Multidimensional dynamic visualization of air pollution based on air pollution diffusion model[J].High Technology Letters,2013,23(7):728-734.
[11]艾洪福,石莹.基于BP人工神经网络的雾霾天气预测研究[J].计算机仿真,2015,32(1):402-405.AI H F,SHI Y.Study on haze prediction based on BP artificial neural network[J].Computer Simulation,2015,32(1):402-405.
[12]周国亮,刘希玉,武鲁英.BP神经网络模型在空气质量级别评价中的应用[J].计算机工程与设计,2009,30(2):392-394.ZHOU G L,LIU X Y,WU L Y.Application of BP neural network model in air quality level evaluation[J].Computer Engineering and Design,2009,30(2):392-394.
[13]AVRIL C,FRANCESCO P,LAURENCE G.Prediction of indoor air exposure from outdoor air quality using an artificial neural network model for inner city commercial buildings[J].International Journal of Environmental Research&Public Health,2015,12(12):15233-15253.
[14]PEI X K,ZHANG J P,DING P X,et al.Genetic algorithm back propagation(GABP)neural network based controller workload prediction method and system,CN 105160201 A[P].2015.
[15]WANG J,NIU T,WANG R.Research and application of an air quality early warning system based on a modified least squares support vector machine and a cloud model[J].International Journal of Environmental Research&Public Health,2017,14(3):249.