Design Of Environmental Monitoring And Analysis System For Smart Community

With the rapid development of urban economy,air pollution problem has seriously endangered the health of urban residents,and has become the primary problem of urban environmental pollution.The Community is the most basic organization of social composition and the basic place for the life urban residents.The community environment is closely related to the physical and mental health of residents.Ensuring the air quality and safety in the community is the bottom line for urban residents to live and daily travel healthily.Currently,there are many environmental websites that provide current air quality information,but generally they are based on the data given by each state-controlled monitoring site.Such real-time data can only reflect the regional air quality information of the monitoring site in the city,can not represent the real situation of the community in which the residents live,and the website design does not fit the user experience,so the residents get air quality information very easily inconvenient.In view of this situation,this thesis has designed and implemented an intelligent community environment monitoring and analysis system based on the daily travel needs of community residents,real-time monitoring of air quality information and providing suggestions for residents’ daily travel.We used sensor technology,machine learning,software development technology to build a set of environmental monitoring and analysis system that can collect and process air quality data in real time,predict air quality changes in the next 24 hours,and provide travel suggestions to users.In addition,users can receive meteorological,air quality,traffic travel information in APP and make complaints about environmental pollution in their lives.The government on the web side and property staff can work together to co-ordinate the environmental pollution problems in the community.The innovations and contributions of this thesis include: 1)aiming at the problems of lack of decision-making basis and low participation of residents in community environmental governance at this stage,big data mining and statistical analysis methods are used to select evaluation indicators,and based on AHP method,the environmental quality evaluation system of smart community is constructed from four aspects: community ecological environment,infrastructure construction,public service,energy saving and emission reduction,To provide basis for the construction of environment-friendly smart community.2)We used Arduino development board and gas sensor to build the air quality information collection terminal,which can collect a variety of pollutant concentrations at the same time.At the same time,based on machine learning theory,it used random forest algorithm to screen important features,and built the long-term and short-term memory network air quality prediction model of LSTM.3)J2EE technology has been used to develop a system with front and back ends separated.Spring boot framework has been used to build the back end.Vue.js has been used to develop the web end and mobile app end.Shiro framework has been used to isolate the rights of different user roles in the web end,and app end is responsible for displaying information.This thesis first introduces the background of air quality monitoring system and related algorithms of air quality prediction,then is to introduce the front-end and back-end technologies and machine learning model theory used in this thesis.After that,the requirements of each module of the system are analyzed,and the functions of each module are designed in detail.Experiments verify the accuracy of deep learning in predicting air quality,train air quality prediction model and set up air quality information collection terminal to provide data source for the system,then implement the whole system according to the design.