Design And Implementation Of Noise And Dust Monitoring And Data Analysis System For Construction Site

The rapid development of China's urbanization has brought about the rapid development of the domestic construction industry.However,urban construction also produces many pollutants,which have certain impacts on the living environment of the residents and the natural environment of the city.Researches shows that dust created by construction is an important source of urban atmospheric particulate pollutants.Besides,the noise caused by construction is the main source of urban noise.Therefore,monitoring and managing noise and dust can effectively prevent and control dust pollution and noise pollution,which indirectly protects the urban environment.In this thesis,data mining technology is used to mine and analyze the historical data.The effective information gained from the data also has certain guiding significance for construction supervision.In view of the above problems,This thesis analyzes the problems in the current supervision process,and settles the functional modules of the proposed system.The main functions of the system are as follows: The Regional Management vertically divides the management at the business level to ensure the independence of services between different regions.Project Management manages the municipal project details.Device Management manages and configures monitoring devices.Task Management automatically or manually generates tasks for monitoring projects that exceed the standards for a long time.GIS Global Map and Video Monitoring module is based on GIS geographic information service and network video monitoring equipments.It realizes video monitoring and statistical analysis of the projects.This module visually displays monitoring data to supervisors to help supervisors to carry out their work.This system uses J2 EE architecture.To realize high performance optimization,it uses Nginx service and Tomcat cluster to achieve load balancing to improve concurrency.The system applies the Redis cache cluster service to cache data with weak consistency but frequent queries.The Redis cluster also avoids the single point failure problem.The system adopts the MySQL cluster solution and applies the keepalived fault automatic cancellation service to avoid data loss and single point of failure,which improves system availability.Based on common air pollutants,this thesis also studies the artificial neural network model to predict the mean value of PM10 concentration.The main work has the following six aspects: 1.It studies common air pollutant concentration prediction models and selects an appropriate model as the prediction model for this study.2.It gets and sorts out the historical data of air quality and the historical data of the system.3.Data preprocessing deals with data missing processing and data normalization,etc.4.Determine the input factors and output results of the model.5.Design and train BP neural networks based on common air pollutant data.6.Adjust the important parameters of the optimization model to achieve better prediction accuracy.Finally,the experimental analysis proves the forecasting model has certain predictive ability.Now,the system has been running stably for two years in a government department in Xi'an.It has realized the functions of automatically monitoring the dust and noise.It also met the regulatory requirements of the department and improved the efficiency of departmental supervision.