Design Of Cloud Service System For Smart City Drainage System And Optimization Of Monitoring Points Of Urban Waterlogging

Drainage system is an indispensable infrastructure of the city,related to our work and life can be running normally.Urban drainage infrastructures have challenges such as information silo,information delay,and operation scheduling dependent on field experiences.The establishment of a real-time urban waterlogging monitoring system has become a hot issue in recent years.With the development of science and technology,especially the development of modern Internet technology,mobile Internet and big data and cloud computing technology prevent a new way to Control city waterlogging.Using a pilot drainage system model built in laboratory,a cloud management system for smart city drainage system was designed and implemented,based on ubiquitous broadband Internet.Technologies such as Internet of Things,mobile internet transmission and cloud computing were applied to achieve functionnalities including real-time collection and visualization of monitoring data,equipments real-time monitoring and automatic control,big data analytics,video-surveillance,and information lookup support for mobile devices.The results of laboratory tests show that the system is capable of stable operation and development,which can greatly reduce urban waterlogging and overflow pollution if be used in the management of urban drainage system.The quality of the urban waterlogging monitoring points directly affects the accuracy of the information provided by the monitoring system to the urban waterlogging information.The direct fuzzy clustering algorithm and the improved K-means algorithm based on Euclidean distance are applied to optimize the monitoring points of urban warerlogging with the purpose to allocate the quantity to reflect the waterlogging condition of the whole river basin with minimum monitoring points.34 waterlogging monitoring sites in a river basin of the Pearl River Delta were selected.The two monitoring algorithms were used to optimize the monitoring points.Finally,We can get the reasonable monitoring points and two common algorithms were chosen to verify the common waterlogging sites as the monitoring points.In practice,the two algorithms can complement each other,and it is more reasonable and accurate to select the monitoring points.