With the penetration of science and information technology into the water industry, the combination of new generation information technology represented by cloud computing, big data, Internet of Things, mobile Internet and artificial intelligence with urban water supply has promoted the rapid development of urban water supply, not only promoting the digitization and visualization of the whole process of water supply, It also enables water companies to understand the health status of urban water supply networks and water demand, and provide targeted water supply services to meet the basic needs of social production and people's life.
In the context of big data, smart water system is an indispensable part of smart city planning. In order to better meet the needs of social production and people's daily life, the smart water system, based on the perception of the Internet of Things, takes the application scenario as the medium, and big data analysis as the starting point, integrates the data of the entire water supply chain, predicts water supply demand, conducts scientific scheduling, reduces pipe bursts and water leaks, and achieves better water supply.
In the era of big data, it is imperative to build a smart water system and improve the utilization rate of water resources and water service capacity in combination with the actual needs of society and people's production and life. However, as far as the current development situation is concerned, the construction of water system in some cities in China still faces some unresolved constraints, which seriously hampers the effective functioning of smart water system. First of all, the problem of isolated water data is prominent. At present, in the application process of urban smart water system, there are often duplicate business systems. Chimney systems and zombie systems are ubiquitous, leading to untimely data acquisition, imperfect data collaboration, and difficult data value. Secondly, the accuracy and availability of water data are very prominent. Many water supply companies have not established pipe network GIS systems or the accuracy and coverage of pipe network GIS data are not high, which brings troubles to pipe network zoning, pipe network leakage detection and water supply scheduling. Then, it is difficult to maintain and manage the device. Many water supply companies have not yet established enterprise asset management systems, and they also lack equipment life cycle management and health assessment, resulting in decentralized equipment management, passive maintenance and high use costs.
