Intelligent Water Monitoring System Based on the Internet of Things
DOI: 10.23977/acss.2023.070403 | Downloads: 158 | Views: 902
Author(s)
Wang Zehui 1, Sun Chunzhi 1, Ma Yanbin 1, Wang Yuhang 1, You Jingxue 1
Affiliation(s)
1 School of Electronic and Electrical Engineering, Shangqiu Normal University, Shangqiu, 476000, China
Corresponding Author
Wang ZehuiABSTRACT
This paper introduces an intelligent water monitoring system based on the Internet of Things, which includes water use information acquisition board, mobile phone APP and Internet of Things cloud platform. The system uses STM32G030F6P6 as the main control chip, and combines with water quality detection module, water pressure detection module, water flow module, solenoid valve control module, ESP8266-01S WIFI module and OneNet cloud platform big data analysis technology to realize real-time supervision, monitoring water data recording and water leakage warning. The system can detect information such as water quality, temperature, total water use and water leakage, and transmit the data to the cloud platform in real time for the convenience of subsequent big data analysis and resource recycling. At the same time, users can realize remote monitoring of data and remote control of valve switch through mobile phone APP, so as to avoid the occurrence of leakage accidents in the home. The system is not only practical, but also has certain promotion value. It can be extended to various fields, such as household, industry and agriculture, so as to promote sustainable water resource utilization and management. The intelligent water supervision system proposed in this paper has certain innovation and practical application value, which is of great significance for improving the utilization efficiency of water resources and managing water resources.[1]
KEYWORDS
Internet of Things; Monitoring system; STM32G030F6P6; Water quality testingCITE THIS PAPER
Wang Zehui, Sun Chunzhi, Ma Yanbin, Wang Yuhang, You Jingxue. Intelligent Water Monitoring System Based on the Internet of Things. Advances in Computer, Signals and Systems (2023) Vol. 7: 21-29. DOI: http://dx.doi.org/10.23977/acss.2023.070403.
REFERENCES
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