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Design and Analysis of a Water Quality Monitoring Data Service Platform

Computers, materials & continua/Computers, materials & continua (Print) 2020 21 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Jianjun Zhang, Yifu Sheng, Yifu Sheng, Weida Chen, Haijun Lin, Guang Sun, Peng Guo

Summary

Researchers designed a water quality monitoring data service platform that integrates real-time data collection with analysis and data mining capabilities, addressing gaps in traditional systems that focus solely on collection while ignoring dirty data and transmission failures.

Water is one of the basic resources for human survival. Water pollution monitoring and protection have been becoming a major problem for many countries all over the world. Most traditional water quality monitoring systems, however, generally focus only on water quality data collection, ignoring data analysis and data mining. In addition, some dirty data and data loss may occur due to power failures or transmission failures, further affecting data analysis and its application. In order to meet these needs, by using Internet of things, cloud computing, and big data technologies, we designed and implemented a water quality monitoring data intelligent service platform in C# and PHP language. The platform includes monitoring point addition, monitoring point map labeling, monitoring data uploading, monitoring data processing, early warning of exceeding the standard of monitoring indicators, and other functions modules. Using this platform, we can realize the automatic collection of water quality monitoring data, data cleaning, data analysis, intelligent early warning and early warning information push, and other functions. For better security and convenience, we deployed the system in the Tencent Cloud and tested it. The testing results showed that the data analysis platform could run well and will provide decision support for water resource protection.

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