Incheon National University, Incheon 22012, Republic of Korea
Traditional water pollution(WP) detection is mainly based on the manual use of water quality testing equipment to identify samples at fixed points, this method of detection is a huge amount of work, and to pay the high cost of testing, in addition, in some unsuitable for operators to enter the WP testing environment, the use of traditional testing equipment is difficult to complete the sampling, so that the testing work can not be carried out as scheduled, and, the current water resources collection every six months or once a quarter, in this way to collect water quality data on a long time line, can not do water quality status updated. In order to solve these problems and build a stable water environment ecosystem, this paper studies WP detection equipment, builds a WP detection system based on artificial intelligence and sensors, and integrates intelligent means to detect pollutants and prevent WP. The system can complete the abnormal detection and recording of water pollutants through different interfaces and software systems, and its advantages of miniaturization and intelligence will be applied to actual production and life.
Artificial Intelligence, Water Pollution Prevention, Anomaly Detection, Water Resource Data
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