Retraction Note: Data mining-based air pollution characteristics and real-time monitoring of college students’ physical and mental health

Abstract

Due to the significant impact of air pollution on visibility, it is also the most visible environmental problem for the public. This paper analyzes the application scenarios of data mining in the air pollution monitoring system, combined with the target of air pollution anomaly detection, mainly researches classification algorithms and outlier detection algorithms, and proposes an air pollution feature detection method based on data mining. A large number of experiments were carried out before the system integration to verify its effectiveness. Based on the abovementioned new architecture, this paper designs and implements an air pollution real-time monitoring system, which can display air pollution data in real time through rich charts, and integrates and applies air pollution anomaly detection methods to the system’s alarm module. The system can help data center managers monitor the air pollution in the data center and notify the managers to check the atmospheric abnormalities in time. In this article, data mining is also applied to the real-time monitoring of college students’ physical and mental health. A real-time monitoring system is designed for college students’ physical and mental health. A new system architecture is proposed through frequent data push and data IO scenarios, which can effectively monitor the physical and mental health of college students. In this article, data mining technology is used to monitor the characteristics of air pollution and the physical and mental health of college students in real time, which provides a new method for the treatment of air pollution and the protection of the physical and mental health of college students.