Simulation application of sensors based on Kalman filter algorithm in student psychological crisis prediction model

Abstract

The mental health and psychological crisis of some Chinese college students today are extremely abnormal, which has attracted the attention of many relevant personnel. Due to various external reasons, the psychological construction of Chinese college students is very pessimistic. Kalman filter is a regression calculation method for processing data. The standard calculation of this filter has the smallest data error, so that relevant data can be recursive. Within the relevant time domain, this calculation method can select suitable filters to accurately calculate high-dimensional and low-dimensional system data. This paper mainly solves some problems encountered, thus proving the effectiveness of Kalman filter calculation method. Finally, we can get the advantages and disadvantages of these filter systems, so as to improve these disadvantages, and finally improve the Rate of convergence of this calculation method. Through the corresponding experimental results, we can see that these calculation methods are correct. By analyzing these data, the analysis results show that this calculation method can effectively predict students' mental health problems, and the designed system can reduce the occurrence of psychological crisis events among college students.