Spatiotemporal evolution characteristics of extreme rainfall based on intelligent recognition and evaluation of music teaching effect in colleges and universities

Abstract

Intelligent recognition system is a new technology based on signal processing, artificial intelligence, cybernetics, computer technology, and other disciplines. The increase of greenhouse gas emissions accelerates global warming and water cycle and increases the frequency of extreme precipitation events. People’s personal safety has been threatened, and also it has brought huge property losses and also increased social unrest. In recent years, extreme rainfall events often occur in China, causing irreparable losses. As a result, the national government departments have paid great attention to extreme precipitation events, which has become one of the hot issues in the academic circles. Based on the advantages of convolution neural network algorithm in image processing, intelligent recognition system can realize many functions. Based on intelligent recognition technology, this paper evaluates the evolution characteristics of extreme rainfall weather and the later evaluation of music teaching. Through on-site detection, people can intelligently identify and demonstrate extreme rainfall weather. In today’s music classroom teaching, self-playing and singing is one of the core teaching skills of music teachers. To cultivate the independent performance ability and singing skills of music education major in university, not only the comprehensive music literacy of students will be improved, but also the practicability of students’ music learning will be increased, which will play a great role in the future music education work. In addition, the support of intelligent department system makes music education effect evaluation more efficient.