Control Method of Complex Environmental Engineering Machinery Cooling System Integrating Deep Learning and Feature Clustering

Abstract

In modern industrial production, due to the continuous development and progress of construction machinery. In the field of construction engineering, the control of mechanical cooling system has attracted more and more attention. In the current construction machinery industry, it is of great significance to study and practice the cooling technology of the heating system. In order to improve the ability of engineering cooling system control, this paper proposes the method of deep learning and feature clustering to optimize the system. This paper mainly uses the experimental method and the comparative method to study the control method of the heat dissipation system of the construction machinery. The experimental data shows that the temperature in different stages is lower than 80 degrees Celsius, and the change in the later stage is lower than 0.3, so the heat dissipation system can well control the heat generated by the IGBT module.