Design and optimization of intelligent orchard frost prevention machine under low-carbon emission reduction

In this paper, a self-heating small frost prevention machine is designed according to the agricultural frost prevention demand in mountainous and hilly areas. Firstly, the frost prevention machine's overall structure and key components are meticulously designed and modeled using UG 3D modeling software, tailored to the specific characteristics of the stepped planting environment in the mountainous area. At the same time, the flow field of the frost prevention machine is calculated and analyzed using jet diffusion theory and thermodynamic theory. The frost prevention machine's wind speed and temperature attenuation models are established, and subsequently fine-tuned and validated using Fluent to optimize its attenuation performance and operational range. To enhance frost prevention performance, a multi-objective bionic optimization strategy is applied to systematically optimize the structural and working parameters of the machine. Moreover, an adaptive mutation hybrid frog jump algorithm is introduced to further enhance the optimization accuracy. The simulation experimental results show that structural improvements can reduce axial speed and temperature attenuation loss by approximately 45%, while slightly increasing the machine's power consumption. On the other hand, optimizing working parameters enhances wind resistance and reduces overall power consumption. The actual experimental results show that the optimized frost prevention machine achieves a frost prevention coverage radius of 10 m within the orchard, even under maximum ambient wind speeds of 2 m/s, and it dynamically adapts to environmental changes. Additionally, the synergy of multiple frost prevention machines, powered by solar panels, enhances the overall frost prevention efficiency, leading to improved economy and reduced energy consumption.