MULTIMODAL SCENARIO CONTROL METHOD FOR LOW-TEMPERATURE GRAIN STORAGE BASED ON THE ADABELIEF ALGORITHM

To address the issues of slow convergence in traditional methods, this study integrates adaptive gradient correction with a belief update mechanism based on the AdaBelief algorithm, and proposes a multimodal scenario control model for low-temperature grain storage. Experimental results indicate that the proposed model can maintain the grain storage temperature at approximately 12°C under various environmental conditions, and the average conductivity of wheat, rice, corn, and soybean is kept below 45 μs.cm-1. These findings demonstrate that the proposed model significantly improves the level of intelligent control in low-temperature grain storage systems and provides a novel approach for the precise regulation of grain storage environments.