Analysis of Grain Condition in Improved Granary Based on Grey Prediction Algorithm

Scientific grain storage plays an important role in ensuring food security and promoting high-efficiency energy-saving operations. The paper provides more accurate reference datas for grain storage work. It can easily monitor the grain situation during the reserve period, and can scientifically predict the future grain development trend more accurately. It takes countermeasure in advance to prevent food disaster and further reduce. The workload of the warehouse clerk and related staff, while ensuring the safe and stable operation of the grain storage. Compared with the traditional Gery Model, the residual correction method is proposed to improve the data prediction accuracy. Combined with the Grey Verhulst model, a new residual-corrected Verhulst model is proposed. The simulation prove that the improved model is more traditional than the traditional one. The model is more conducive to the prediction of volatility data and the prediction accuracy is greatly improved.