To ensure the safety of stored grain,it's essential to regularly collect environmental data such as temperature,humidity,moisture,and CO2 levels and determine whether the grain is safe.Previously,this process required manual labor from grain storage managers,which was both time-consuming and prone to human error.This could even result in unnecessary loss of grain due to delayed detection of abnormal grain conditions.To reduce labor and achieve timely processing,this paper proposes using an optimized Kalman filter algorithm to construct a grain prediction model.The model predicts the environmental conditions of the grain storage,enabling managers to anticipate changes in these conditions in advance.The grain predic-tion model is compared with the exponential smoothing model,and the results show that the former has a smaller average error for each environmental variable.This makes it an effective and practical tool for pre-dicting environmental conditions in grain storage facilities.
维普
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