Efficient grain storage management is of great significance to the country, and grain storage temperature is one of the key indicators to judge grain storage security. Accurately predicting the temperature of stored grain and making timely and appropriate protective measures can effectively reduce grain storage losses. In order to address the limitations of traditional prediction models, this paper introduces a novel network model integrating a time-domain convolutional network (TCN), a self-attention mechanism, and a bi-directional gated recursive unit (BiGRU). First of all, the local features of grain storage temperature data are obtained by TCN, and the SelfAttention mechanism is incorporated into the network according to the temporal characteristics of grain storage temperature to assign weights to different grain features, highlighting the features that have a greater impact on the prediction of grain storage temperature. In addition, BiGRU network is used to learn the bi-directional dependency of grain condition sequences to obtain more information in the sequences and achieve the prediction of grain storage temperature. The experimental results show that the RMSE of the model is 0.389 5, the MAE is 0.328 1, and the R² is 0.9912. Compared with other models, the proposed method reduces the error and improves the prediction accuracy, thus providing a solid foundation for decision-making in grain storage temperature control.

粮食仓储管理对于国家具有重要意义，储粮温度是判断粮食仓储安全的重要指标之一。准确地预测储粮温度并及时做出相应的防护措施能够有效降低粮食仓储损耗。针对传统储粮温度预测模型预测准确度较低的问题，提出一种融合时域卷积网络（TCN）、自注意力机制（Self-Attention）和双向门控循环单元（BiGRU）的网络模型。首先通过TCN提取储粮温度数据的局部特征，并根据储粮温度数据的时序特征将自注意力机制加入网络为不同粮情特征分配权重，突出对储粮温度预测影响更大的特征，之后利用BiGRU网络学习粮情序列的双向依赖关系来获取序列中的更多信息，实现对储粮温度的预测。结果表明，所提出的模型均方根误差RMSE为0.389 5，平均绝对误差MAE为0.328 1，确定系数R²为0.991 2，与其他模型相比误差小，预测精度高，能够为粮仓的温度管控提供决策依据。