Temperature Forecasting for Stored Grain: A Deep Spatiotemporal Attention Approach
Temperature Forecasting for Stored Grain: A Deep Spatiotemporal Attention Approach
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DOI：
10.1109/jiot.2021.3078332
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发表时间：
2021-12-01
影响因子：
10.6
通讯作者：
Zhang, Yuan
中科院分区：
文献类型：
--
作者：
Duan, Shanshan;Yang, Weidong;Zhang, Yuan
物联网技术的发展推动了粮情检测分析系统的进步。温度监测是保障粮食品质的重要环节，有效控制粮温是粮食安全储藏的关键。为了准确预测储粮温度，提出了一种具有注意力机制的编码-译码模型。考虑到温度面梯度方向上的点对目标点的温度影响较大，采用Sobel算子提取目标点的局部特征。此外，考虑到感官数据中的相关性结构，利用注意力机制提取目标点的全局特征。将提取的空间特征送入长短期记忆(LSTM)网络，得到空间要素的长期状态信息。利用LSTM单元和卷积神经网络对目标点的空间特征进行编码。以气象因素作为译码的外部输入，利用时间注意机制和LSTM单元完成译码过程，实现对未来粮温的预测。与卡尔曼修正的最小绝对收缩和选择算子(卡尔曼修正的LASSO)、时间图卷积网络(T-GCN)、LSTM、CNN-LSTM和卷积LSTM(Conv-LSTM)等算法相比，该模型具有显著的优势。
The development of Internet-of-Things (IoT) technology promotes the advances of grain condition detection and analysis systems. Temperature monitoring is a main element to maintain grain quality, and effective control of grain temperature is crucial to safe storage of grain. In this article, an encoder-decoder model with attention mechanism is proposed to accurately forecast the temperature of stored grain. Considering that the points on the gradient direction of the temperature surface have a great influence on the temperature of the target point, the Sobel operator is used to extract the local characteristics of the target point. In addition, considering the correlation structure in the sensory data, the attention mechanism is used to extract the global features of the target point. The extracted spatial features are fed into long short-term memory (LSTM) networks to obtain the long-term state information of spatial factors. LSTM unit and convolutional neural network are used to encode the spatial features of the target points. Taking meteorological factors as the external input of the decoder, temporal attention mechanism and LSTM unit are used to complete the decoding process and realize the prediction of grain temperature in the future. The results with real grain storage data show that the proposed model outperforms several schemes, including Kalman-modified the least absolute shrinkage and selection operator (Kalman-modified LASSO), temporal graph convolutional network (T-GCN), LSTM, CNN-LSTM, and convolutional LSTM (Conv-LSTM), with considerable gains.