基于一维卷积神经网络的烟叶仓储霉变预测方法研究

针对烟叶存储期间的霉变问题，传统的防治措施效果欠佳，且已有的烟叶霉变预测模型的准确率较低，不能有效减少烟叶霉变现象的发生。为了提高预测烟叶霉变状态的准确率，提出了一种基于一维卷积深度神经网络（1D-CNN）的方法。以采集终端传感器数据为基础，对其进行标准化处理，得到模型训练特征，训练一个1D-CNN来预测烟叶霉变状态，优化网络结构，实验结果表明所提方法的预测准确率高于其它传统模型。最后，设计并实现了烟叶仓储霉变智能监测系统，实现了烟叶霉变的实时预测功能，取得了较好的效果。

Design and optimization of CCFDoverlapping grid parallel algorithm

Aiming at the problem of mildew during the storage of tobacco leaves, the traditional prevention and control measures are not effective, and the existing tobacco leaf mildew prediction model has low accuracy, which cannot effectively reduce the occurrence of tobacco leaf mildew. In order to improve the accuracy of tobacco leaf mildew state prediction, a method based on one-dimensional convolution deep neural network (1D-CNN) is proposed. Based on the collection of terminal sensor data, it is stan- dardized and processed to obtain the model's training features. A 1D-CNN is trained to predict the mildew state of tobacco leaves, and the network structure is optimized. The experimental results show that the proposed method has higher prediction accuracy than other traditional models. Finally, an intelligent monitoring system for tobacco leaf storage mildew is designed and implemented to realize the real-time prediction function of tobacco leaf mildew, and good results are achieved.