基于GRU神经网络模型的冷链运输温度时序预测

doi:10.19788/j.issn.2096-6369.220109

农业大数据学报 ›› 2022, Vol. 4 ›› Issue (1): 82-88.doi: 10.19788/j.issn.2096-6369.220109

• 专题——农产品冷链物流智能管控与大数据 • 上一篇下一篇

基于GRU神经网络模型的冷链运输温度时序预测

陈谦¹(), 杨涵¹, 王宝刚², 李文生², 钱建平¹()

^1.中国农业科学院农业资源与农业区划研究所/农业农村部农业遥感重点实验室，北京 100081
^2.北京市农林科学院农产品加工与食品营养研究所，北京 100093

收稿日期:2021-12-20 出版日期:2022-03-26 发布日期:2022-06-29
通讯作者: 钱建平 E-mail:chenqian940910@163.com;qianjianping@caas.cn
作者简介:陈谦，博士，研究方向：农产品品质安全控制，复杂系统建模；Email：chenqian940910@163.com
基金资助:
国家自然科学基金项目(31971808);中央级公益性科研院所基本科研业务费专项(CAAS-ZDRW202107)

Time Series Prediction of Cold-chain Transportation Temperature Based on GRU Neural Network Model

Qian Chen¹(), Han Yang¹, Baogang Wang², Wensheng Li², Jianping Qian¹()

^1.Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
^2.Institute of Agricultural Products Processing and Food Nutrition, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, China

Received:2021-12-20 Online:2022-03-26 Published:2022-06-29
Contact: Jianping Qian E-mail:chenqian940910@163.com;qianjianping@caas.cn

摘要/Abstract

摘要：

冷链可以有效维持易腐食品品质、保障质量安全、降低过程损耗，冷链环境监控对于充分发挥冷链物流效能至关重要。当前，冷链环境监控技术可以满足多点、无线、实时等监测需求，并通过与新一代智能信息技术结合开始向冷链物流环境精准预测方向快速发展。本文针对冷链运输温度预测问题，从挖掘历史数据时序信息角度出发，提出了一种基于门控循环单元网络（Gated recurrent unit，GRU）的冷链运输温度时序预测方法。首先，滤除冷链运输温度时序数据的异常值和缺失值，利用拉格朗日插值法进行填补修正后归一化处理；然后，根据三种不同时间序列预测尺度的GRU神经网络模型预测性能对比结果，选择构建GRU时序预测模型用于预测冷链中实验冷藏厢体未来10 min的温度变化，并与循环神经网络（Recurrent neural network，RNN）模型、反向传播神经网络（Back propagation，BP）模型进行预测准确性对比试验。对比冷链厢体温度真实值与预测值发现，基于 GRU 神经网络模型的对应厢体预测温度均方根误差和平均绝对误差分别为 0.156 和 0.760 ℃，平均绝对百分比误差为0.236%，与其他模型相比，以上误差指标值均处于相对最低水平；在温度时间序列预测模型中，GRU时序预测模型表现出更优的预测效果。该研究成果对于食品冷链物流中预测预警食品安全、精细控制冷链环境具有重要的实际指导意义。

关键词: 易腐食品, 冷链运输温度, 时序预测, GRU神经网络, 食品安全, 冷链运输

Abstract:

Cold-chain transportation maintains the quality and safety of perishable food, thus reducing process losses. Monitoring the cold-chain environment is very important in ensuring its optimal performance. At present, the technology for monitoring the cold-chain environment enables multi-point, wireless, real-time measurements. Combined with new-generation intelligent information technology, there have been developments toward accurate predictions of the cold-chain environment. With a focus on predicting the cold-chain transportation temperature, this study proposes a method of time series temperature prediction based on a gated recurrent unit (GRU) artificial neural network that mines the time series information of historical data. First, the outliers and missing values of temperature data collected from refrigerated compartments are filtered out, then filled and corrected by Lagrange interpolation before being normalized. Comparisons of the prediction performance of the GRU models using time series data on three different scales lead to the construction of a GRU model for predicting the temperature changes of an experimental refrigerated compartment over the next 10 min. Finally, a recurrent neural network(RNN) and backpropagation neural network(BP) are used for comparative experiments. Comparing the actual temperatures with the predicted values, we find that the root mean square and average absolute errors of the compartment temperature predictions using the GRU model are 0.156°C and 0.760℃, respectively, and the average absolute percentage error is 0.236%. The GRU model exhibits better prediction performance than the recurrent neural network and backpropagation neural network. The research results are of significance for accurate early warning of food safety and precise control of the cold-chain environment.

Key words: perishable food, cold chain transportation temperature, time series prediction, gated recurrent unit neural network, food safety, cold chain transportation

中图分类号:

TU205

陈谦, 杨涵, 王宝刚, 李文生, 钱建平. 基于GRU神经网络模型的冷链运输温度时序预测[J]. 农业大数据学报, 2022, 4(1): 82-88.

Qian Chen, Han Yang, Baogang Wang, Wensheng Li, Jianping Qian. Time Series Prediction of Cold-chain Transportation Temperature Based on GRU Neural Network Model[J]. Journal of Agricultural Big Data, 2022, 4(1): 82-88.

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Temperature /℃	GRU			RNN			BP
Temperature /℃	MAE/℃	MAPE/℃	RMSE/%	MAE/℃	MAPE/℃	RMSE/%	MAE/℃	MAPE/℃	RMSE/%
M₁	0.169	0.832	0.241	0.356	1.707	0.416	0.485	2.104	0.699
M₂	0.110	0.567	0.182	0.196	1.102	0.307	0.230	1.242	0.340
M₃	0.188	0.882	0.286	0.155	0.784	0.391	0.362	1.588	0.528
均值	0.156	0.760	0.236	0.236	1.198	0.371	0.359	1.645	0.522

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基于GRU神经网络模型的冷链运输温度时序预测

Time Series Prediction of Cold-chain Transportation Temperature Based on GRU Neural Network Model

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