摘要
近年来,消费者对果蔬冷链产品的安全和品质提出了更高要求,而现有的系统仅从温度和湿度两方面预测果蔬安全状态,没有综合考虑人员操作和设备等因素对果蔬品质的影响。针对上述问题,分析果蔬在冷链过程中出现安全隐患的因素,整合供应链上的追溯信息和监测信息,建立果蔬预警指标体系,采用BP神经网络搭建安全预警模型,并对模型进行训练和预测。预警结果表明,该方法较传统的时间序列、回归分析方法,在解决实际问题中预测误差小,可以有效提高果蔬在冷链物流中风险预警的准确性。
In recent years, consumers ask for a higher demand on the security and quality of fruit and vegetable products. But the existing system can only predict the state of fruits and vegetables based on the temperature and humidity of the environment, and factors such as personnel operation and equipment are not taken into account. To solve these problems, we analyze the factors which affect the quality of fruits and vegetables in the cold-chain process, establish the early warning index system, and integrate the traceable and monitorable information in the supply chain. The BP neural network is utilized to build the security warning model, and we train it and make prediction. The results show that the proposed method has fewer errors than traditional time-series and regression analysis methods when solving prac- tical problems, and can effectively increase the security risks and hazards warning accuracy of fruits and vegetables in cold-chain logistics.
出处
《计算机工程与科学》
CSCD
北大核心
2015年第9期1707-1711,共5页
Computer Engineering & Science
基金
陕西省农业科技创新与攻关资助项目(2014K01-29-01)
陕西省社会科学基金资助项目(13SC011)
陕西省教育厅资助项目(14JK1093)
陕西科技大学科研启动基金项目(BJ12-21)
关键词
冷链物流
预警
神经网络
cold-chain logistics
early warning
neural network