BP神经网络在果蔬冷链物流预警中的应用

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。

Application of BP neural network in the early-warning of fruits and vegetables cold-chain logistics

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 practical problems, and can effectively increase the security risks and hazards warning accuracy of fruits and vegetables in cold-chain logistics.