大数据环境下基于神经网络技术的食品安全监管

从阐述中国食品安全监管模式经历的几个重要时期着手,分析了当前中国食品安全监管存在的不足,指出应借鉴美国等发达国家较为成熟的监管策略,将大数据相关技术应用于食品安全监管中,使数据信息更具时效性和公开性;提出了将BP神经网络运用于食品检测数据分析中,实现预测某类食品在之后多个监管周期内的风险系数,提高对食品安全事故的预警能力。

Food safety supervision based on neural network technology in large data environment

In the environment of large data,the application value of data mining and neural network technology in food safety supervision is discussed to provide ideas for the innovation of supervision mode in this field in China.Several important safety supervision models of Chinese traditional food first analyzed.Based on the analysis of the deficiencies of current supervision,the more mature supervision strategy of developed countries such as the United States is used to apply big data related technology to the food safety supervision,so as to make the data information timelier and more open.BP neural network is applied to the analysis of food testing data to predict the risk coefficient of a certain type of food in the subsequent multiple regulatory cycles,and to give early warning.