用机器学习设计食品烹调过程卫生问题管理系统

Pengyu Wang
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引用次数: 0

摘要

在信息高速交互的时代,数据源趋向网络化和数字化,数据结构复杂化和多样化,数据之间的相关性特别隐蔽和难以检测。面对复杂的海量食品数据信息,传统的信息研究与判断技术已不能满足其需求。因此,本文分析了机器学习的领域范畴、机器学习的结构和机器学习的关联规则,讨论了烹饪过程导航系统策略和自动烹饪系统设计策略,构建了一个食品烹饪专家系统。结果表明,当卷积核的尺度为1-2-3时,卷积神经网络对配方成分的多标签分类效果最好。
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Using Machine Learning to Design the Management System of Hygienic Problems in the Process of Food Cooking
In the era of high-speed information interaction, data sources tend to be networked and digitized, data structures become complex and diverse, and the correlation between data is particularly hidden and difficult to detect. Facing the complex massive food data information, the traditional information research and judgment technology can not meet its needs. Therefore, this paper analyzes the domain category of machine learning, the structure of machine learning and the association rules of machine learning, discusses the strategy of cooking process navigation system and the design strategy of automatic cooking system, and constructs a food cooking expert system. The results show that when the scale of convolution kernel is 1-2-3, the convolution neural network is the best in the multi label classification of formula components.
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