用于设计人工智能驱动的智能餐饮系统的功能要求和面向对象的系统建模
Kaihong Feng1, Fang Zhao2, Qinkai Yang3
1Faculty of Information Science and Technology, The National University of Malaysia; norbertfeng199371044047p147151@asu.edu.pl.
Journal of visualized experiments : JoVE
|November 17, 2025
概括
人工智能 (AI) 通过非接触式选项,个性化的建议和满意度预测来增强餐厅服务. 这种由人工智能驱动的系统显著提高了食品行业的效率和客户体验.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 由于全球化和技术进步,食品行业正在迅速发展.
- 人工智能 (AI) 和物联网 (IoT) 是现代化食品生产和服务的关键驱动力.
- 客户的期望正在转向更加个性化和高效的餐饮体验.
研究的目的:
- 开发一个人工智能驱动的智能系统,以提高餐厅餐饮服务.
- 使用自然语言处理 (NLP) 和线性差异分析 (LDA) 实现无接触服务解决方案.
- 通过一个卷积循环神经网络 (Conv-RNN) 提供个性化的食物建议,并使用一个卷积长短期记忆 (Conv-LSTM) 模型预测客户满意度.
主要方法:
- 利用NLP和LDA进行非接触式服务改进.
- 采用Conv-RNN模型进行个性化的食物建议.
- 应用了一个优化的Conv-LSTM模型来预测客户满意度.
- 集成的人工智能和物联网用于全面的系统升级.
主要成果:
- Word2Vec-LDA模型实现了91.5%的准确性,91%的精度,91.1%的回忆,以及89.7%的F1得分.
- Conv-RNN模型的准确度为98.5%,损失为0.02.
- 在Conv-LSTM系统中,RMSE值为0.1011和R2值为0.9812.
- 拟议的人工智能系统的性能优于传统的基于规则的方法.
结论:
- 人工智能显著增强了餐厅餐饮服务,提高了效率和客户满意度.
- 不接触式服务,个性化的建议和准确的满意度预测是可以实现的.
- 该研究强调了人工智能在自动化和优化餐饮行业的变革潜力.
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