为实现便捷、个性化的健康饮食，一种针对中国菜肴的饮食推荐方案被提出。首先，通过自建数据库训练能进行三餐分类的ResNet-18神经网络模型，得到2167道中国菜肴的三餐置信度作为推荐依据。然后构建优化算法数学模型，将菜肴的营养素、食材、类别等信息抽象为决策变量，营养与饮食习惯等目标设为约束条件，利用自适应的Gurobi优化器来计算推荐菜肴及其克数的最优解。实验证明，系统可在三个层面达成设定目标：在营养素层面，能够满足健康用户或30种慢性病人的19种营养素的摄入指标；在食材层面，能够确保推荐食材的多样性，完成喜好菜肴筛选与忌口成分规避；在菜肴层面，能够实现菜肴种类搭配和三餐分别推荐的功能。

In order to make healthy and personalized diets conveniently, a dietary recommendation system for Chinese cuisine is proposed. Firstly, the ResNet-18 neural network model that can classify three meals was trained through the self-built database, and the three-meal confidence of 2167 Chinese dishes was obtained as the basis for recommendation. Then, the optimization model was constructed, where the information of nutrients, ingredients and categories of dishes were abstracted as decision variables, and the objectives of nutrition and eating habits were set as constraints. The optimal solution of the recommended dishes and their grams was calculated by using the adaptive Gurobi optimizer. Experiments show that the system can achieve the set goals at three levels: at the nutrient level, it can meet the intake targets of 19 nutrients for healthy users or 30 chronic patients; At the level of food ingredients, it can ensure the food diversity, complete the selection of favorite dishes and the avoidance of taboo ingredients; At the dish level, it can realize the function of pairing dishes and recommending three meals respectively.

TP399???????

国家自然科学基金项目（61771338），天津市重大科技专项资助项目（18ZXRHSY00190）。