The invention discloses a
deep learning-based diabetic diet scheme self-adaptive generation
system, which comprises a collection and storage module, which is configured to collect and make data of total
calorie dynamic change of a patient, and according to basic
metabolism, occupational consumption, exercise consumption, diet supplement and a difference value between a
waist-hip ratio and a fasting blood-glucose mean value, the data of the total
calorie dynamic change of the patient is calculated; constructing a personalized
data set for generating a heat supplement plan; the calculation module is used for importing the personalized
data set into an improved
linear regression model and carrying out data training to obtain an
optimal weight corresponding to each piece of data in the
data set; and the evaluation and feedback module is used for automatically calculating a diet
calorie supplement value by setting a double-gradient target according to the
optimal weight and the deviation value of the calculation module, generating a personalized diet scheme and timely feeding back and optimizing the diet scheme. According to the method, the personalized data set of the total calorie change of the patient is constructed, and the
deep learning method is introduced into the formulation of the diet scheme of the patient, so that more accurate and scientific diet guidance is provided for the
diabetic patient.