The present invention discloses a self-adaptive generation
system of a diabetes diet plan based on
deep learning, including: a collection and storage module, configured to collect and produce data on the dynamic changes of the total calories of patients, and construct a personalized
data set for generating a
calorie supplement plan according to the
basal metabolic rate, occupational consumption, exercise consumption,
dietary supplement, and the difference between the
waist-to-hip ratio and the mean of fasting blood glucose; a calculation module, which imports the personalized
data set into an improved
linear regression model and performs data training to obtain the corresponding
optimal weight of each data in the
data set; an evaluation and feedback module, which automatically calculates the dietary
calorie supplement value according to the
optimal weight and deviation value of the calculation module by setting up a dual gradient target, generates a personalized diet plan, and timely feedback optimization. The present invention constructs a personalized data set of the total
calorie changes of patients, and introduces a
deep learning method into the formulation of the patient's diet plan, providing more accurate and scientific dietary guidance for diabetic patients.