A method,
system, and medium for dynamically controlling infusion drip rate based on patient physiological parameters include: acquiring real-time physiological parameter data,
medical history data, and
drug attribute data of the patient; real-time physiological parameter data including glomerular
filtration rate and B-type
natriuretic peptide concentration; obtaining a safe drip rate range based on one or more of the real-time physiological parameter data,
medical history data, and
drug attribute data according to a preset rule base; calculating a recommended drip rate based on a
machine learning model and a standard drip rate; obtaining a preset drip rate threshold range, and generating and outputting a first drip
rate control command based on the recommended drip rate, the safe drip rate range, and the preset drip rate threshold range; monitoring the actual drip rate in real time and calculating the error value between the actual drip rate and the recommended drip rate; and dynamically adjusting the first drip
rate control command based on the error value using a closed-
loop control algorithm to generate and output a second drip
rate control command.