The present application relates to the technical field of medical
Internet of Things, and in particular to a self-adaptive infusion collaborative control method and
system based on
intelligent decision-making. The method comprises: an end device analyzes multi-
modal physiological
time series data and infusion parameters through an end-to-end
patient state evaluation model integrated in a sub-body
infusion unit, and outputs a
patient state index and infusion suggestions; combined with a pipeline pressure
signal, an adaptive flow
rate control algorithm based on LSTM is used to generate
motor control instructions to realize closed-
loop control. The
edge device periodically aggregates local
model parameter updates of multiple end devices through a mother
base station. The cloud device fuses all updates uploaded by the edge devices based on a federated averaging
algorithm, generates an optimized
global model, and issues the
global model. The present application realizes a paradigm shift from isolated execution to collaborative
perception-decision-evolution of infusion, drives continuous evolution of group intelligence under the premise of protecting data privacy, and improves the accuracy, safety and overall intelligent level of
infusion therapy in complex environments.