The invention relates to the field of
artificial intelligence, in particular to a ventilation man-
machine asynchronous detection model training method and device based on DQN
reinforcement learning, and the method comprises the steps: obtaining a capacity
data segment during
breathing and ventilation; preprocessing the capacity
data segment to obtain training data and
test data which can be used for DQN
reinforcement learning; based on the training data and the
test data, constructing a scene problem suitable for applying DQN
reinforcement learning processing; describing or depicting the scene problem; and constructing a specific DQN network, setting training parameters of a reinforcement learning model, and training a DQN reinforcement learning model. A man-
machine asynchronous detection problem of
breathing ventilation is converted into a scene problem, so that the man-
machine asynchronous problem can be identified or detected by using a reinforcement learning scheme. A
processing method of
breathing ventilation man-machine asynchronous detection is expanded, the problem of automatic detection of man-machine asynchronous events occurring in the breathing ventilation process is solved, the monitoring burden is effectively relieved, and the
nursing efficiency of work is improved.