The invention provides a
train driver behavior state detection method and
system based on a neural
network model, and the method specifically comprises the steps: S1, collecting a driver
image sequence, an operation sequence, a
train operation sequence,
signal display information and line attributes, and forming a behavior sequence, an operation sequence and an environment sequence; s2, constructing a graph structure of a driver, a
train, a
signal, a line and an equipment node, and setting a control, sensing, power and feedback relation edge; s3, a graph neural
differential equation is constructed, node states are generated by short-time action branches and long-time trend branches, events trigger continuous time advance, and edge weights are adjusted along with working conditions; s4, forming a self-adaptive Koopman operator by using the basic Koopman operator group and the weight of the
risk factor; s5, generating a driver state trend, a train state trend and a danger trend
score in linear extrapolation; and S6, outputting the driver behavior level, the operation
risk level and the joint
risk index. According to the method, the reliability of risk prediction is improved through combined modeling of driver behaviors, train operation and environment constraints.