The invention relates to the technical field of charging switching control, in particular to a
dynamic switching system for
cooperative work of
wireless charging and wired charging, which performs node modeling and edge weight propagation on multiple parameters such as
magnetic field intensity, current fluctuation ratio,
contact resistance and temperature threshold through a graph neural network. Structured expression and dynamic updating of a
nonlinear coupling relation among parameters are achieved, abnormal
feature aggregation and channel state characterization are more refined,
fuzzy reasoning and
weight adjustment are conducted on multiple variables such as
power output, the heat growth rate, the
voltage drop rate and the energy efficiency ratio through
fuzzy logic control, the stability of a channel priority sequence is enhanced, and the stability of the channel priority sequence is improved. Through dynamic detection of power curve intersection points and transition section
time sequence calculation, continuous power distribution is converted into a smooth function interval,
instantaneous energy abrupt change during switching is reduced, closed-loop correction of multi-period signals is achieved, and the
system keeps stable operation and energy efficiency output consistency under high-dimensional parameter disturbance.