The invention discloses an
analysis method and
system for AI recognition of
physical exercise trajectory data. The method comprises the following steps: step 1, constructing a space-time-environment-biological
feature fusion network, integrating collected multi-
source data, capturing association of trajectory coordinates, skeleton joint angles and electromyographic signals, constructing a space-time dynamic graph, and comprehensively sensing the influence of a dynamic environment on a motion trajectory; 2, introducing a
generative adversarial network to simulate
extreme environment interference, and improving model robustness; forcing the model to learn a defensive decision through
physical layer disturbance and behavior layer disturbance; distinguishing a real track from a generated track by using a
discriminator, and trying to cheat the
discriminator by a generator to enable the model to adapt to a
noise environment; 3, cross-athlete skill migration is conducted, a parameter freezing strategy and dynamic
weight adjustment are adopted, the basic exercise mode recognition capacity is reserved, and meanwhile individual differences are adapted; and step 4, in combination with deep
reinforcement learning and an interpretable
rule engine, carrying out instant tactical adjustment, and generating an action through a DRL strategy network.