The invention discloses an unmanned aerial vehicle cluster efficient online segmentation learning method and
system, and the method comprises the steps that a
server unmanned aerial vehicle splits a
global model and distributes the
global model to a
client unmanned aerial vehicle, and the
client unmanned aerial vehicle screens samples through a dynamic
sample processing module, completes the training calculation through the combination of a local model, and feeds back a result to the
server unmanned aerial vehicle. And the
server unmanned aerial vehicle aggregates the model and optimizes
resource allocation to form a closed-loop process of model splitting, sample screening, distributed training,
model aggregation and resource optimization. Segmentation learning and
online learning are deeply fused, a two-factor importance evaluation mechanism is adopted to screen high-value samples, and a sub-channel allocation and
power control algorithm is combined, so that the unmanned aerial vehicle cluster can capture dynamic samples in real time in an unknown scene, the model detection precision is improved through distributed cooperative training, meanwhile, the training
delay is reduced, and the training efficiency is improved. And the characteristics of high maneuverability,
limited resources and dynamic environment of the unmanned aerial vehicle cluster are effectively adapted.