The present application relates to the technical field of wind
turbine operation and maintenance, and particularly relates to a wind
turbine defect cross-domain guidance collaborative detection method and
system, comprising: acquiring global scanning data of wind
turbine blades and
tower drums of non-stop wind turbines after a
drone is calibrated by cross-
domain space-
time synchronization; acquiring near-domain reference data collected by a climbing drum
robot at a preset reference position of the
tower drum; taking the near-domain reference data as an
anchor point, splicing the global scanning data to determine a defect suspected area and a spatial position; scheduling the climbing drum
robot to a
target site for spot detection, and acquiring multi-
modal detection data; and combining real-time
wind field conditions and
unit operation parameters to perform interference compensation on the multi-
modal data to obtain defect characteristic information. Through the present application, space-time unification and
collaboration of the detection data of the
drone and the climbing drum
robot are realized, and the reliability of wind turbine defect identification and positioning is improved.