The invention discloses a
machine learning
icing recognition system and method for a skymirror
system, and relates to the technical field of measurement, and the method comprises the following steps: S1, transmitting an optical
signal to an ice layer, and judging the type of the ice layer based on the features of the returned optical
signal; s2, acquiring an ice layer
capacitance value, and calculating the thickness of the ice layer according to the
capacitance value; s3, acquiring an image containing a
water body, a sensitive zone at the junction of the water and the ice layer and the ice layer, and calculating the thickness of the sensitive zone according to image features and optical
signal features; s4, acquiring the signal-to-
noise ratio of the
capacitance measurement data, determining a first
weight coefficient according to the signal-to-
noise ratio, and determining a second
weight coefficient according to the
contrast ratio of the image; and S5, performing weighted fusion on the capacitance measurement thickness and the image thickness based on the first
weight coefficient and the second weight coefficient to obtain the corrected ice
layer thickness. According to the invention, through multi-sensor data fusion, weight dynamic distribution and construction of prediction modeling, the problems of ice type identification, thickness measurement and growth law prediction are solved.