The invention discloses a ship refined
perception full-voyage track fitting and optimization method, which comprises the steps of drawing historical data acquired by an AIS (
Automatic Identification System) to obtain a full-voyage-segment ship
route track diagram, and dividing the full-voyage-segment ship
route track diagram into a
training set and a
test set, the
training set being used for generating an input image and a training
label; based on the input image and the training
label, loading historical weight parameters at the same time, and training to obtain a
deep learning track separation model; testing set data is processed and then input into a
deep learning track separation model, the model outputs an image, track data is
cut and divided into a straight line stage and a curve stage, and track fitting is carried out in a segmented mode; combining the segmented tracks to obtain an overall track of the ship
route; comparing the difference between the fitted curve and the actual curve, calculating a precision index, and if the precision is improved, iteratively updating a weight parameter of the
deep learning trajectory separation model; otherwise, repeatedly training the iteration model until the precision index meets the requirement. According to the invention, the track features can be effectively identified, and the track fitting precision is effectively improved.