The invention discloses an SVDD-based operation condition classification method and
system for autonomous
shovel loading of a
loader, and the method comprises the steps: collecting the autonomous operation data of the
loader under different operation conditions, and carrying out the preprocessing of the autonomous operation data, so as to extract a
shovel loading operation segment and the characteristics of the
shovel loading operation segment; training an SVDD model under each working condition by using the features, obtaining the center and
radius of a
hypersphere of each working condition grade category, and constructing an SVDD category
incremental learning model; when new operation data are input, the model can judge whether the data belong to known working conditions or not; if yes, calculating a support function value to determine a specific working condition grade; and if not, identifying the working condition as a new working condition and updating
model parameters. According to the method, by collecting and analyzing the operation data of the loading
machine and utilizing the SVDD model and the
incremental learning ability of the SVDD model, precise classification and dynamic
adaptation of the operation working conditions under all working conditions are achieved, and the autonomous operation efficiency and precision are improved.