The invention discloses a
tunnel boring machine rock
mass grade classification method based on weak
supervised learning, and the method comprises the steps: collecting TBM multi-channel sensor
time sequence data and geological
record labels, and constructing a
data set with
noise labels; carrying out preprocessing and
sequence alignment on the data; dividing a transition section and a stable section based on the geological
label change points, and generating a probabilistic soft
label guided by domain information; a teacher-student
network architecture is constructed,
feature learning and
label correction are performed through a double-graph self-
distillation mechanism of a semantic graph and a category graph, and
model parameters are optimized in combination with prototype learning and a composite
loss function; and finally, reasoning the real-time TBM data by using the trained student network, and outputting a rock
mass grade
classification result. According to the method, robust rock
mass feature representation can be automatically learned from the
engineering labels containing
noise, correction and stable classification of the
noise labels are achieved, and the self-adaptive tunneling capability and construction reliability of the TBM under the complex geological condition are improved.