The application discloses a subway tunnel structure intelligent three-
dimensional modeling and
disease diagnosis method and
system, relates to the technical field of intelligent operation and maintenance of
urban rail transit infrastructure, adopts master-slave cooperative inspection units to collect
laser radar point clouds, high-definition images,
infrared thermal images, ultrasonic wave data and
environmental sensing data, pre-identifies suspected
disease areas after
time synchronization and
processing, constructs a self-adaptive separable four-parameter model M(beta, theta,
delta, epsilon), wherein beta is a structure form parameter set, theta is a structure posture parameter set,
delta is a
local disease parameter set, and epsilon is an environmental correlation parameter set, and completes model initialization based on a design BIM model and historical data. The LSTM
time sequence prediction model of the application realizes 12-month structure state
change prediction based on a historical optimal parameter set, generates targeted maintenance suggestions, supports
predictive maintenance decision-making, and reduces operation risks and maintenance costs caused by sudden diseases.