The application discloses a transport hoisting intelligent scheduling optimization method based on multi-dimensional screening and
trajectory analysis, which collects and analyzes multi-dimensional data of transport trajectories, task nodes, environmental parameters, operation timeliness and safety events and the like of historical hoisting operations, and constructs a hoisting operation path
knowledge base containing space-time characteristics, risk weight and efficiency index; by using
big data mining and
machine learning
algorithm, the optimal path mode and dynamic adjustment strategy under the typical operation scene are extracted; before actual operation, the optimal hoisting operation path is matched or generated from the
knowledge base in combination with current task parameters and real-time
environmental data, and dynamic optimization and real-time correction are supported. The application solves the problems that the traditional hoisting operation path relies on artificial experience, lacks data-driven decision and cannot adapt to complex dynamic environment, and significantly improves the scientificity, safety and efficiency of path planning, and reduces transport cost and
accident risk.