The invention relates to the technical field of
control system data processing, in particular to a ship intelligent workshop crane hoisting path
planning method, which comprises the following steps: collecting crane coordinates, obstacle distribution, load swing angle and material weight, and inputting a physical constraint model to generate a structured environment state
data set; initializing wolf pack
algorithm parameters based on the structured environment state
data set, calculating a compressed detection wolf walking step length according to an obstacle distance, generating a global task
queue by an industrial cloud platform according to a dynamic parameter set, and outputting an execution state
data set by a crane end; counting emergency braking times from an execution state data set to optimize a walk step length function, comparing task time to optimize a wolf investigation proportion, analyzing historical swing data to establish a step length and swing mapping
library, and optimizing a parameter set to feed back and update a physical constraint model. According to the invention, cooperation of environment
perception, dynamic path planning and closed-loop
verification is realized, and accident risks and production
delay in a ship workshop narrow space dynamic obstacle scene are reduced.