京杭运河吴江段船舶碰撞事故致因模型及应用OA

The shipping system of the Beijing-Hangzhou Grand Canal may be regarded as relatively stable,with a relatively high frequency of vessel collisions,yet lacking quantitative analytical methods for their causal factors.This paper proposes converting time-series data into complex network models using visible graph algorithms,introducing formal concept analysis models to extract attribute sets through text mining methods.This methodology conducted time-series feature analysis on 217 collision incidents occurring consecutively over five years(2018-2022)along the Wujiang section of the Beijing-Hangzhou Grand Canal.Two representative accident black spots within this section were selected as the object set,establishing a formal causal background to construct a conceptual lattice of collision accident causes tailored to canal scenarios.The methodology was validated against collision incidents occurring over three consecutive years(2023-2025)in the Wujiang section.Results indicate the primary causal factors for canal vessel collisions are:vessel manoeuvres entering/exiting the main navigation channel;inconsistent intentions during overtaking manoeuvres;and the relatively narrow effective width of the canal navigation channel.