Since the tobacco retail stores in cities are dense,traditional path planning algorithms for solving the optimal supervision path will consume a lot of time,and cannot guarantee the effect within the specified time.In addition,existing methods seldom consider the network characteristics and the explainability of the candidate subset.This study proposes a graph attention-based node selection and path optimization algorithm(GA-SGPO),which iteratively selects the optimal coordinate node subset and performs calculation on the subset to reduce computa-tion time.In addition,the structural similarity between nodes is calculated to reduce the sparsity of training samples.The experimental data in-cludes the coordinates of 40,000 retail stores in Dongguan City.The experimental results show that the GA-SGPO model ensures the solution accuracy while the solution time is reduced by an average of 48%.The GA-SGPO can significantly save computational time and is closer to practical application scenarios.The attention mechanism and node similarity calculation can provide visualization basis for optimal node selec-tion.
维普
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