The application relates to the field of
trip planning, and provides a personalized
trip planning method and
system based on a large
language model and a recall strategy, which comprises the following steps: analyzing and refining demand information on a demand field; screening and calculating a point-of-interest recall
score to obtain initial candidate points of interest; clustering the initial candidate points of interest to obtain a first cluster including a cluster boundary, constructing a virtual point of interest to obtain a virtual point of interest and merging the virtual point of interest into the first cluster to obtain a second cluster, calculating a point-of-interest
standard score, and screening to obtain a high-recommendation-degree point of interest according to the point-of-interest
standard score; obtaining a point-of-interest cluster, inserting the second cluster to obtain a third cluster, calculating a cluster density, rearranging the third cluster to obtain a fourth cluster, calculating a center
turning angle, and obtaining a target cluster; obtaining an initial
global optimal solution based on a greedy search target function and the target cluster, optimizing the initial
global optimal solution based on a multilayer nested target function, and completing the planning of the trip.