Vehicle transportation route planning method and device, storage medium and terminal

Abstract

The invention discloses a vehicle transportation route planning method and device, a storage medium and a terminal. The method comprises the steps of determining a starting point position and a terminal point position, and searching a starting point road section and a terminal point road section of the starting point position and the terminal point position, when the road section identifiers are different, initializing a search unit deduplication set, determining search nodes according to the starting road section and determining end nodes according to the terminal road section, querying a target road section communicated with the search node, and generating a first search unit, a first weight value and a second weight value based on the target road section, when the first search unit does not exist in the deduplication set, putting the first search unit and the previous search unit in a route planning queue and recording in the search unit deduplication set, executing route planning queue dequeue based on the first weight value and the second weight value to generate a second search unit, and when the rear node of the road section corresponding to the second search unit is a terminal node, generating a transportation route of the vehicle according to the second search unit. The route planning accuracy can be improved.

Description

technical field [0001] The present invention relates to the technical field of intelligent transportation, in particular to a vehicle transportation route planning method, device, storage medium and terminal. Background technique [0002] The current mainstream Internet truck navigation solution is to generate routes through route planning algorithms by collecting information such as road height limits, weight limits, and time limits, combined with data such as route connectivity and route speed limits. Common navigation route planning algorithms include algorithms such as Dijkstra, A-star, and hierarchical reduction. Dijkstra's algorithm can calculate the minimum weight very accurately, but it runs slowly. The hierarchical reduction method runs very fast, but after the data weight rules change, it needs a long time to compile the data, and the timeliness is poor. Although the A-star algorithm is much faster than Dijkstra's, errors may occur when calculating the minimum we...

AI Technical Summary

Problems solved by technology

Dijkstra's algorithm can calculate the minimum weight very accurately, but it runs slowly

The hierarchical reduction method runs very fast, but after the data weight rules change, it takes a long time to compile the data, and the timeliness is poor

Although the A-star algorithm is much faster than Dijkstra's, it may make mistakes when calculating the minimum weight, and cannot accurately meet the user's requirements for the route.

Images