Logistics transportation personalized recommendation path planning method and system based on cloud platform

By constructing a heterogeneous traffic graph model and a heterogeneous graph neural network, combining user semantic intentions and edge computing, the problem of user preferences overlooked in logistics path recommendation is solved, and efficient and real-time recommendation of personalized path planning is achieved.

CN120494243AActive Publication Date: 2025-08-15XIAN HUODA NETWORK TECH CO LTD
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Patent Information

Application Number
CN202510977752.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-16
Publication Date
2025-08-15
Estimated Expiration
2045-07-16

AI Technical Summary

Technical Problem

The existing logistics path recommendation methods lack deep modeling of user transportation intentions and personal preferences in scenarios such as cross-regional, complex road network structure and abnormal weather, resulting in lagging responses to recommendation results or not meeting user preferences and reducing transportation efficiency.

Method used

Build a heterogeneous traffic graph model that integrates roads, geographical points of interest and traffic events, collects user historical transportation task records to generate semantic intent tags, and jointly model them through heterogeneous graph neural networks, combines edge computing devices to obtain traffic status in real time, dynamically update semantic intent mapping relationships, and realizes personalized path recommendations.

Benefits of technology

It improves the matching accuracy and user satisfaction of path recommendations, realizes personalized path online recommendations with low latency and high adaptability, and improves the intelligence level and response capabilities of logistics and transportation in complex traffic environments.

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Abstract

The invention discloses a logistics transportation personalized recommendation path planning method and system based on a cloud platform, and belongs to the technical field of logistics transportation, and the method comprises the steps: constructing a heterogeneous traffic map model fusing road sections, geographic interest points and traffic event information; collecting historical transportation task records of a plurality of users, generating user semantic intention tags based on delivery behaviors, and mapping the user semantic intention tags to traffic map nodes and edge attributes to form a semantic constraint graph structure; performing joint modeling on the user intention and the path reachability by using a heterogeneous graph neural network to generate a path scoring sequence; after the transportation task is completed, abnormal events are collected, and the semantic intention label mapping relation is dynamically updated based on reverse alignment errors; the model and the mapping module are deployed on a cloud platform, the traffic state is obtained in real time in combination with edge equipment, and path recommendation online generation and rapid pushing are achieved; according to the method, the personalized matching degree and the real-time response capability of path recommendation can be effectively improved, and the method is suitable for intelligent logistics path planning in a complex scene.
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