一种物流车辆智能路径规划方法、软件系统、装置
By training with multimodal graph datasets and deep multimodal graph clustering, the problem of insufficient multimodal data fusion in logistics vehicle route planning is solved, enabling real-time route updates and efficient route decisions, thereby improving the safety and efficiency of logistics transportation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- LULIANG UNIV
- Filing Date
- 2026-02-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing logistics vehicle route planning methods rely on a single data source, lack the ability to fuse multimodal data, and are difficult to achieve real-time dynamic route updates, thus failing to meet the real-time, intelligent, and refined requirements of modern logistics operations for route optimization.
By employing a multimodal graph dataset and a deep multimodal graph clustering training method, and acquiring road network topology, vehicle behavior, and intersection environment data, an intelligent path planning model is constructed to achieve the fusion of multimodal data and real-time path updates.
It improves the accuracy of road condition perception and route decision-making capabilities, enabling the identification of road operation modes and the prediction of potential risks, thereby enhancing the accuracy and adaptability of route planning.
Smart Images
Figure CN121702417B_ABST