Construction and Optimization of a Precise Positioning Model for Logistics Vehicles Based on Sustainable Operation

Abstract

The booming development of e-commerce drives the demand of the logistics industry. An effective logistics vehicle positioning system is crucial for improving logistics operational efficiency. However, current positioning systems based on global positioning systems and global system mobile communication suffer from issues such as low positioning accuracy and poor real-time performance. A current research focus is to improve and optimize it. To address this issue, a logistics vehicle precise positioning model based on deep learning was constructed. High-definition images were captured using digital cameras, and data augmentation and preprocessing techniques were introduced to adapt to various environments and vehicle types. These experiments confirmed that through this model, the vehicle positioning accuracy reached up to 93.3%, and the positioning accuracy under urban road conditions was 96%. The AP of different types of logistics vehicles ranged from 92.4% to 94.7%, far exceeding other positioning algorithms. For CPU usage, the optimization algorithm gradually increased to 77% within 120 minutes of experimental time. Overall, this research model provides strong technical support for the logistics industry and an effective way to improve logistics operational efficiency and service quality.