智能传感器与边缘计算驱动的电商物流实时追踪模块设计

Design of a Real-Time Tracking Module for E-Commerce Logistics Driven by Intelligent Sensors and Edge Computing

摘要: 针对电商物流场景中货物实时追踪精度不足的问题，本研究设计一种基于STM32G070微控制器与多模态传感器的智能边缘计算追踪模块。系统集成华大北斗/GPS双模定位、温湿度传感器，通过硬件层构建定位信息与环境参数的复合感知矩阵。采用卡尔曼滤波算法对多源异构数据进行融合处理，有效抑制了车载动态环境下的信号噪声干扰，将定位轨迹的均方根误差降低至1.5米以内。模块依托STM32G070的Cortex-M0⁺内核实现边缘侧实时数据处理，开发具有自适应权值调整功能的数据融合框架，在保持7.2 mA低功耗特性的同时，完成运动状态识别与环境异常检测的并行计算。研究成果为智能物流提供了具备边缘决策能力的嵌入式解决方案，有效支持运输过程可视化与供应链数字化升级。

Abstract: To address the insufficient real-time tracking accuracy in e-commerce logistics scenarios, this study develops an intelligent edge computing tracking module based on the STM32G070 microcontroller and multimodal sensors. The system integrates Huada Beidou/GPS dual-mode positioning and temperature-humidity sensors, constructing a composite perception matrix of positioning information and environmental parameters through hardware-layer integration. A Kalman filtering algorithm is employed to fuse multi-source heterogeneous data, effectively suppressing signal noise interference in vehicular dynamic environments and reducing the root mean square error (RMSE) of positioning trajectories to below 1.5 meters. Leveraging the Cortex-M0⁺ core of the STM32G070, the module achieves real-time edge-side data processing and implements a data fusion framework with adaptive weight adjustment functionality. This architecture enables parallel computation for motion state recognition and environmental anomaly detection while maintaining low power consumption at 7.2 mA. The developed solution provides an embedded system with edge decision-making capabilities for smart logistics, effectively supporting transportation process visualization and digital supply chain upgrades.