基于物联网技术的电力物资配送实时优化决策模型

Real-Time Optimization Decision Model for Power Material Distribution Based on Internet of Things Technology

摘要: 随着电力行业的快速发展和物联网技术的广泛应用，电力物资运输的实时优化决策成为提高物流效率、降低运输成本的关键问题。本文提出了一种基于物联网技术的电力物资运输实时优化决策模型。该模型利用信息物理系统(CPS)和物联网技术，通过车载GPS、RFID物资标签、路侧传感器、交通摄像头以及气象API等多种感知设备，实时采集客户订单、车辆位置、物资状态、道路实时交通流速度、事故信息、施工路段位置和精确的天气数据(如降雨量、风速、能见度)，构建了融合多源异构数据的动态运输环境感知层。数据通过MQTT协议传输至云端CPS平台，进行清洗、融合(如将GPS坐标与高德地图的路网匹配，将天气信息关联到具体路段)，并转化为模型可用的参数(如路段通行时间、事故导致的道路封闭状态、天气影响因子)。在此基础上，建立了配送路径优化模型，提出了一种改进的大邻域搜索算法，以提高算法的收敛速度和优化效果。实验结果表明，所提出的模型和算法能够有效提高物流配送效率，降低运输成本，相较于传统静态规划方法，总行驶里程平均降低13.3%，车辆使用数量减少25%，载重利用率提升21.1%，动态事件响应时间缩短至5分钟内，为电力物资运输的智能化管理提供了理论支持和借鉴。

Abstract: With the rapid development of the electric power industry and the widespread application of Internet of Things (IoT) technology, real-time optimization decision-making for electrical material transportation has become a key factor in enhancing logistics efficiency and reducing transportation costs. This paper proposes a real-time optimization decision model for electric material transportation based on IoT technology. The model leverages Cyber-Physical Systems (CPS) and IoT devices, including vehicle-mounted GPS, RFID tags, roadside sensors, traffic cameras, and meteorological APIs, to collect data in real time on customer orders, vehicle locations, material statuses, road traffic flow velocities, accident information, construction zones, and precise weather conditions (such as rainfall, wind speed, and visibility). These heterogeneous multi-source data are integrated into a dynamic transportation environment awareness layer. The data are transmitted to a cloud-based CPS platform via MQTT protocol, where they undergo cleaning, fusion (such as matching GPS coordinates with Gaode Map’s road network and associating weather information with specific road segments) and transformation into model-ready parameters (e.g., segment travel times, road closures due to accidents, and weather impact factors). Based on this enriched information, a route optimization model is established, and an improved large-neighborhood search algorithm is proposed to enhance convergence speed and optimization effect. Experimental results demonstrate that the proposed model and algorithm significantly improve logistics delivery efficiency and reduce transportation costs. Compared to traditional static planning methods, the approach reduces total travel mileage by an average of 13.3%, decreases vehicle utilization by 25%, enhances load utilization by 21.1%, and shortens the response time to dynamic events to within 5 minutes, thereby providing theoretical support and valuable insights for the intelligent management of electric material transportation.