Research on Edge Node Data Processing Technology in Multi-Tier Supply Chains

Edge node data processing in supply chain networks faces challenges such as real-time performance, reliability, and resource optimization. This paper analyzes the data characteristics and processing requirements of edge nodes in multi-tier supply chains and proposes a three-tier edge computing architecture. It designs differentiated processing strategies tailored to various levels of nodes, achieving core functions such as data preprocessing, distributed computation, and real-time analysis. By utilizing an edge-cloud collaborative framework and a resource scheduling algorithm based on deep reinforcement learning, the overall system performance is significantly improved. Experimental results demonstrate that this solution outperforms traditional architectures in terms of processing capability, response time, and resource utilization, offering a feasible technological approach and practical reference for large-scale supply chain network edge data processing.