A Low Delay Edge-End Collaborative Data Processing Method Based on Electric Service Characteristics

With the continuous emergence of new electric services, a large amount of data is generated at the distribution network, imposing stricter requirements on transmission delay, data volume, and reliability. Traditional optimization methods focusing on individual aspects such as data compression or resource scheduling are insufficient to meet the differentiated requirements of services, resulting in high processing delay and low efficiency. To address this issue, this paper proposes a low delay edge-end collaborative data processing method based on electric service characteristics. First, the differentiated characteristics of typical electric services are analyzed in terms of real-time performance, data volume, and reliability, and an edge-end collaborative system model integrating compression, transmission, and decoding is established. Then, a joint optimization problem is formulated with the objective of minimizing the weighted sum of delay and throughput based on service characteristics. Finally, a deep Q-network (DQN) algorithm is introduced to dynamically adjust the compression ratio, thereby achieving multi-stage collaborative optimization. Simulation results demonstrate that the proposed method can dynamically balance delay and throughput according to the specific requirements of services.