文献类型：期刊文献

英文题名：Sensor-Generated In Situ Data Management for Smart Grids: Dynamic Optimization Driven by Double Deep Q-Network with Prioritized Experience Replay

作者：Zhang, Peiying[1,2];Li, Siyi[1,2];Li, Dandan[3];Ding, Qingyang[3];Shi, Lei[4]

第一作者：Zhang, Peiying

通讯作者：Li, DD[1]

机构：[1]China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China;[2]Shandong Key Lab Intelligent Oil & Gas Ind Softwar, Qingdao 266580, Peoples R China;[3]Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China;[4]Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China

第一机构：China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2025

卷号：15

期号：11

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20252418594803);Scopus(收录号：2-s2.0-105007678848);WOS:【SCI-EXPANDED(收录号:WOS:001505780300001)】；

基金：This work is partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023LZH017 and ZR2024MF066, the National Natural Science Foundation of China under Grant 62471493, and the Beijing Social Science Foundation Program under Grant 23GLC037. Additionally, it is supported by the open research subject of the State Key Laboratory of Intelligent Game (No. ZBKF-24-12) and the Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE (No. 202306). This work is also supported by the Teaching Reform Project of the Beijing Union University "Research on the Construction of an Innovation and Entrepreneurship Education Ecosystem for E-commerce Majors Empowered by Digital Intelligence" (No. JJ2025Y039).

语种：英文

外文关键词：smart grid; in situ data; in situ server system; data management; double deep Q-network

摘要：According to forecast data from the State Grid Corporation of China, the number of terminal devices connected to the power grid is expected to reach the scale of 2 billion within the next five years. With the continuous growth in the number and variety of terminal devices in the smart grid, traditional cloud-edge-end architecture will face the increasing issue of response latency. In this context, in situ computing, as a paradigm for local or near-source data processing within cloud-edge-end architecture, is gradually becoming a key technological pathway in industrial systems. The in situ server system, by deploying servers near terminals, enables the near-data-source processing of terminal-generated in situ data, representing an important implementation of in situ computing. To enhance the processing efficiency and response capability of in situ data in smart grid scenarios, this study designs an in situ data processing mechanism and an access demand management framework. Due to the heterogeneity of in situ server performance, there are variations in response capabilities for access demands across rounds. This study introduces a double deep Q-network with prioritized experience replay to assist in response decision-making. Simulation experiments show that the proposed method reduces waiting latency and response latency by an average of 67.69% and 68.77%, respectively, compared to traditional algorithms and other reinforcement learning algorithms, verifying its effectiveness in in situ data management. This scheme can also be widely applied to in situ computing scenarios with low-latency data management, such as smart cities and industrial IoT.