就地计算、云边协同:传感云与边缘计算的一体化框架(特邀)

doi:10.19678/j.issn.1000-3428.0253308

摘要/Abstract

摘要： 面向大规模感知与智能应用场景,集中式计算在时延、带宽、能耗与隐私保护的多重约束下逐渐呈现边际效益递减,计算范式因此由单一的"万物上云"模式,逐步转向"就地计算与云边协同"的新形态。在此背景下,本文首先梳理集中化计算路径在不同发展阶段所具备的优势及其适用边界,进而界定边缘计算在端-云之间所扮演的关键角色。在此基础上,进一步概述"传感云-边缘-端"协同计算框架,重点分析其中的核心机制,包括数据"必要即上行"的传输原则、面向服务级别协议(SLA)感知的任务分配与双层调度策略,以及边侧即时闭环执行与云侧全局策略治理之间的分工与协同关系。随着计算与智能能力向边缘侧持续下沉,本文进一步讨论边缘智能的发展方向,涵盖模型轻量化与本地学习机制、联邦学习与知识蒸馏的协同范式,以及面向边缘环境的智能运维(AIOps for Edge)与多级降级机制所支撑的自治能力。同时,强调构建以端到端闭环效率、系统韧性与可追责性为导向的综合评价体系的重要性。最后,结合教育等典型应用场景以及产业实践,论证就地计算与云边协同在保障确定性时延、提升系统整体韧性以及实现跨域一致性方面的现实有效性,并据此指出计算范式由边缘计算向云边智能协同演进的必然趋势与发展方向。

关键词: 云边协同, 传感云, 边缘智能, 就地计算, 大小模型协同

Abstract: Centralized computing exhibits diminishing returns under latency, bandwidth, energy, and privacy constraints in large-scale sensing and intelligent applications. Consequently, the architectural focus shifts from an ″everything to the cloud″ to near-source computing combined with cloud—edge collaboration. This paper reviews the stage-specific advantages and limitations of centralization. It characterizes edge computing as a near-data layer situated between endpoints and the cloud that uses local processing and closed-loop control to satisfy deterministic latency and resilience. From this perspective, the paper outlines a sensor-cloud—edge—device collaborative framework. This framework adopts upload-when-necessary data paths, Service Level Agreement (SLA)-aware task placement with two-tier scheduling, and a division of labor in which the edge closes loops instantly while the cloud performs policy and model governance. The paper then discusses the trajectory toward edge intelligence, including lightweight and on-device learning; federated learning and knowledge distillation; AIOps for Edge with multilevel degradation; and an evaluation regime oriented to end-to-end closed-loop efficiency, resilience, and auditability. Evidence from educational scenarios and current industral pratices demonstrate the the practical effectiveness of near-source computing and cloud—edge collaboration in ensuring deterministic latency, enhancing overall system resilience, and achieving cross-domain consistency, and accordingly identify the inevitable evolution of the computing paradigm from edge computing toward cloud—edge intelligent collaboration.

Key words: cloud—edge collaboration, sensor cloud, edge intelligence, near-source computing, small—large model collaboration

中图分类号:

TP18

王田, 李雨婷, 王文华. 就地计算、云边协同:传感云与边缘计算的一体化框架(特邀)[J]. 计算机工程, 2026, 52(2): 7-12.

WANG Tian, LI Yuting, WANG Wenhua. Near-Source Computing and Cloud-Edge Collaboration: An Integrated Architecture for Sensor Cloud and Edge Computing (Invited)[J]. Computer Engineering, 2026, 52(2): 7-12.

https://www.ecice06.com/CN/Y2026/V52/I2/7

参考文献

[1] 张晓丽, 杨家海, 孙晓晴, 等. 分布式云的研究进展综述[J]. 软件学报, 2018, 29(7): 2116-2132. ZHANG X L, YANG J H, SUN X Q, et al. Survey of geo-distributed cloud research progress[J]. Journal of Software, 2018, 29(7): 2116-2132. (in Chinese)
[2] WANG L, LI L, XU L M, et al. Failure-resilient distributed inference with model compression over heterogeneous edge devices[J]. IEEE Transactions on Mobile Computing, 2024, 23(12): 12680-12692.
[3] NIU C Y, DING Y C, LU J H, et al. Collaborative learning of on-device small model and cloud-based large model: advances and future directions[EB/OL].[2025-10-25]. https://arxiv.org/abs/2504.15300.
[4] 曾建电, 王田, 贾维嘉, 等. 传感云研究综述[J]. 计算机研究与发展, 2017, 54(5): 925-939. ZENG J D, WANG T, JIA W J, et al. A survey on sensor-cloud[J]. Journal of Computer Research and Development, 2017, 54(5): 925-939. (in Chinese)
[5] 梁玉珠, 梅雅欣, 杨毅, 等. 一种基于边缘计算的传感云低耦合方法[J]. 计算机研究与发展, 2020(3): 639-648. LIANG Y Z, MEI Y X, YANG Y, et al. A low-coupling method in sensor-cloud systems based on edge computing[J]. Journal of Computer Research and Development, 2020(3): 639-648. (in Chinese)
[6] 王田, 王文华, 徐旸. 传感云与边缘计算[M]. 北京: 人民邮电出版社, 2025. WANG T, WANG W H, XU Y. Sensor-cloud and edge computing[M]. Beijing: People’s Posts and Telecommunications Press, 2025. (in Chinese)
[7] WANG Z Y, GOUDARZI M, BUYYA R. TF-DDRL: a Transformer-enhanced distributed DRL technique for scheduling IoT applications in edge and cloud computing environments[J]. IEEE Transactions on Services Computing, 2025, 18(2): 1039-1053.
[8] 王思轩. 数字化转型架构[M].北京: 电子工业出版社, 2021. WANG S X. Digital transformation architecture: methodology and cloud-native practice[M]. Beijing: Publishing House of Electronics Industry, 2021. (in Chinese)
[9] LIU Z Y, CHEN X, WU H, et al. Integrated sensing and edge AI: realizing intelligent perception in 6G[J]. IEEE Communications Surveys & Tutorials, 2025, 28: 2725-2770.
[10] LUO Q Y, HU S H, LI C L, et al. Resource scheduling in edge computing: a survey[J]. IEEE Communications Surveys & Tutorials, 2021, 23(4): 2131-2165.
[11] HUANG F Y, WANG W H, LIU Q, et al. DRMQ: dynamic resource management for enhanced QoS in collaborative edge-edge industrial environments[J]. IEEE Transactions on Services Computing, 2025, 18(2): 743-757.
[12] 唐朝刚, 李召, 肖硕, 等. 一种面向车载边缘计算基于服务缓存的任务协同卸载算法[J]. 计算机学报, 2025, 48(4): 864-876. TANG C G, LI Z, XIAO S, et al. Service caching based collaborative task offloading algorithm in vehicular edge computing[J]. Chinese Journal of Computers, 2025, 48(4): 864-876. (in Chinese)
[13] 国务院. 关于印发"十四五"数字经济发展规划的通知(国发〔2021〕29号)[J]. 中国军转民, 2022(1): 6-12. State Council. Notice on issuing the "14th Five-Year" plan for digital economy development (state council document No. 29〔2021〕)[J]. China Defence Industry Conversion, 2022(1): 6-12. (in Chinese)
[14] 国家发展改革委, 国家数据局, 中央网信办, 工业和信息化部, 国家能源局. 关于深入实施"东数西算"工程加快构建全国一体化算力网的实施意见(发改数据〔2023〕1779号)[EB/OL].[2025-10-25]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202312/t20231229_1363000.html. National Development and Reform Commission, National Data Administration, Cyberspace Administration of China, Ministry of Industry and Information Technology, National Energy Administration. Implementation opinions on further deepening the "East Data, West Computing" project to accelerate the construction of a national integrated computing power network (development and reform data 〔2023〕 No. 1779)[EB/OL].[2025-10-25]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202312/t20231229_1363000.html. (in Chinese)
[15] SALI S, MERIBOUT A, MAJEED A, et al. Real-time object detection and associated hardware accelerators targeting autonomous vehicles: a review[EB/OL].[2025-10-12]. https://arxiv.org/abs/2509.04173.
[16] KAREEM AWAD W, ZAINOL ARIFFIN K A, AHMAD NAZRI M Z, et al. Resource allocation strategies and task scheduling algorithms for cloud computing: a systematic literature review[J]. Journal of Intelligent Systems, 2025, 34: 20240441.
[17] 工业和信息化部, 中央网络安全和信息化委员会办公室, 教育部, 国家卫生健康委员会, 中国人民银行, 国务院国有资产监督管理委员会. 算力基础设施高质量发展行动计划(工信部联通信〔2023〕180号)[EB/OL].[2025-10-25]. https://www.gov.cn/zhengce/zhengceku/202310/content_6907900.htm. Ministry of Industry and Information Technology, Office of the Central Cyberspace Administration of China, Ministry of Education, National Health Commission, People’s Bank of China, State-owned Assets Supervision and Administration Commission of the State Council. Action plan for the high-quality development of computing power infrastructure(joint MIIT communication document 〔2023〕 No. 180)[EB/OL].[2025-10-25]. https://www.gov.cn/zhengce/zhengceku/202310/content_6907900.htm. (in Chinese)
[18] 鲁飞鸿, 罗杨一飞, 高士淇, 等. 基于不确定性校准的云边协同推理框架[J]. 计算机学报, 2025, 48(10): 2487-2507. LU F H, LUO Y Y F, GAO S Q, et al. An uncertainty-calibrated cloud-edge collaborative inference framework[J]. Chinese Journal of Computers, 2025, 48(10): 2487-2507. (in Chinese)
[19] WANG Z, WU F, YU F, et al. Federated continual learning for edge-AI: a comprehensive survey[EB/OL].[2025-10-25]. https://arxiv.org/abs/2411.13740.
[20] WANG L P, CHEN S, JIANG L N, et al. Parameter-efficient fine-tuning in large language models: a survey of methodologies[J]. Artificial Intelligence Review, 2025, 58(8): 227.
[21] LI Y T, WANG W H, WANG T. pFSSL-D: generalization meets personalization in dual-phase federated semi-supervised learning[C]//Proceedings of the IEEE 41st International Conference on Data Engineering (ICDE). Washington D.C., USA: IEEE Press, 2025: 2188-2200.
[22] YANG C P, ZHU Y, LU W, et al. Survey on knowledge distillation for large language models: methods, evaluation, and application[J]. ACM Transactions on Intelligent Systems and Technology, 2025, 16(6): 1-27.
[23] BECKER S, SCHMIDT F, GULENKO A, et al. Towards AIOps in edge computing environments[C]//Proceedings of the IEEE International Conference on Big Data (Big Data). Washington D.C., USA: IEEE Press, 2020: 3470-3475.
[24] AHMED I, SYED M A, MAARUF M, et al. Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches[J]. Computing, 2024, 107(1): 2.
[25] SUSNJAK T, MCINTOSH T R, BARCZAK A L C, et al. Over the edge of chaos? Excess complexity as a roadblock to artificial general intelligence[J]. IEEE Transactions on Cybernetics, 2026, 56(1): 29-41.

选择文件类型/文献管理软件名称

选择包含的内容