欢迎访问《昆明冶金职业大学学报》,

昆明冶金高等专科学校学报 ›› 2025, Vol. 41 ›› Issue (05): 59-.DOI: 10.3969/j.issn.1009-0479.2025.05.010

• 电子信息技术 • 上一篇    下一篇

一种基于分散式边缘计算的 IAPVS平台设计

  

  1. 1.南昌理工学院电子与信息学院,江西 南昌 330044;2.昆明冶金高等专科学校建筑工程学院,云南 昆明 65003
  • 出版日期:2025-10-01 发布日期:2026-03-25
  • 作者简介:程晓玲 (1990-),女,安徽铜陵人,讲师,工程硕士,主要从事电子通信技术、传感器技术等研究。
  • 基金资助:
    江西省教育厅科技项目 “单片机驱动的水下清洁机器人移动控制系统设计”(GJJ2402614)。

  1. 1. School of Electronics and Information, Nanchang Institute of Technology, Nanchang 330044, China;2. Faculty of Architectural Engineering, Kunming Metallurgy College, Kunming 650033, China
  • Online:2025-10-01 Published:2026-03-25

摘要: 在科技强国战略背景下,智能化视频监控在多领域需求猛增。但传统视频监控及集中式视频云计算架 构面临诸多局限,如端侧智能不足、传输延迟、网络容量及计算效率问题等。深入剖析智能视频监控技术发展 历程与现状,创新提出基于分散式边缘计算的 IAPVS设计模式。该模式含视频边缘计算节点与数据中心两大核 心,边缘节点于现场或近场布设,运用 YOLO算法对视频数据预处理,精准识别目标并过滤数据后上传;数据 中心涵盖多平台与模型仓库,可进行模型训练、修正复用等操作。IAPVS平台逻辑设计与工作流过程亦有详细 阐述,此设计突破传统架构局限,实现端—边—网—云协同,构建高效智能供给体系,提升数据处理与资源利 用效率,为多样化场景的视频智能分析平台开发提供全新总体解决方案,助力我国智能化视频监控技术发展。

关键词: 智能化视频监控, 分散式边缘计算, IAPVS设计模式, 端一边一网一云协同, YOLO算法

Abstract: In the context of the strategy of building a powerful country through science and technology.the demand for intelligent video surveillance has soared in many fields, However, traditional video sur-veillance and centralized video cloud computing architectures face numerous limitations, such as insuffi-cient end-side intelligence, transmission delays, issues related to network capacity, and low computingefficiency. This paper deeply analyzes the development process and current status of intelligent video sur-veillance technology, and innovatively proposes the IAPVS (Intelligent Analysis Platform for Video Sur-veillance) design mode based on distributed edge computing. This mode consists of two core components:video edge computing nodes and a data center. Edge nodes are deployed on-site or in near-field environ-ment and the YOLO algorithm is used for video data preprocessing-accurately identify targets, filtering da-ta, and then uploading the processed data. The data center integrates muliple platforms and a model re-pository, enabling operations such as model training, correction and reuse. The logic design and workflowof IAPVS Platform are also elaborated in detail. This design breaks through the limitations of traditionalarchitectures, realizes end-edge-network-cloud collaboration, establishes an efficient intelligent supplysystem, and improves data processing and resource utilization. It provides a new overall solution for thedevelopment of video intelligent analysis platforms in diverse scenarios, facilitating the development of in-telligent video surveillance technology in China.

Key words: intelligent video surveillance, distributed edge computing, IAPVS (Intelligent analysis plat-form for video surveillance) design model, terminal-edge-network-cloud collaboration, YOLO algorithm