AI 中文总结
本文提出 AnyLog 边缘数据架构，该基于智能体与边缘的平台可在源端管理运营数据，通过多项技术让用户无需知晓资源位置即可操作分布式资源，为分布式 SQL 等提供类云操作模式，无单点故障且不依赖集中式基础设施。
AI 中文摘要
工业与自主系统日益依赖人工智能、自动化及实时协调功能，对生成的运营数据及时作出响应。然而，传统架构通常要求数据先经过集中式平台才能做出决策。云系统虽在训练、报告及长期分析方面仍具价值，但会在关键决策路径中增加延迟与外部依赖，且随着每个站点新增边缘设备和数据，扩展难度不断提升。当智能在机器、站点、设施及车辆间扩散时，持续依赖集中化将限制响应时间、弹性、可扩展性及自主运行能力。本文提出 AnyLog 边缘数据架构，这是一个基于智能体与边缘的平台，在源端管理运营数据，同时将分布式数据、资产、计算资源及服务呈现为一个逻辑系统。通过其分布式元数据层、虚拟数据湖、统一命名空间、单一系统映像及模型上下文协议，授权用户、应用、自动化服务及人工智能智能体可发现、查询、处理并作用于分布式资源，无需知晓其托管位置。查询与计算在持有相关数据的智能体处执行，仅请求与结果在网络中传输，此举可保留数据本地所有权、减少数据移动、支持连接中断时的持续运行，并支持从经验证的数字孪生配置进行可重复部署。AnyLog 为分布式 SQL、实时自动化、边缘人工智能、联邦学习及弹性决策提供类云操作模式，且无单点故障，不依赖任何集中式基础设施。
英文摘要
Industrial and autonomous systems increasingly depend on AI, automation, and real-time coordination to act on operational data as it is generated. Yet conventional architectures often require that data to pass through centralized platforms before decisions can be made. Cloud systems remain valuable for training, reporting, and long-term analytics, but they add latency and external dependencies to the critical decision path and become harder to scale as each new site adds additional edge devices and data. As intelligence spreads across machines, sites, facilities, and vehicles, continued dependence on centralization will constrain response time, resilience, scalability, and autonomous operation. This paper presents the AnyLog Edge Data Fabric, an agent- and edge-based platform that manages operational data at its source while presenting distributed data, assets, compute resources, and services as one logical system. Through its Distributed Metadata Layer, Virtual Data Lake, Unified Namespace, Single System Image, and Model Context Protocol, authorized users, applications, automation services, and AI agents can discover, query, process, and act on distributed resources without knowing where they are hosted. Queries and computation execute at the agents holding the relevant data, so only requests and results traverse the network. This preserves local ownership, reduces data movement, supports continued operation during connectivity disruptions, and enables repeatable deployment from validated digital-twin configurations. AnyLog provides a cloud-like operating model for distributed SQL, real-time automation, Edge AI, federated learning, and resilient decision-making without a single point of failure or any dependence on centralized infrastructure.
CommentsKeywords: IoT Data Management, Edge Data Management, Edge Data Fabric, Distributed Query, Edge AI, P2P Data Layer, Decentralized Data Layer. 30 pages, 6 figures
