用知识图谱实现工业物联网流数据的上下文感知管理。
A Context-Aware Knowledge Graph Platform for Stream Processing in Industrial IoT
- 构建知识图谱统一异构设备与数据流，支持语义级集成。
- 结合上下文推理实现动态权限控制与流发现，延迟低于500ms。
- 适合工业5.0场景下的智能系统集成与实时决策需求。
工业物联网生态系统整合传感器、机器与智能设备，在工业环境中协同运行。这些系统生成大量异构、高速的数据流，需具备互操作性、安全性与上下文感知能力。当前多数流管理架构仍依赖语法层面的集成机制，导致在复杂工业5.0场景中灵活性、可维护性和可解释性不足。本文提出一种面向数据流管理的上下文感知语义平台，通过知识图谱统一表示设备、数据流、代理、转换管道、角色与权限，实现灵活的数据采集、可组合的流处理管道及基于代理上下文的动态权限访问。平台基于 Apache Kafka 与 Apache Flink 实现实时处理，结合 SPARQL 与 SWRL 推理实现上下文相关的流发现。实验表明，该模型在工业5.0环境下有效支持互操作数据工作流，显著提升系统语义理解与响应能力。
原文摘要 · Abstract (English)
Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.
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