Automation of High-Level Context (HLC) reasoning for intelligent systems at scale is imperative due to the unceasing accumulation of contextual data in the IoT era, the trend of the fusion of data from multi-sources, and the intrinsic complexity and dynamism of the context-based decision-making process. To mitigate this issue, we propose an automatic context reasoning framework CSM-H-R, which programmatically combines ontologies and states at runtime and the model-storage phase for attaining the ability to recognize meaningful HLC, and the resulting data representation can be applied to different reasoning techniques. Case studies are developed based on an intelligent elevator system in a smart campus setting. An implementation of the framework - a CSM Engine, and the experiments of translating the HLC reasoning into vector and matrix computing especially take care of the dynamic aspects of context and present the potentiality of using advanced mathematical and probabilistic models to achieve the next level of automation in integrating intelligent systems; meanwhile, privacy protection support is achieved by anonymization through label embedding and reducing information correlation. The code of this study is available at: https://github.com/songhui01/CSM-H-R.

翻译：随着物联网时代上下文数据的持续积累、多源数据融合的趋势，以及基于上下文的决策过程固有的复杂性和动态性，大规模智能系统的高层上下文（HLC）自动推理势在必行。为缓解这一问题，我们提出了一种自动上下文推理框架CSM-H-R，该框架通过编程方式将本体与运行时及模型存储阶段的状态相结合，以获得识别有意义HLC的能力，并且产生的数据表示可应用于不同的推理技术。基于智慧校园环境下的智能电梯系统开展了案例研究。该框架的实现——CSM引擎，以及将HLC推理转化为向量和矩阵计算的实验，特别关注了上下文的动态特性，并展示了利用高级数学和概率模型实现智能系统集成下一阶段自动化的潜力；同时，通过标签嵌入匿名化和降低信息相关性实现了隐私保护支持。本研究的代码可从以下网址获取：https://github.com/songhui01/CSM-H-R。