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：大规模智能系统的高层上下文推理自动化势在必行，这源于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与动态性。为解决此问题，我们提出自动上下文推理框架CSM-H-R，该框架通过运行时与模型存储阶段的程序化本体与状态组合，实现有意义的HLC识别能力，所得数据表示可应用于不同推理技术。基于智慧校园场景下的智能电梯系统开展案例研究。框架实现（CSM引擎）及将HLC推理转化为向量与矩阵计算的实验，特别关注上下文的动态特性，展示了利用高级数学与概率模型实现智能系统集成自动化升级的潜力；同时通过标签嵌入匿名化与降低信息相关性实现隐私保护支持。本研究的代码可访问：https://github.com/songhui01/CSM-H-R。