Building Management Systems (BMS) are essential for optimizing energy efficiency and operational performance in modern buildings. However, the lack of standardization across BMS points from different manufacturers creates significant barriers to integration and data utilization. While the Brick schema offers a standardized ontology for building systems, mapping BMS points to appropriate Brick classes presents three critical challenges: (i) the extensive number of Brick classes (936 in the latest version), (ii) limited domain-specific knowledge in large language models (LLMs), and (iii) substantial manual effort required for verification. To address these challenges, we propose Brick-DICL, a two-stage dynamic in-context learning framework for automated Brick schema classification. Brick-DICL consists of two primary components: metadata-RAG, which retrieves relevant examples to enhance LLMs' domain knowledge, and class-RAG, which narrows down potential Brick classes to address the large classification space. Additionally, we implement a multi-LLM filtering mechanism that compares predictions across multiple models, flagging low-confidence classifications for human review. As a result: (i) General: Brick-DICL is applicable to any building management system regardless of manufacturer or metadata format; (ii) Novel and Powerful: as the first dynamic in-context learning approach for Brick schema classification, Brick-DICL achieves significant classification accuracy improvements on building datasets, outperforming existing methods; (iii) Efficient: our multi-LLM filtering strategy reduces manual verification effort, enabling rapid digital building onboarding. Extensive experiments demonstrate Brick-DICL's effectiveness across diverse building datasets, accelerating the path toward standardized, interoperable building management systems.

翻译：摘要：楼宇管理系统（BMS）对优化现代建筑的能效与运营性能至关重要。然而，不同厂商BMS数据点缺乏标准化，导致集成与数据利用面临重大障碍。尽管砖模式（Brick schema）为楼宇系统提供了标准化本体，但将BMS数据点映射至对应砖类时仍面临三大关键挑战：(i) 砖类数量庞大（最新版本达936类），(ii) 大型语言模型（LLM）缺乏领域专业知识，(iii) 验证过程需大量人工投入。针对上述挑战，我们提出Brick-DICL——面向自动化砖模式分类的两阶段动态上下文学习框架。该框架包含两大核心组件：元数据检索增强生成（metadata-RAG）通过检索相关示例增强LLM的领域知识，类检索增强生成（class-RAG）则缩小候选砖类范围以应对大规模分类空间。此外，我们引入多LLM过滤机制，通过对比多模型预测结果，将低置信度分类标记以供人工复核。实验结果表明：(i) 通用性：Brick-DICL可适用于任意楼宇管理系统，不受厂商或元数据格式限制；(ii) 创新性与高效性：作为首个面向砖模式分类的动态上下文学习方法，该框架在建筑数据集上显著提升分类精度，性能超越现有方法；(iii) 经济性：多LLM过滤策略减少了人工验证工作量，实现建筑数字化的快速部署。广泛实验验证了Brick-DICL在多类建筑数据集上的有效性，加速了标准化、可互操作楼宇管理系统的实现进程。