「英中对照」Brick-DICL：面向楼宇管理系统Brick模式自动分类的动态上下文学习框架

View PDF HTML (experimental)查看 PDF HTML（实验性）

Abstract: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. 摘要：楼宇管理系统（BMS）对于优化现代建筑的能源效率和运行性能至关重要。然而，不同制造商的 BMS 点位缺乏标准化，给集成和数据利用带来了巨大障碍。尽管 Brick 模式为楼宇系统提供了标准化的本体，但将 BMS 点位映射到合适的 Brick 类面临三个关键挑战：(i) Brick 类数量庞大（最新版本中有 936 个），(ii) 大语言模型（LLM）中特定领域知识有限，(iii) 验证需要大量人工工作。为应对这些挑战，我们提出了 Brick-DICL，这是一种用于自动化 Brick 模式分类的两阶段动态上下文学习框架。Brick-DICL 由两个主要组件组成：metadata-RAG 用于检索相关示例以增强 LLM 的领域知识；class-RAG 用于缩小潜在 Brick 类的范围，以应对庞大的分类空间。此外，我们实现了一种多 LLM 过滤机制，可比较多个模型的预测结果，标记低置信度的分类以供人工审核。结果表明：(i) 通用性：Brick-DICL 适用于任何楼宇管理系统，不受制造商或元数据格式的限制；(ii) 新颖且强大：作为首个用于 Brick 模式分类的动态上下文学习方法，Brick-DICL 在楼宇数据集上实现了显著的分类准确率提升，优于现有方法；(iii) 高效：我们的多 LLM 过滤策略减少了人工验证工作量，能够实现快速的数字楼宇接入。大量实验证明了 Brick-DICL 在各类楼宇数据集上的有效性，加速了迈向标准化、可互操作的楼宇管理系统的进程。

Submission history提交历史

From: Yiyue Qian [view email]
[v1] Tue, 16 Jun 2026 07:46:51 UTC (1,992 KB)
来自：Yiyue Qian [查看邮箱]
[v1] Tue, 16 Jun 2026 07:46:51 UTC (1,992 KB)