用大模型让普通人也能轻松问空调系统问题，答得准还懂上下文。
LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction
- 分两阶段处理：先理解用户问题，再查数据生成回答。
- 在真实空调系统上测试，答对率显著高于其他方法。
- 特别适合想轻松管理空调的非专业人士使用。
基于大语言模型（LLM）的问答接口为提升非专业用户与暖通空调（HVAC）系统交互体验提供了新路径。然而，实现准确、实时且上下文敏感的交互面临独特挑战，包括频繁更新的传感器数据整合、领域知识定位以及多阶段推理一致性。本文提出 JARVIS，一种面向传感器驱动的 HVAC 系统交互的双阶段 LLM-QA 框架。JARVIS 使用专家级大模型将高层用户查询转化为结构化执行指令，并通过代理完成基于 SQL 的数据检索、统计处理及最终响应生成。为应对 HVAC 特有挑战，该框架集成三项关键技术：（1）自适应上下文注入策略，高效融合 HVAC 及部署特定信息；（2）参数化 SQL 构建与执行器，提升数据访问可靠性；（3）自底向上的规划机制，保障多阶段响应生成的一致性。我们利用商业 HVAC 系统采集的真实数据与专家构建的基准问答数据集进行评估，结果表明，JARVIS 在自动化和用户中心评估中均持续优于基线及消融版本，展现出高响应质量与准确性。
原文摘要 · Abstract (English)
Question-answering (QA) interfaces powered by large language models (LLMs) present a promising direction for improving interactivity with HVAC system insights, particularly for non-expert users. However, enabling accurate, real-time, and context-aware interactions with HVAC systems introduces unique challenges, including the integration of frequently updated sensor data, domain-specific knowledge grounding, and coherent multi-stage reasoning. In this paper, we present JARVIS, a two-stage LLM-based QA framework tailored for sensor data-driven HVAC system interaction. JARVIS employs an Expert-LLM to translate high-level user queries into structured execution instructions, and an Agent that performs SQL-based data retrieval, statistical processing, and final response generation. To address HVAC-specific challenges, JARVIS integrates (1) an adaptive context injection strategy for efficient HVAC and deployment-specific information integration, (2) a parameterized SQL builder and executor to improve data access reliability, and (3) a bottom-up planning scheme to ensure consistency across multi-stage response generation. We evaluate JARVIS using real-world data collected from a commercial HVAC system and a ground truth QA dataset curated by HVAC experts to demonstrate its effectiveness in delivering accurate and interpretable responses across diverse queries. Results show that JARVIS consistently outperforms baseline and ablation variants in both automated and user-centered assessments, achieving high response quality and accuracy.
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