用AI代理框架让普通人也能轻松模拟智能家居，助力社会5.0研究。
S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation
- 通过大语言模型和智能代理实现自然语言配置仿真，无需编程。
- 检索增强生成技术使设备行为与真实物联网数据高度一致。
- 适合跨领域研究人员使用，尤其适合安全、能源、健康等方向。
智能家居是社会5.0以人为本愿景的关键领域。随着技术快速演进，研究需多元化且保持与社会5.0目标一致。推动智能家居研究民主化，可吸引更广泛的创新者参与。这要求具备包容性的仿真框架，支持产业与学术界跨领域研究。然而现有仿真工具需高技术门槛、适应性差、缺乏自动演化能力，无法满足社会5.0的全面需求，阻碍了对安全、能源、健康、气候及社会经济等领域的高效仿真实验。为此，本文提出面向社会5.0的智能家居环境仿真代理（S5-HES Agent），一个由AI自主调度的智能体仿真框架。该框架通过可替换的大语言模型协调专用智能体，实现自然语言驱动的端到端仿真配置，无需编程知识。采用融合语义、关键词与混合搜索的检索增强生成（RAG）管道获取智能家居知识。在S5-HES Agent上的综合评估表明，RAG管道达到近最优检索保真度，仿真设备行为与威胁场景与真实物联网数据一致，仿真引擎在不同家庭配置下表现可预测的扩展性，为社会5.0智能家居研究奠定了稳定基础。源代码开源于https://github.com/AsiriweLab/S5-HES-Agent（MIT许可）。
原文摘要 · Abstract (English)
The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio-economic research. To address these challenges, this paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent), an agentic simulation framework that transforms traditional smart home simulation through autonomous AI orchestration. The framework coordinates specialized agents through interchangeable large language models (LLMs), enabling natural-language-driven end-to-end smart home simulation configuration without programming expertise. A retrieval-augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge. Comprehensive evaluation on S5-HES Agent demonstrates that the RAG pipeline achieves near-optimal retrieval fidelity, simulated device behaviour and threat scenarios align with real-world IoT datasets, and simulation engine scales predictably across home configurations, establishing a stable foundation for Society 5.0 smart home research. Source code is available under the MIT License at https://github.com/AsiriweLab/S5-HES-Agent.
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