基于去中心化自治组织、数字孪生和大语言模型的建筑设施管理数据驱动与分布式治理
Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models
AI总结：
本文提出一种融合DAO、数字孪生、大语言模型和区块链的建筑管理分布式治理框架，以提升决策透明度和系统安全性。
摘要：
尽管传统AI和数据驱动的设施管理方法提升了建筑运营效率，但其受集中化组织结构限制，易受网络攻击、缺乏上下文理解且排除关键利益相关者参与治理。本文提出一种新型AI和数据驱动的分布式治理框架，整合去中心化自治组织（DAOs）、数字孪生、大语言模型（LLMs）和区块链技术。该框架通过DAO治理平台实现透明集体决策，利用物联网和数字孪生实现数据驱动管理，采用LLM虚拟助手增强决策支持，并利用区块链实现安全建筑自动化。开发了一个全栈去中心化应用以促进用户与这些组件的交互。系统通过系统可用性量表（SUS）评估了成本效益、可扩展性、数据安全性和可用性，并通过专家访谈评估其实际效益和实施挑战。
英文摘要：
While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
