基于数字孪生的高校碳足迹实时感知与动态优化研究

Research on Real-Time Perception and Dynamic Optimization of Carbon Footprint in Colleges and Universities Based on Digital Twin

摘要: 本研究针对高校碳排放监测手段粗放、排放源头难以追溯以及动态调控能力不足的现实问题，创新性地构建了一套由数字孪生技术所驱动的高校碳足迹实时感知与动态优化框架。借助所设计的多源异构数据融合机制，该研究将来自物联网终端、能源管理系统以及各类活动记录的数据进行了有效集成，从而构建起一个动态的碳足迹模型架构，成功实现了对校园碳排放情况精细化与全息化的实时映射。在此基础之上，研究进一步识别了高校内部诸如能源消耗、交通出行以及实验活动等关键排放节点，并开发了一套用于碳足迹归因分析与责任划分的框架，为实施精准化的碳排放管理提供了关键依据。进而，研究团队研发了集成跨域数据接入与边缘计算能力的数字孪生平台，该平台不仅实现了碳排放态势的可视化交互，还具备了动态预警功能，能够支持管理者直观地掌握校园碳流的实时动态。本项工作的核心贡献在于，提出了一套基于强化学习理论的动态优化调度算法。该算法能够根据实时感知到的数据，自动生成并验证可行的碳排放调控策略，从而在有效提升校园整体能效与资源利用率方面发挥了重要作用。最终，本研究设计了一套涵盖从感知、分析、优化到反馈环节的全生命周期碳管理闭环机制，并探索了与之相配套的制度协同路径，为高校乃至更广泛的城市社区实现智能化、可持续的碳治理，提供了一套系统的技术解决方案与理论参考。

Abstract: This study addresses the current issues of extensive carbon emission monitoring, difficulties in traceability, and insufficient dynamic regulation in universities. It innovatively constructs a digital twin-driven framework for real-time perception and dynamic optimization of carbon footprints in higher education institutions. By designing a multi-source heterogeneous data fusion mechanism, the framework integrates Internet of Things (IoT), energy management systems, and activity data to build dynamic carbon footprint model architecture, achieving refined and holographic real-time mapping of campus carbon emissions. On this basis, the research identifies key emission nodes such as energy consumption, transportation, and experimental activities in universities, and develops a carbon footprint attribution analysis and responsibility allocation framework, providing a basis for precise management. Furthermore, a digital twin platform integrating cross-domain data access and edge computing is developed, enabling visual interaction and dynamic early warning, supporting administrators in intuitively understanding carbon flow dynamics. The core contribution lies in proposing a set of reinforcement learning-based dynamic optimization scheduling algorithms, which can automatically generate and validate carbon reduction strategies based on real-time perception data, effectively improving energy efficiency and resource utilization. Finally, the research designs a closed-loop carbon management mechanism covering the entire lifecycle from perception, analysis, optimization to feedback, and explores pathways for institutional coordination, providing systematic technical solutions and theoretical references for intelligent and sustainable carbon governance in universities and even urban communities.