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

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a methodology for integrating digital twin, IoT, and reinforcement learning for carbon footprint optimization in complex systems, offering a novel approach for automated carbon management.

🏢実務担当者:Offers a practical framework for campus energy managers to implement real-time carbon monitoring and automated optimization, potentially reducing energy costs and improving sustainability performance.

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.

• semanticscholar https://doi.org/10.12677/airr.2026.153074first seen 2026-06-23 06:13:20