The Application of Digital Twin Technology in the Evaluation of Environmental Design Solutions

As urbanization accelerates, the need for effective environmental design grows. This paper proposes the Digital Twin-Based Environmental Multi-Objective Optimization System (DTEMOS) to address challenges in environ-mental evaluation and optimization. By integrating multi-source heterogeneous data using tensor decomposition and deep neural networks, DTEMOS enables comprehensive environmental assessment. An adaptive evaluation framework based on meta-learning and reinforcement learning is introduced, alongside a value-aligned reward mechanism. A dynamic weight adjustment system enhances optimization flexibility, while human-in-the-loop feedback incorporates expert judgment. The proposed system provides a robust and automated approach to opti-mizing environmental design.