Design and Dynamic Optimization of Smart Urban Environmental Landscape Ecological Restoration Schemes Empowered by Digital Twins

Urban landscape ecological restoration faces challenges such as dynamic environmental changes and long-term lag in feedback on the effectiveness of restoration measures. This paper proposes a dynamic design and optimization method for smart environmental landscape ecological restoration schemes empowered by digital twins. First, a high-fidelity twin model is constructed by integrating multi-source data such as oblique photography, LiDAR point clouds, and IoT sensors, and embedding mechanistic models of soil moisture transport and pollutant migration-transformation to simulate ecological processes. Second, a dynamic optimization engine is established that couples the mechanistic model and the data-driven proxy model, generating Pareto-optimal restoration schemes based on multi-objective intelligent algorithms. Finally, a dynamic closed-loop control mechanism is constructed to continuously optimize the restoration strategy based on real-time monitoring data. Application in a waterfront area in East China shows that, driven by this method, vegetation coverage reached 85.2% within 18 months, and the ammonia nitrogen concentration in the water body was reduced to 0.95 mg/L ahead of schedule, meeting the standard. The total cost was approximately 4.458 million yuan, achieving simultaneous optimization of ecological restoration effects and resource efficiency.