Digital twin comprehensive models: a study of ancient tree ecological environment quality assessment based on a cyber-physical system

Abstract

This study leverages cyber-physical system (CPS) technology to create a digital twin model for assessing the ecological quality of ancient trees. Integrating multi-source data and machine learning, our model provides tailored conservation strategies, supports ecological restoration, and enhances disaster response capabilities. Key findings illustrate that the model is precise in monitoring tree health, managing water resources, and predicting the impacts of natural disasters. This innovative approach provides significant advantages in real-time monitoring and long-term ecological management, ensuring the sustainability of ancient tree ecosystems. Our results highlight the model's potential to transform ecological conservation practices and offer a reliable tool for researchers and practitioners in environmental science.

Keywords: Ancient tree conservation; Ecosystem sustainability; Habitat quality assessment; Machine learning in ecology; Multi-source data integration; Real-time monitoring.