A Rough Set-Cloud Model Approach for Digital Twin-Enabled Natural Heritage Protection Evaluation: A Case Study of Huanglong Scenic Area, China

Hang Yin; Zhichao Hong; Yong Yao; Chunpeng Wang; Jianxun Cui; Junchuan Huang

doi:10.6981/FEM.202607_7(7).0006

Authors

Hang Yin School of Economics and Management, Sichuan Tourism University, Chengdu 610000, China
Zhichao Hong School of Economics and Management, Sichuan Tourism University, Chengdu 610000, China
Yong Yao School of International Relations, Sichuan University, Chengdu 610000, China
Chunpeng Wang National Key Laboratory of Fundamental Science on Synthetic Vision, Chengdu 610000, China
Jianxun Cui Civil-Military Integration Center, China Geological Survey, Chengdu 610000, China
Junchuan Huang Civil-Military Integration Center, China Geological Survey, Chengdu 610000, China

DOI:

https://doi.org/10.6981/FEM.202607_7(7).0006

Keywords:

Digital Twin; Rough Set; Cloud Model; Natural Heritage; Huanglong Scenic Area; Smart Conservation.

Abstract

While digital twin technology has emerged as a promising tool for natural heritage conservation, few studies have put forward systematic evaluation frameworks specifically designed for assessing the operational effectiveness of such platforms. Building on existing research, this paper introduces a hybrid evaluation methodology that combines rough set theory with cloud modeling techniques to measure the performance of digital twin platforms in heritage protection contexts. To address the complexity of multi-dimensional evaluation systems with intricate interrelationships among indicators, we employ rough set theory grounded in conditional information entropy to filter out redundant metrics and compute objective weight values. The cloud model component translates qualitative assessments provided by domain experts into quantitative evaluation clouds through forward and backward cloud generation algorithms, while a thickness-based iterative scoring process helps converge divergent expert opinions toward consensus. By weighting each indicator cloud according to its importance, a comprehensive evaluation cloud can be synthesized. We test the proposed method using data from Huanglong Scenic Area, a UNESCO World Natural Heritage site in southwestern China where an integrated digital twin platform-incorporating multi-source data fusion capabilities and real-time monitoring systems for conservation management-has been deployed and running for several years. Our findings indicate that this approach not only successfully pinpoints key performance indicators but also overcomes the difficulties associated with converting qualitative judgments into quantitative measures, while offering intuitive three-dimensional visualizations of evaluation results. The evaluation framework presented in this study provides a practical, replicable model for assessing digital technology implementations in protected area management settings.

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