Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-182
Cloud Model-Driven Digital Twin Security Assessment for Water Conservancy
Associate Professor, Director of the Academic Affairs Department, Dean’s Office, Yunnan Water Resources and Hydropower Vocational College, Kunming, 650400, China, E-mail: Cdl9527@yeah.net (corresponding author).
Project Management
Received August 25, 2025; revised October 14, 2025; accepted October 17, 2025
Available online March 7, 2026
Abstract: Long-term water conservancy engineering projects face safety issues during operation, and effective assessment is key to ensuring stable operation. Therefore, this study proposes an intelligent digital evaluation technology for water conservancy engineering based on digital twin technology. The study uses a cloud model to quantify safety indicators and utilizes three parameters, namely expectation, entropy, and super entropy, to establish a two-way mapping between qualitative concepts and quantitative values. This mapping is used to construct a four-level safety status discrimination criterion. Second, the study combines an improved mutation series method with deep learning models, integrating physical interpretability with data-driven advantages to construct a hybrid evaluation framework. In a case study of a certain water conservancy hub, the cloud model achieved 100% consistency between its evaluation results for 32 indicators, including horizontal displacement and uplift pressure, and the actual conditions, outperforming comparable technologies. In water conservancy parameter identification, the research model had an error rate of 0.017, with an anomaly detection accuracy of 97.25%, demonstrating the best performance. Finally, an overall assessment of water conservancy engineering was conducted, with a total mutation level of 0.93 to 0.95, all of which were classified as “normal”. In summary, this research technology has good application results and provides more efficient and reliable technical support for digital twin security assessment for water conservancy engineering.
Keywords: Cloud model, digital twin, mutation algorithm, safety and security, water conservancy.
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Citation: Chen, D. (2026). Cloud Model-Driven Digital Twin Security Assessment for Water Conservancy. Journal of Engineering, Project, and Production Management, 16(2), 2025-182.
