Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field.

Urban–Rural Water supply integration is one of the effective ways to address rural drinking water safety issues. With the rapid economic development and urbanization, the gap between urban and rural water supply has become increasingly evident, and rural areas face severe challenges such as insufficient water sources and substandard water quality. Yunnan Province, due to its unique topography and natural environment, encounters numerous difficulties in promoting the construction and development of urban–rural water supply integration, and there is a severe lack of research specifically focused on sustainable development measures for urban–rural water supply integration in Yunnan Province. In light of this, this paper first reviews the current research status on urban–rural water supply integration both domestically and internationally, pointing out that existing studies mainly focus on optimizing urban water supply systems, addressing rural drinking water safety issues, and exploring water supply management models. It then analyzes the challenges faced by Yunnan Province in advancing urban–rural water supply integration, including limitations in engineering construction, inadequate operation and maintenance, complex and variable water source conditions, and insufficient public awareness of water conservation. In response to these issues, a series of sustainable development measures are proposed, including revitalizing existing assets, enhancing construction planning and quality, promoting water pricing reforms, upgrading the technical system for drinking water safety assurance, and advancing the information technology construction of urban–rural water supply integration, with the aim of providing references and insights for the sustainable development of urban–rural water supply integration in Yunnan Province and other regions.

Greenhouse cultivation plays an important role in food security, resource-use efficiency, and sustainable agricultural development. Temperature management is particularly critical because it directly affects crop growth, yield and quality, energy consumption, and environmental control. To clarify the technological pathways, application boundaries, and future directions of intelligent greenhouse temperature management, this review synthesizes recent studies from a closed-loop perspective. A structured narrative approach was adopted to organize the literature around an integrated framework linking multi-source sensing and data governance, predictive modeling, control strategies, system deployment, and feedback-based operation and maintenance. The review first examines the roles of sensors, Internet of Things (IoT) technologies, wireless sensor networks, and multi-source data fusion in reliable greenhouse state perception. It then compares mechanistic, machine-learning, deep-learning, hybrid and physics-guided models in terms of prediction capability, interpretability, generalization, uncertainty, and control compatibility. Control strategies ranging from rule-based and fuzzy control to model predictive control, reinforcement learning, and RL-MPC are further evaluated with respect to constraints, adaptability, energy efficiency, and operational safety. Finally, edge–cloud collaboration, digital twins, safety fallback, and model lifecycle management are discussed as key enablers for translating prediction and control methods into deployable closed-loop systems. Overall, recent advances indicate that high prediction accuracy alone is insufficient for practical greenhouse management; reliable sensing, trustworthy modeling, safe control, system integration, and long-term maintainability must be considered jointly. Future research should therefore move toward crop-oriented, energy-efficient, trustworthy, and practically deployable closed-loop systems for intelligent greenhouse temperature management.