# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 6 Summary and Prospect  
The smart water conservancy system based on digital twins uses a new generation of information technology to build data and model integration methods to realize real-time monitoring, analysis and decision-making of water conservancy management. On the basis of previous studies, the framework of the intelligent water conservancy system based on digital twins is proposed, but future research should focus on the challenges that may be faced in the verification process, such as data security, complexity of system integration and technical standardization. This study summarizes its core technologies including integrated monitoring, big data and artificial intelligence, cloud network integration, digital twin platforms, knowledge graphs and blockchain security technology, which are widely used in water security, water resources and hydraulic engineering. These technical frameworks and methodologies promote the transformation of the water conservancy industry from traditional management to intelligent management, and ensure the sustainable use and security of water resources. By integrating a variety of advanced technologies, real-time monitoring and accurate management of water conservancy systems can be realized, resource allocation can be optimized, flood prevention early warning and water quality management efficiency can be improved, safety and reliability of water conservancy projects can be enhanced, and the scientific basis for policy formulation can be provided. Future smart water conservancy construction is benefiting from the rapid development of new monitoring methods and cutting-edge technologies. With the help of the Beidou Communication remote air and earth monitoring system, 6G + IPV6 Internet system and water remote sensing satellite, all-round water resources monitoring and data collection can be achieved. Deep integration of the physical watershed and virtual (or digital) watershed in the construction of the digital twin watershed model is regarded as the core of future research. The digital twin virtual model driven by data, mechanism and knowledge is constructed. Promoting independent research and development of large water conservancy models, and applying domain training and knowledge graph technology is required to achieve advanced, practical, integrated, open, reliable and safe large water conservancy models [101]. These models not only have core capabilities, but also can play an important role in various \"2 + N\" business scenarios, resulting in the establishment of a large water conservancy model system that is co-built and shared. In addition, the construction of the coupling system of scientific prediction, early warning, rehearsal and preplan is also an important part of future smart water conservancy. Combined with the smart security protection system based on the cryptographic mechanism, the innovative integration of cryptographic technology, security situational awareness and dynamic defense technologies will provide a safe and reliable guarantee for smart water conservancy and promote the sustainable development of the field. These measures will certainly bring about a qualitative leap in the assessment, prediction and management of global water resources, and promote the development of smart water conservancy in a more intelligent and efficient direction. **Author Contributions:** [PERSON] and [PERSON] revised the paper. [PERSON], [PERSON], [PERSON] and [PERSON] processed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the General Project of the Basic Research Program of Yunnan Province under Grant no. 202201 AT070981, the General Project of Agricultural Basic Research Joint Special Program of Yunnan Province under Grant no. 202101 BD070001-081, the Opening Foundation of the Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province under Grant no. 2022 ZHNY04, the Initial Scientific Research Fund of Yunnan Agricultural University under Grant no.