Digital Twin Smart Water Conservancy: Status, Challenges, and Prospects

Abstract

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.

1. Introduction

With climate change and rapid socio-economic development on a global scale, water resources are becoming increasingly scarce, water quality pollution is serious, and the risk of floods is increasing, which seriously jeopardizes global water security, food security, and ecological and environmental security [1]. The sustainable management and optimal use of water resources have become core issues of national and regional concern [2]. Currently, traditional water conservancy is difficult to meet the specialized, refined, and scientific management needs of the new era of economic and social development. The management and decision-making mode characterized by experience-based, post-event summary, and manpower-intensive tactics not only consumes time and energy, but also is difficult to achieve satisfactory results, leading to the water conservancy engineering system and management system struggling to exert the synergistic effect of "1+1>2" in practical work [1]. As a result, the global water industry has been trying to break through the bottleneck of traditional management with more modern methods in pursuit of efficient, precise and convenient water management [3].

The Internet of Things, cloud computing, artificial intelligence, digital twins and other technologies are maturing, making the “smart water” technology environment constantly complete, so the high-quality development of water conservancy and “smart” combination has become a general trend. Among the many new technologies, digital twin technology is an efficient, convenient and reliable method, both in terms of policy support and the advantages of the technology itself [2,3]. As shown in Figure 1, digital twin is oriented to the whole life cycle process of the product, which is a kind of technology that makes full use of the model, data and intelligence and integrates multiple disciplines to play the role of a bridge and link connecting the physical world and the information world to provide more real-time, efficient, and intelligent services [4,5,6,7].

The concept of “digital twin” is no longer just a technology but a development model, a transformation path and a driving force for the transformation of the water conservancy industry [8]. Therefore, combining digital twin technology with smart water conservancy has become an important part of water conservancy work for the present and future. Digital twin water conservancy is an important symbol of the high-quality development of water conservancy. In accordance with the requirements of ‘demand-driven, application-first, digital empowerment, and capacity enhancement’, the construction of digital twin watersheds [9], digital twin water networks [10], and digital twin projects will be coordinated to build a digital twin water conservancy system with the function of ‘four preemptions’, as shown in Figure 2.

However, the current so-called “digital twin” water conservancy, which mainly uses GIS geographic information data, BIM modeling and other technical methods to achieve a certain degree of virtual mapping of water conservancy projects, has not yet been realized, with the real physical water conservancy projects between virtual and real symbiosis, i.e., twins and common growth, still being in the initial stage of development [11,12]. At the same time, the original water conservancy information methods cannot meet reservoir flood control scheduling, dam safety control smart upgrades and other business needs. Therefore, there is an urgent need to apply GIS+BIM+IoT technology to the whole life cycle of water conservancy project construction, operation and maintenance based on modern information technology methods such as “cloud, big matter, mobile intelligence” and establish a digital twin smart water conservancy project control platform covering forecasting and dispatching, engineering safety, reservoir management and other business activities [13]. Exploring the best path for the integration and development of digital twin technology and water conservancy projects, accelerating the smart water conservancy governance system and governance capacity and assisting the high-quality development of water conservancy are the urgent tasks of water conservancy scientists at present. Therefore, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares the differences between the “digital twin water conservancy model” and the “traditional water conservancy model” and then proposes the digital twin smart water conservancy five-dimensional model, which contains five dimensions of water conservancy physical entities, water conservancy perception data, ubiquitous real-time interaction, and digital empowerment services. Based on this model, this study reviews the research progress of digital twin smart water conservancy in six dimensions: water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and service application, and it provides an outlook on the current technological deficiencies and future technological developments with the aim of providing references and lessons for research on and the application of digital twin smart water conservancy.

2. Digital Twin Smart Water Conservancy Modeling

2.1. Development History and Concept of Digital Twin Smart Water Conservancy

Digital twins, as the name suggests, are twins in digital form [14], as shown in Figure 3, and its earliest use can be traced back to 1969, when National Aeronautics and Space Administration (NASA) planned to use digital twin technology to digitally model and simulate spacecraft [15]. And most people believe that the explicit concept of digital twins was first introduced by Prof. Grieves in 2003 as “the concept of digital equivalents or digital twins of physical products” in a product lifecycle management (PLM) course at the University of Michigan [16]. Due to the limitations of technology and cognitive understanding at that time, this concept did not attract public attention [17]. In 2010, NASA introduced the concept of digital twin for the first time in the space technology roadmap [18] and used digital twin technology to achieve the comprehensive diagnostic maintenance of flight systems. In 2011, the U.S. Air Force Research Laboratory and NASA collaborated on the concept of digital twins, which utilize high-fidelity simulation models, historical data, and real-time sensor data to construct a complete virtual map of the vehicle to predict vehicle health, remaining life and mission accessibility [17]. In 2012, NASA released a roadmap for “Modeling, Simulation, Information Technology and Processing”, and the concept of digital twins attracted widespread public attention [19]. It can be seen that digital twins have not been in the industry for a long time, and there is no uniform definition of its concept and connotations. Table 1 summarizes the different conceptual understandings of digital twin technology by different organizations and scholars.

In 1998, Al Gore former U.S. Vice President Al, proposed the concept of “Digital Earth”, an information model offering seamless global coverage of the Earth, integrating information obtained through various channels and organizing it according to geographic coordinates and demonstrating the links between various types of information for easy retrieval and use [20]. In the same year, General Secretary Jiang Zemin mentioned “Digital Earth”, and hundreds of experts and scholars expressed their opinions on China’s “Digital Earth” science and technology development strategy through several symposiums, making “Digital Earth” a major social issue at that time [20]. In 2001, Prof. Zhang Yongchuan and Prof. Wang from Huazhong University of Science and Technology proposed the important topic of “Digital Watershed”. In November 2008, IBM Corporation released, in New York, the United States, the “Smart Earth: the next generation of leaders in the agenda” theme report, which proposed that a new generation of information technology be fully utilized in all walks of life, proposing a “Smart Earth”. In 2013, the Ministry of Housing and Urban-Rural Development of China issued the first batch of national pilot projects for smart cities, integrating advanced concepts such as intensification, low-carbon, ecological, and smart into the specific process of urbanization. This initiative involved a reexamination of urbanization development and planning from a methodological perspective, with an emphasis on smartly planning and managing urban areas, and intelligently allocating urban resources.

Around 2011, Siemens started to research digital twins and included them in the German Industry 4.0 strategy. By 2016, Siemens Industry Software began to try to use digital twins to improve Industry 4.0 applications and formally released a complete digital twin application model at the end of 2017, becoming the first digital twin practitioner [21]; the Digital China Smart Society then came into being, and digital twins began to be integrated with smart cities. If Industry 4.0 is the era of smart, then Water Conservancy 4.0 can be called the era of smart water conservancy. So-called smart water conservancy is the innovative application of relevant advanced concepts and high technology in the water conservancy industry generated in the construction of a smart society represented by a smart city [22]. Table 2 combs existing scholars’ different understanding and awareness of the concept of smart water conservancy.

In 2021, the Ministry of Water Resources formally proposed the “digital twin watershed” to carry out smart simulation; support accurate decision-making; comprehensively promote the construction of data, algorithms and computing power; and accelerate the construction of smart water conservancy systems with the functions of forecasting, early warning, rehearsal and preplanning. So far, digital twin and smart water conservancy were formally integrated. In 2022, the Ministry of Water Resources has proposed a digital twin water conservancy project, digital twin water network and top-level design, so that the digital twin watershed, digital twin water network and digital twin water conservancy project come together to form a digital twin smart water conservancy series—the three factors are physical watersheds, physical water networks, and physical water conservancy projects in the digital space of mapping; the three relationships determine the relationships between the three physical entities; and the three physical entities are inter-related. The relationship between the three is determined by the inter-relationship of the three physical entities, which are not alternative to each other, have their own focus, are relatively independent, are interconnected, and conduct information sharing.

Table 1. Different conceptual understandings of digital twin technology.

Proposer	Concept of Digital Twin
E Tuegel [23] B T Gockel [24]	DT is used only for aircraft and is called “Airframe Digital Twin” or ADT, which is a computational model that manages aircraft throughout their lifecycles.
Tao Fei [7]	A digital twin digitally replicates a physical entity by using data to simulate its behavior in a real-world environment. It enhances the physical entity by incorporating new capabilities through virtual–real interactive feedback, data fusion analysis, and iterative decision-making optimization.
NASA [25]	An integrated, multi-physics, multi-scale, probabilistic simulation model of a vehicle or system that incorporates current physical models, updated sensor data, historical data, and more to accurately represent the state of the flight entity based on that model.
GE [26]	By integrating physical machinery with analytical techniques, machines are tested, debugged, and optimized in a virtual environment.
Gartner [15]	A digital twin is a virtual copy of a physical object, meaning that it can be a product, structure, facility or system.
PTC [6,26]	Extending the product lifecycle management (PLM) process to the subsequent design cycle establishes a closed-loop product design process that facilitates the predictive maintenance of products.
Siemens [6,26]	By utilizing a consistent data model throughout all stages of the product life cycle, accurate and realistic simulations of certain real-life operations can be achieved.
Oracle [15]	The actual complexity of physical entities is simulated through virtual models of equipment and products that are projected into the application process.
ANSYS [26]	Combining superior simulation capabilities with powerful data analytics can help organizations to gain strategic insights.
Dassault [6,26]	The 3D Experience Platform enables designers and customers to engage with products throughout the design or manufacturing process, gaining insights into their functionality.
SAP [26]	It drives product development and innovation through real-time data collection and analysis, facilitated by the construction of digital models.
Altair [26]	Utilizing advanced virtual simulation technology, we develop virtual models that incorporate multiple physical characteristics to enhance product features.

Note(s): Drawing from industry and academic definitions of digital twins, working groups for standardization and management guidelines of intelligent manufacturing professional terminology in Industry 4.0 offer a comprehensive perspective on digital twin technology. They define digital twins as leveraging data such as physical models, sensor updates, and operational history to integrate diverse disciplines and simulation processes across multiple scales and probabilities. This allows for comprehensive mapping in virtual space to accurately mirror the behavior of corresponding physical equipment [27]. This interpretation is widely acknowledged within the field.

Table 1. Different conceptual understandings of digital twin technology.

Proposer	Concept of Digital Twin
E Tuegel [23] B T Gockel [24]	DT is used only for aircraft and is called “Airframe Digital Twin” or ADT, which is a computational model that manages aircraft throughout their lifecycles.
Tao Fei [7]	A digital twin digitally replicates a physical entity by using data to simulate its behavior in a real-world environment. It enhances the physical entity by incorporating new capabilities through virtual–real interactive feedback, data fusion analysis, and iterative decision-making optimization.
NASA [25]	An integrated, multi-physics, multi-scale, probabilistic simulation model of a vehicle or system that incorporates current physical models, updated sensor data, historical data, and more to accurately represent the state of the flight entity based on that model.
GE [26]	By integrating physical machinery with analytical techniques, machines are tested, debugged, and optimized in a virtual environment.
Gartner [15]	A digital twin is a virtual copy of a physical object, meaning that it can be a product, structure, facility or system.
PTC [6,26]	Extending the product lifecycle management (PLM) process to the subsequent design cycle establishes a closed-loop product design process that facilitates the predictive maintenance of products.
Siemens [6,26]	By utilizing a consistent data model throughout all stages of the product life cycle, accurate and realistic simulations of certain real-life operations can be achieved.
Oracle [15]	The actual complexity of physical entities is simulated through virtual models of equipment and products that are projected into the application process.
ANSYS [26]	Combining superior simulation capabilities with powerful data analytics can help organizations to gain strategic insights.
Dassault [6,26]	The 3D Experience Platform enables designers and customers to engage with products throughout the design or manufacturing process, gaining insights into their functionality.
SAP [26]	It drives product development and innovation through real-time data collection and analysis, facilitated by the construction of digital models.
Altair [26]	Utilizing advanced virtual simulation technology, we develop virtual models that incorporate multiple physical characteristics to enhance product features.

Note(s): Drawing from industry and academic definitions of digital twins, working groups for standardization and management guidelines of intelligent manufacturing professional terminology in Industry 4.0 offer a comprehensive perspective on digital twin technology. They define digital twins as leveraging data such as physical models, sensor updates, and operational history to integrate diverse disciplines and simulation processes across multiple scales and probabilities. This allows for comprehensive mapping in virtual space to accurately mirror the behavior of corresponding physical equipment [27]. This interpretation is widely acknowledged within the field.

Table 2. Different scholars have different understanding and awareness of the concept of smart water conservancy.

Proposer	The Concept of Smart Water Conservancy
Jiang Yunzhong [1]	Utilizing cutting-edge information and communication technologies like big data, artificial intelligence, the Internet of Things, cloud computing and mobile internet, intelligent water management aims to deliver the optimal quantity and quality of water to the right location at the right time. This system is built upon natural water systems, water conservancy engineering systems, and water management systems to ensure sustainable water supply, high-quality water resources, a habitable water environment and a healthy water ecosystem.
Liu Guofeng [28]	By integrating technologies such as cloud computing, the Internet of Things, big data, mobile internet, and artificial intelligence, and comprehensively perceiving, interconnecting, intelligently applying, and providing ubiquitous services to water objects and activities, the comprehensive system is conducive to improving the modernization, informatization, and intelligence level of water governance.
Guo Hua [29]	Facilities and equipment and their status data are digitized and then loaded with models and algorithms for processing and analysis, providing support for watershed management, reservoir scheduling, urban water services, farmland irrigation, etc.
Wang Zhongjing [30]	The concept of water networking is based on the concept of the Internet of Things (IoT) and intelligent water conservancy is another expression of water networking.
Zhao Ranhang [31]	A scale information ecosystem for water conservancy applications that integrates information processes such as data collection, analysis, decision-making, control, and feedback to realize autonomous survival, autonomous self-examination, autonomous response and autonomous learning.
Cai Yang [32]	Applying new-generation information technology to realize thorough perception, comprehensive interconnection, intelligent application and ubiquitous service for water conservancy objects and activities.
Cai Xudong [33]	Intelligent mega-systems that comprehensively address water resources, water management, water safety, water ecology, water environment and other water-related matters.
Zhang Jianyun [22]	Harnessing a new wave of information and communication technologies to enhance intelligent water conservancy planning, engineering construction, operation, management and social services.

Note(s): Smart water conservancy refers to the use of advanced information technology methods and intelligent management methods to monitor, predict, dispatch and manage water resources in an all-round, all-process, all-element way to realize the efficient, safe and sustainable use and protection of water resources. This concept is recognized by most scholars, and intelligent water conservancy has become a research hotspot and development trend in the field of global water conservancy.

Table 2. Different scholars have different understanding and awareness of the concept of smart water conservancy.

Proposer	The Concept of Smart Water Conservancy
Jiang Yunzhong [1]	Utilizing cutting-edge information and communication technologies like big data, artificial intelligence, the Internet of Things, cloud computing and mobile internet, intelligent water management aims to deliver the optimal quantity and quality of water to the right location at the right time. This system is built upon natural water systems, water conservancy engineering systems, and water management systems to ensure sustainable water supply, high-quality water resources, a habitable water environment and a healthy water ecosystem.
Liu Guofeng [28]	By integrating technologies such as cloud computing, the Internet of Things, big data, mobile internet, and artificial intelligence, and comprehensively perceiving, interconnecting, intelligently applying, and providing ubiquitous services to water objects and activities, the comprehensive system is conducive to improving the modernization, informatization, and intelligence level of water governance.
Guo Hua [29]	Facilities and equipment and their status data are digitized and then loaded with models and algorithms for processing and analysis, providing support for watershed management, reservoir scheduling, urban water services, farmland irrigation, etc.
Wang Zhongjing [30]	The concept of water networking is based on the concept of the Internet of Things (IoT) and intelligent water conservancy is another expression of water networking.
Zhao Ranhang [31]	A scale information ecosystem for water conservancy applications that integrates information processes such as data collection, analysis, decision-making, control, and feedback to realize autonomous survival, autonomous self-examination, autonomous response and autonomous learning.
Cai Yang [32]	Applying new-generation information technology to realize thorough perception, comprehensive interconnection, intelligent application and ubiquitous service for water conservancy objects and activities.
Cai Xudong [33]	Intelligent mega-systems that comprehensively address water resources, water management, water safety, water ecology, water environment and other water-related matters.
Zhang Jianyun [22]	Harnessing a new wave of information and communication technologies to enhance intelligent water conservancy planning, engineering construction, operation, management and social services.

Note(s): Smart water conservancy refers to the use of advanced information technology methods and intelligent management methods to monitor, predict, dispatch and manage water resources in an all-round, all-process, all-element way to realize the efficient, safe and sustainable use and protection of water resources. This concept is recognized by most scholars, and intelligent water conservancy has become a research hotspot and development trend in the field of global water conservancy.

2.2. Comparison of Digital Twin Water Conservancy Modeling with Traditional Water Conservancy Modeling

At present, many people are still confused about the difference between the digital twin water conservancy model constructed by digital twin technology and the traditional water conservancy model constructed by traditional modeling and simulation, and they even call the traditional water conservancy model a “digital twin water conservancy model”. In order to solve this problem,

Table 3. Differences between digital twin water conservancy modeling and traditional water conservancy modeling [34,35,36,37,38].

Comparison of Content	Traditional Water Conservancy Modeling	Digital Twin Water Conservancy Modeling
Application method	Analytical and support computational tools for businesses based on physical equations and data-driven static descriptions	Based on real-time data and machine learning [39,40,41] for dynamic description, deeply integrated with digital business systems, achieving automatic closed-loop intelligent water resource management and decision-making activities
Application phase	Mostly used in the planning phase	Full life cycle of planning, design, construction and operation
Modeling methodology	Manual offline approach, mechanism-driven modeling, mathematical and statistical modeling as the focus	Automated approach, supplemented by a manual approach; multi-dimensional and multi-scale high-fidelity models such as geometric, mechanistic–analytical, mathematical–statistical and hybrid models
Model update	Updating model structure and model parameters manually	Adopting a data-driven approach, the model structure, state variables and parameters are automatically updated by continuously acquiring real-time operational data [42,43,44].
Model validation	Possible validation of models through comparison with historical data or known results	Validate model accuracy by comparing real-time data with physical entities
Model Extensibility	The structure and scope of the application of the model may be fixed at the design time, with limited extensibility	Designed to be scalable to accommodate changing system requirements and data sources
Model integration	Lack of standardization support, poor standardization of model interfaces and poor connectivity of models with different dimensions and scales	Use standardized interfaces to connect physical water conservancy entities, water digital twins, data and services, with the ability to integrate, add and replace digital models
accessibility	Requires specific hardware facilities and software support that can only be accessed and operated by users of specific devices	Receive real-time monitoring data from water conservancy systems via the internet and remotely control and monitor physical equipment
model performance	Predominantly 2D presentation	Predominantly 3D presentation

systematically analyzes and compares the differences between the two from multiple perspectives, aiming to help people to more clearly recognize the advantages and application potentials of digital twin technology and then better explore the application prospects of digital twins in the field of water conservancy.

2.3. Digital Twin Water Conservancy Model Construction

In 2014, Professor Grieves proposed the digital twin 3D conceptual model, as shown in Figure 4, which includes three main parts: physical products in the physical space, virtual products in the virtual space, and the connection of data and information linking the virtual and physical products together [45]. Subsequently, [46] proposed a conceptual framework of the digital twin modeling process containing three layers, a physical entity, data layer and information processing and optimization layer, to guide the construction of digital twin models for industrial production; moreover, [47] proposed a framework for the realization of full-parameter digital twins, dividing digital twins into three layers, a physical layer, information processing layer and virtual layer, and realizing the upper layer based on the processes of data acquisition, transmission, processing, matching, etc., for digital twin applications.

Because the digital twin three-dimensional model cannot meet the current stage of technological development and application needs, Tao Fei et al. [48] proposed a digital twin five-dimensional model, as shown in Figure 5, which includes physical entities, virtual entities, services, data and connections. They also discussed the ideas and solutions for the application of the digital twin five-dimensional model in many fields, which were widely accepted.

Based on the above digital twin five-dimensional model, combined with the requirements of the scientific development of smart water conservancy and the needs of integrated management practices, a digital twin five-dimensional model of smart water conservancy is constructed that contains a physical water conservancy entity, a digital twin of water conservancy, heaven, Earth, space and space perception data, ubiquitous real-time interactions and digital empowerment services, as shown in Figure 6.

(1) Water conservancy physical entity: The water conservancy project-related information is fed back to the water conservancy digital twin in a timely manner, including the natural topography and geomorphology, the water conservancy project entity and its construction impact area and other monitoring and control information technology facilities and equipment associated with it. On the basis of the traditional water conservancy monitoring perception system, we integrate new monitoring technologies to form an air, sky and ground integrated perception network; enhance the monitoring capability; and collect all the water conservancy perception data in real time through a variety of sensors, monitoring equipment and IoT technologies to provide data inputs for the upper-level applications and digital twins.

(2) Water conservancy digital twin: We focus on constructing a multi-dimensional and multi-scale virtual model or digital twin model, including a geometric model, mechanism analysis model, mathematical and statistical model, mixed model, intelligent identification model, behavioral model and visualization model, which can dynamically describe the water conservancy project entity from multi-temporal and spatial scales and multi-dimensions.

(3) Heaven, earth and space perception data: Data provide the power source of digital twin smart water conservancy, as well as the basis for the construction of digital twin smart water conservancy, mainly including all kinds of sensor perception data. Due to the influence of the external environment on the accuracy of the sensor, data loss in the data transmission process makes the acquired data have certain errors, so it is also necessary to process the sensed data (such as data conversion, data preprocessing, data classification, data association, data integration, data fusion, etc.).

(4) Ubiquitous real-time interaction: Composed of business networks, industrial control networks, wireless networks, the internet, sensor networks and transmission protocols such as IPv4/IPv6, input/output devices, security facilities and related technologies, it provides efficient transmission services, collaborative interactive control and synchronous iterative optimization for the transfer of various types of data such as physical hydraulic entities, hydraulic digital twins, real-time connectivity interactions, digital empowerment services and twin hydraulic data between subjects and objects.

(5) Digital empowerment services: Service application is the purpose of digital twin smart water conservancy, facing different users such as water administration authorities, water conservancy project operation and management units, the public, technology development and operation and maintenance personnel, etc., and constructing a digital twin smart water conservancy application system around water resource management and scheduling, smart flood control and scheduling, panoramic water conservancy projects and early warning of river basin risks.

2.4. Key Technologies of Digital Twin Water Conservancy Model

2.4.1. Digital Twin Water Conservancy Data Perception

Data acquisition is mainly conducted through reliable sensors (e.g., satellite remote sensing, high-definition cameras, drones, intelligent robots, flow meters, water-level gauges, etc., as shown in Figure 7) and distributed sensing networks for the real-time and accurate sensing acquisition of physical device data, which is a key technology for realizing digital twins [49]. Traditional sensor networks lack real-time synchronization and fault-tolerant capabilities, making it difficult to meet the requirements of digital twin systems [50,51,52]. To this end, [53] proposes a City Sensing Base Station (CSBS) sensing technology, which improves the efficiency and accuracy of data collection through collaborative observation, strengthens the ability of real-time monitoring and management of the urban environment and achieves high-density, diverse and high-precision sensor sensing in a distributed manner to provide smarter and more efficient sensing data services. Ref. [54] proposes a study based on “geospatial sensor integration management”, which aims to achieve multiple sensing of surface environmental elements through the integration of different remote sensing satellites in order to improve the reliability and accuracy of macro-environmental sensing data. Ref. [55] proposes a prototype data acquisition system based on the Cyber–Physical System (CPS) architecture, which improves the fault tolerance and reliability of data acquisition by adding the estimation of physical layer device errors to the sensor data acquisition process. Ref. [56] uses energy measurement correction methods, Coulomb efficiency estimation techniques and optimization algorithm-based state-of-charge estimation methods to dynamically adjust the estimation of sensor errors and improve the accuracy and reliability of the data collected by the sensor. Wireless sensor placement is another key issue in digital twin data acquisition, and its main research goal is to identify sensor layouts that can achieve performance metrics using the minimum number of sensors. Ref. [57] proposed a quantum-inspired tabu search algorithm with an entanglement algorithm based on quantum-inspired tabu search and quantum entanglement properties for solving the minimum number of sensors and their positional distributions to meet the performance requirements. Ref. [58] proposes a non-consistent sensor arrangement strategy to determine the density of sensor nodes based on the distance to the target node, which improves the lifetime of the sensor network while satisfying the connectivity and coverage requirements. Ref. [59] introduces a 5G-enabled battery-less smart skins sensor that adopts a Van Atta array design, enabling ubiquitous local strain monitoring of the monitoring target with a wide detection angle. Ref. [60] proposes a data-driven sensor layout optimization technique for nuclear digital twins, integrating spatial constraints into the optimization framework to minimize reconstruction errors under noisy sensor measurement conditions, ensuring that data transmitted from physical processes enable remote monitoring and real-time control.

2.4.2. Digital Twin Water Conservancy Data Transmission

Currently, research on data transmission focuses on transmission protocols, congestion control and Quality-of-Service (QoS) management [61,62]. The current common network transmission protocol (such as TCP/IP) is based on the idea of best-effort transmission; for the high real-time requirements of the digital twin system, it is difficult to ensure the transmission effect. Unpredictable transmission time will affect the reliability of the virtual entity and even lead to the whole system not being stable. Ref. [63] proposes a reliable multipath routing selection algorithm that ensures end-to-end data transmission reliability by leveraging internet redundancy and multipath transmission principles through online path quality monitoring and multipath selection. Ref. [64] introduces the Ada-MAC protocol based on the IEEE 802.15.4 protocol, which enhances data transmission reliability and real-time performance while maintaining low power consumption and low latency. Ref. [65] developed a prototype framework of an all-element digital twin model connectivity module based on OPC UA, through which the construction of information network for all elements of the IoT (Internet of Things), the IoB (Internet of Behavior) and the IoR (Internet of Rule) was completed, and a complete information path to achieve the high real-time accuracy of all-element data transmission in digital twins. Ref. [66] proposes an EEDC (Energy-Efficient Data Communication) network protocol based on region-based hierarchical clustering for efficient routing, achieving uniform load distribution and efficient long-distance communication between network nodes, reducing sensor node energy consumption and improving data packet transmission reliability by converting long-distance communication into multiple short-distance communications. Due to the limited channel resources of communication networks, data transmission may suffer from channel congestion, which triggers problems such as transmission delay and jitter and thus affects the quality of data transmission. Therefore, many studies focus on reducing the congestion condition by reducing the amount of data transmission, thus improving the efficiency of real-time data transmission. However, this approach usually leads to a large amount of data loss, which in turn may bring large estimation errors, making it difficult to reliably map virtual entities to physical entities. To address this problem, ref. [67] proposes a congestion–adaptive data acquisition scheme that employs adaptive lossy compression to mitigate congestion while limiting the overall data estimation error in a distributed manner. By effectively solving the data congestion problem under the premise of ensuring data accuracy, ref. [68] proposes a Real-Time Transport Protocol (RTP)-based state estimation method for networked control systems, aiming to address the impact of time delay, packet loss and data rate limitation on the performances of networked control systems that may occur when sensors and controllers are connected over a static, memoryless digital communication channel. It improves the accuracy of networked control system state estimation, enhances the robustness of the system and provides effective data transmission for real-time networked control systems.

In summary, the data, as the basis of the digital twin water conservancy perception object, perception data, perception mode and transmission mode, are specifically shown in

Table 4. Digital twin water conservancy data.

Perception Object	Perception Data	Perceptual Method	Transmission Method
Natural water system, topography, reservoirs, dams, gates, pumps, embankments, power stations, irrigation districts, meteorology, soil, facilities and equipment, ecological environment, users, etc.	Hydrological data including real-time monitoring data such as water level, flow, flow rate, water temperature, etc.; water quality data including pH, dissolved oxygen, ammonia nitrogen, total phosphorus and other water quality indicators; meteorological data such as rainfall, wind speed, wind direction, temperature, etc.; soil data including soil moisture, soil type, soil erosion, etc.; water conservancy facilities data involving reservoirs, sluice gates, pumping stations, embankments, and other water conservancies, such as the operational status of the water conservancy facilities, maintenance records, etc.; water resource utilization data including data on water consumption and water use efficiency in agricultural irrigation, industrial water use, urban water supply, etc. Ecological environment data such as the number of aquatic organisms populations, biodiversity, vegetation cover, etc.	The Internet of Things, sensors, satellite remote sensing, drones, unmanned boats, high-definition video, ground robots, underwater robots, manual recording, etc.	ZigBee wireless protocol, Bluetooth (BT) wireless protocol, Multi-hop Communication protocol, Cooperative Communication protocol, UAV-Assisted Communication protocol, Wi-Fi wireless protocol, long-range radio (LoRa) protocol, Intelligent Reflecting Surface-Assisted Cooperative Relaying, SigFox protocol, flat routing protocol, hierarchical routing protocol, Energy-Efficient Data Communication protocol, Ada-MAC protocol, Real-Time Transport Protocol, etc.

.

2.4.3. Digital Twin Water Conservancy Data Analysis and Processing

In practice, the digital twin system is in a complex environment, as the influence of the external environment on the accuracy of the sensor, data loss in the process of data transmission, etc., makes the acquired data have certain errors [69,70], so it is necessary to use a variety of data processing techniques to solve the above problems, as shown in Figure 8.

Due to the characteristics of perception data collected by various sensors, such as multi-source, heterogeneous, multi-scale and high noise, it is necessary to perform unified cleaning and management to enhance the data’s standardization, consistency and availability and avoid data redundancy and conflicts. First, algorithms such as machine learning and rule constraints are used to handle issues such as data absence, redundancy, conflicts, and errors [71,72]. Subsequently, multi-source perception data need to be fused [69] to enhance the robustness and reliability of twin data and expand the modeling dimensions of virtual entities. Common methods for multi-sensor fusion include fuzzy set theory, neural networks, wavelet analysis, support vector machines, etc. [73,74]. In digital twins, sensor data are typically fused and mapped with models based on IoT middleware, feature extraction and information fusion methods. For example, [75] combines the characteristics of computer-aided design systems, computer-aided manufacturing systems, etc., and proposes a model fusion method based on semantic feature fusion, which in turn fuses multi-sensor data into a model. In this process, it is impractical to process the twin data on a personal computer due to the oversized data volume. Usually, they need to be processed in parallel mode based on tools such as MapReduce or based on cloud computing [76]. Additionally, data mining techniques can be combined to discover relationships between all elements of a water conservancy system and the rules governing the entire process of water conservancy governance activities, using statistical, machine learning, pattern recognition, and other methods from data resources. For example, [77] used data mining techniques to automatically explore and construct virtual models from real-time construction data collected by many IoT devices for in-depth analyses to identify bottlenecks in the construction process and predict construction progress. Ref. [72] provides efficient integration and in-depth analysis of large-scale hydrological and water quality data through data mining and analysis, which improves the efficiency of data utilization in complex water environments. Ref. [78] uses data mining techniques to explore temperature rise boundaries in a data center and mine more pairs of data to solve the problem of insufficient data in a physical data center, improve the robustness of the model and achieve better energy saving results. Ref. [79] uses data mining techniques to calculate the importance of each flood risk indicator to identify the most critical risk indicators, which not only accelerates the process of data generation and map modeling but also improves the quality of disaster prevention decisions. By making full use of the above data analysis and processing tools, it can provide effective support for model construction and the realization of business requirements, thus improving the accuracy and reliability of the model. This can better meet the needs of various business applications and promote innovation and business development.

2.4.4. Digital Twin Water Conservancy Model Construction

In digital twin modeling applied to water conservancy, constructing a high-fidelity digital twin water conservancy model requires multi-dimensional modeling of the geometry, physical properties, behavioral patterns and operational rules of physical water conservancy facilities to ensure that it can simulate the spatiotemporal states, behaviors and functions of physical entities [80,81]. Therefore, the digital twin is not a singular digital technique, but the construction of a digital twin water conservancy model describing the whole water conservancy system based on the cross-integration of multiple enabling technologies [82,83,84], as shown in Figure 9.

Since the digital twin system contains a wide variety of sub-modules, it is necessary to build corresponding sub-models for each module of the system, and then perform model fusion to achieve a complete digital twin model. Therefore, [85] proposes a digital twin modeling method based on model fusion, which constructs complex virtual entities by combining multiple mathematical and physical simulation models and proposes a virtual entity calibration method based on anchors. Ref. [86] puts forward an automatic model generation and online simulation digital twin modeling method. Initially, a static simulation model is selected as the initial model, then a dynamic simulation model is automatically generated from the static model based on data matching methods, combined with various models to enhance simulation accuracy, and finally achieves online simulation through real-time data feedback. Currently, the water conservancy industry usually adopts BIM (building information modeling), GIS (geographic information system), DEM (digital elevation model), 3D scanning, mathematical models, mathematical and statistical models, and simulation engines. techniques are combined to model water conservancy systems, as shown in Figure 9. For example, [87] combined GIS, high-resolution 3D geospatial modeling, artificial intelligence techniques, hydraulic and hydrological simulation models to construct a digital twin dam and watershed management platform. Ref. [82] used BIM, GIS, 3D scanning, satellite remote sensing, and laser point cloud to construct a digital twin watershed information model that integrates a smart flood early warning model, a smart power dispatch model, and a hydrological information monitoring model. Ref. [83] used BIM, GIS, 3D scanning, satellite remote sensing, and laser point cloud to construct a digital twin watershed information model that integrates an intelligent flood warning model, an intelligent power dispatch model, and a hydrological information monitoring model. Ref. [84] used a digital twin watershed information model that integrates a smart flood warning model, an intelligent power dispatch model, and a hydrological information monitoring model. twin watershed information model. Ref. [88] used digital twin technology combined with GIS, remote sensing data, hydrological simulation and machine learning algorithms to build a large-scale hydrological digital twin (HDT) model using the GEO frame modeling framework. Ref. [89] used low-altitude unmanned aerial tilt-photo surveying, GIS and BIM 3D modeling techniques to integrate different types of vector, raster, scene, live model, BIM model, and numerical computation model in the watershed, and constructed a multi-scale spatial geographic information model of the digital watershed. The digital twin water conservancy models established now are far from enough, and most of them stay in the modeling of some application scenarios, with a low degree of model integration, without really realizing the comprehensive construction of digital twin water conservancy models. In order to further drive the synergistic and efficient computing of various types of water conservancy models, and to promote the intelligent simulation of smart water conservancy management activities. Ref. [90] integrates various mainstream BIM platform data models with water conservancy GIS scene data in-depth, realizing the fusion of integrated data with different scale digital scenes, constructing a water conservancy digital twin scene that combines multiple sources, dimensions, and scales of GIS + BIM fusion. This enables three-dimensional simulation and emulation of various customized water conservancy operations, combined with cloud rendering technology to enhance the fidelity and quality of three-dimensional visualization, providing strong support and technological drive for the development of digital twin smart water management.

2.4.5. Virtual–Real Interaction Techniques

Virtual–real interaction is the key link of digital twins. The virtual entity monitors the state of the physical entity through sensor data, realizes real-time dynamic mapping and then verifies the control effect through simulation in the virtual space, and it realizes the operation of the physical entity through the control process [25]. Interactions and collaborations in digital twins include physical–physical, virtual–virtual and physical–virtual forms and cover a variety of elements such as people, machines, objects and environments. Among other things, physical–physical interaction and collaboration allows physical devices to communicate, coordinate and collaborate with each other to accomplish tasks that cannot be accomplished by a single device [91]; Virtual–virtual interaction and collaboration can connect multiple virtual models to form an information sharing network [49]; Physical–virtual interaction and collaboration synchronizes the virtual model with changes in the physical objects and allows the physical objects to be dynamically adapted to direct commands from the virtual model [92]. Currently, there are relatively few studies on the deep interaction and collaboration of digital twins [48], and there are only some studies on theories of real-time data acquisition and human–computer interaction. A four-dimensional convergence framework consists of “physical convergence, model convergence, data convergence, and service convergence” [5]. It can provide a reference framework for realizing the interaction and collaboration of digital twins, in which physical fusion can realize the intelligent perception and interconnection of heterogeneous elements of the system based on the intelligent interconnection protocol of the Internet of Things and accurately control the behavioral collaboration of heterogeneous resources of the system under the complex dynamic environment, and the related technologies include intelligent perception and interconnection technology [93], data transmission and fusion technology, distributed control technology, etc., which can provide support for interaction and collaboration at the physical–physical-level interaction and synergy. Data fusion is based on cleaning, clustering, mining, fusion and other methods for mining real-time sensing data, model data, simulation data, etc., to truly portray the system’s operating state, element behavior and other dynamic evolutionary processes and laws [94]. Service convergence is based on twin data analytics driving and influencing the operation of physical and virtual entities [93,95], providing decision support for the intelligent management and precise control of the system.

Therefore, data fusion and service fusion work together to realize the physical–virtual two-way interaction and synergistic process, making full use of the advantages of data and models, which can realize more intelligent and flexible intelligent water conservancy system control.

2.4.6. Digital Twin Water Conservancy Service Applications

Service application is the ultimate goal of digital twin smart water conservancy. Based on specific business scenarios, it integrates various sensor data and operational data of water conservancy systems. By constructing various digital mechanism models, combining technologies such as big data, machine learning algorithms, data-driven approaches, visualization, etc., the digital twin smart water conservancy system is designed to meet various business applications including water resource management and allocation, water conservancy engineering operation and maintenance management, hydrological forecasting, flood and drought warning, scheduling simulation, emergency decision-making, and more. ① Water management and allocation. Ref. [96] combines digital twin technology with optimization algorithms to construct a digital twin water network model for rapid leakage detection and the optimal setting of pressure control valves in the water distribution network to improve the energy efficiency of the whole system and the utilization of water resources. ② Four early warnings. Ref. [97] proposes a digital twin smart city model integrating machine learning and deep learning to enable decision makers to proactively solve potential problems and optimize system operations, dynamically adjust the operation of stormwater infrastructure and adaptively respond based on real-time data and forecasts to effectively combat changing weather conditions to prevent flooding or infrastructure damage. Ref. [98] describes the overall architecture of the digital twin Yangtze River construction and uses analogue simulation technology to achieve the whole process simulation of flood forecasting, early warning, previewing and preplanning, as well as the intelligent dispatching of the project, for the results of the digital twin Yangtze River construction. Ref. [99] proposes a probabilistic digital twin model based on generative deep learning and Bayesian inference for locating leaks in a water distribution network, which locates leaks by combining the sensor observation results with the model output, which outputs a true posterior distribution approximating the location of possible leaks. ③ Decision support. Ref. [100] proposes a digital twin model for modeling the fate and transport of pollutants in urban and natural drainage networks, which not only improves pollutant tracking and source identification but also performs proactive water quality management through the real-time control of water conservancy infrastructure, in addition to supporting water environment capacity assessment and risk analysis, assisting decision makers in developing pollutant reduction strategies. Ref. [101] presents a new framework for updating 3D virtual city models with numerous visualization data to support risk-orientated decision-making through interactive and immersive visualizations, helping to enhance the performance of virtual city models in terms of local vulnerability, support risk-informed decision-making for urban infrastructure management and improve visibility in disaster situations. Ref. [87] combines GIS, high-resolution 3D geospatial modeling, artificial intelligence techniques, real-time data synchronization, hydraulic and hydrological simulation models, geological safety assessments and UAV monitoring and AI CCTV video surveillance capabilities to build a comprehensive digital twin dam and watershed management platform for supporting flood response and decision-making for water resource management in watersheds. ④ Water conservancy project operation and maintenance management. Ref. [102] proposes a water conservancy project operation management system based on digital twin technology, which realizes the perception of the operation state of the physical world by deploying a variety of sensors on the physical entities, and it achieves intelligent operation, precise control and reliable operation and maintenance services through the mutual mapping and real-time interaction between the virtual model and the physical entities in cyberspace. Ref. [103] establishes a digital twin visualization and intelligent management platform for river engineering management by integrating intelligent prediction and visualization management.

3. Challenges and Problems of Digital Twins in Smart Water Conservancy Applications

Digital twin technology transforms complex product development, manufacturing and operations and maintenance into relatively low-cost digital models to reduce errors, uncertainties and inefficiencies in water conservancy systems or processes [96,104]. Through digital twins, the construction and redesign process of water conservancy projects can be accelerated [4,105], costs [106] can be reduced, real-time simulation and prediction [4] can be achieved, solutions and improved maintenance can be provided [17] and remote monitoring and control [106], risk alerts [23], the optimal management of resources and data management and security can be achieved [107,108] and can contribute to the development of smart water conservancy to a large extent.

However, in practical applications, digital twin technology faces technical difficulties. There are relatively few cases of its successful application in water conservancy projects [82,102], and coupled with the influence of various practical factors, smart water conservancy applications based on digital twins are still facing many challenges and problems.

3.1. It Is Difficult to Achieve Comprehensive Perception of Digital Twin Water Conservancy Data

With the continuous development of IoT technology, a part of the perceptual collection system has initially been established in the fields of water resources, water safety, water ecology, water environment, water disasters, water project and so on. However, to realize the requirements of digital twin smart water conservancy, there is still a certain gap to fill, as mainly reflected in the following: ① Sensor deployment is ponderous. Due to the diverse and complex objects that digital twin water conservancy data need to perceive (such as weather, environment, dams, users, natural water systems, etc.), the current main method of perception is deploying sensors. However, the single perception method used by sensors of various brands and types cannot achieve a unified standard, leading to complex sensor deployment. ② The perception cost is too high and difficult to fully implement. Data perception and acquisition mainly rely on sensors and IoT devices. The “one-to-one” and “point-to-point” deployment methods require a large investment in sensors and other equipment, and the subsequent management and maintenance of these devices also require a lot of manpower, material resources, and financial resources. This all results in high costs for water data perception, making it difficult to implement comprehensively. ③ Network requirements are very high, so it is difficult to comprehensively cover. Many water engineering projects are located in remote areas where network coverage is inadequate or nonexistent, preventing real-time data perception and transmission. ④ A lack of equipment exists for perception data, so we are unable to obtain real-time data. Digital twin water conservancy involves a wide range of objects and data. Not all relevant data can be collected by corresponding devices. Some crucial data for certain objects may lack necessary monitoring equipment and still rely on manual observation and data entry. In conclusion, digital twin water management has high requirements for data accuracy, real-time capabilities, and coverage range. However, existing issues with perception equipment pose challenges during the data perception process, making it a primary concern for the development of smart water management.

3.2. Difficulty in Processing Digital Twin Water Conservancy Perception Data

Data serve as the basis for simulation and decision-making in digital twin technology, and the establishment of a high-fidelity digital twin water conservancy model requires a large amount of information collection through information technology to ensure subsequent operations. However, in the actual operation link, the raw data collected by digital twin technology in the application of smart water management often contain a large amount of useless data and noise [67,70,109], resulting in the error of information, which in turn restricts the accuracy of the digital twin technology. ① The problem of heterogeneous data from multiple sources [29]. The data involved in smart water conservancy systems come from various sources such as different devices, sensors, monitoring points and satellite remote sensing. These data are diverse and heterogeneous, with differences in data formats, units, accuracy, etc. The data quality varies, making data integration and fusion challenging, and it becomes difficult to unify management and interpretation. ② The issue of data silos [110,111]. Different water conservancy management departments or units have their own data systems and databases, and the data are often isolated from each other. The lack of a unified data sharing and exchange mechanism makes it difficult to manage and integrate data uniformly. ③ The problem of shallow data mining [78,112]. Currently, various water conservancy systems are mainly developed independently, leading to fragmented business data. The data from different departments have not been integrated and shared. The lack of data integration directly results in the inability to establish inherent connections and relationships between data, making it challenging to form comprehensive data correlation services. It is difficult to conduct correlation analysis and value mining on the data, and currently, water model libraries and learning algorithm libraries have not been fully established, thus failing to achieve deep mining of massive data.

3.3. The Lack of Standards and Evaluation Systems for Digital Twin Water Conservancy

Through the theoretical research and application practice of the digital twin model in recent years, it has been found that the evaluation index system and methodology of digital twin model are not perfect enough [113], and there are a lack of systematic methods to measure the quality, performance, applicability and symbiosis, adaptability and value of the digital twin model in the construction and verification, operation and management, reconfiguration and optimization, migration and reuse and circulation and delivery of the digital twin model. This absence makes it difficult to comprehensively and objectively evaluate the performance and effectiveness of digital twin models, and it hinders the quantification of the roles and impacts of digital twin models regarding water systems. Additionally, it leads to subjectivity and arbitrariness in the evaluation process, failing to ensure the scientific validity and credibility of the evaluation results. As a result of this issue, further problems emerge such as the opacity of digital twin model quality, unclear performance distribution, strong blind spots in reconfiguration optimization, vulnerability in transfer and reuse and issues such as being “marketed but lacking value”. These derivative problems seriously impede the widespread and deep application of digital twin technology [114].

3.4. Serious Network Security Issues in Digital Twin Water Conservancy

Network security issues pose a critical challenge to the implementation of digital twin technology [115,116,117]. This is mainly reflected in the following points: ① Security challenges arising from the convergence of multiple technologies. The realization of digital twin water conservancy involves the integration of traditional water conservancy technology, big data, cloud computing, the IoT, AI and many other modern information technologies. In the process of cross-integrating these technologies, more vulnerabilities are inevitably exposed, and traditional information security technologies or protection schemes are no longer as effective as before [118,119,120,121,122]. Therefore, the integration of multiple technologies will lead to numerous security issues. ② Security challenges triggered by AI technology. AI security has become a global focus of attention and a hotspot in research areas [123,124,125,126,127]. Compared to traditional network security, on one hand, the vulnerabilities, complexities and opacities of AI technologies make it challenging to ensure the security of digital twin water conservancy. On the other hand, AI technology makes cyber-attacks more intelligent, frequent, covert and adversarial, posing a significant threat to the security of digital twin water conservancy. ③ Security challenges caused by internal staff. In digital twin water conservancy systems, internal staff, system users and relevant stakeholders have varying levels of technical expertise and experience. They have their own permissions within the allowable range to manipulate data, and individual data manipulation behaviors can affect data security [128,129]. If conflicts of interest or other reasons arise, breaking established data security boundaries and inadvertently or maliciously conducting threat actions such as theft, sabotage or the deletion of data will pose data security risks.

3.5. The Intelligence Level of Digital Twin Water Conservancy Needs to Be Improved

There are multiple deficiencies in smart applications. [130,131] At present, the application of new-generation information technology in the water conservancy industry is still in the primary stage; technologies such as big data, artificial intelligence and virtual reality have not yet been widely used; and the intelligent functions have not been fully realized [22]. In terms of intelligence, the model capability is insufficient, and the decision-making accuracy is not high; for example, forecasting mainly relies on centralized and empirical models, the degree of refinement of early warning and planning is insufficient and the preview capability is difficult to support multi-scenario preference. There is also a lack of intelligence in business management, as the application system has been built to fit real-time online monitoring data integration and display and business management systems, while synergistic and auxiliary decision-making support difficulties exist, such as water project scheduling intelligence not being high, the water resource allocation of business synergy not being enough, sand mining supervision not being intelligent enough and so on. The business application system has been constructed in a decentralized manner, a unified business application system has not been formed, the degree and level of information technology application is not high, the ability of business intelligent scheduling is insufficient, the informatization level of businesses such as engineering management is low and the application of intelligent identification technology in river and lake management is insufficient to realize the automatic discovery of water-related problems. The depth of the integration of water conservancy business and information technology is insufficient, the analysis and decision-making capability is insufficient and there is a gap between the realization of accurate decision-making in digital scenarios.

4. Future Development Trends in Digital Twins Smart Water Conservancy

As a new frontier technology, digital twin, based on simulation technology, plays a larger role in promoting the construction of smart water conservancy. Data twin technology will enable real-time the monitoring and prediction of water conservancy systems, providing decision makers with more accurate and timely decision support, reducing flood risk and improving water use efficiency. However, in terms of technology applications, multimodal data fusion, large-scale data processing, high-precision intelligent modeling, network information security and other aspects are still difficult to practically apply, and there is a lack of more mature, intelligent and systematic application service platforms. Therefore, I believe that future research should focus on the following aspects:

4.1. High-Throughput Sensing and Processing of Multi-Source Data from IoT Sensors

Currently, most of the devices such as IoT sensors are the same device that can only collect a set amount of single data, which is the biggest limitation that exists in sensor devices. This limitation stems from the design and function of sensors, which are typically designed to monitor and collect specific types of data; for example, water pressure sensors can only sense water pressure parameters. This single data collection mode limits the scope and flexibility of IoT devices to some extent. In practical applications, it is often necessary to gather various types of data for comprehensive analysis and decision-making. However, due to the singularity of sensor devices, it may be necessary to deploy multiple types of sensors to meet this requirement, adding complexity and cost to maintenance. To overcome this limitation, future IoT sensor devices need to move in multifunctional and intelligent directions. Specifically, improvements and innovations can be made in the following areas: ① Integrated design of multiple sensors. Integrating multiple types of sensors into a single device allows it to collect multiple types of data at the same time. This design not only reduces the number of devices and complexity of wiring but also enhances the efficiency and accuracy of data collection. ② Integration of sensor adaptive learning algorithms. Introducing adaptive learning algorithms to enable sensors to automatically adjust data collection types and frequencies based on environmental changes and device requirements, thus allowing them to adapt more flexibly to different scenarios and application needs. ③ Edge computing capabilities of sensors. Integrating edge computing capabilities into sensor devices so that they can perform data preprocessing and analysis locally, thereby alleviating the load on cloud servers and improving data processing speed and efficiency. ④ Standardization and interoperability of sensing devices. Promoting the standardization and interoperability of IoT sensing devices (sensors, drones, satellite remote sensing, high-definition cameras, etc.) to enable different brands and types of sensing devices to connect and collaborate with each other contributes to the development of a more open and scalable IoT ecosystem. In conclusion, through improvements and innovations in integrated design, adaptive learning algorithms, edge computing capabilities, standardization, and interoperability, future IoT sensing devices will be able to overcome the limitations of single data collection modes while possessing high-throughput acquisition capabilities [78,132], representing the future development trend.

4.2. Highly Integrated Collaborative Control of Data-Driven and Model-Driven Approaches

Water conservancy project physical entities generally have individual complexity and group constraint characteristics. Complex physical systems often struggle to establish accurate mathematical and physical models, and it is not possible to analyze the mathematical and physical models by way of state assessment and control optimization. Digital twins, using a data-driven approach with the historical and real-time operational data of the system, can not only update, correct, connect and supplement mathematical models but also help the system to learn patterns from massive data, identify anomalies and predict trends. Model fusion can provide a deep understanding of the physical processes of water conservancy systems, aiding in building more accurate and reliable system models, thus offering comprehensive information support for decision-making. Most of the existing research is based on complex algorithms such as machine learning, deep learning, etc., to transform the data into an alternative to physical models, and the models are not sufficiently interpretable, making it difficult to deeply portray or characterize the mechanisms of physical entities. Therefore, I believe that in the future, we should focus on the deeper integration and optimization of data-driven and model fusion collaborative control to effectively combine high-precision sensing data with the system mechanism in depth, obtain a better state assessment and system characterization effect, provide a more scientific and effective solution for the management of smart water conservancy and push the water conservancy system in the direction of intelligent and sustainable development.

4.3. Network Security Protection System with Artificial Intelligence Technology as the Core

In the context of digital twin water conservancy, a network security protection system with AI technology at its core plays a crucial role. Firstly, the autonomous learning and recognition capabilities of AI technology make it a key component in network security protection systems. Through deep learning and pattern recognition, AI can analyze network traffic and behavior in real time and promptly detect anomalies and potential threats. This capability is particularly important for the digital twin water conservancy platform, as it involves a large amount of sensitive data and critical business operations, where any security vulnerabilities could have serious consequences. Furthermore, the automation capability of AI enhances the efficiency of network security protection. Traditional security measures often rely on manual operations and rule matching, resulting in inefficiency and a risk of overlooking hidden threats. In the digital twin water conservancy environment, this approach cannot guarantee network security. In contrast, AI-based security protection systems can monitor network status in real time and automatically take defensive actions upon detecting abnormal behaviors, such as isolating suspicious devices and blocking malicious traffic. Therefore, the future network security protection system of digital twin water conservancy will be centered around AI technology. On one hand, AI technology can be applied to safeguard the network security of digital twin water conservancy, and on the other hand, the established security protection system can thwart and isolate malicious attacks based on AI technology.

4.4. Development Trends of Digital Twin Water Conservancy with Multi-Technology and Multi-Industry Integration

Digital twin water conservancy is not an isolated system but rather requires the integration of multiple technologies and comprehensive solutions across multiple industries. With the rapid development of technology and deep integration of industries, digital twin water conservancy will present broader application prospects and profound social impacts. Firstly, the continuous advancements of new-generation information technologies provide robust technical support for digital twin water conservancy. IoT technology enables the comprehensive sensing and real-time monitoring of water conservancy projects, cloud computing technology offers efficient data processing and storage capabilities, big data technology allows for the in-depth exploration and analysis of massive data and AI technology enhances decision support and management optimization through techniques such as machine learning and pattern recognition. The organic integration and application of these technologies enable digital twin water conservancy to achieve higher levels of intelligence and automation, improving the operational efficiency and safety of water conservancy projects. Secondly, digital twin water conservancy needs to be deeply integrated and co-developed with multiple industries. As a crucial infrastructure for national economic and social development, the construction and management of water conservancy projects involves multiple industries and fields. Digital twin water conservancy needs to collaborate closely with industries such as agriculture, industry, transportation and environmental protection to jointly promote the rational utilization of water resources and the sustainable development of water conservancy projects, contributing to the overall sustainable development and ecological civilization construction of society.

5. Conclusions

At the present stage of the development process of water conservancy projects, due to the continuous increase in social demand for water resources, traditional water conservancy project management technology struggles to meet the needs of social development. On this basis, it is necessary to combine advanced technology and digital twin technology into a water conservancy project to realize the construction of a smart water conservancy project.

Digital twins, as emerging technologies, have yet to realize their full potential in the water conservancy field. This study discusses the concept and connotations of digital twin smart water conservancy from the perspective of digital twin technology and application services and constructs a corresponding technical system. At present, digital twin technology has made some progress in the field of smart water conservancy, covering monitoring and prediction, decision support, smart water conservancy equipment, ecological environmental protection, etc., which provides a development path for the intelligence and ecologicalization of water conservancy systems. However, insufficient definition and value perception, insufficient data collection and processing, model accuracy needing to be improved, the lack of standards and regulations, the lack of technical talents, and insufficient cybersecurity have posed great challenges to the construction of a framework for digital twin smart water conservancy. In the future development, studies should focus on the high-throughput acquisition and processing of water conservancy data, the deeper integration of data-driven and model fusion collaborative control, and the improvement of network security and protection capabilities to ensure that digital twin technology can be fully applied to bring substantial improvements to the water conservancy industry.