A digital twin is a digital replica based on virtual models and real-time data that simulates and optimizes the performance and behavior of a system during real-world operations.
The application of digital twins in the whole value chain of water projects can cover the design and planning stage, the construction and construction stage, the application and management stage, as well as the decision support and visualization stage.
To build a digital twin, we must first lay a good foundation, and the foundation is to build a good data baseboard, model platform and knowledge platform.
Data backplane. The data base of the digital twin platform refers to the data foundation that supports the operation of the digital twin model and system.
The digital twin backplane includes real-time data, historical data, geospatial data, management data, and external data.
Real-time data comes from data from the water system, including sensor data, monitoring data, real-time operation status, etc. This real-time data is used to update the digital twin to reflect the current state of the actual system.
Historical data includes past operating records, event data, fault data, etc. Historical data is used to construct the initial state of the digital twin, as well as for model validation and calibration.
Geospatial data contains spatial data about water systems, including network topology, equipment locations, pumping station layouts, and more. The construction of water conservancy geospatial data is classified as L1, L2 L3 bottom plate.
360° Case Study] L1, L2, L3** base plates of Nantong water network digital twin.
Business management data includes device information, maintenance records, and O&M plans. This management data is used to support the digital twin platform's capabilities such as device management, operation and maintenance planning, and workflow optimization.
Sharing data across industries requires the acquisition and integration of data from external sources, such as weather data, water quality data, water demand data, etc. This external data can be used as boundary conditions, scenario analysis, and decision support in the digital twin.
The data base of the digital twin platform must have the ability to collect, store, process, and analyze data.
At the same time, the data base of the digital twin platform also needs to have data security and privacy protection measures to ensure the confidentiality and integrity of sensitive data. In addition, the data base needs to provide a user-friendly interface and data visualization tools so that users can intuitively understand and analyze the data to support decision-making and optimize the operation of the water system.
Model platform. In the water conservancy industry, digital twin platforms generally include professional water conservancy models, visual models and digital simulation engines. Together, these components form a platform for the development and application of digital twins in the field of water conservancy.
Professional hydraulic models are a core component of the digital twin platform. It is a tool for modeling and improving hydraulic systems to simulate the behavior and performance of hydrological processes, water resource dispatch, water supply and drainage systems, and more. Professional hydraulic models can accurately simulate and improve the water conservancy system based on physical principles, mathematical models and statistical analysis.
Visual models are an important component of the digital twin platform to present the results of the hydraulic model to users in an intuitive way. The visual model can display the operating status, data changes, and scenario analysis results of the water conservancy system in the form of charts, maps, animations, etc., to help users understand and analyze the complexity of the water conservancy system.
The Digital Simulation Engine is a key technology of the Digital Twin Platform for simulating and computing the behavior and response of water systems. It is able to perform numerical calculations and perform numerical calculations based on the input data of the model, and integrate different physical, mathematical and statistical models to achieve accurate simulation of the behavior of the water conservancy system.
When choosing a digital twin platform, you should consider the technical capabilities, scalability, and user-friendliness of the platform, as well as how well it matches the actual needs of the water conservancy system. At the same time, aspects such as data security, reliability, and visualization of the platform need to be evaluated and compared.
Knowledge platform. The knowledge platform plays an important role in the field of water conservancy, mainly involving the water conservancy knowledge engine and the water knowledge base of rivers and lakes, flood control, etc., including comprehensive knowledge base, forecast and dispatch database, scheme plan database, historical flood scenario database, business rules database, expert experience database and other information.
The Water Knowledge Engine is used to collect, organize, and manage knowledge in the field of water conservancy. Based on expert experience, domain knowledge, and real-world data, it can build knowledge models and rules to support optimal decision-making and problem solving in water systems.
The river and lake knowledge base is used to store and manage data, information and knowledge related to rivers and lakes. It includes geographic information, hydrological data, water quality data, ecological environment data, etc. of rivers and lakes.
The flood control knowledge base includes historical flood data, flood control engineering design standards, flood control scheduling plans, flood control emergency plans, etc.
The comprehensive knowledge base is used to store and manage comprehensive information and knowledge in the field of water conservancy. It can include knowledge of water planning, water resource management, water supply and drainage system design, water environment protection, etc. The forecast and dispatch database includes meteorological forecast data, rainfall forecast data, hydrological model parameters, water level and flow relationship curves, etc.
The Historical Flood Scenario Library includes flood history, flood process data, flood disaster assessment, and more.
The business rule library includes water resources management rules, reservoir scheduling rules, drainage system management specifications, etc.
The expert experience base includes expert opinions, suggestions, cases, etc. The expert experience base supports expert systems and intelligent decision support for water systems.
The construction and management of the knowledge platform can be realized through technical means such as knowledge graphs, databases, and model algorithms. The construction of the knowledge platform must ensure the accuracy and reliability of the knowledge base through the collation and standardization of expert knowledge, as well as the collection and cleaning of data.