Digital twin refers to the integration of a multidisciplinary, multi-scale simulation process by making full use of physical models, sensors, operational history and other data, which serves as a mirror image of the physical product in virtual space and reflects the full lifecycle process of the corresponding physical entity product.

hen we talk about twins, we may think of twins, and we can think of digital twins as digital twins; digital twins are not two identical individuals but two systems, one system existing in the real physical world and one system existing in the virtual digital world.

The model presents five major driving elements: sensors and actuators for the physical world, integration, data and analytics, and continuously updated digital twin applications.

Sensors: Sensors configured in the production process emit signals through which the digital twin can obtain operational and environmental data related to the actual process.

Data: The actual operational and environmental data provided by the sensors will be merged with the enterprise data after aggregation, which includes bill of materials, enterprise systems and design specifications, etc. Other types of data include engineering drawings, connections to external data sources, and customer complaint records.

Integration: Sensors reach data transfer between the physical and digital worlds through integrated technologies, including edge, communication interfaces and security.

Analytics: The digital twin uses analytics to carry out algorithmic simulations and visualization procedures, which in turn analyze the data and provide insights.

Digital Twin: The "digital" dimension of the diagram refers to the digital twin itself. The application combines all these elements to create a quasi-real-time digital model of physical entities and processes. The digital twin aims to identify anomalies in the different dimensions that deviate from the ideal state.

Enabler: If it is determined that a practical action should be taken, the digital twin will initiate the practical action through an enabler with human intervention to move the practical process forward.

There are three key words to remember when understanding the digital twin: "full lifecycle", "real-time/quasi-real-time", and "bi-directional".

Full lifecycle: This means that the digital twin can be used throughout the entire product cycle, including design, development, manufacturing, service, maintenance and even end-of-life recycling. It is not limited to helping companies build their products better, but also includes helping users use them better.

Real-time/Quasi-real-time: It means that between the ontology and the twin, a full real-time or quasi-real-time connection can be established. The two are not completely independent, and the mapping relationship possesses a certain degree of real time.

Bidirectional: It means that the data flow between the ontology and the twin can be bidirectional. It is not that only the ontology can output data to the twin, but the twin can also provide feedback to the ontology. The enterprise can take further actions and interventions on the ontology based on the feedback from the twin.

3D digital twin system, the model simulation of the production process in the physical world to establish a virtual mirror, through 3D digital modeling and other technologies to restore the scene module of the digital twin, through data visualization of physical objects for dynamic virtual simulation control.