Data Insights

Data-driven insights

The basis for building an intelligent digital twin industrial model is data. A large number of sensors need to be deployed on site to collect real-time data such as temperature, vibration, and pressure of the equipment, as well as data services such as order information and replenishment processes in production. These multi-source data are processed by edge computing and transmitted to the digital twin platform to be integrated with the virtual model to ensure that the virtual model can truly reflect the state of the physical entity. For example, in an automobile manufacturing plant, sensors compress, weld, paint and other production line equipment in real time, and these data are synchronously updated to the corresponding digital twin model, providing a basis for subsequent artificial intelligence analysis.

Embedding artificial intelligence algorithms into digital twin models is the core of realizing system intelligence. Deep learning algorithms can be used for fault prediction. By learning from the historical operation data and fault cases of equipment, a fault prediction model is established. When the digital twin model detects abnormal trends in the equipment operation parameters, the artificial intelligence algorithm can predict the possibility of faults in advance and give maintenance suggestions. Reinforcement learning algorithms can be applied to production scheduling optimization. With production efficiency, energy consumption, etc. as objective functions, different scheduling strategies are continuously simulated in the digital twin environment to find the optimal production plan. For example, in chemical production, reinforcement learning algorithms dynamically adjust production plans based on factors such as raw material supply and equipment status to reduce production costs.