Key features and benefits

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  Modeling

  A digital twin starts with the exact digitum model of a physical object or system.
  This model mimics physical properties, behavior, components, and more.

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  Data collection

  Data is collected from real systems via sensors and IoT devices and reflected in digital twin models. This data can be real-time or historical information, including sensor data, temperature, pressure, vibration, location information, etc.

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  Analysis

  Analyze the collected data to understand the status and performance of digital twin models. Identify patterns and trends and detect problems using big data analysis, artificial intelligence, and machine learning techniques.

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  Simulation

  Digital twins can simulate the behavior of real systems.
  This allows you to test preliminary scenarios or simulate the behavior of the system.

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  Prediction and optimization

  Digital twins are used to predict future conditions and optimize systems.
  For example, you can predict failures in the production process or optimize maintenance schedules.

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  Flexibility

  Digital twins allow you to respond to a variety of scenarios and changes without changing the design of your physical system.

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  Applications

  Digital twins are used in manufacturing, energy, construction, automotive, aerospace, healthcare, and many other industries.