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Engineering digital twins

Enriching the digital twin with appropriate solutions and resources (AI, Big Data, etc.).

Enriching the digital twin with appropriate solutions and resources (AI, Big Data, etc.).

Objectifs

  • Develop solutions specific to a given use that enhance the services provided by the digital twin
  • Create AI modules capable of extracting relevant data and processing it in real time
  • Create predictive models based on digital twin data and integrate them to optimize operations.
Enriching the digital twin with appropriate solutions and resources (AI, Big Data, etc.).

Acteurs concernés

  • Local authorities / EPCI
  • Dealers/ Operators
  • Manufacturers/ Equipment suppliers
Enriching the digital twin with appropriate solutions and resources (AI, Big Data, etc.).

Avantages

  • Decision support in complex multi-use environments
  • Decision support based on self-learning predictive tools
  • Integration of interconnected, interoperable business applications with the digital twin
  • Simulate and compare behavior under real-life conditions, especially for safety-critical applications

A digital twin fully anchored in its environment can now enter a phase of muscularizing its operational capabilities, with an innate sense of absorbing the most innovative technologies!

This step marks the transition to an advanced level of intelligence and functionality. By developing solutions specific to a given use, this action customizes the digital twin to meet the particular needs of the targeted application. The integration of AI modules capable of extracting and processing relevant data in real time considerably enhances the digital twin’s ability to provide dynamic analysis, right up to autonomous decision-making. What’s more, the creation of predictive models based on data from the digital twin makes it possible to anticipate future developments, notably through simulations, thus truly optimizing operations. This stage represents the final phase before operation, as it prepares the digital twin to operate autonomously, intelligently exploiting data to improve performance and decision-making.

Enriching the digital twin with appropriate solutions and resources (AI, Big Data, etc.).

Avantages

  • Decision support in complex multi-use environments
  • Decision support based on self-learning predictive tools
  • Integration of interconnected, interoperable business applications with the digital twin
  • Simulate and compare behavior under real-life conditions, especially for safety-critical applications

Les retours d’expérience

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