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Solutions

Digital twin data supply

Feed your model with actual shop-floor data.

Digital transformation and engineering teams

Map physical equipment identities to the digital model. Provide process values with time, units and quality so the model can distinguish valid readings from stale data.

Typical data points

  • Asset identifier
  • Process value, unit and quality
  • Source timestamp
  • State and alarm information
  • Data age

How the flow works

  1. Connect

    The OPC UA Client reads sensor and equipment tags.

  2. Transform

    Values are converted to the format the model expects, with asset identifier, unit, time and quality fields.

  3. Transfer

    They are delivered to the model or simulation environment over MQTT or REST; when the maximum data age is exceeded the value is flagged as stale.

Decisions before connecting

  1. 01Asset identity mapping
  2. 02Sampling and latency needs
  3. 03Maximum data age
  4. 04Model behaviour with missing data

Pilot acceptance criterion

Compare source measurements with model values for one asset; verify that the model marks stale data when the connection fails.

Connectors used in this scenario

Connector availability, version compatibility and licence scope are verified for each project.

Implementation guide

Frequently asked questions

What data rate does a digital twin need?

There is no single answer; it depends on which decision the model supports. Sampling frequency, acceptable data age and the model’s behaviour when the connection fails must be defined together.

Should it run at the edge or on a central server?

It depends on data rate, outage tolerance and the competence of the operating team. The edge versus central guide walks through the decision.

Your next step

Let’s talk about the systems you need to connect.

Share your source, destination and intended outcome. We’ll help define the project scope.

Talk to usDemo