ILLUSTRATIVE TECHNOLOGY CASE · OBJECTLOGIC DIGITAL TWIN
Understand the model. Explain the decision.
AI-generated code still needs to be understood and tested. In an ObjectLogic digital twin, engineering knowledge is explicit: pumps, tanks, supply dependencies, operating limits and the rules that connect them. Engineers can review the model in terms of the system they know.
A cooling system with visible reasoning
A tank feeds a pump. A cooling consumer supplies a machine. A production line depends on that machine. These relationships and the equipment’s operating limits are represented explicitly.
The tank level is 8%. Its configured minimum is 15%. The pump fed by that tank is running. The rule derives dry-run risk. The measurements, relationship and rule show why.
Correct the tank level and the risk clears. Change a supply dependency and the affected production assets change. A “running” pump with zero measured speed triggers an explicit feedback inconsistency rule.
Reuse knowledge your team can inspect
In this demonstrator, a second cooling loop is added using equipment, configuration and measurement facts. The same engineering rules apply. OntoBroker follows the configured dependencies to derive affected assets and recommendations that applications can query.
Conventional software can produce the same outcomes. ObjectLogic’s value is a shared, semantically explicit model: domain knowledge remains available for review, explanation and reuse. Unexpected cases can be turned into explicit conditions and regression checks.
Illustrative software demonstrator. Conclusions depend on the supplied facts, relationships and rules; modelled reconfiguration does not operate physical equipment.
