Another interesting aspect of a #DigitalTwin Model is the use of virtual, or calculated properties to derive additional #IoT value. #IIoT
While “virtual properties” don’t have a 1:1 relationship with the “telemetry properties” (sensors) I described in my previous post, that are actually sending data from a device, they’re super-valuable. The value assigned to a virtual property is typically derived from a mathematical combination of values from one or more telemetry properties and possibly other reference data. For instance, calculating miles per hour (speed) of a car is a good example of a virtual property where a combination of telemetry properties like the rotating drive shaft and magnetic sensors use simple analytics to tell you how fast you’re going.
While not always necessary, virtual properties deliver additional value and insights.
Your #DigitalTwin Model and its telemetry properties are an essential part of the #IoT #data flow from device to #analytics. #IIoT
A digital twin model is used to define a class, or type, of entity/thing. In other words, you define all aspects of a type of thing just once, rather than defining it over and over again for each individual thing. Each instance of a digital twin derived from a digital twin model will inherit its attributes. The digital twin model tells the event processing engine in your Internet of Things platform what to expect by providing “telemetry properties” that include the data labels, data types, and units of measure for the incoming data to assist with pattern matching. No matter what level of analytics or machine learning you’re using, you won’t be able to derive any actionable insights without knowing the details of the data that’s arriving from your IoT endpoints.
The digital twin model indispensable.