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== DT Resolution and Update Frequency ==
== DT Resolution and Update Frequency ==
As mentioned earlier, key questions which must be answered by the solution architect are regarding the DT resolution and update frequency.
A good example here is a DT for a soccer game. Depending on the role of the different stakeholders, they would have different requirements regarding resolution and update frequency. For example, a betting office might only need the final score of the game. The referee (well, plus every body else playing or watching) needs more detailed information about whether the ball has actually crossed the line of the goal, in case of a shot on the goal. The audience usually wants an even higher "resolution" for the Internet live feed, including all significant events (goals, fouls, etc.). The team coach might require a detailed heat map of the position of each player during every minute of the game. And finally the team physicial wants additional information about the bio rythm of each player during the entire game.
[[File:0.3.1-SoccerExample.png|800px|frameless|center|Digital Twin: Soccer Example]]
[[File:0.3.1-SoccerExample.png|800px|frameless|center|Digital Twin: Soccer Example]]
So there are a number of key questions here: How high should the resolution of the DT be? And how can a combination of sensors and reconstruction algorithm deliver on this resolution?
For the individual goal recognication, a dedicated sensor could be embedded in the ball, with a counterpart in the goal posts. This would require a modification of the ball and the goals posts, but would allow for very straight-forward reconstruction, e.g. via a simple rule.
Things become more complicated for the reconstruction process if a video camara is used instead. Here, AI/ML could be utilized, e.g. for goal recognition.
For the bio rythm, chances are that a specialized type of sensor will be somehow attached to the player´s body, e.g. in his shorts or t-shirt. For the reconstruction process, probably advanced analytics will be required.
[[File:0.3.1-SoccerDetails.png|800px|frameless|center|Digital Twin: Soccer Example (Details)]]
[[File:0.3.1-SoccerDetails.png|800px|frameless|center|Digital Twin: Soccer Example (Details)]]


== Conclusion ==
== Conclusion ==
[[File:0.3.1 DT Conclusions.png|800px|frameless|center|Conclusions - Digital Twin and AIoT]]
[[File:0.3.1 DT Conclusions.png|800px|frameless|center|Conclusions - Digital Twin and AIoT]]

Revision as of 21:52, 5 June 2021

More...More...More...More...More...More...More...More...More...More...More...More...More...DigitalTwin 101

Digital Twins can be used in order to create a digital representation of the physical entities. They can help with managing complexity and establishing a semantic layer on top of the more technical layers. This in turn can make it easier to realize business goals and implement AI/ML solutions using machine data. The following provides an overview, some concrete examples, as well as a discussion in which situations the Digital Twin approach should be considered in an AIoT initiative.

Overview

Digital Twin - Overview

Example

A good example for a Digial Twin is a system which makes route recommendations to drivers of electric vehicles, including stop points at available charging stations. For these recommendations, the system will need a representation of the vehicle itself (including charging status), as well as the charging stations along the chosen route. If this information is logically aggregated as a Digital Twin, and AI in the backend can then use this DT in order to make the route calculation, without having to worry about technical integration with the vehicle and the charging stations in the field.

Similarly, the feature responsible for reserving a charging stations after a stop has been selected can benefit if the charging station is made available in the form of a Digital Twin - allowing to make the reservation without having to deal with the underlying complexity of the remote interaction.

The Digital Twin in this case is providing a higher level of abstraction than would be made available, for example, via a basic API architecture. Especially if the Digital Twin is taking care of data synchronization issues.

DigitalTwin Example

Digital Twin and AIoT

In an AIoT initiative, the Digital Twin concept can play an important role in providing a semantic abstraction layer. IoT plays the role of providing connectivity services. AI, on the other hand, can play two roles:

  • Reconstruction: AI can be an important tool for the reconstruction process, i.e. the process which is reconstructing the virtual representation based on the raw data from the sensors.
  • Application: Once the Digital Twin is reconstructed, AI and be applied to the semantically rich representation of the Digital Twin to support the business goals
Digital Twin and AIoT

Example 1: Electric Vehicle

The first example to demonstrate this concept is building on the EV scenario from earlier on. In addition, the DT concept is now also applied to the Highly Automated Driving Function of the vehicle, which includes short term trajectory and long term path planning.

For the short term planning, a digital twin of the vehicle surroundings is created (here, the AI is supporting the reconstruction of the DT). Next, an AI is using the semantically rich interfaces of the digital twin of the vehicle surroounding to perform the short term trajectory planning. This AI will also take the long-term path into consideration, e.g. to determin necessary turns on a highway.

Digital Twin and AIoT - Example

Example 2: Particle Collider

The second example is a partile collider, such as the Large Hadron Collider at CERN.

The particle collider is using a 3D grid of ruggedized radio activity sensors in a cavern of the collider to capture radio activity after the collision. This data is fed into a hugely complex tier of compute nodes, which are applying advanced analytics concepts in order to create a digital reconstruction of the particle collision. This Digital Twin is then the foundation of the analyis of the physical phenomens that could be observed.

AIoT & Digital Twin: Particle Collider Example

DT Resolution and Update Frequency

As mentioned earlier, key questions which must be answered by the solution architect are regarding the DT resolution and update frequency.

A good example here is a DT for a soccer game. Depending on the role of the different stakeholders, they would have different requirements regarding resolution and update frequency. For example, a betting office might only need the final score of the game. The referee (well, plus every body else playing or watching) needs more detailed information about whether the ball has actually crossed the line of the goal, in case of a shot on the goal. The audience usually wants an even higher "resolution" for the Internet live feed, including all significant events (goals, fouls, etc.). The team coach might require a detailed heat map of the position of each player during every minute of the game. And finally the team physicial wants additional information about the bio rythm of each player during the entire game.

Digital Twin: Soccer Example

So there are a number of key questions here: How high should the resolution of the DT be? And how can a combination of sensors and reconstruction algorithm deliver on this resolution?

For the individual goal recognication, a dedicated sensor could be embedded in the ball, with a counterpart in the goal posts. This would require a modification of the ball and the goals posts, but would allow for very straight-forward reconstruction, e.g. via a simple rule.

Things become more complicated for the reconstruction process if a video camara is used instead. Here, AI/ML could be utilized, e.g. for goal recognition.

For the bio rythm, chances are that a specialized type of sensor will be somehow attached to the player´s body, e.g. in his shorts or t-shirt. For the reconstruction process, probably advanced analytics will be required.

Digital Twin: Soccer Example (Details)

Conclusion

Conclusions - Digital Twin and AIoT