Using ChatGPT for AVEVA Intouch, System Plaftorm and OMI

Heyy I was thinking if there's a way to use ChatGPT for AVEVA Intouch, System Platform , OMI, Historian , Edge for developing graphics, tags, basically all the leg work.

Features i'd like to see:

1) Write scripts for me

2) Help me upgrade to industrial graphics

3) Better help guide section 

4) build tags , UDTs, objects for me

5) use situational awareness library to build graphics based on P&ID.

Any thoughts from AVEVA ?

I tried seeing what ChatGPT gives me currently and it's not half bad at all

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  • We are also looking into how AI would apply to Enterprise SCADA. Things like, automatically summarise the previous shift for a handover report, automatic screen comparisons as part of Parallel Operation Verification step that most of our pipeline users must do as part of their regulated change management process. 

    We are also exploring the potential for AI to automate routine tasks performed today by the operator following multiple steps. The Enterprise SCADA Automation Bundle for example could receive an Action Sequence that has been proposed by the AI, for the operator to approve and launch. Keeping the Human in the Loop will be essential here, even if we were able to do things like a What if analysis which would perform the action sequence against a simulator to show how the asset will likely respond to the sequence of commands.

    Other avenues to explore would be HMI screen translations to streamline the adoption of our next-gen HMI, and code completion as part of our SDKs. However, AI hallucinations remain part of this landscape today, so human inspection and comprehensive testing would be required before putting anything into production.

    Asset Inferencing is another promising avenue of investigation: a way to keep up to date the various mappings between different data models as part of the Unified Namespace / Common Data Model solution that would facilitate Common Configuration.

    Lastly, I have been suggesting that customers think ahead about what kind of data to collect given that we dont yet know what data the AI would need to be trained on. So, having as much time-corrolated, clean data as possible in a central historian that is normalised as much as possible to a master asset model will be invaluable in the future, once a few years of such data is available to train on. This should include data from outside the control system as well, such as maintenance and commercial data. 

  • Hi Jake, thanks for your reply, I'm working for an AVEVA Distributor and i have seen Enterprise SCADA but I'm embarrased to say I don't understand Enterprise SCADA, initially thought it was System Platform Enterprise but when i went through it said about Oil and Gas Pipeline SCADA and much more. When I see this, I'm thinking Enterprise SCADA is beyond the reach for most customers but I would definitely like to understand more. We have big customers but none use this, would like to know what it does and where it fits in

  • Hi Rainer - yes, it is not surprising that Enterprise SCADA is not as widely known as System Platform. Formally known as OASyS from the Telvent company, it specialises in Oil & Gas, but also very large regional water districts, and has been used in the Transportation, Electrical and Weather industried to a lessor extant in the past.

    Its strengths are suited for very large systems (geographically dispersed) with high availability requirements (99.999% uptime), with multiples layers or arbitration and redundancy, and a fully distributed operating model with a flexible deployment architecture.

    Out of the box it supports all the current North American standards for Midstream transportation, as well as downstream distribution, particularly around the Control Room experience and hydrocarbon measurement.

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  • Hi Rainer - yes, it is not surprising that Enterprise SCADA is not as widely known as System Platform. Formally known as OASyS from the Telvent company, it specialises in Oil & Gas, but also very large regional water districts, and has been used in the Transportation, Electrical and Weather industried to a lessor extant in the past.

    Its strengths are suited for very large systems (geographically dispersed) with high availability requirements (99.999% uptime), with multiples layers or arbitration and redundancy, and a fully distributed operating model with a flexible deployment architecture.

    Out of the box it supports all the current North American standards for Midstream transportation, as well as downstream distribution, particularly around the Control Room experience and hydrocarbon measurement.

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