Method for providing interactive co-pilot in an industrial operation and a system thereof

A system for providing interactive co-pilot in an industrial operation, comprising:

• one or more processors;
• a memory storing program instructions which, when executed by the one or more processor, causes the processor to:
• provide an interactive user interface (UI), wherein the user interface provides natural language processing capability for processing one or more user requests;
• access a real-time digital twin of one or more processes and associated assets with the one or more processes, wherein the real-time digital twin includes an asset digital twin and a process digital twin;
• receive the one or more user requests including at least one of execution of optimization scenarios, monitoring anomaly, and unplanned event detection;
• monitor one or more asset parameters associated with assets from the asset digital twin;
• monitor one or more process parameters associated with processes from the process digital twin;
• simulate, by a machine learning (ML) model, one or more scenarios corresponding to the one or more user requests to generate one or more outcomes, based on the one or more asset parameters and the one or more process parameters;
• and
• present to a user, via the interactive user interface, the one or more outcomes corresponding to the user request for user selection.

A non-transitory computer-readable storage medium storing program instructions for providing interactive co-pilot in an industrial operation, the instructions, when executed, perform the steps of:

• providing an interactive user interface (UI), wherein the user interface provides natural language processing capability for processing one or more user requests;
• accessing a real-time digital twin of one or more processes and associated assets with the one or more processes, wherein the real-time digital twin includes an asset digital twin and a process digital twin;
• receiving one or more user requests including at least one of execution of optimization scenarios, monitoring anomaly, and unplanned event detection;
• monitoring one or more asset parameters associated with assets from the asset digital twin;
• monitoring one or more process parameters associated with processes from the process digital twin;
• simulating, by a machine learning (ML) model, one or more scenarios corresponding to the user request to generate one or more outcomes, based on the one or more asset parameters and the one or more process parameters;
• and
• presenting to a user, via the interactive user interface, the one or more outcomes corresponding to the user request for user selection.