Control Strategy of Distributed Control Systems Based on Operator Actions

A computer-implemented method for amending and/or augmenting an engineering tool that is configured to generate application code which, when executed on one or more controllers in a distributed control system of an industrial plant, causes the industrial plant to be controlled according to a control strategy that is implemented in the application code, the method comprising:

• acquiring state variables that characterize an operational state of at least one industrial plant;
• predicting, based on the state variables, using at least one trained machine learning model, one or more interaction events that at least one plant operator is likely to initiate on the distributed control system via a human-machine interface in response to said operational state;
• and
• mapping the one or more predicted interaction events to an amendment and/or augmentation for the engineering tool that has generated the application code for the distributed control system such that, when the application code is re-generated by the amended and/or augmented engineering tool and executed in the distributed control system, the plant operator is likely to manually interact with the distributed control system less frequently, and/or to spend less time interacting with the distributed control system.

A computer-implemented method for training at least one machine-learning model, comprising:

• providing records of training input data with state variables that characterize an operational state of at least one industrial plant;
• providing labels as to which interaction events at least one plant operator has initiated in the distributed control system in response to said operational states;
• mapping, by the machine learning model, the records of training input data to predictions of one or more interaction events that at least one plant operator will initiate in response to the operational states in the training input data;
• rating, utilizing a predetermined cost function, how well the prediction by the machine learning model corresponds to the label of the respective record of training input data;
• and
• optimizing parameters that characterize a behavior of the machine learning model towards the goal that when further records of training input data are processed by the machine learning model, this will result in a better rating by the cost function.