Insight driven programming tags in an industrial automation environment

A non-transitory computer-readable medium storing instructions to perform variable optimization in an industrial automation environment, wherein the instructions, in response to execution by a processor, cause the processor to drive a system to perform operations comprising:

• identifying program tags that represent process variables in an industrial process, ingesting feature vectors that represent the program tags, utilizing machine learning algorithms to process the feature vectors and determine correlations between the program tags, and generate a correlation matrix that indicates the correlations between the program tags based on the utilization of the machine learning algorithms;
• generating a control program configured for implementation by a programmable logic controller and selecting a program tag in the control program that represents a target variable in the industrial process, wherein target variable comprises one of the process variables;
• accessing the correlation matrix, selecting ones of the program tags that represent independent process variables that are correlated with the selected program tag that represents the target variable in the industrial process based on the correlations indicated by the correlation matrix, and generating a recommendation that indicates the ones of the program tags;
• and
• modifying the control program using the ones of the program tags to optimize the target variable based on the correlations between the independent process variables represented by the ones of the program tags and the target variable represented by the selected program tag and transferring the control program for implementation by the programmable logic controller.

A method to perform variable optimization in an industrial automation environment, the method comprising:

• identifying program tags that represent process variables in an industrial process, ingesting feature vectors that represent the program tags, utilizing machine learning algorithms to process the feature vectors and determine correlations between the program tags, and generate a correlation matrix that indicates the correlations between the program tags based on the utilization of the machine learning algorithms;
• generating a control program configured for implementation by a programmable logic controller and selecting a program tag in the control program that represents a target variable in the industrial process, wherein the target variable comprises one of the process variables;
• accessing the correlation matrix, selecting ones of the program tags that represent independent process variables that are correlated with the selected program tag that represents the target variable in the industrial process based on the correlations indicated by the correlation matrix, and generating a recommendation that indicates the ones of the program tags;
• and
• modifying the control program using the ones of the program tags to optimize the target variable based on the correlations between the independent process variables represented by the ones of the program tags and the target variable represented by the selected program tag and transferring the control program for implementation by the programmable logic controller.