Data assessment and selection for industrial automation environments
Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a user interface component configured to display a programming environment for editing control logic, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline. A machine learning-based data science engine is configured to process the operational data from the industrial automation environment to generate processed data and identify a portion of the processed data relevant to a component of the control logic. The user interface component is further configured to surface the portion of the processed data in the programming environment.
1. A system comprising:
a memory that stores executable components; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to display, in a user interface, a programming environment comprising a block-based representation of control logic executable by a programmable logic controller (PLC) in an industrial automation environment, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline, and wherein the control logic is editable within the programming environment; and
a machine learning-based data science engine configured to:
process the operational data from the industrial automation environment to generate processed data for the industrial automation environment; and
identify a portion of the processed data related to the control logic; and
the user interface component further configured to display, in the user interface, the portion of the processed data in the programming environment.
2. The system of claim 1 , the executable components further comprising:
the user interface component further configured to display a data science environment for analyzing the operational data from the industrial automation environment, wherein control data from the programming environment is accessible from within the data science environment through the data pipeline;
a machine learning-based context engine configured to process the control data from the programming environment to generate contextual data; and
the user interface component further configured to display, in the user interface a portion of the contextual data in the data science environment.
3. The system of claim 2 , wherein the portion of the contextual data comprises a model of the control logic related to a selected portion of the operational data.
4. The system of claim 1 , wherein the machine learning-based data science engine comprises:
at least one machine learning model configured to use the operational data from the industrial automation environment as input and produce the processed data for the industrial automation environment as output; and
at least one other machine learning model configured to take the processed data as input and identify the portion of the processed data related to the control logic.
5. The system of claim 1 , the executable components further comprising a machine learning-based recommendation engine configured to, in the programming environment, generate a recommendation to add a new component to the control logic based at least in part on the operational data.
6. The system of claim 5 , wherein the new component comprises at least one of a variable, a tag, a sensor, a model, a device, an input, and an output.
7. The system of claim 1 , wherein displaying the portion of the processed data in the programming environment comprises surfacing at least one of a table and a graph in a region of the user interface proximate to an icon of the PLC.
8. A system comprising:
a memory that stores executable components; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a user interface component configured to:
display, in a user interface, a data science environment for analyzing operational data from an industrial automation environment, wherein control data from a programming environment associated with the industrial automation environment is accessible from within the data science environment through a data pipeline, and
receive, via the user interface, a user selection of a portion of the operational data;
a machine learning-based context engine configured to:
process the control data from the programming environment to generate contextual data, and
identify a portion of the contextual data related to the selected portion of the operational data; and
the user interface component further configured to display, in the user interface, the portion of the contextual data in the data science environment.
9. The system of claim 8 , the executable components further comprising:
the user interface component further configured to display, in the user interface, the programming environment comprising a block-based representation of control logic executable by a programmable logic controller (PLC) in the industrial automation environment, wherein the operational data from the industrial automation environment is accessible from within the programming environment through the data pipeline, and wherein the control logic is editable within the programming environment; and
a machine learning-based data science engine configured to:
process the operational data from the industrial automation environment to generate processed data for the industrial automation environment; and
identify a portion of the processed data related to the control logic; and
the user interface component further configured to display, in the user interface, the portion of the processed data in the programming environment.
10. The system of claim 9 , wherein the machine learning-based data science engine comprises:
at least one machine learning model configured to use the operational data from the industrial automation environment as input and produce the processed data for the industrial automation environment as output; and
at least one other machine learning model configured to take the processed data as input and identify the portion of the processed data relevant to the component of the control logic.
11. The system of claim 9 , wherein displaying the portion of the processed data in the programming environment comprises displaying at least one of a table and a graph in a region proximate to a portion of the control logic that is relevant to the contextual data.
12. The system of claim 9 , the executable components further comprising a machine learning-based recommendation engine configured to, in the programming environment, generate a recommendation to add a new component to the control logic based at least in part on the operational data.
13. The system of claim 12 , wherein the new component comprises at least one of a variable, a tag, a sensor, a model, a device, an input, and an output.
14. The system of claim 8 , wherein the portion of the contextual data comprises a model of the control data.
15. A method of proving contextual data in an industrial programming environment, the method comprising:
displaying, by a system comprising a processor and in a user interface, a programming environment comprising a block-based representation of control logic executable by a programmable logic controller (PLC) in an industrial automation environment, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline, and wherein the control logic is editable within the programming environment; and
processing, by a machine learning-based data science engine of the system, the operational data from the industrial automation environment to generate processed data for the industrial automation environment; and
identifying, by the machine learning-based data science engine of the system, a portion of the processed data related to the control logic; and
displaying, by the system and in the user interface, the portion of the processed data in the programming environment.
16. The method of claim 15 further comprising:
displaying, by the system and in the user interface, a data science environment for analyzing the operational data from the industrial automation environment, wherein control data from the programming environment is accessible from within the data science environment through the data pipeline;
processing, by a machine learning-based context engine of the system, the control data from the programming environment to generate contextual data; and
displaying, by the system and in the user interface, a portion of the contextual data in the data science environment.
17. The method of claim 16 wherein the portion of the contextual data comprises a model of the control logic relevant to a selected portion of the operational data.
18. The method of claim 15 wherein the machine learning-based data science engine comprises:
at least one machine learning model configured to use the operational data from the industrial automation environment as input and produce the processed data for the industrial automation environment as output; and
at least one other machine learning model configured to take the processed data as input and identify the portion of the processed data related to the control logic.
19. The method of claim 15 further comprising, by a learning-based recommendation engine of the system, in the programming environment, generating a recommendation to add a component to the control logic based at least in part on the operational data.
20. The method of claim 19 wherein the component comprises at least one of a variable, a tag, a sensor, a model, a device, an input, and an output.