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 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 machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

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 machine learning-based recommendation engine configured to, an industrial programming environment, generate a recommendation to add a component to control logic based on an existing portion of the control logic. A notification component is configured to surface the recommendation in the programming environment. A programming component is configured to, in the programming environment, add the component to the control logic. A configuration component is configured to configure the component based at least in part on the existing portion of the control logic.

An industrial integrated development environment (IDE) includes analytic features that generate control project telemetry data based on analysis of a control system design and uses this project telemetry data as the basis for control system design guidance and recommendations. The IDE system can analyze the control system design and identify industrial devices use by the control project, device features that are utilized by the control project, resource utilizations, and other such project telemetry. The IDE system can generate recommendations for improving the control system design based on this project telemetry. The project telemetry data can also be provided to product vendors, offering insights into how the vendors' products are being used by end customers.

An engineering support system that supports engineering of a process control system, includes: a storage; and a processor connected to the storage and that: transforms design drawings into semantic models and outputs the semantic models to the storage; and generates a combined semantic model by combining the semantic models based on a degree of similarity among the semantic models and outputs the combined semantic model to the storage, wherein each of the semantic models is expressed by first information indicating elements included in the design drawings and second information indicating a relationship between the elements.

Various embodiments include prediction model learning methods for industrial systems, including a simulation according to a simulation task on a platform. Some methods include: using statistical metrics to quantify a simulation task to extract features; extracting parameter groups from system modules, adjusting the values of the parameter groups, and triggering a simulation for the industrial system on the simulation platform based on a plurality of parameter groups having different values; recording performance metrics according to the values of the parameter groups after the parameter adjustment; and training data with a machine learning algorithm based on the features of the simulation task, the corresponding parameter groups having different values and the performance metrics, and generating a prediction model.

Systems, methods, and devices are disclosed herein for the integrated design and deployment of process control logic and power control logic in the context of industrial automation environments. In an implementation a computer displays, in an integrated design environment, representations of process control logic for controlling manufacturing devices an industrial automation process and of power control logic for controlling electrical devices in the industrial automation process. The computer further deploys the process control logic and the power control logic to a programmable logic controller (PLC) that controls the industrial automation process.

Multi-target libraries, projects, and activities for robotic process automation (RPA) are disclosed. Some embodiments multiple target platforms can be handled in the same project. The target platform(s) can be specified at the automation and/or activity level in order to provide the supported functionality for each. This may also allow previously built automations to be applied to new target frameworks without starting from scratch.

In a method for analyzing a mechatronic system which has one or more mechatronic components, structure data is provided. The structure data is representative of a predefined structure for a network. The structure has a plurality of layers and a respective layer is representative in each case of a technical domain of the mechatronic system. A model in the form of a multilayer network is generated depending on a multiplicity of input data relating to the mechatronic system and to the predefined structure. The multilayer network comprises a multiplicity of nodes and a plurality of connections in each case between two nodes. Each node of the plurality of nodes is assigned to one of the plurality of layers. The mechatronic system is analyzed depending on the multilayer network.

A machining control system according to an aspect of the present disclosure includes: a program storage unit which stores a machining program including a plurality of data blocks; a program cache unit which temporarily stores a part of the machining program; a schedule storage unit which stores schedule information specifying a transmission sequence to the program cache unit of the plurality of data blocks; a transmission control unit which controls transmission of the data blocks to the program cache unit from the program storage unit, based on the schedule information; and a machining processing unit which reads out the data blocks from the program cache unit and performs processing required in machining.

Systems, methods, and devices are disclosed herein for the integrated design and deployment of process control logic and power control logic in the context of industrial automation environments. In an implementation, a programmable logic controller executes power control logic to control a power control module on the network of the electrical subsystem of an industrial automation process. The power control module relays electrical device information to the programmable logic controller which issues power control commands according to the power control logic. The power and process control logic executed by the programmable logic controller are designed in an integrated design environment which includes representations of process control logic for controlling manufacturing devices of the industrial automation process and of power control logic for controlling electrical devices in the industrial automation process.