An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

An industrial development hub (IDH) supports industrial development and testing capabilities that are offered as a cloud-based service. The IDH comprises an enhanced storage platform and associated design tools that serve as a repository on which customers can store control project code, device configurations, and other digital aspects of an industrial automation project. The IDH system can facilitate discovery and management of digital content associated with control systems, and can be used for system backup and restore, code conversion, and version management. The IDH also supports storage and instantiation of virtual machine images preconfigured with digital engineering applications or project conversion that can be instantiated and executed remotely as part of a digital engineering services framework.

A server that is a program providing device includes: a provision processing unit that provides a program part constituting a control program being a program to be executed in a controller; an authentication unit that authenticates an operation simulation module being a program for simulatively performing operation in accordance with the program part on a basis of a result of verification on whether or not the operation simulation module can simulate operation of the controller to be performed by execution of the program part; and an operation checking unit that checks operation of the program part by using the authenticated operation simulation module.

An industrial integrated development environment (IDE) includes a training component that improves the IDE's automated design tools over time based on analysis of aggregated project data submitted by developers over time. The industrial IDE can apply analytics (e.g., artificial intelligence, machine learning, etc.) to project data submitted by developers across multiple industrial enterprises to identify commonly used control code, visualizations, device configurations, or control system architectures that are frequently used for a given industrial function, machine, or application. This learned information can be encoded in a training module, which can be leveraged by the IDE to generate programming, visualization, or configuration recommendations. The IDE can automatically add suitable control code, visualizations, or configuration data to new control projects being developed based on an inference of the developer's design goals and knowledge of how these goals have been implemented by other developers.

Provided is a production system including: a first industrial machine configured to control a second industrial machine; and circuitry configured to acquire data relating to an operation of at least one of the first industrial machine or the second industrial machine, wherein the first industrial machine comprises a synchronous area regularly subjected to synchronization and an asynchronous area different from the synchronous area, and wherein the first industrial machine is configured to: write the data into the asynchronous area; and transmit the data written in the asynchronous area to an external device.

An industrial integrated development environment (IDE) supports collaborative tools that allow multiple designers and programmers to remotely submit design input to the same automation system project in parallel while maintaining project consistency. The industrial IDE also permits localized development of system projects, and provides an infrastructure for intelligently brokering between conflicting edits submitted to common portions of the system project. Project edits submitted to the IDE system, both applied and unapplied, are stored as edit records, allowing edits to be selectively undone or applied either manually or as part of a rollback to a milestone version.

In a method of automating engineering design a knowledge base (KB) is queried for a template to map with new control loop (CL) data of a new CL that was identified in new digitized design data for a new engineering project, the query including the new CL data. The KB is trained to map past CL data of past CLs identified in past digitized design data from past engineering projects to respective templates based on past instantiation of the respective templates with the past CLs by the past engineering projects. The method further includes receiving a selected template in response to the query, wherein the selected template is selected based on its mapping with past CL data that matches the new CL data, and providing configuration data, including an instantiation of the selected template with the new CL data, for implementation of the new CL in an engineering system.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A process control system includes an engineering system for a project configuration of hardware and software components of a process control system, an operator system having a runtime component for operator control and monitoring of a technical process, and an archive system for archiving project configuration inputs of the engineering system and for archiving operator inputs in the operator system, via which a project engineer and/or an operator may be provided with the relationships between engineering-relevant actions or events and runtime-relevant actions or events.

An operation control system includes: a storing processing part configured to store collected information on abnormality in countermeasure progress information storage parts in which information on an abnormality or countermeasure information on a countermeasure to the abnormality is stored; an equipment abnormality monitor part configured to, when a length of a time period during which the abnormality continues exceeds a prescribed time period, update a state of the abnormality; a countermeasure monitor part configured to monitor a state of the countermeasure, and, when the state of the countermeasure changes, update the state of the countermeasure; and an output processing part configured to change how to output alarm information in accordance with the length of the time period during which the abnormality continues and the state of the countermeasure.