A method of generating control code for an industrial plant comprises: defining control logic for controlling the industrial plant by editing a cause-and-effect matrix provided by an engineering tool, wherein the defining comprises defining both instrument-based control logic and service-based control logic using the same cause-and-effect matrix; and generating the control code for controlling the industrial plant on the basis of the defined control logic.

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.

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

A maintenance planning system for a production plant comprises: a production planning system for determining a production sequence for the production plant; an automation system for controlling production in the production plant; a state monitoring system for acquiring states of the production plant and its components; and a business planning system for the economic management of production and maintenance in the production plant. The maintenance planning system is designed for determining maintenance measures for the production plant. When determining the maintenance measures, the maintenance planning system takes into account the information of the production planning system, the automation system, the state monitoring system and the business planning system and performs optimization with regard to an economic utilization of the production plant. The disclosure further relates to a method for determining maintenance measures for a production plant and corresponding computer programs.

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.

Some implementations of the disclosure are directed to tapping input/output (I/O) data from an industrial control system (ICS) or applying the tapped I/O data to a learned model to perform predictive or prescriptive maintenance. In one implementation, a method comprises: tapping I/O data from a controller of an ICS while the controller executes first control code to control one or more devices of the ICS; transmitting the tapped I/O data over a network to a second system; and executing, via the second system, second control code comprising an original or modified version of all or a subset of the first control code of the ICS, wherein the second control code executes in response to receiving the tapped I/O data. The output of executing the second control code may be provided to a model to predict a future event involving the ICS or to prescribe maintenance of the ICS.

An access control apparatus and method for controlling a configuration of an automation apparatus. The method includes: reading authentication information from an electronic tag; transmitting the authentication information to a networked service; receiving access rights from the networked service; and controlling the configuration of the automation apparatus according to the access rights.

[Object] To appropriately produce a product generated by printing on a medium. [Solving Means] A production management system that manages production of a product generated by executing printing on a medium determines (S104, S114) a recommended condition of processing by an electronic device scheduled to be used for generation of a product, based on processing condition information indicating, for each step of generating the product, a relationship among quality of the product, a type and an installing place of the electronic device that performs at least one step of generating the product, and a recommended condition of processing by the electronic device, the target quality of the product, and the type and the installing place of the electronic device scheduled to be used for generating the product.

Systems, devices, and methods including selecting one or more sequences of machining types for a feature of one or more features, where the selection of the one or more sequences of machining types is based on the feature and a database of prior selections of machining types; selecting one or more tools for the selected one or more sequences of machining types, where the selection of the one or more tools is based on the feature, the selected one or more sequences of machining types, and a database of prior selections of one or more tools; and selecting one or more machining parameters for the selected one or more tools, where the selected machining parameters are based on the feature, the selected one or more sequences of machining types, the selected one or more tools, and a database of prior selections of one or more machining parameters.

A machine learning system and method for optimizing a production process. For instance, the method includes several steps as follows: selecting different values for a plurality of input parameters of a digital model of the production process for simulation; running the digital model using the different values for the plurality of input parameters and at least some of real-time data of the production process; determining a plurality of output parameters of the digital model; analyzing the plurality of output parameters; learning an optimized plurality of input parameters corresponding to the plurality of output parameters; and programming the production process to use the optimized plurality of input parameters to run the production process.