One aspect of the invention is a system for mobile device authentication. The system includes a public-facing server configured to interface with a mobile device. The system also includes a secure server configured to interface with the public-facing server and an authorization station. The authorization station includes processing circuitry configured to establish authorization limits for the mobile device and generate an authentication key associated with the authorization limits. The processing circuitry is further configured to provide the authentication key and an identifier of the mobile device to the secure server, and generate an authorization code including an encoded version of the authentication key and an address of the public-facing server. The processing circuitry is also configured to provide the authorization code to the mobile device to establish authentication for the mobile device to receive data from a control system network as constrained by the authorization limits.

A method may include receiving, via a processing system, a request for information associated with an industrial automation system from a user, identifying a prompt associated with the request, and identifying one or more datasets associated with the request based on the prompt and the information. The method may also involve receiving the one or more datasets from one or more data sources, formatting the request and the one or more datasets into a package, and sending the package to a generative artificial intelligence (AI) system. The method may then involve receiving a response from the generative AI system, such that the response may be presented via a display of a human machine interface (HMI) system.

Implementations of the present invention provide a digital twin intelligent edge terminal and an operation method therefor pertaining to the field of digital twin terminals. The terminal comprises: a state perception module physically connected to a cloud digital twin system and single or multiple application objects and sensors via various data interfaces; a decision reception and execution module configured to perform the dispatch of decision information according to whether a control system for controlling application objects has an instruction interface and a transmission protocol; a human-computer interaction module; a digital twin local control module interacting with the cloud digital twin system or collaborating with the state perception module, the decision reception and execution module, and the human-computer interaction module in different working modes; and a functional module manager implementing information interaction between the modules.

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

A human-machine interface (HMI) development and runtime system leverages generative artificial intelligence (AI) to create HMI dashboards or graphical interfaces without the need for manual coding based on natural language prompts submitted by the user, which specify the information the user wishes to see. In one or more embodiments, the system can support industry-specific prompt engineering services that assist a user in generating a customized HMI dashboard that satisfies specified criteria using natural language prompts that describe functional and visual requirements of the dashboard. The system can make use of a generative AI model and associated neural networks to generate suitable dashboards in accordance with functional requirements provided to the system as intuitive natural language inputs (e.g., spoken or written natural language text).

A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.