210₁-210_N. Control program 204 can be substantially any type of code used to process input signals read into the controller 118 and to control output signals from the controller 118, including but not limited to ladder logic programming, sequential function charts, function block diagrams, or structured text. Data read into or generated by controller 118 can be stored in a data table 206 within the controller's memory.

Controller 118 can exchange data with the input and output devices of the controlled processes 210₁-210_Nover the plant network 116, or over another hardwired or networked connection. For example, controller 118 can be equipped with native hardwired input and output points that exchange digital and analog signals with the field devices to effect control of the devices. The native controller I/O can include digital I/O that transmits and receives discrete voltage signals to and from the field devices, or analog I/O that transmits and receives analog voltage or current signals to and from the devices. The controller 118 translates input signals from the controlled processes 210 into digital and analog data values, which are stored in the controller's data table 206. The control program 204 processes these input data values in accordance with a user-defined control algorithm and sets values of the controller's digital and analog output signals based on this processing. The values of the output signals, and any other values calculated by the control program 204, are stored in the data table 206.

HMI 114 leverages data stored in the controller's data table 206 to visualize information relating to the controlled processes 210₁-210_Nas graphical and alphanumeric information. To this end, the HMI 114 communicates with the controller 118 via the plant network 116 or via a direct connection, and reads data from and writes data to the data table 206 over this connection. The HMI 114 renders navigable interface display screens that present current operational or status information for the controlled processes 210₁-210_N. In some implementations, the display screens can render graphical representations of the machines that carry out the controlled processes 210₁-210_N, and can animate these graphical representations based on the current statuses of the corresponding machines, as determined based on the data values contained in the controller's data table 206. These animations can include, for example, setting a color of a graphical element based on a state of a corresponding machine component, altering the height of a fill graphic based on a corresponding fill level of a tank, setting a position or orientation of a graphical element based on a corresponding position or orientation of a machine component, displaying alphanumeric text conveying metered values (e.g., temperatures, pressures, flows, etc.), or other such animations.

Operators can also interact with the HMI's display screens to send commands to the controller 118 that alter operation of the controlled processes 210₁-210_N. These commands can include, for example, altering control setpoints, initiating start or stop commands, changing an operating mode of a machine or process, clearing alarm messages render by the HMI 114, or other such commands. To provide a means to issue these commands, the display screens can include interactive graphical controls-such as graphical pushbuttons, data entry fields, or other such controls-that are linked to corresponding data tags defined in the data table 206. Through interaction with these controls, the operator can write digital or analog values to these data tags, and these values are processed by the control program 204 in connection with controlling the industrial processes 210₁-210_N.

In general, an HMI 114 comprises a computer terminal with display capabilities that executes an HMI runtime application 202. The HMI runtime application 202 defines the display screens that are presented to the operator (including definitions of the graphical elements and controls included on each display screen and the arrangements of those elements), the navigation structure for navigating between the display screens, and the data links or bindings between the graphical elements and corresponding data tags in the controller's data table 206. HMI developers typically design these aspects of an HMI using an HMI development platform, which compiles the design into an HMI runtime application 202 that can be downloaded to, and executed on, the HMI terminal. These HMI development platforms typically support a graphical and menu-driven development workflow, in which the developer selects graphical display and control elements from a library of elements for inclusion on each display interface, and manipulates these selected elements—e.g., via drag-and-drop interactions—on a mock-up of the display interface to yield a desired layout. For elements whose appearance or behavior is a function of a value of a data tag defined in the controller's data table 206, the developer typically defines the binding to the appropriate data tag by invoking the element's properties window and specifying the data tag in an appropriate property field of the window. Similarly, for elements designed to write data to the controller 118—such as graphical pushbuttons and data entry fields—users typically set the data tags to which those elements write their data via interaction with the elements' properties windows. This graphical development approach can also be cumbersome and time-consuming.

As an alternative to this graphical HMI development approach, one or more embodiments described herein provide an HMI development system that supports the use of generative artificial intelligence (AI) to assist in development and use of industrial HMI applications. In one or more embodiments, the HMI development system can support industry-specific prompt engineering services that assist a user in developing industrial visualization projects using natural language prompts that describe functional and visual requirements of the HMI. To this end, the HMI development system can make use of a generative AI model and associated neural networks to generate portions of an HMI project—including display screen content and layouts, screen navigational structures, links between animated graphics and data sources such as controller data tags, alarm definitions, color settings, and other such aspects—in accordance with functional requirements provided to the HMI development system as intuitive natural language inputs (e.g., spoken or written natural language text). The HMI development system can include a specialized prompt engineering layer and associated custom models—trained using knowledge of various types of industrial control applications, knowledge of specific types of industrial assets, vertical-specific industrial standards and best practices, sample HMI layouts, and other such training data—that generate prompts or meta-prompts based on a user's natural language inputs for submission to generative AI models such as large language models (LLMs).

FIG. 3 is a block diagram of an example HMI development system 302 according to one or more embodiments of this disclosure. Aspects of the systems, apparatuses, or processes explained in this disclosure can constitute machine-executable components embodied within machine(s), e.g., embodied in one or more computer-readable mediums (or media) associated with one or more machines. Such components, when executed by one or more machines, e.g., computer(s), computing device(s), automation device(s), virtual machine(s), etc., can cause the machine(s) to perform the operations described.

HMI development system 302 can include a user interface component 304, an HMI generation component 306, an HMI deployment component 308, a generative AI component 310, a training component 312, one or more processors 318, and memory 320. In various embodiments, one or more of the user interface component 304, HMI generation component 306, HMI deployment component 308, generative AI component 310, training component 312, the one or more processors 318, and memory 320 can be electrically and/or communicatively coupled to one another to perform one or more of the functions of the HMI development system 302. In some embodiments, components 304, 306, 308, 310, and 312 can comprise software instructions stored on memory 320 and executed by processor(s) 318. HMI development system 302 may also interact with other hardware and/or software components not depicted in FIG. 3. For example, processor(s) 318 may interact with one or more external user interface devices, such as a keyboard, a mouse, a display monitor, a touchscreen, or other such interface devices.

User interface component 304 can be configured to receive user input and to render output to the user in any suitable format (e.g., visual, audio, tactile, etc.). In some embodiments, user interface component 304 can be configured to generate and serve interface displays to a client device 3 (e.g., a laptop computer, tablet computer, smart phone, etc.), and exchange data via these interface displays. Input data that can be received via various embodiments of user interface component 304 can include, but is not limited to, natural language chat input or prompts, interactions with an HMI development interface (e.g., selection input, drag-and-drop input, alphanumeric input, etc.), or other such input. Output data rendered by various embodiments of user interface component 304 can include natural language responses to chat input or prompts, HMI displays in both design mode and runtime mode, answers to user-submitted questions or requests, HMI development suggestions, or other such outputs.

HMI generation component 306 can be configured to create an HMI project based on design input, including natural language requests or functional descriptions, received via the user interface component 304. HMI deployment component 308 can be configured to commission the HMI application created by the HMI generation component 306 to an appropriate execution platform, such as an HMI terminal

Generative AI component 310 can be configured to assist the HMI generation component 306 in generating or analyzing portions of the HMI project—including generating, formatting, and configuring HMI display screens—using generative AI. To this end, the generative AI component 310 can implement prompt engineering functionality using associated custom models 322 trained with domain-specific industrial training data. The generative AI component 310 can generate and submit prompts or meta-prompts to one or more generative AI models and associated neural networks, where these prompts are generated based on natural language requests or queries submitted by the designer as well as domain-specific information contained in the custom models 322. Depending on the nature of the designer's request or query, the responses returned by the generative AI model in response to the prompts can be used by the HMI generation component 306 or the user interface component 304 to generate portions of the HMI project, to render answers to designer's questions about a portion of the HMI project or about the design platform itself. Training component 312 can be configured to train one or more custom models 322 with various types of relevant training data. These custom models 322 are used by the system 302 in connection with generating and customizing portions of an HMI application, as well as generating suitable prompts to a generative AI model as needed to assist with this HMI development.

The one or more processors 318 can perform one or more of the functions described herein with reference to the systems and/or methods disclosed. Memory 320 can be a computer-readable storage medium storing computer-executable instructions and/or information for performing the functions described herein with reference to the systems and/or methods disclosed.

FIG. 4 is a diagram illustrating example data flows associated with creation of an HMI project 402 using the HMI development system 302 according to one or more embodiments. Some embodiments of the HMI development system 302 can be implemented on a cloud platform and made accessible to multiple industrial customers having authorized access to use the HMI development system's services. Cloud-based implementations can also facilitate collaborative project development whereby multiple developers contribute design and programming input to a common HMI project 402. Alternatively, some embodiments of HMI development system 302 may execute at least partially on a local client device while accessing remote services and repositories as needed.

A client device 404 (e.g., a laptop computer, tablet computer, desktop computer, mobile device, wearable AR/VR appliance, etc.) owned by a user with suitable authentication credentials can access the HMI development system's project development tools and leverage these tools to create an HMI project 402—including display screen definitions, layouts of graphical elements on the display screens, screen navigation structures, animation definitions for graphical elements, links to data tags whose value drive the animations, or other such aspects of an HMI project 402—for visualizing status and operational information for an industrial automation system. Through interaction with development interfaces generated by the system's user interface component 304 and delivered to the client device 404, developers can submit design input 412 to the HMI development system 302 in various supported formats. Design input 412 can include explicit HMI design input such as menu-driven screen creation, graphic element selection and placement (e.g., via drag-and-drop interactions with the development interfaces), manual definition of animation properties and data links, and other such input 412.

Additionally, the HMI system's development services can include a chat-based interface and associated natural language processing services that leverage generative AI to assist the user in creating, editing, or customizing an HMI project 402 for an industrial application, as well as to search for answers to specific questions relating to the HMI project 402 or to the HMI development platform itself. To this end, the HMI development system 302 can include a generative AI component 310 that responds to natural language prompts submitted by the user as part of design input 412. These prompts can comprise, for example, natural language descriptions of desired visualization layouts, screen navigation definitions, graphical content to be included on a display screen and the screen locations at which the content is to reside, alarm requirements, data sources or tags that are to control the animation properties of selected graphical objects, questions regarding development tools supported by the HMI development system 302, request for HMI design recommendations, or other such prompts. Depending on the nature of the prompt, the generative AI component 310 can create or edit portions of the HMI project 402, generate development recommendations for consideration by the user, answers to questions about the HM project 402 or about development tools that are available as part of the HMI development system's development platform, or other such content designed to assist the user in developing an HMI project 402.

The generative AI component 310 can implement prompt engineering functionality using associated custom models 322 trained with domain-specific industrial training data, and can interface with a generative AI model 406 (e.g., an LLM or another type of model) and associated neural networks. FIG. 5 is a diagram illustrating training of the custom models 322 used by the generative AI component 310. In some embodiments, the generative AI model 406 can reside and execute externally from the HMI development system 302, and the generative AI component 310 can include suitable connectivity tools and protocols, application programming interfaces (APIs), or other such services that allow the generative AI component 310 to exchange prompts and responses with the generative AI model 406. Custom models 322 can be trained using sets of training data 502 representing a range of domain-specific industrial knowledge that can assist the generative AI component 310 in generating or modifying portions of an HMI project 402 in a manner having a high probability of satisfying a user's natural language design descriptions as well as satisfying any application-specific or vertical-specific requirements. Example training data 502 that can be used to train the custom models 322 includes, but is not limited to, information defining industrial standards (e.g., global or vertical-specific safety standards, food and drug standards, design standards such as the ISA-88 standard, etc.), technical specifics or design standards for various types of industrial control applications (e.g., batch control processes, die casting, valve control, agitator control, etc.), knowledge of specific industrial verticals (e.g., automotive, food and beverage, pharmaceuticals, oil and gas, textiles, mining, etc.), knowledge of industrial best practices, technical specifications for various types of industrial devices or assets (e.g., industrial controllers, motor drives such as variable frequency drives, sensors, etc.), control design rules, sample HMI display layouts for various types of control applications or use cases, customer-specific training data describing in-house HMI design preferences or standards (e.g., preferred screen layout formats, preferred types of graphics, preferred text fonts, etc.), or other such training data.

During the project development process, the generative AI component 310 can, as needed, formulate and submit prompts 504 to the generative AI model 406 designed to obtain responses 506 that assist with HMI project development tasks. These prompts 504 are generated based on the user's natural language inputs as well as the industry knowledge and reference data encoded in the trained custom models 322. The generative AI component 310 can reference custom models 322 as needed in connection with processing a user's natural language queries or requests (which may be submitted as design input 412) and prompting the generative AI model 406 for responses 506 that assist the user interface component 304 and HMI generation component 306 in processing these requests and queries.

Returning to FIG. 4, based on the user's design input 412, user interface component 304 can render design feedback 418 designed to assist the developer in connection with developing an HMI project 402. At least some of this design feedback 418 can comprise natural language chat prompts generated by the generative AI component 310 requesting specific items of information from the user (e.g., as part of an iterative dialogue with the user intended to ascertain the user's design requirements), HMI design recommendations (e.g., recommendations for HMI screen layouts or navigation controls), design views of the HMI project 402 being developed, responses to natural language queries submitted by the user about the HMI project 402 or the system's HMI development tools, or other such feedback.

Embodiments of the HMI development system 302 can use prompt engineering services to process natural language design input 412 submitted by the user via the user interface component 304 (e.g., via a spoken word interface or text-based chatbot). These prompt engineering services can leverage the industry knowledge encoded in the custom modules 322 (as learned from training data 502), together with responses 506 prompted from the generative AI model 406, to accurately ascertain the designer's design needs and generate portions of the HMI project 402 to address those needs, or to provide refined answers to design queries.

When a user submits natural language design input 412 describing an HMI design request to the HMI development system 302, the generative AI component 306 analyzes the input 412 based on domain-specific industrial knowledge and design rules encoded in the custom models 322 (that is, the knowledge and rules represented by training data 502). Based on this analysis, and depending on the nature of the design input 412, the generative AI component 306 creates or modifies a portion of the HMI project 402 in a manner that satisfies a design requirement described by the design input 412. In the case of design input 412 comprising a natural language query about the HMI project 402 being edited, or about the system 302 itself, the generative AI component 310 generates and returns, as design feedback 418, a response to the query. This response may be, for example, an answer to a question about HMI project 402 being viewed, guidance as to an appropriate development tool supported by the system 302 that can be used to solve an HMI design problem specified by the query, or other such responses.

In addition to referencing the information contained in the custom models 322, the generative AI component 310 can also, as needed, prompt the generative AI model 406 for responses 506 (see FIG. 5) that assist in formulating suitable HMI designs (or natural language responses) in response to the user's natural language design input 412. For example, in response to receipt of a natural language design request, the generative AI component 310 can determine whether a sufficiently accurate HMI design action (e.g., creation of an HMI project 402, creation or modification of a display screen defined by the project 402, placement of a suitably configured graphical object on the display screen, removal of a graphical object from a display screen, configuration of an animation data link for an animated graphical object, etc.) can be performed based on relevant information contained in the custom models 322 alone, or, alternatively, whether supplemental information from the generative AI model 406 is necessary in order to determine a suitable a HMI design action having a sufficiently high probability of satisfying the design request described by the natural language design input 412. If supplemental information from the generative AI model 406 is deemed necessary, the generative AI component 310 can formulate prompts 504 based on analysis of the design input 412 and relevant subsets of the industrial knowledge encoded in the custom models 322 (or corresponding subsets of the training data 502). These prompts 504 are designed to obtain responses 506 from the generative AI model 406 that can be used to generate or modify portions of the HMI project 402 in a manner that satisfies the user's natural language design request.

In the case of formulating responses to a user's question about an HMI project 402, or about design tools supported by the HMI development system 302, the generative AI component 310 can aggregate information from the custom models 322 determined to be relevant to the query (e.g., knowledge of common design standards for a type of industrial control application of interest, technical or specification data for industrial assets or devices, industrial design standard information, vertical-specific industrial standards, knowledge of the IDE system's development platform, etc.) with language-specific compositional or syntax information obtained as responses 506 from the generative AI model 406 to formulate a natural language answer to the user's query.

If necessary, the system 302 can also guide iterative natural language chat exchanges with the user if necessary in order to ascertain the user's HMI design request or to improve the probability that the generative AI component 310 will formulate an HMI design action that satisfies the user's design request. For example, the generative AI component 310 can parse an initial natural language design input 412 to determine the type of design requirement being described by the input 412, and refine and contextualize the initial input 412 in a manner expected to assist the custom models 322 or the generative AI model 406 to quickly and accurately arrive at the desired HMI design solution. If the generative AI component 310 determines that additional information from the user would yield a design solution having a higher probability of satisfying the user's initial request (that is, a probability that exceeds a defined threshold), the generative AI component 310 can formulate and render, as design feedback 418, one or more query responses that prompt the user for more refined information that will allow the generative AI component 310 to provide a more complete or accurate HMI design solution to the user's request (that is, a solution estimated to have an accuracy in excess of a defined threshold). Through iterations of such chat exchanges, the generative AI component 310 can collaborate with the user in exploring potential HMI content and configuration variations likely to satisfy the user's needs. The generative AI component 310 can guide these natural language dialogs with the user based in part on learned knowledge of the types of questions that need to be answered in order to generate an HMI project 402, or portions thereof, that align with user's needs.

Users can interact with the system 302 using such iterative natural language dialogs to facilitate incremental design and refinement of an HMI project 402. For example, using natural language design requests submitted as design input 412, the user can initiate creation of a new HMI project 402 and proceed to refine the project 402 by adjusting color schemes (e.g., “Change the background color of the Station #6 screen to grey.”), adding or remove machining stations or other graphical elements (e.g., “Add a redundant backup pump in parallel with Pump #6.”), relocating or resizing graphical objects (e.g., “Move the Leak Test station to the top of the screen,” “Make the tank smaller,” etc.), or performing other such refinements. As needed, the generative AI component 310 can guide this refinement process by rendering, as design feedback 418, natural language prompts requesting additional information from the user that may be helpful in converging on a suitable HMI design for the user's control system, or providing suggestions for improving or optimizing the HMI project 402 based on the current design state of the project 402 (e.g., recommendations for rearranging or resizing graphical objects in a manner that makes more efficient use of space, recommendations for modifying the display screen navigation structure for the project 402, etc.).

In addition to allowing the user to submit free-form natural language design input 412, some embodiments of the user interface component 304 can present pre-composed or pre-loaded prompts to the user for selection and submission to the generative AI component 310. These pre-composed prompts can be stored in a prompt repository 408 of the HMI development system 302 and represent questions or development tasks that are commonly submitted by users of the HMI development system 302. In an example scenario, the user interface component 304 may render 10 of the most common questions or requests submitted by users of the system 302 as a list of selectable natural language prompts, where selection of a prompt from the list causes the prompt to be submitted to the generative AI component 310 for processing. If appropriate, the user interface component 304 can allow the user to customize one or more parameters of a selected pre-loaded prompt to accord with a specific need (e.g., by indicating a specific display screen, graphical object, data tag, controller definition, program instruction, or industrial asset to which the selected pre-written prompt is to be directed).

In another example, the generative AI component 310 may determine a general design topic to which a user's initial design input 412 is directed (e.g., creation of a suitable layout of graphical objects on a display screen, design of a suitable screen navigation structure for the HMI project 402, etc.), and select a subset of pre-written prompts from the prompt repository 408 that are determined to be helpful for the design topic. The user interface component 304 can render these prompts on the system's development interface for selection by the user if desired. Selection of a pre-written prompt causes the selected prompt to the submitted to the generative AI component 310 for processing as design input 412.

The generative AI component 310 can use a range of approaches for processing natural language design input 412 submitted by the user, and for formulating prompts 504 to the generative AI model 406 designed to yield responses 406 that assist with the user's design request. According to an example approach, the generative AI component 310 can access an archive of chat exchanges between the generative AI component 310 and other users of the system 302, and identify chat sessions that were initiated by user queries having similarities to the initial design input 412 submitted by the present user. Upon identifying these archived chat sessions, the generative AI component 310 can analyze these past chat sessions to determine types of design actions that were ultimately performed by the system 302 on an HMI project 402 as a result of these sessions (e.g., creation or configuration of a display screen having content that is a function of specific keywords of the user's query, addition of a certain type of graphical object to a display screen, configuration of a data link that is to control an animation property of a graphical object, adding a controller definition to the HMI project 402 defining a controller with which the project 402 is to exchange data, etc.), and update the HMI project 402 based on the results of these past chat sessions and adapted to the user's initial request, or, if necessary.

Analysis of these archived chat sessions, as well as any other relevant industrial knowledge or expertise encoded in the custom models 322, can also assist the generative AI component 310 in inferring the user's needs from an initially vaguely worded natural language design input 412, and to modify the design of the HMI project 402 in a manner that addresses these needs. If the generative AI component 310 determines that supplemental information from the generative AI model 406 is necessary to determine a design modification to the HMI project 402 having a sufficiently high probability of satisfying the user's request, the generative AI component 310 can also formulate a prompt 504 designed to prompt the generative AI model 406 for at least a portion of the information inferred to be of interest to the user. This may include, for example, formulating the prompt 504 to request, from the generative AI model 406, a specific type of information that may not have been specified in the user's design input 412 but which the generative AI component 310 ascertained to be the information that would address the user's needs. In this way, the generative AI component 310 and its associated custom models 322 can actively frame a user's natural language design input 412 in a manner that quickly and accurately leads the generative AI model 406 to the user's desired HMI design solution (e.g., generation of an HMI project 402 satisfying design requirements implied by the natural language design input 412).

In another example approach, the generative AI component 310 can enhance the user's natural language design input 412 with additional information from the custom models 322 that contextualizes the user's request, and integrate this additional information with the user's request to yield the prompt 504 submitted to the generative AI model 406. The types of additional contextual information added to the design input 412 can depend on the nature of the design request and can include, but are not limited to, information obtained from vendor knowledgebases or device documentation for industrial devices known to be relevant to the user's design request.

Various example HMI design actions—e.g., HMI project creation and editing actions—that can be performed by the system 302 based on processing of a user's natural language design input 412 are now described. In general, the system 302 can perform substantially any type of HMI project creation, development, and editing action based on, and in accordance with, natural language requests or design descriptions submitted as design input 412. For example, in response to receipt of a natural language request to create a new HMI project 402 (e.g., “Create a new project”), the HMI generation component 306 (with the assistance of the generative AI component 310 as needed) can initiate a new HMI project 402 within the system's development environment. As noted above, this natural language design input 412 can be submitted to the system 302 as typed or spoken natural language text, which may be entered via a chatbot interface or another type of natural language dialog interface rendered by the user interface component 304.

As part of this initial request to create an HMI project 402 or via subsequent separate natural language design requests, the user can submit natural design requirements or configuration requests directed to the newly created HMI project 402. Example HMI design actions that can be performed by the system 302 based on such natural language design inputs 412 can include, but are not limited to, creating an HMI project 402, defining display screens that make up the HMI project 402, defining navigation schemas or controls for navigating between the display screens (including adding graphical navigation controls to selected display screens and defining which other display screens these navigation controls will invoke), selecting and adding both static and animated graphical objects to the display screens, defining animation controls for animated graphical elements (including specifying a data tag of an industrial controller or another data source that is to control an animated state of a graphical object), specifying layouts or locations of the graphical objects on the display screens, defining color schemes for display screens, defining conditions that are to trigger alarms (e.g., a high or low limit of a specified data tag that is to trigger a high level or low level alarm), defining the text of respective alarms, defining multiple versions of a given HMI project 402 to be used under different environmental conditions (e.g., bright light conditions, outdoor use, high-particulate environments, etc.), defining scripts to be triggered in response to specified conditions detected by the HMI during runtime, naming display screens or graphical objects, adding a controller definition to the HMI project 402 that defines an industrial controller with which the HMI will be exchanging data values, or defining other such properties of the HMI project 402.

For example, a user may submit, as an example natural language design input 412, “Create a new project, with left nav, top right help and login, alarm summary centered on the top and company logo on the left. Create three sub-areas, alarms history, top ten alarms, across all units.” The generative AI component 310 can analyze this input 412, leveraging industry or application knowledge encoded in the custom models 322 as well as responses 506 prompted from the generative AI model 406 as needed, to determine or infer the user's HMI design requirements and instruct the HMI generation component 306 to create or edit the HMI project 402 in a manner that satisfies these requirements. In this example, the HMI generation component 306 will define an HMI project 402 in which a display screen is defined, a navigation button control is added to the left side of the screen, help and login button controls are added to the top right corner of the screen, an alarm summary is centered near the top of the screen, and a company logo is added to the left side of the screen.

A user's natural language design input 412 can include explicit references to portions or elements of the HMI project 402 as needed in order to describe an HMI configuration action to be performed. These references can include display screen names (e.g., “Add a button for navigating to the Overview screen to the bottom left of the Line 1 screen.”), names or identities of graphical elements or the properties thereof (e.g., “Set the OPEN state color of the #1 Valve graphic to green and link it to the ValveOpen tag of Controller1.”), or other such explicit references.

In some scenarios, rather the submitting natural language design input 412 describing HMI configuration actions with explicit reference to elements or configuration functions of the HMI project 402 itself, the user may submit natural language design input 412 describing in more general terms—that is, without reference to specific elements of an HMI project 402—visualization requirements of an industrial automation system for which an HMI project 402 is required. According to this approach, the user may describe, via natural language design input 412, industrial assets, devices, or machines that are part of an automation system and that are to be visualized on the HMI (e.g., tanks, valves, stamping presses, conveyors, machining stations, etc.), functional relationships between the assets (e.g., “I need a tank called Tank #1 that feeds material to another tank called Tank #2 via an inlet valve called Valve #1”), and information about the assets that the user wishes to visualize (“Show me the states of the valves and the fill levels of the tanks.”). The user may also provide other specifics about the assets or application, such as the capacities of the tanks, maximum flow rates between the tanks, or other relevant information about the assets. The HMI generation component 306, assisted by the generative AI component 310, can process these functional descriptions to determine the user's visualization requirements and develop the HMI project 402 to fulfil these requirements. Development actions performed by the system 302 in response to this natural language design input 412 can include adding suitable graphical objects to the HMI's display screens, arranging these graphical objects in a manner that reflects the functional relationships between the assets represented by the graphical objects, determining and configuring data links between animation properties of these objects and corresponding controller data tags, or other such HMI development functions.

In other example scenarios, the user's natural language design input 412 may describe the type of industrial automation system or control application to be visualized, together with application-specific details of the user's system that can be used by the system 302 to create and customize a suitable HMI project 402 for the automation system. For example, the user may specify, via natural language design input 412, that an HMI is required for a batch process for producing a specified type of material (e.g., a plastic, a chemical, a medication, etc.). This design input 412 can describe any functional details that can be used by the generative AI component 310 and HMI generation component 306 to determine suitable graphical content, layouts, and animations for visualizing the control application.

The generative AI component 310 can formulate a suitable HMI configuration action in response to any of the various types of natural language design input 412 described above based in part on industry-specific knowledge encoded in the custom models 322 (or the underlying training data 502). The industry knowledge encoded in the custom models 322 can assist the generative AI component 310 and HMI generation component 306 in determining a preferred or standards-compliant configuration for the HMI project 402 that also satisfies the user's design requirements as specified in the natural language design input 412. This encoded knowledge can reduce the burden on the user to provide highly granular details of the control application or automation system for which the HMI is being designed, since the system 302 can leverage domain-specific knowledge recorded in the custom models 322 to infer the user's needs and to formulate suitable HMI designs that satisfy these inferred needs.

For example, some of the training data 502 used to train the custom models 322 can include knowledge of standard or common system designs for respective different types of industrial control applications (e.g., batch processing applications, die casting applications, material handling applications, machining applications, etc.). This knowledge can comprise the types, numbers, and configurations of respective types of industrial assets typically used in such applications (e.g., machines, monitoring and control devices, conveyors, machining stations, vats or tanks, piping, ovens, presses, etc.), functional relationships between these assets (e.g., the arrangements of the assets, the direction and order of part or material flow through the assets, etc.), or other such domain-specific knowledge. In response to receipt of a user's natural language design input 412 describing a functional requirement for visualizing an automation system designed to implement a specific type of industrial control application, the generative AI component 310 can access relevant domain-specific information about the specified type of control application from the custom models 322 and use this information in connection with formulating aspects of an HMI design having a high probability of satisfying the requirements described by the user's natural language design input 412. This can include, for example, inferring a collection of industrial assets having a high likelihood of being part of the automation system to be visualized (e.g., based on the type of control application or industrial vertical described by the user's design input 412) and generating an HMI project 402 having a display screen with a preliminary layout of graphical objects representing these assets. In such scenarios, the generative AI component 310 and HMI generation component 306 can infer a suitable set of graphical asset representations, and arrangements of these graphical representations, even if the user's natural language design input 412 did not specify the identities or types of assets that will make up the automation system.

In some embodiments, the custom models 322 can be trained with information about specific custom-built machines, such as a machine or production line built for the customer by an OEM, or the custom models 322 may include pre-trained models provided by the OEM. This machine-specific training data 502 can include, for example, names and functions of work stations that make up the machine or line (e.g., tooling stations, quality check stations, ovens, etc.), the identities and arrangements of components that make up the machine (e.g., valves, vats, pumps, industrial robots, pushers, stoppers, etc.) specification data for the machine, or other such information. The generative AI component 310 can leverage the machine-specific information used to train these models 322 to suitably configure an HMI project 402 for visualizing this custom machine in accordance with a user's design input 412. Custom models 322 trained with machine-specific training data 502 for machines known to be deployed at the customer facility can assist the HMI development system 302 in generating an HMI project 402 that accurately reflects the customer's equipment with relatively little design input 412 required from the user.

By supporting HMI development using natural language design input 412 describing functional requirements of the HMI project 402 at varying degrees of granularity or specificity, the HMI development system 302 can allow users of various roles within the industrial enterprise to contribute to the design of HMI projects 402, including plant engineers or controls engineers who design the corresponding automation system, maintenance staff who will be maintaining and troubleshooting the automation system, and machine operators who will be operating the automation system. These various user roles may require different information presentations from the HMI project 402, and members of these roles can ensure that the HMI project 402 caters to these role-specific needs by providing natural language design input 412 describing these role-specific requirements at various levels of specificity. For example, control engineers may submit natural language design input 412 describing visual requirements in terms of the specific equipment or device names, explicitly identified controller data tags that are to drive the animation properties of graphical objects, or other technical language and references that may not be familiar to machine operators. Additionally, machine operators may be invited to contribute natural language design input 412 describing their HMI requirements in terms information that they wish to see and how they would like to see this information organized (e.g., “I'd like to see the tank fill level and outlet flow rate on the same screen.”). In all these scenarios, the system 302 can process these natural language inputs, leveraging the domain-specific knowledge encoded in the custom models 322 and responses from the generative AI model 406 as needed, to implement development actions on the HMI project 402 that are expected to satisfy the requirements of these various users.

The system 302 can support multi-user collaborative development of the HMI project 402, receiving natural language design input 412 from users having these various roles. In some such collaborative development scenarios in which users of various roles contribute to the development of an HMI project 402, some embodiments of the system 302 can process design input 412 directed to the HMI project 402 as a function of the role of the user from whom the design input 412 was received. For example, some embodiments of the HMI development system 302 may create multiple versions of an HMI project 402 being developed for a given automation system, where each version of the project 402 is created using design input 412 received from users of a single common role (e.g., machine or line operator, plant engineer, maintenance personnel, etc.). This yields multiple versions of the HMI project 402 that are specific to respective different user roles, and which can be invoked during runtime by users having those roles. In this way, the system 302 allows users of different roles to create versions of the HMI project 402 catered to their role-specific requirements.

Alternatively, the HMI generation component 306 can integrate design input 412 from multiple users of different roles into a single HMI project 402 that satisfies all requirements specified by the multi-user design input 412. In some such embodiments, the system 302 may prioritize design requests of one role over another role if requests from these roles conflict with each other. For example, if the system 302 determines that design input 412 received from a plant engineer is in conflict with design input 412 submitted by a machine operator (e.g., in terms of placement or orientation of graphical objects, color schemes, content to be included or omitted from the HMI, etc.), the system 302 may opt to implement the plant engineer's design input 412 in the HMI project 402 while overriding the design input 412 received from the machine operator.

In a related aspect, the generative AI component 310 can infer the user's level of expertise based on the wording of the natural language design input 412, and formulate design feedback 418 (e.g., follow-up questions regarding the user's design request, answers to questions about the HMI design or the development tools offered by the system 302) based on this inferred level of expertise. The user's level of expertise can be inferred, for example, based on a determination of whether the words, phrases, or terms used to in the design input 412 are likely to be used by one of relatively advanced expertise who can be expected to understand more technically advanced design feedback 418 or, alternatively, a layman who may require more fundamental information included in the feedback 418.

For users at these various levels of expertise, the generative AI component 310 can word any design feedback 418 or responses to the design input 412—including responses prompting the user for additional information to assist the system 302 in determining suitable HMI development actions having a likelihood of satisfying the user's request—at a level deemed appropriate to the user's inferred level of presumed understanding. This can affect the choice of words used in the feedback 418, as well as the granularity of the response's content.

Some embodiments of the HMI development system 302 can also be configured to process design input 412 comprising various types of design documentation that was generated as part of the design process for the automation system to be visualized. For example, the user may submit, as design input 412, digital design drawings (e.g., computer-aided design (CAD) drawings) of the automation system or machine for which an HMI project 402 is required. Example CAD drawings that can be submitted as design input 412 can include electrical drawings, I/O drawings, mechanical drawings, panel layout drawings, or other such documentation. The generative AI component 310, assisted by the custom models 322 or responses prompted from the generative AI model 406, can determine suitable content and formatting for a corresponding HMI project 402 based on analysis of these drawings, and generate an HMI project 402 having this content and formatting.

In an example scenario, the generative AI component 310 can identify types of machines or devices included in the automation system, as well as communicative or functional relationships between these machines and devices, based on analysis of I/O drawings, mechanical drawings, or electrical drawings for the automation system. The generative AI component 310 can also infer a type of industrial process being carried out by the automation system based on analysis of these drawings. Also, based on knowledge of the types of information that is typically of interest to operators for the inferred type of industrial application (as encoded in the custom models 322, or based on analysis of other archived HMI applications for similar types of industrial applications), the generative AI component 310 can instruct the HMI generation component 306 to generate an HMI project 402 containing graphical objects representing a subset of the machines or devices that are known to typically be of interest (e.g., tanks, valves, motors, conveyors, etc.), and organized to represent the functional relationships between the machines or devices. HMI generation component 306 can also define data links between the animation properties of these graphical objects and their corresponding data tags or addresses in the industrial controller that will be monitoring and controlling the automation system (as determined, for example, from the I/O drawings).

Once development on an HMI project 402 has been completed and the HMI generation component 306, the HMI project 402 can be deployed to an HMI terminal 114 or other computing platform with display capabilities for execution. FIG. 6 is a diagram illustrating commissioning of an HMI project 402 to an HMI terminal 114. The system's HMI deployment component 308 can communicatively interface with an HMI terminal 114 via either a direct connection between the HMI development system 302 and the terminal 114 or, in the case of embodiments of the system 302 that execute on a cloud platform or other remote platform, via a remote connection to the terminal 114. When executed on the HMI terminal 114, the complied HMI application 602 renders an HMI having the display screens and associated graphical elements and behaviors defined by design input 412 and at least partially generated and configured based on processing of the developer's natural language prompts. In cases in which the HMI project 402 also defines communication parameters for the HMI terminal 114 itself, execution of the HMI application 602 will set the terminal's networking or communication settings in accordance with the communication settings defined in the HMI project 402. This may include, for example, setting the terminal's networking parameters to operate on the plant network 116 as well as setting the HMI's communication settings so that the terminal 114 will communicate with the industrial devices (such as controller 118) with which the HMI terminal 114 will exchange data.

After deployment of an HMI project 402 as a runtime application 602, some embodiments of the HMI development system 302 can also support dynamic runtime modifications to the resulting HMI application 602 using natural language prompts. FIG. 7 is a diagram illustrating use of the HMI development system 302 to implement dynamic edits 704 to the HMI application 602 during runtime using natural language prompts 706. At any time during runtime operation of the HMI application 602, an operator or other user can submit a natural language prompt 706 to the application 602 requesting a modification to a visual aspect of the HMI application 602. These natural language prompts 706 can be submitted via a text-based or verbal chat interface integrated into the HMI application 602, and are then routed to the HMI development system 302 for processing. The generative AI component 310 can process these natural language prompts 706 to determine the nature of the modification being requested by the user, and return HMI edits 704 that implement the requested modification on the HMI application 602.

In an example scenario, the user may indicate, via a natural language prompt 706, that the display screens rendered by the HMI application 602 are difficult to see due to environmental conditions of the area in which the HMI is being used, such as bright ambient light or relative darkness. The prompt 706 may specify the nature of the visual difficulty—e.g., “I can't see the screens in this bright light,” “This control room is dark, can you brighten up the display?”—and based on assessment of this prompt 706 the generative AI component 310 can make an adjustment to the application 602 or to the HMI terminal 114 itself to correct the issue (e.g., by setting a brightness or contrast level to counteract bright ambient conditions). Another example prompt 706 may request a countermeasure for a visual impairment (e.g., “I can't see blues in this environment, can we correct that?” “I can't see yellow very well, can you pick an alternative color?”), and in response to such prompts 706 the generative AI component 310 can determine and implement a suitable countermeasure for compensating for the indicated impairment (e.g., adjusting the brightness, contrast, or color choices on the HMI's displays).

Natural language prompts 706 can also be used to alter the content of the displays rendered by the HMI application. For example, the user may submit a natural language prompt 706 requesting that a graphical element, such as an animated graphical object or a numerical value, be added or removed from the currently rendered display. In response to receipt and processing of this prompt, the generative AI component 310 will return an HMI edit 704 that implements the indicated modification on the HMI application 602.

Users can also use natural language prompts 706 to dynamically create new customized display screens as needed. For example, a given machine operator may wish to aggregate graphical objects that are currently distributed across multiple different screens onto a single new display screen. To achieve this, the operator can submit a natural language prompt 706 describing the content that the operator wishes to see (e.g., “Get me the valve from Display 2, the pump from Display 3, and build me a new display.”), and based on translation of this prompt 706 the generative AI component will return an HMI edit 704 that creates and adds a screen having the described content to the HMI application 602.

The system 302 can also process natural language prompts 706 that, rather than explicitly identifying the content that the operator wishes to see in terms of specific graphical objects, describes the functionality that the operator wishes to see in more abstract terms. For example, a natural language prompt 706 may indicate that the operator wishes to see a display that assists in calibrating a valve's open and closed conditions (e.g., “Give me a screen for calibrating Valve #2”). Based on analysis of this prompt 706, industry knowledge encoded in the custom models 322 (which may include knowledge of the types of information known to be helpful in performing such calibrations), and analysis of the HMI application 602 itself, the generative AI component 310 can determine suitable content and associated data links (that is, links to appropriate controller data tags or other sources of required data) expected to be useful for performing the valve calibration, and return an edit 704 that creates a new screen that includes this content. In some cases, content for the new screen may be drawn from other screens already defined in the HMI application 602.

As in the design time scenario, the system 302 can also leverage pre-loaded or pre-written prompts during runtime of the HMI application 602. For example, the generative AI component 310 can retrieve selected pre-composed prompts from the prompt repository 408 when appropriate and present these pre-composed prompts to the user via the HMI application 602 for selection and processing by the generative AI component 310. The generative AI component 310 can select a suitable subset of pre-composed prompts for presentation to the user based on a current condition of the HMI application 602 or its associated automation system, or based on a pattern of the user's interactions with the HMI application 602. For example, the generative AI component 310 may determine, based on the user's interactions with the HMI application 602, that the user is attempting to perform a particular operational task. Based on this determination, the generative AI component 310 may select and render a subset of pre-composed prompts from the prompt repository 408 determined to be relevant to the operational task. Selection of one of these pre-written prompts by the user can cause the selected prompt to be processed by the generative AI component 310.

In some embodiments, after the system 302 has implemented a dynamic runtime modification to the HMI application 602 in accordance with a user's natural language prompt 706, the user may submit a request to save the resulting modified version of the HMI application 602 as a template 702 to be stored with the HMI project 402 from which the runtime HMI application 602 was compiled. Each template 702 represents a version of the base HMI project 402 that has been modified in accordance with a given set of natural language prompts 706 as described above, and can be given a name or other type of identifier to distinguish the template 702 from other template 702 associated with the project 402. Users can invoke a selected template via interaction with the HMI application 602; e.g., by requesting a selected template via a natural language prompt 706. When a selected template 702 is invoked, the user interface component 304 can modify the HMI application 602 currently executing on the HMI terminal 114 in accordance with the selected template 702. In another example scenario, a template 702 may record dynamic modifications that were made to the HMI application 602 to render the HMI more suitable for a given environment, context, or user. These may include modifications to adapt the HMI to a bright light environment, a dark environment, a user with visual impairments, or other such contextual considerations. The system 302 can invoke one of these templates 702 either in response to an explicit request to adapt the HMI application 602 in accordance with the selected template 702 or automatically in response to a determination by the system 302 that the contextual condition for which the template 702 was created is currently active.

As another runtime feature, the HMI development system 302 can also process natural language prompts 706 comprising questions about the HMI application 602 itself or about current operation of the automation system being visualized by the application 602. For example, the user may submit a natural language prompt 706 requesting assistance in diagnosing a performance issue with the automation system being visualized, or an active alarm that has been generated by the HMI application 602. Such prompts 706 may, for example, request a suggested countermeasure for addressing a current alarm condition being reported by the HMI or a performance issue observed by the operator and described by the prompt 706. In response to such prompts 706, the generative AI component 310 can formulate a natural language response 708 to the prompt 706 based on the nature of the question described by the prompt 706, as well as one or more of knowledge of the specific automation system and its components as encoded in the custom models 322 (e.g., if one of the custom models 322 is a machine-specific model, such as a model provided by an OEM), more general knowledge encoded in the custom models 322 about the type of industrial application being carried out by the automation system, or responses 506 prompted from the generative AI model 406. The system 302 can render the resulting natural language responses 708 on the HMI via the chat interface used to submit the prompt 706. Depending on the nature of the user's query, these responses 708 may describe recommended workflows for addressing the alarm condition or performance issue, answer questions regarding the nature of the alarm or issue, or other such information. The ability to process chat-based queries regarding alarm conditions or performance concerns can streamline the diagnostic process and assist machine operators or other plant personnel in quickly resolving performance problems.

In some embodiments, system 302 can further refine this diagnostic process by monitoring the workflows or HMI interaction patterns of machine operators or engineers in response to specific alarm conditions or performance issues and, based on these observed interaction patterns, training one or more of the custom models 322 with knowledge of learned workflows that are known to successfully resolve these performance issues. The generative AI component 310 can then use these trained models 322 to generate, as natural language responses 708, recommendations for addressing similar types of alarms or performance issues. For example, the generative AI component 310 may determine that, when a given alarm condition is triggered on the HMI, the corresponding alarm is resolved most quickly or effectively when operators perform a particular set of interactions with the HMI and the automation system (e.g., navigating to a particular display screen, changing a state of a binary tag or a value of a setpoint via an HMI interaction, performing a sequence of control panel or HMI interactions, clearing parts from a work station of the automation system, etc.). Accordingly, the training component 312 can train one or more of the custom models 322 with this learned optimal interaction pattern. If the alarm is triggered in subsequent operating schedules, the generative AI component 310 can leverage these updated trained models 322 to generate and render a natural language response 708 describing the actions to be taken by the operator to address the alarm condition. This guidance can be rendered either in response to detection of the alarm condition or in response to a request from the operator (submitted as a natural language prompt 706) for assistance in addressing the alarm.

Since the system 302 can track and record the interaction patterns of different operators during runtime, the system 302 can present these past user interaction patterns in response a user's natural language prompt 706. For example, if the automation system experiences an alarm condition or performance issue that the current machine operator is not familiar with, the operator may submit a natural language prompt 706 asking for assistance in addressing the issue, either by expressly requesting previous interaction patterns that were performed by other operators to successfully address the issue or by requesting assistance with the issue more generally. In response to such prompts 706, the generative AI component 310 can retrieve information regarding one or more past interaction sequences known to have successfully mitigated the alarm condition or performance issue and render this information on the HMI. An interaction sequence can comprise an ordered sequence of actions performed by previous operators to address the issue, where these actions may include interactions with the HMI (e.g., navigations to specific displays screens, interactions with specific HMI controls such as graphical push buttons or data entry fields, etc.), interactions with the automation system's control panel (e.g., pressing a pushbutton, setting a position of a selector switch, placing the automation system in a particular operating mode, manually moving the automation system's machine components to their home position, etc.), or another interaction with the automation system that is detectable by the system 302 (e.g., clearing parts or debris from a location on the automation system's conveyor that is monitored by a presence sensor). In response to a request for these past interactions, the system's user interface component 304 can render information about these past interactions on the HMI in any suitable format, including but not limited to a natural language description of these interactions or a graphical demonstration that visualizes the interactions.

In another example, rather than requesting a recommended set of interactions for addressing a specific alarm or performance issue, a user may submit a natural language prompt 706 requesting information on all interactions that were performed on the HMI and its associated automation system a given range of time (e.g., interactions for the previous work shift, interactions for the past six hours, etc.), and the system 302 can formulate and render a description of these interactions via the HMI.

FIGS. 8a-9 illustrate various methodologies in accordance with one or more embodiments of the subject application. While, for purposes of simplicity of explanation, the one or more methodologies shown herein are shown and described as a series of acts, it is to be understood and appreciated that the subject innovation is not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all illustrated acts may be required to implement a methodology in accordance with the innovation. Furthermore, interaction diagram(s) may represent methodologies, or methods, in accordance with the subject disclosure when disparate entities enact disparate portions of the methodologies. Further yet, two or more of the disclosed example methods can be implemented in combination with each other, to accomplish one or more features or advantages described herein.

FIG. 8a illustrates a first part of an example methodology 800a for developing an industrial HMI project using natural language design input. Initially, at 802, a natural language request describing a functional requirement for an HMI project being developed using an HMI development system is received via a chat interface or another type of natural language interface supported by the HMI development system. The chat interface can be invoked via an interaction with the HMI development system's development interface. The natural language request can describe substantially any type of function requirement that the user wishes to be supported by the HMI application. For example, the user may submit written or spoken natural language descriptions of desired visualization layouts, screen navigation definitions, graphical content to be included on a display screen and the screen locations at which the content is to reside, alarm requirements, data sources or tags that are to control the animation properties of selected graphical objects, or other such natural language input. The natural language request may also describe the functional requirements in more general terms; e.g., by describing the types of information generated by the automation system that the user wishes to see rendered on the HMI. In some scenarios, the natural language request may reference an existing element or property of the HMI project being developed, such as a graphical object or data link definition that has already been added to the project (e.g., “Link the fill animation for Tank 2 to the Material 1 Tank Level tag”; “Move Valve 1 to the right side of the screen”, etc.).

At 804, the request received at step 802 is analyzed by the HMI development system using trained custom models or a generative AI to determine if sufficient information can be inferred from the request to determine an HMI development action that will satisfy the functional requirement. The custom models can be trained using sets of training data representing a range of domain-specific industrial knowledge. Example training data that can be used to train the custom models includes, but is not limited to, information defining industrial standards (e.g., global or vertical-specific safety standards, food and drug standards, design standards such as the ISA-88 standard, etc.), technical specifics or design standards for various types of industrial control applications (e.g., batch control processes, die casting, valve control, agitator control, etc.), knowledge of specific industrial verticals (e.g., automotive, food and beverage, pharmaceuticals, oil and gas, textiles, mining, etc.), knowledge of industrial best practices, technical specifications for various types of industrial devices or assets (e.g., industrial controllers, motor drives such as variable frequency drives, sensors, etc.), control design rules, sample HMI display layouts for various types of control applications or use cases, customer-specific training data describing in-house HMI design preferences or standards (e.g., preferred screen layout formats, preferred types of graphics, preferred text fonts, etc.), or other such training data. As part of the analysis, the system can also generate and submit prompts to the generative AI model, and use the content of the generative AI model's responses in connection with analyzing the user's request and generating natural languages responses directed to the user if necessary.

At 806, a determination is made as to whether more information is needed from the user in order to determine the user's functional requirements and apply a suitable HMI development action determined to satisfy these requirements. If additional information is required (YES at step 806), the methodology proceeds to step 808, where the HMI development system determines the additional information required, and renders a natural language prompt designed to guide the user toward providing the additional information. In determining the nature of the necessary additional information, the system can reference the industry knowledge encoded in the trained models as well as responses prompted from the generative AI model. At 810, a response to the prompt generated at step 808 is received via the chat interface.

Steps 806-810 are repeated as a natural language dialog with the user until sufficient information translatable to a set of functional requirements for the requested HMI development action has been obtained. When no further information is required from the user (NO at step 806), the methodology proceeds to the second part 800b illustrated in FIG. 8b. At 812, the HMI development system determines a development action to be applied to the HMI project based on at least one of analysis if the user's natural language request and responses as obtained at steps 802 and 810, content of the trained custom models, or responses prompted from the generative AI model. The development action may be, for example, addition of a graphical element representing an industrial asset or device to a display screen, removal of a graphical element from a screen, creation or modification of a data link between an animation property of a graphical object and a controller data tag that is to control the animation property, creation or deletion of a display screen, definition of a screen navigation schema or architecture, addition and configuration of a control object (e.g., a graphical push button or a data entry field), or other such development actions. At 814, the development action determined at step 812 is implemented on the HMI project by the HMI development system.

In general, the natural language requests received at step 802 can be submitted by multiple users having different roles within the industrial enterprise—including plant engineers, maintenance staff, and machine or line operators—in a collaborative development scenario. The prompts generated by the HMI development system at step 808 may be formulated based on an inferred level of expertise of the user, as determined based on an analysis of the terminology used in the user's original natural language request received at step 802, such that the prompts have a high likelihood of being understood by the recipient. In some embodiments, the HMI development system can process natural language development requests based on the role of the user submitting the request. For example, the system may develop multiple different role-specific versions of the HMI application in parallel, such that each version is developed using natural language development requests submitted by a given one of the multiple user roles. Alternatively, the system may apply all natural language development requests to the same version of the HMI application, prioritizing the development requests of one role over another role (e.g., by overriding a development request of a line operator in favor of a development request of a plant engineer if the two development requests conflict with one another).

FIG. 9 illustrates an example methodology 900 for providing dynamic alarm assistance via an industrial HMI application. Initially, at 902, an HMI runtime application that visualizes status and operational information for an automation system, is deployed and executed on an HMI terminal. At 904, interaction patterns with the HMI and with the automation system are monitored and recorded during operation of the automation system. These interaction patterns can include, for example, HMI screen navigation interactions, interactions with graphical controls on the HMI (e.g., graphical pushbuttons, switches, data entry fields, etc.) or with control panel devices, entry of data values to selected data entry fields of the HMI, clearance of parts or debris from areas of the automation system monitored by presence sensors, entry of setpoint values, placement of the automation system in a particular operating mode (e.g., automatic, semi-automatic, manual, etc.), manual movement of machine components during manual mode via interaction with control panel devices, or other such interactions.

At 906, a correlation between an instance of an alarm condition on the automation system reported by the HMI application and a sequence of interactions that resolve the alarm condition is determined based on the monitoring performed at step 904. At 908, a determination is made as to whether a request for assistance with a current instance of the alarm condition is received. The request can be submitted, for example, as a natural language request for assistance submitted via an integrated chat interface of the HMI application, or may be submitted via another interaction with the HMI. If a request for assistance is received (YES at step 908), the methodology proceeds to step 910, where information regarding the learned sequence of interactions is rendered via the HMI application. This information maybe a natural language description of the steps to be performed to address the alarm condition or a graphical demonstration of the learned interactions.

Embodiments, systems, and components described herein, as well as control systems and automation environments in which various aspects set forth in the subject specification can be carried out, can include computer or network components such as servers, clients, programmable logic controllers (PLCs), automation controllers, communications modules, mobile computers, on-board computers for mobile vehicles, wireless components, control components and so forth which are capable of interacting across a network. Computers and servers include one or more processors—electronic integrated circuits that perform logic operations employing electric signals—configured to execute instructions stored in media such as random access memory (RAM), read only memory (ROM), a hard drives, as well as removable memory devices, which can include memory sticks, memory cards, flash drives, external hard drives, and so on.

Similarly, the term PLC or automation controller as used herein can include functionality that can be shared across multiple components, systems, and/or networks. As an example, one or more PLCs or automation controllers can communicate and cooperate with various network devices across the network. This can include substantially any type of control, communications module, computer, Input/Output (I/O) device, sensor, actuator, and human machine interface (HMI) that communicate via the network, which includes control, automation, and/or public networks. The PLC or automation controller can also communicate to and control various other devices such as standard or safety-rated I/O modules including analog, digital, programmed/intelligent I/O modules, other programmable controllers, communications modules, sensors, actuators, output devices, and the like.

The network can include public networks such as the internet, intranets, and automation networks such as control and information protocol (CIP) networks including DeviceNet, ControlNet, safety networks, and Ethernet/IP. Other networks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus, Profibus, CAN, wireless networks, serial protocols, and so forth. In addition, the network devices can include various possibilities (hardware and/or software components). These include components such as switches with virtual local area network (VLAN) capability, LANs, WANs, proxies, gateways, routers, firewalls, virtual private network (VPN) devices, servers, clients, computers, configuration tools, monitoring tools, and/or other devices.

In order to provide a context for the various aspects of the disclosed subject matter, FIGS. 10 and 11 as well as the following discussion are intended to provide a brief, general description of a suitable environment in which the various aspects of the disclosed subject matter may be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

The illustrated embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data.

Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.