Patent Publication Number: US-11640566-B2

Title: Industrial programming development with a converted industrial control program

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of, and claims priority to, U.S. patent application Ser. No. 16/584,298 (now U.S. Pat. No. 11,042,362), filed on Sep. 26, 2019, and entitled “INDUSTRIAL PROGRAMMING DEVELOPMENT WITH A TRAINED ANALYTIC MODEL,” the entirety of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     The subject matter disclosed herein relates generally to industrial automation systems, and, for example, to industrial programming development platforms 
     BRIEF DESCRIPTION 
     The following presents a simplified summary in order to provide a basic understanding of some aspects described herein. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of the various aspects described herein. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
     In one or more embodiments, a system for developing industrial applications is provided, comprising a user interface component configured to render integrated development environment (IDE) interfaces and to receive, via interaction with the IDE interfaces, industrial design input that specifies aspects of an industrial automation control project; a project generation component configured to perform an analysis on the industrial design input based on an analytic model and to generate system project data based on inferences about the industrial design input determined based on results of the analysis; and a training component configured to train the analytic module based on training analysis performed on aggregated system project data collected by the system from multiple sets of system project data. 
     Also, one or more embodiments provide a method for creating industrial applications, comprising rendering, by a system comprising a processor, integrated development environment (IDE) interfaces on a client device; receiving, by the system via interaction with the IDE interfaces, industrial design input that defines aspects of an industrial control and monitoring project; analyzing, by the system, the industrial design input based on an analytic model; generating, by the system, system project data based on inferences about the industrial design input determined based on results of the analyzing; performing, by the system, training analysis on aggregated system project data collected from multiple sets of system project data including the system project data; and training, by the system, the analytic module based on results of the training analysis. 
     Also, according to one or more embodiments, a non-transitory computer-readable medium is provided having stored thereon instructions that, in response to execution, cause a system to perform operations, the operations comprising rendering integrated development environment (IDE) interfaces on a client device; receiving, from the client device via interaction with the IDE interfaces, industrial design input that defines control design aspects of an industrial automation project; analyzing the industrial design input using an analytic model; generating system project data based on inferences about the industrial design input learned based on results of the analyzing; performing training analysis on aggregated system project data collected from multiple sets of system project data including the system project data; and training the analytic module based on results of the training analysis. 
     To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of various ways which can be practiced, all of which are intended to be covered herein. Other advantages and novel features may become apparent from the following detailed description when considered in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an example industrial control environment. 
         FIG.  2    is a block diagram of an example integrated development environment (IDE) system. 
         FIG.  3    is a diagram illustrating a generalized architecture of an industrial IDE system. 
         FIG.  4    is a diagram illustrating several example automation object properties that can be leveraged by the IDE system in connection with building, deploying, and executing a system project. 
         FIG.  5    is a diagram illustrating example data flows associated with creation of a system project for an automation system being designed using an industrial IDE system. 
         FIG.  6    is a diagram illustrating an example system project that incorporates automation objects into a project model. 
         FIG.  7    is a diagram illustrating commissioning of a system project. 
         FIG.  8    is a diagram illustrating an example architecture in which cloud-based IDE services are used to develop and deploy industrial applications to a plant environment. 
         FIG.  9    is a diagram illustrating an example architecture in which cloud-based IDE services are accessed by multiple users across different industrial enterprises to develop and deploy industrial applications for their respective plant environments. 
         FIG.  10    is a diagram illustrating training of an analytic module over time by an IDE system&#39;s training component. 
         FIG.  11    is a diagram illustrating submission of a legacy control project to an IDE system for conversion into an object-based system project. 
         FIG.  12    is a flowchart of an example methodology for developing an industrial automation system project using an industrial IDE system with the aid of a trained analytic model. 
         FIG.  13    is a flowchart of an example methodology for converting a legacy industrial control program to an upgraded format by an industrial IDE system using a trained analytic module. 
         FIG.  14   a    is a flowchart of a first part of an example methodology for converting a legacy industrial control program to an upgraded format by an industrial IDE system using a trained analytic module. 
         FIG.  14   b    is a flowchart of a second part of the example methodology for converting a legacy industrial control program to an upgraded format by an industrial IDE system using a trained analytic module. 
         FIG.  15    is an example computing environment. 
         FIG.  16    is an example networking environment. 
     
    
    
     DETAILED DESCRIPTION 
     The subject disclosure is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the subject disclosure can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate a description thereof. 
     As used in this application, the terms “component,” “system,” “platform,” “layer,” “controller,” “terminal,” “station,” “node,” “interface” are intended to refer to a computer-related entity or an entity related to, or that is part of, an operational apparatus with one or more specific functionalities, wherein such entities can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical or magnetic storage medium) including affixed (e.g., screwed or bolted) or removable affixed solid-state storage drives; an object; an executable; a thread of execution; a computer-executable program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. Also, components as described herein can execute from various computer readable storage media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry which is operated by a software or a firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can include a processor therein to execute software or firmware that provides at least in part the functionality of the electronic components. As further yet another example, interface(s) can include input/output (I/O) components as well as associated processor, application, or Application Programming Interface (API) components. While the foregoing examples are directed to aspects of a component, the exemplified aspects or features also apply to a system, platform, interface, layer, controller, terminal, and the like. 
     As used herein, the terms “to infer” and “inference” refer generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. 
     In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from the context, the phrase “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, the phrase “X employs A or B” is satisfied by any of the following instances: X employs A; X employs B; or X employs both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from the context to be directed to a singular form. 
     Furthermore, the term “set” as employed herein excludes the empty set; e.g., the set with no elements therein. Thus, a “set” in the subject disclosure includes one or more elements or entities. As an illustration, a set of controllers includes one or more controllers; a set of data resources includes one or more data resources; etc. Likewise, the term “group” as utilized herein refers to a collection of one or more entities; e.g., a group of nodes refers to one or more nodes. 
     Various aspects or features will be presented in terms of systems that may include a number of devices, components, modules, and the like. It is to be understood and appreciated that the various systems may include additional devices, components, modules, etc. and/or may not include all of the devices, components, modules etc. discussed in connection with the figures. A combination of these approaches also can be used. 
       FIG.  1    is a block diagram of an example industrial control environment  100 . In this example, a number of industrial controllers  118  are deployed throughout an industrial plant environment to monitor and control respective industrial systems or processes relating to product manufacture, machining, motion control, batch processing, material handling, or other such industrial functions. Industrial controllers  118  typically execute respective control programs to facilitate monitoring and control of industrial devices  120  making up the controlled industrial assets or systems (e.g., industrial machines). One or more industrial controllers  118  may also comprise a soft controller executed on a personal computer or other hardware platform, or on a cloud platform. Some hybrid devices may also combine controller functionality with other functions (e.g., visualization). The control programs executed by industrial controllers  118  can comprise substantially any type of code capable of processing input signals read from the industrial devices  120  and controlling output signals generated by the industrial controllers  118 , including but not limited to ladder logic, sequential function charts, function block diagrams, or structured text. 
     Industrial devices  120  may include both input devices that provide data relating to the controlled industrial systems to the industrial controllers  118 , and output devices that respond to control signals generated by the industrial controllers  118  to control aspects of the industrial systems. Example input devices can include telemetry devices (e.g., temperature sensors, flow meters, level sensors, pressure sensors, etc.), manual operator control devices (e.g., push buttons, selector switches, etc.), safety monitoring devices (e.g., safety mats, safety pull cords, light curtains, etc.), and other such devices. Output devices may include motor drives, pneumatic actuators, signaling devices, robot control inputs, valves, pumps, and the like. 
     Industrial controllers  118  may communicatively interface with industrial devices  120  over hardwired or networked connections. For example, industrial controllers  118  can be equipped with native hardwired inputs and outputs that communicate with the industrial devices  120  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 I/O can communicate with a controller&#39;s processor over a backplane such that the digital and analog signals can be read into and controlled by the control programs. Industrial controllers  118  can also communicate with industrial devices  120  over a network using, for example, a communication module or an integrated networking port. Exemplary networks can include the Internet, intranets, Ethernet, DeviceNet, ControlNet, Data Highway and Data Highway Plus (DH/DH+), Remote I/O, Fieldbus, Modbus, Profibus, wireless networks, serial protocols, and the like. The industrial controllers  118  can also store persisted data values that can be referenced by their associated control programs and used for control decisions, including but not limited to measured or calculated values representing operational states of a controlled machine or process (e.g., tank levels, positions, alarms, etc.) or captured time series data that is collected during operation of the automation system (e.g., status information for multiple points in time, diagnostic occurrences, etc.). Similarly, some intelligent devices—including but not limited to motor drives, instruments, or condition monitoring modules—may store data values that are used for control and/or to visualize states of operation. Such devices may also capture time-series data or events on a log for later retrieval and viewing. 
     Industrial automation systems often include one or more human-machine interfaces (HMIs)  114  that allow plant personnel to view telemetry and status data associated with the automation systems, and to control some aspects of system operation. HMIs  114  may communicate with one or more of the industrial controllers  118  over a plant network  116 , and exchange data with the industrial controllers to facilitate visualization of information relating to the controlled industrial processes on one or more pre-developed operator interface screens. HMIs  114  can also be configured to allow operators to submit data to specified data tags or memory addresses of the industrial controllers  118 , thereby providing a means for operators to issue commands to the controlled systems (e.g., cycle start commands, device actuation commands, etc.), to modify setpoint values, etc. HMIs  114  can generate one or more display screens through which the operator interacts with the industrial controllers  118 , and thereby with the controlled processes and/or systems. Example display screens can visualize present states of industrial systems or their associated devices using graphical representations of the processes that display metered or calculated values, employ color or position animations based on state, render alarm notifications, or employ other such techniques for presenting relevant data to the operator. Data presented in this manner is read from industrial controllers  118  by HMIs  114  and presented on one or more of the display screens according to display formats chosen by the HMI developer. HMIs may comprise fixed location or mobile devices with either user-installed or pre-installed operating systems, and either user-installed or pre-installed graphical application software. 
     Some industrial environments may also include other systems or devices relating to specific aspects of the controlled industrial systems. These may include, for example, a data historian  110  that aggregates and stores production information collected from the industrial controllers  118  or other data sources, device documentation stores containing electronic documentation for the various industrial devices making up the controlled industrial systems, inventory tracking systems, work order management systems, repositories for machine or process drawings and documentation, vendor product documentation storage, vendor knowledgebases, internal knowledgebases, work scheduling applications, or other such systems, some or all of which may reside on an office network  108  of the industrial environment. 
     Higher-level systems  126  may carry out functions that are less directly related to control of the industrial automation systems on the plant floor, and instead are directed to long term planning, high-level supervisory control, analytics, reporting, or other such high-level functions. These systems  126  may reside on the office network  108  at an external location relative to the plant facility, or on a cloud platform with access to the office and/or plant networks. Higher-level systems  126  may include, but are not limited to, cloud storage and analysis systems, big data analysis systems, manufacturing execution systems, data lakes, reporting systems, etc. In some scenarios, applications running at these higher levels of the enterprise may be configured to analyze control system operational data, and the results of this analysis may be fed back to an operator at the control system or directly to a controller  118  or device  120  in the control system. 
     The various control, monitoring, and analytical devices that make up an industrial environment must be programmed or configured using respective configuration applications specific to each device. For example, industrial controllers  118  are typically configured and programmed using a control programming development application such as a ladder logic editor (e.g., executing on a client device  124 ). Using such development platforms, a designer can write control programming (e.g., ladder logic, structured text, function block diagrams, etc.) for carrying out a desired industrial sequence or process and download the resulting program files to the controller  118 . Separately, developers design visualization screens and associated navigation structures for HMIs  114  using an HMI development platform (e.g., executing on client device  122 ) and download the resulting visualization files to the HMI  114 . Some industrial devices  120 —such as motor drives, telemetry devices, safety input devices, etc.—may also require configuration using separate device configuration tools (e.g., executing on client device  128 ) that are specific to the device being configured. Such device configuration tools may be used to set device parameters or operating modes (e.g., high/low limits, output signal formats, scale factors, energy consumption modes, etc.). 
     The necessity of using separate configuration tools to program and configure disparate aspects of an industrial automation system results in a piecemeal design approach whereby different but related or overlapping aspects of an automation system are designed, configured, and programmed separately on different development environments. For example, a motion control system may require an industrial controller to be programmed and a control loop to be tuned using a control logic programming platform, a motor drive to be configured using another configuration platform, and an associated HMI to be programmed using a visualization development platform. Related peripheral systems—such as vision systems, safety systems, etc.—may also require configuration using separate programming or development applications. 
     This segregated development approach can also necessitate considerable testing and debugging efforts to ensure proper integration of the separately configured system aspects. In this regard, intended data interfacing or coordinated actions between the different system aspects may require significant debugging due to a failure to properly coordinate disparate programming efforts. 
     To address at least some of these or other issues, one or more embodiments described herein provide an integrated development environment (IDE) for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Embodiments of the industrial IDE can be used to configure and manage automation system devices in a common way, facilitating integrated, multi-discipline programming of control, visualization, and other aspects of the control system. 
     In general, the industrial IDE supports features that span the full automation lifecycle, including design (e.g., device selection and sizing, controller programming, visualization development, device configuration, testing, etc.); installation, configuration and commissioning; operation, improvement, and administration; and troubleshooting, expanding, and upgrading. 
     Embodiments of the industrial IDE can include a library of modular code and visualizations that are specific to industry verticals and common industrial applications within those verticals. These code and visualization modules can simplify development and shorten the development cycle, while also supporting consistency and reuse across an industrial enterprise. 
     Some embodiments of the industrial IDE system can also include a training component that improves several of the system&#39;s automated design tools over time based on analysis of project data submitted by developers. For example, the IDE system 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 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 system to generate recommendations regarding control programming, suitable visualizations, device parameter configurations, control system architectures, or other automation system aspects. The IDE system can also automatically add suitable control code, visualizations, device parameter settings or configurations, engineering drawings, or other such project aspects to new control projects being developed based on an inference of the developer&#39;s design goals and knowledge of how these goals have been implemented by other developers. 
     Some embodiments of the IDE system can also be configured to convert legacy control programs to a new format supported by the IDE system and supporting industrial devices. These control project conversions can be performed based in part on common approaches to implementing certain design goals learned by the training component and encoded in the trained analytic module. 
       FIG.  2    is a block diagram of an example integrated development environment (IDE) system  202  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. 
     IDE system  202  can include a user interface component  204  including an IDE editor  224 , a project generation component  206 , a project deployment component  208 , a training component  210 , a conversion component  212 , an encryption component  214 , one or more processors  218 , and memory  220 . In various embodiments, one or more of the user interface component  204 , project generation component  206 , project deployment component  208 , training component  210 , conversion component  212 , encryption component  214 , the one or more processors  218 , and memory  220  can be electrically and/or communicatively coupled to one another to perform one or more of the functions of the IDE system  202 . In some embodiments, components  204 ,  206 ,  208 ,  210 ,  212 , and  214  can comprise software instructions stored on memory  220  and executed by processor(s)  218 . IDE system  202  may also interact with other hardware and/or software components not depicted in  FIG.  2   . For example, processor(s)  218  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  204  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  204  can be configured to communicatively interface with an IDE client that executes on a client device (e.g., a laptop computer, tablet computer, smart phone, etc.) that is communicatively connected to the IDE system  202  (e.g., via a hardwired or wireless connection). The user interface component  204  can then receive user input data and render output data via the IDE client. In other embodiments, user interface component  314  can be configured to generate and serve suitable interface screens to a client device (e.g., program development screens), and exchange data via these interface screens. Input data that can be received via various embodiments of user interface component  204  can include, but is not limited to, programming code, industrial design specifications or goals, engineering drawings, AR/VR input, DSL definitions, video or image data, legacy control projects, or other such input. Output data rendered by various embodiments of user interface component  204  can include program code, programming feedback (e.g., error and highlighting, coding suggestions, etc.), programming and visualization development screens, etc. 
     Project generation component  206  can be configured to create a system project comprising one or more project files based on design input received via the user interface component  204 , as well as industrial knowledge, predefined code modules and visualizations, and automation objects  222  maintained by the IDE system  202 . Project generation component  206  can generate at least a portion of the system project based on a training module generated based on analysis of multiple sets of project data submitted to the industrial IDE system  202 . Analysis of these multiple sets of project data trains the project generation component  206  to accurately convert design input submitted by the user to suitable control code, visualizations, device configurations, etc. 
     Project deployment component  208  can be configured to commission the system project created by the project generation component  206  to appropriate industrial devices (e.g., controllers, HMI terminals, motor drives, AR/VR systems, etc.) for execution. To this end, project deployment component  208  can identify the appropriate target devices to which respective portions of the system project should be sent for execution, translate these respective portions to formats understandable by the target devices, and deploy the translated project components to their corresponding devices. 
     Training component  210  can be configured to analyze multiple sets of project data submitted by developers in order to train the project generation component  206  to accurately convert design input submitted to the IDE system  202  to suitable executable project data (e.g., industrial controller programming, HMI applications or dashboards, device parameter settings, engineering drawings, etc. 
     Conversion component  212  an be configured to convert industrial control programming in a legacy format to an upgraded format supported by the IDE system  202 . This can include, for example, mapping control segments discovered in the legacy control programming to automation objects, associated suitable visualizations with selected control segments, or other such conversion functions. Encryption component  214  can be configured to encrypt customer-specific project or design data for embodiments of the IDE system  202  that are embodied on a cloud platform as a cloud-based industrial design service. 
     The one or more processors  218  can perform one or more of the functions described herein with reference to the systems and/or methods disclosed. Memory  220  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.  3    is a diagram illustrating a generalized architecture of the industrial IDE system  202  according to one or more embodiments. Industrial IDE system  202  can implement a common set of services and workflows spanning not only design, but also commissioning, operation, and maintenance. In terms of design, the IDE system  202  can support not only industrial controller programming and HMI development, but also sizing and selection of system components, device/system configuration, AR/VR visualizations, and other features. The IDE system  202  can also include tools that simplify and automate commissioning of the resulting project and assist with subsequent administration of the deployed system during runtime. 
     Embodiments of the IDE system  202  that are implemented on a cloud platform also facilitate collaborative project development whereby multiple developers  304  contribute design and programming input to a common automation system project  302 . Collaborative tools supported by the IDE system can manage design contributions from the multiple contributors and perform version control of the aggregate system project  302  to ensure project consistency. 
     Based on design and programming input from one or more developers  304 , IDE system  202  generates a system project  302  comprising one or more project files. The system project  302  encodes one or more of control programming; HMI, AR, and/or VR visualizations; device or sub-system configuration data (e.g., drive parameters, vision system configurations, telemetry device parameters, safety zone definitions, etc.); or other such aspects of an industrial automation system being designed. IDE system  202  can identify the appropriate target devices  306  on which respective aspects of the system project  302  should be executed (e.g., industrial controllers, HMI terminals, variable frequency drives, safety devices, etc.), translate the system project  302  to executable files that can be executed on the respective target devices, and deploy the executable files to their corresponding target devices  306  for execution, thereby commissioning the system project  302  to the plant floor for implementation of the automation project. 
     To support enhanced development capabilities, some embodiments of IDE system  202  can be built on an object-based data model rather than a tag-based architecture. Automation objects  222  serve as the building block for this object-based development architecture.  FIG.  4    is a diagram illustrating several example automation object properties that can be leveraged by the IDE system  202  in connection with building, deploying, and executing a system project  302 . Automation objects  222  can be created and augmented during design, integrated into larger data models, and consumed during runtime. These automation objects  222  provide a common data structure across the IDE system  202  and can be stored in an object library (e.g., part of memory  220 ) for reuse. The object library can store predefined automation objects  222  representing various classifications of real-world industrial assets  402 , including but not limited to pumps, tanks, values, motors, motor drives (e.g., variable frequency drives), industrial robots, actuators (e.g., pneumatic or hydraulic actuators), or other such assets. Automation objects  222  can represent elements at substantially any level of an industrial enterprise, including individual devices, machines made up of many industrial devices and components (some of which may be associated with their own automation objects  222 ), and entire production lines or process control systems. 
     An automation object  222  for a given type of industrial asset can encode such aspects as 2D or 3D visualizations, alarms, control coding (e.g., logic or other type of control programming), analytics, startup procedures, testing protocols, validation reports, simulations, schematics, security protocols, and other such properties associated with the industrial asset  402  represented by the object  222 . Automation objects  222  can also be geotagged with location information identifying the location of the associated asset. During runtime of the system project  302 , the automation object  222  corresponding to a given real-world asset  402  can also record status or operational history data for the asset. In general, automation objects  222  serve as programmatic representations of their corresponding industrial assets  402 , and can be incorporated into a system project  302  as elements of control code, a 2D or 3D visualization, a knowledgebase or maintenance guidance system for the industrial assets, or other such aspects. 
       FIG.  5    is a diagram illustrating example data flows associated with creation of a system project  302  for an automation system being designed using IDE system  202  according to one or more embodiments. A client device  504  (e.g., a laptop computer, tablet computer, desktop computer, mobile device, wearable AR/VR appliance, etc.) executing an IDE client application  514  can access the IDE system&#39;s project development tools and leverage these tools to create a comprehensive system project  302  for an automation system being developed. Through interaction with the system&#39;s user interface component  204 , developers can submit design input  512  to the IDE system  202  in various supported formats, including industry-specific control programming (e.g., control logic, structured text, sequential function charts, etc.) and HMI screen configuration input. Based on this design input  512  and information stored in an industry knowledgebase (predefined code modules  508  and visualizations  510 , guardrail templates  506 , physics-based rules  516 , etc.), user interface component  204  renders design feedback  518  designed to assist the developer in connection with developing a system project  302  for configuration, control, and visualization of an industrial automation system. 
     In addition to control programming and visualization definitions, some embodiments of IDE system  202  can be configured to receive digital engineering drawings (e.g., computer-aided design (CAD) files) as design input  512 . In such embodiments, project generation component  206  can generate portions of the system project  302 —e.g., by automatically generating control and/or visualization code—based on analysis of existing design drawings. Drawings that can be submitted as design input  512  can include, but are not limited to, P&amp;ID drawings, mechanical drawings, flow diagrams, or other such documents. For example, a P&amp;ID drawing can be imported into the IDE system  202 , and project generation component  206  can identify elements (e.g., tanks, pumps, etc.) and relationships therebetween conveyed by the drawings. Project generation component  206  can associate or map elements identified in the drawings with appropriate automation objects  222  corresponding to these elements (e.g., tanks, pumps, etc.) and add these automation objects  222  to the system project  302 . The device-specific and asset-specific automation objects  222  include suitable code and visualizations to be associated with the elements identified in the drawings. In general, the IDE system  202  can examine one or more different types of drawings (mechanical, electrical, piping, etc.) to determine relationships between devices, machines, and/or assets (including identifying common elements across different drawings) and intelligently associate these elements with appropriate automation objects  222 , code modules  508 , and/or visualizations  510 . The IDE system  202  can leverage physics-based rules  516  as well as pre-defined code modules  508  and visualizations  510  as necessary in connection with generating code or project data for system project  302 . 
     The IDE system  202  can also determine whether pre-defined visualization content is available for any of the objects discovered in the drawings and generate appropriate HMI screens or AR/VR content for the discovered objects based on these pre-defined visualizations. To this end, the IDE system  202  can store industry-specific, asset-specific, and/or application-specific visualizations  510  that can be accessed by the project generation component  206  as needed. These visualizations  510  can be classified according to industry or industrial vertical (e.g., automotive, food and drug, oil and gas, pharmaceutical, etc.), type of industrial asset (e.g., a type of machine or industrial device), a type of industrial application (e.g., batch processing, flow control, web tension control, sheet metal stamping, water treatment, etc.), or other such categories. Predefined visualizations  510  can comprise visualizations in a variety of formats, including but not limited to HMI screens or windows, mashups that aggregate data from multiple pre-specified sources, AR overlays, VR objects representing 3D virtualizations of the associated industrial asset, or other such visualization formats. IDE system  202  can select a suitable visualization for a given object based on a predefined association between the object type and the visualization content. 
     In another example, markings applied to an engineering drawing by a user can be understood by some embodiments of the project generation component  206  to convey a specific design intention or parameter. For example, a marking in red pen can be understood to indicate a safety zone, two circles connected by a dashed line can be interpreted as a gearing relationship, and a bold line may indicate a camming relationship. In this way, a designer can sketch out design goals on an existing drawing in a manner that can be understood and leveraged by the IDE system  202  to generate code and visualizations. In another example, the project generation component  206  can learn permissives and interlocks (e.g., valves and their associated states) that serve as necessary preconditions for starting a machine based on analysis of the user&#39;s CAD drawings. Project generation component  206  can generate any suitable code (ladder logic, function blocks, etc.), device configurations, and visualizations based on analysis of these drawings and markings for incorporation into system project  302 . In some embodiments, user interface component  204  can include design tools for developing engineering drawings within the IDE platform itself, and the project generation component  206  can generate this code as a background process as the user is creating the drawings for a new project. In some embodiments, project generation component  206  can also translate state machine drawings to a corresponding programming sequence, yielding at least skeletal code that can be enhanced by the developer with additional programming details as needed. 
     Also, or in addition, some embodiments of IDE system  202  can support goal-based automated programming. For example, the user interface component  204  can allow the user to specify production goals for an automation system being designed (e.g., specifying that a bottling plant being designed must be capable of producing at least 5000 bottles per second during normal operation) and any other relevant design constraints applied to the design project (e.g., budget limitations, available floor space, available control cabinet space, etc.). Based on this information, the project generation component  206  will generate portions of the system project  302  to satisfy the specified design goals and constraints. Portions of the system project  302  that can be generated in this manner can include, but are not limited to, device and equipment selections (e.g., definitions of how many pumps, controllers, stations, conveyors, drives, or other assets will be needed to satisfy the specified goal), associated device configurations (e.g., tuning parameters, network settings, drive parameters, etc.), control coding, or HMI screens suitable for visualizing the automation system being designed. 
     Some embodiments of the project generation component  206  can also generate at least some of the project code for system project  302  based on knowledge of parts that have been ordered for the project being developed. This can involve accessing the customer&#39;s account information maintained by an equipment vendor to identify devices that have been purchased for the project. Based on this information the project generation component  206  can add appropriate automation objects  222  and associated code modules  508  corresponding to the purchased assets, thereby providing a starting point for project development. 
     Some embodiments of project generation component  206  can also monitor customer-specific design approaches for commonly programmed functions (e.g., pumping applications, batch processes, palletizing operations, etc.) and generate recommendations for design modules (e.g., code modules  508 , visualizations  510 , etc.) that the user may wish to incorporate into a current design project based on an inference of the designer&#39;s goals and learned approaches to achieving the goal. To this end, some embodiments of project generation component  206  can be configured to monitor design input  512  over time and, based on this monitoring, learn correlations between certain design actions (e.g., addition of certain code modules or snippets to design projects, selection of certain visualizations, etc.) and types of industrial assets, industrial sequences, or industrial processes being designed. Project generation component  206  can record these learned correlations and generate recommendations during subsequent project development sessions based on these correlations. For example, if project generation component  206  determines, based on analysis of design input  512 , that a designer is currently developing a control project involving a type of industrial equipment that has been programmed and/or visualized in the past in a repeated, predictable manner, the project generation component  206  can instruct user interface component  204  to render recommended development steps or code modules  508  the designer may wish to incorporate into the system project  302  based on how this equipment was configured and/or programmed in the past. 
     In some embodiments, IDE system  202  can also store and implement guardrail templates  506  that define design guardrails intended to ensure the project&#39;s compliance with internal or external design standards. Based on design parameters defined by one or more selected guardrail templates  506 , user interface component  204  can provide, as a subset of design feedback  518 , dynamic recommendations or other types of feedback designed to guide the developer in a manner that ensures compliance of the system project  302  with internal or external requirements or standards (e.g., certifications such as TUV certification, in-house design standards, industry-specific or vertical-specific design standards, etc.). This feedback  518  can take the form of text-based recommendations (e.g., recommendations to rewrite an indicated portion of control code to comply with a defined programming standard), syntax highlighting, error highlighting, auto-completion of code snippets, or other such formats. In this way, IDE system  202  can customize design feedback  518 —including programming recommendations, recommendations of predefined code modules  508  or visualizations  510 , error and syntax highlighting, etc.—in accordance with the type of industrial system being developed and any applicable in-house design standards. 
     Guardrail templates  506  can also be designed to maintain compliance with global best practices applicable to control programming or other aspects of project development. For example, user interface component  204  may generate and render an alert if a developer&#39;s control programing is deemed to be too complex as defined by criteria specified by one or more guardrail templates  506 . Since different verticals (e.g., automotive, pharmaceutical, oil and gas, food and drug, marine, etc.) must adhere to different standards and certifications, the IDE system  202  can maintain a library of guardrail templates  506  for different internal and external standards and certifications, including customized user-specific guardrail templates  506 . These guardrail templates  506  can be classified according to industrial vertical, type of industrial application, plant facility (in the case of custom in-house guardrail templates  506 ) or other such categories. During development, project generation component  206  can select and apply a subset of guardrail templates  506  determined to be relevant to the project currently being developed, based on a determination of such aspects as the industrial vertical to which the project relates, the type of industrial application being programmed (e.g., flow control, web tension control, a certain batch process, etc.), or other such aspects. Project generation component  206  can leverage guardrail templates  506  to implement rules-based programming, whereby programming feedback (a subset of design feedback  518 ) such as dynamic intelligent autocorrection, type-aheads, or coding suggestions are rendered based on encoded industry expertise and best practices (e.g., identifying inefficiencies in code being developed and recommending appropriate corrections). 
     Users can also run their own internal guardrail templates  506  against code provided by outside vendors (e.g., OEMs) to ensure that this code complies with in-house programming standards. In such scenarios, vendor-provided code can be submitted to the IDE system  202 , and project generation component  206  can analyze this code in view of in-house coding standards specified by one or more custom guardrail templates  506 . Based on results of this analysis, user interface component  204  can indicate portions of the vendor-provided code (e.g., using highlights, overlaid text, etc.) that do not conform to the programming standards set forth by the guardrail templates  506 , and display suggestions for modifying the code in order to bring the code into compliance. As an alternative or in addition to recommending these modifications, some embodiments of project generation component  206  can be configured to automatically modify the code in accordance with the recommendations to bring the code into conformance. 
     In making coding suggestions as part of design feedback  518 , project generation component  206  can invoke selected code modules  508  stored in a code module database (e.g., on memory  220 ). These code modules  508  comprise standardized coding segments for controlling common industrial tasks or applications (e.g., palletizing, flow control, web tension control, pick-and-place applications, conveyor control, etc.). In some embodiments, code modules  508  can be categorized according to one or more of an industrial vertical (e.g., automotive, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.), an industrial application, or a type of machine or device to which the code module  508  is applicable. In some embodiments, project generation component  206  can infer a programmer&#39;s current programming task or design goal based on programmatic input being provided by the programmer (as a subset of design input  512 ), and determine, based on this task or goal, whether one of the pre-defined code modules  508  may be appropriately added to the control program being developed to achieve the inferred task or goal. For example, project generation component  206  may infer, based on analysis of design input  512 , that the programmer is currently developing control code for transferring material from a first tank to another tank, and in response, recommend inclusion of a predefined code module  508  comprising standardized or frequently utilized code for controlling the valves, pumps, or other assets necessary to achieve the material transfer. 
     Customized guardrail templates  506  can also be defined to capture nuances of a customer site that should be taken into consideration in the project design. For example, a guardrail template  506  could record the fact that the automation system being designed will be installed in a region where power outages are common, and will factor this consideration when generating design feedback  518 ; e.g., by recommending implementation of backup uninterruptable power supplies and suggesting how these should be incorporated, as well as recommending associated programming or control strategies that take these outages into account. 
     IDE system  202  can also use guardrail templates  506  to guide user selection of equipment or devices for a given design goal; e.g., based on the industrial vertical, type of control application (e.g., sheet metal stamping, die casting, palletization, conveyor control, web tension control, batch processing, etc.), budgetary constraints for the project, physical constraints at the installation site (e.g., available floor, wall or cabinet space; dimensions of the installation space; etc.), equipment already existing at the site, etc. Some or all of these parameters and constraints can be provided as design input  512 , and user interface component  204  can render the equipment recommendations as a subset of design feedback  518 . In some embodiments, project generation component  206  can also determine whether some or all existing equipment can be repurposed for the new control system being designed. For example, if a new bottling line is to be added to a production area, there may be an opportunity to leverage existing equipment since some bottling lines already exist. The decision as to which devices and equipment can be reused will affect the design of the new control system. Accordingly, some of the design input  512  provided to the IDE system  202  can include specifics of the customer&#39;s existing systems within or near the installation site. In some embodiments, project generation component  206  can apply artificial intelligence (AI) or traditional analytic approaches to this information to determine whether existing equipment specified in design in put  512  can be repurposed or leveraged. Based on results of this analysis, project generation component  206  can generate, as design feedback  518 , a list of any new equipment that may need to be purchased based on these decisions. 
     In some embodiments, IDE system  202  can offer design recommendations based on an understanding of the physical environment within which the automation system being designed will be installed. To this end, information regarding the physical environment can be submitted to the IDE system  202  (as part of design input  512 ) in the form of 2D or 3D images or video of the plant environment. This environmental information can also be obtained from an existing digital twin of the plant, or by analysis of scanned environmental data obtained by a wearable AR appliance in some embodiments. Project generation component  206  can analyze this image, video, or digital twin data to identify physical elements within the installation area (e.g., walls, girders, safety fences, existing machines and devices, etc.) and physical relationships between these elements. This can include ascertaining distances between machines, lengths of piping runs, locations and distances of wiring harnesses or cable trays, etc. Based on results of this analysis, project generation component  206  can add context to schematics generated as part of system project  302 , generate recommendations regarding optimal locations for devices or machines (e.g., recommending a minimum separation between power and data cables), or make other refinements to the system project  302 . At least some of this design data can be generated based on physics-based rules  516 , which can be referenced by project generation component  206  to determine such physical design specifications as minimum safe distances from hazardous equipment (which may also factor into determining suitable locations for installation of safety devices relative to this equipment, given expected human or vehicle reaction times defined by the physics-based rules  516 ), material selections capable of withstanding expected loads, piping configurations and tuning for a specified flow control application, wiring gauges suitable for an expected electrical load, minimum distances between signal wiring and electromagnetic field (EMF) sources to ensure negligible electrical interference on data signals, or other such design features that are dependent on physical rules. 
     In an example use case, relative locations of machines and devices specified by physical environment information submitted to the IDE system  202  can be used by the project generation component  206  to generate design data for an industrial safety system. For example, project generation component  206  can analyze distance measurements between safety equipment and hazardous machines and, based on these measurements, determine suitable placements and configurations of safety devices and associated safety controllers that ensure the machine will shut down within a sufficient safety reaction time to prevent injury (e.g., in the event that a person runs through a light curtain). 
     In some embodiments, project generation component  206  can also analyze photographic or video data of an existing machine to determine inline mechanical properties such as gearing or camming and factor this information into one or more guardrail templates  506  or design recommendations. 
     Since several design features performed by project generation component  206  as described above may rely on inferences of the developer&#39;s design goals; discovery of common develop behaviors; learned associations between design goals and control code or visualizations, and other such intelligent decision-making, project generation component  206  can employ an associated trainable analytic module  520  in connection with performing its intelligent design functions. As will be described in more detail below, analytic module  520  can be trained by the IDE system&#39;s training component  210  based on analysis of system projects  302  generated and stored for multiple developers across different industrial enterprises. 
     As noted above, the system project  302  generated by IDE system  202  for a given automaton system being designed can be built upon an object-based architecture that uses automation objects  222  as building blocks.  FIG.  6    is a diagram illustrating an example system project  302  that incorporates automation objects  222  into the project model. In this example, various automation objects  222  representing analogous industrial devices, systems, or assets of an automation system (e.g., a process, tanks, valves, pumps, etc.) have been incorporated into system project  302  as elements of a larger project data model  602 . The project data model  602  also defines hierarchical relationships between these automation objects  222 . According to an example relationship, a process automation object representing a batch process may be defined as a parent object to a number of child objects representing devices and equipment that carry out the process, such as tanks, pumps, and valves. Each automation object  222  has associated therewith object properties or attributes specific to its corresponding industrial asset (e.g., those discussed above in connection with  FIG.  4   ), including executable control programming for controlling the asset (or for coordinating the actions of the asset with other industrial assets) and visualizations that can be used to render relevant information about the asset during runtime. 
     At least some of the attributes of each automation object  222  are default properties defined by the IDE system  202  based on encoded industry expertise pertaining to the asset represented by the objects. Other properties can be modified or added by the developer as needed (via design input  512 ) to customize the object  222  for the particular asset and/or industrial application for which the system projects  302  is being developed. This can include, for example, associating customized control code, HMI screens, AR presentations, or help files associated with selected automation objects  222 . In this way, automation objects  222  can be created and augmented as needed during design for consumption or execution by target control devices during runtime. 
     Once development on a system project  302  has been completed, commissioning tools supported by the IDE system  202  can simplify the process of commissioning the project in the field. When the system project  302  for a given automation system has been completed, the system project  302  can be deployed to one or more target control devices for execution.  FIG.  7    is a diagram illustrating commissioning of a system project  302 . Project deployment component  208  can compile or otherwise translate a completed system project  302  into one or more executable files or configuration files that can be stored and executed on respective target industrial devices of the automation system (e.g., industrial controllers  118 , HMI terminals  114  or other types of visualization systems, motor drives  710 , telemetry devices, vision systems, safety relays, etc.). 
     Conventional control program development platforms require the developer to specify the type of industrial controller (e.g., the controller&#39;s model number) on which the control program will run prior to development, thereby binding the control programming to a specified controller. Controller-specific guardrails are then enforced during program development which limit how the program is developed given the capabilities of the selected controller. By contrast, some embodiments of the IDE system  202  can abstract project development from the specific controller type, allowing the designer to develop the system project  302  as a logical representation of the automation system in a manner that is agnostic to where and how the various control aspects of system project  302  will run. Once project development is complete and system project  302  is ready for commissioning, the user can specify (via user interface component  204 ) target devices on which respective aspects of the system project  302  are to be executed. In response, an allocation engine of the project deployment component  208  will translate aspects of the system project  302  to respective executable files formatted for storage and execution on their respective target devices. 
     For example, system project  302  may include—among other project aspects—control code, visualization screen definitions, and motor drive parameter definitions. Upon completion of project development, a user can identify which target devices—including an industrial controller  118 , an HMI terminal  114 , and a motor drive  710 —are to execute or receive these respective aspects of the system project  302 . Project deployment component  208  can then translate the controller code defined by the system project  302  to a control program file  702  formatted for execution on the specified industrial controller  118  and send this control program file  702  to the controller  118  (e.g., via plant network  116 ). Similarly, project deployment component  208  can translate the visualization definitions and motor drive parameter definitions to a visualization application  704  and a device configuration file  708 , respectively, and deploy these files to their respective target devices for execution and/or device configuration. 
     In general, project deployment component  208  performs any conversions necessary to allow aspects of system project  302  to execute on the specified devices. Any inherent relationships, handshakes, or data sharing defined in the system project  302  are maintained regardless of how the various elements of the system project  302  are distributed. In this way, embodiments of the IDE system  202  can decouple the project from how and where the project is to be run. This also allows the same system project  302  to be commissioned at different plant facilities having different sets of control equipment. That is, some embodiments of the IDE system  202  can allocate project code to different target devices as a function of the particular devices found on-site. IDE system  202  can also allow some portions of the project file to be commissioned as an emulator or on a cloud-based controller. 
     As an alternative to having the user specify the target control devices to which the system project  302  is to be deployed, some embodiments of IDE system  202  can actively connect to the plant network  116  and discover available devices, ascertain the control hardware architecture present on the plant floor, infer appropriate target devices for respective executable aspects of system project  302 , and deploy the system project  302  to these selected target devices. As part of this commissioning process, IDE system  202  can also connect to remote knowledgebases (e.g., web-based or cloud-based knowledgebases) to determine which discovered devices are out of date or require firmware upgrade to properly execute the system project  302 . In this way, the IDE system  202  can serve as a link between device vendors and a customer&#39;s plant ecosystem via a trusted connection in the cloud. 
     Copies of system project  302  can be propagated to multiple plant facilities having varying equipment configurations using smart propagation, whereby the project deployment component  208  intelligently associates project components with the correct industrial asset or control device even if the equipment on-site does not perfectly match the defined target (e.g., if different pump types are found at different sites). For target devices that do not perfectly match the expected asset, project deployment component  208  can calculate the estimated impact of running the system project  302  on non-optimal target equipment and generate warnings or recommendations for mitigating expected deviations from optimal project execution. 
     As noted above, some embodiments of IDE system  202  can be embodied on a cloud platform.  FIG.  8    is a diagram illustrating an example architecture in which cloud-based IDE services  802  are used to develop and deploy industrial applications to a plant environment. In this example, the industrial environment includes one or more industrial controllers  118 , HMI terminals  114 , motor drives  710 , servers  801  running higher level applications (e.g., ERP, MES, etc.), and other such industrial assets. These industrial assets are connected to a plant network  116  (e.g., a common industrial protocol network, an Ethernet/IP network, etc.) that facilitates data exchange between industrial devices on the plant floor. Plant network  116  may be a wired or a wireless network. In the illustrated example, the high-level servers  810  reside on a separate office network  108  that is connected to the plant network  116  (e.g., through a router  808  or other network infrastructure device). 
     In this example, IDE system  202  resides on a cloud platform  806  and executes as a set of cloud-based IDE service  802  that are accessible to authorized remote client devices  504 . Cloud platform  806  can be any infrastructure that allows shared computing services (such as IDE services  802 ) to be accessed and utilized by cloud-capable devices. Cloud platform  806  can be a public cloud accessible via the Internet by devices  504  having Internet connectivity and appropriate authorizations to utilize the IDE services  802 . In some scenarios, cloud platform  806  can be provided by a cloud provider as a platform-as-a-service (PaaS), and the IDE services  802  can reside and execute on the cloud platform  806  as a cloud-based service. In some such configurations, access to the cloud platform  806  and associated IDE services  802  can be provided to customers as a subscription service by an owner of the IDE services  802 . Alternatively, cloud platform  806  can be a private cloud operated internally by the industrial enterprise (the owner of the plant facility). An example private cloud platform can comprise a set of servers hosting the IDE services  802  and residing on a corporate network protected by a firewall. 
     Cloud-based implementations of IDE system  202  can facilitate collaborative development by multiple remote developers who are authorized to access the IDE services  802 . When a system project  302  is ready for deployment, the project  302  can be commissioned to the plant facility via a secure connection between the office network  108  or the plant network  116  and the cloud platform  806 . As discussed above, the industrial IDE services  802  can translate system project  302  to one or more appropriate executable files—control program files  702 , visualization applications  704 , device configuration files  708 , system configuration files  812 —and deploy these files to the appropriate devices in the plant facility to facilitate implementation of the automation project. 
     Several of the automation system development services provided by the IDE system  202 —including, for example, identification and integration of reusable code or visualizations for common control functions and equipment, inference of a developer&#39;s design intentions for the purpose of generating design recommendations or automatically generating elements of the system project, simulations for dynamic design feedback and recommendations, conversion of legacy control projects to IDE projects supported by the IDE system  202 , optimization of control code, supervisory monitoring using a digital twin, and other such features—can be continually improved via ongoing training of the IDE system  202 . This training can be based on one or both of development information extracted from multiple control system projects processed by the IDE system  202  over time or real-world data received from physical plant floor systems. For embodiments of the IDE system  202  implemented on a cloud platform  806  and made available to multiple users, the IDE system&#39;s training component  210  can apply analytics (e.g., AI analytics, machine learning, or other types of analytics) to large amounts of system project data received from multiple users of the IDE services and use results of these analytics for training or learning purposes. In this way, the automated development features offered by the IDE system  202  can be automatically improved over time as new project data is collected and analyzed. Data that can be analyzed for training or learning purposes can include both design data (e.g., system project data) from which common design approaches and relationships between project components can be learned, as well runtime production data collected after these system projects have been commissioned for operation. 
       FIG.  9    is a diagram illustrating an example architecture in which cloud-based IDE services  802  are accessed by multiple users across different industrial enterprises  902  to develop and deploy industrial applications for their respective plant environments. As described in previous examples, developers at the respective industrial enterprises  902  can submit design input  512  to the IDE system  202  (implemented as cloud-based IDE services  802  in this example) to facilitate creation of system projects  302  which can then be deployed at the respective industrial enterprises  902 . Using this architecture, client devices at the respective industrial enterprises  902  can leverage the centralized industrial IDE services  802  to develop their own industrial system projects  302 . System projects  302  for each industrial enterprise  902  are securely stored on the cloud platform  806  during development, and can be deployed to automation system devices at the respective industrial enterprises  902  from the cloud platform  806  (as depicted in  FIG.  8   ) or can be downloaded to the respective client devices at the industrial enterprises  902  for localized deployment from the client devices to one or more industrial devices. Since IDE services  802  reside on a cloud-platform  806  with access to internet-based resources, some embodiments of the IDE services  802  can also allow users to access remote web-based knowledgebases, vendor equipment catalogs, or other sources of information that may assist in developing their industrial control projects. 
     Cloud-based IDE services  802  can support true multi-tenancy across the layers of authentication authorization, data segregation at the logical level, and network segregation at the logical level. End users can access the industrial IDE services  802  on the cloud platform  806 , and each end user&#39;s development data—including design input  512 , design feedback  518 , and system projects  302 —is encrypted (e.g., by encryption component  214 ) such that each end user can only view their own data. In an example implementation, an administrator of the cloud-based industrial IDE services  802  may maintain a master virtual private cloud (VPC) with appropriate security features, and each industrial enterprise  902  can be allocated a portion of this VPC for their own access to the IDE services  802 . In an example embodiment, an encrypted multi-protocol label switching (MPLS) channel can protect the entire corpus of an end user&#39;s data such that the data can only be viewed by specific computers or domains that have an appropriate certificate. 
     In order to improve the design and runtime services offered by the cloud-based implementation of IDE system  202 , end users can be encouraged to allow portions of their system project data to be stored on the cloud platform anonymously as aggregated project data  904 . The IDE system&#39;s training component  210  can use this aggregated project data  904  as training data to improve the ability of the project generation component  206  to perform such functions as generating and recommending suitable control code modules or visualizations for a given design goal, inferring design goals based on a developer&#39;s design input  512 , generating equipment recommendations, rendering appropriate design feedback  518  in response to design input  512 , or other such functions. Results of this training can be encoded in an analytic module  520  used by the project generation component  206  to analyze design input  512  and generate suitable design feedback  518 , as well as to auto-generate portions of system projects  302  based on design input  512  in view of design patterns learned by the training component  210 . 
       FIG.  10    is a diagram illustrating training of the analytic module  520  over time by the IDE system&#39;s training component  210 . As developers across multiple industrial enterprises  902  access cloud-based industrial IDE system  202  to develop system projects  302 , selected portions of these diverse system projects  302  can be aggregated as aggregated project data  904  and fed to the IDE system&#39;s training component  210  as training data. Aggregated project data  904  can be collected in a manner that protects the industrial enterprises&#39; proprietary project information. In some embodiments, IDE system  202  may only feed a user&#39;s project data to the training module  210  if the user expressly volunteers to allow their project information to be used to train the IDE system  202 . In some embodiments, data volume thresholds can be defined such that, when the amount of collected project data reaches a defined threshold, anonymization and aggregation of the collected is triggered and the aggregated data is fed to the training component  210 . This can reassure owners of the industrial assets and associated system projects  302  that their proprietary raw data is not being viewed by outside parties. An obfuscation routine can also be applied to the data sets to remove locations, names, or other potentially identifying information from the aggregated project data  904 . 
     Based on training analysis performed on the aggregated project data  904 , training component  210  can learn design patterns and associations from the collected project data to facilitate faster training of the analytic module  520  used by the project generation component  206 . Training component  210  can apply any suitable type of analytics to the aggregated project data  904 , including but not limited to artificial intelligence analysis, machine learning, heuristics, statistical deep learning models, etc. 
     The training analysis performed by the training component  210  can include, for example, analyzing the aggregated project data  904  to identify design patterns, or frequently used approaches to designing certain types of industrial applications or automation functions. For example, training component  210  can identify, based on analysis of the aggregated project data  904 , that certain types of control functions—e.g., palletizing, flow control, web tension control, conveyor control, pick-and-place functions, etc.—are frequently programmed using control code that is generally similar in form across different system projects  302 . Based on this observation, training component  210  can train analytic module  520  to recognize when a designer is developing a system project  302  that includes one of the identified control functions (based on analysis of design input  512 ) and either recommend or insert a code module  508  corresponding to the matching control code. In some embodiments, training component  210  may also generate a new code module  508  that contains the learned control code and store the new code module  508  in the IDE system&#39;s industry knowledgebase for subsequent retrieval by the project generation component  206 . Training module  210  can classify this generated code module  508  in the industry knowledgebase according to one or more of an appropriate industrial vertical (e.g., automotive, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.), industrial application, or type of machine to which the code module  508  relates, per the training analysis results. 
     Training component  210  can apply similar training analysis to identify common ways in which developers create visualizations (e.g., HMI screens or animation objects, dashboards, mashups, AR/VR visualization objects, etc.) for various types of machines, processes, or automation applications. Based on results of such analysis, training component  210  can train analytic module  520  to recognize these design scenarios (e.g., to identify when a developer&#39;s system project  302  includes a machine, process, or automation function for which a common visualization  510  has been identified) and either recommend or automatically add the appropriate visualization  510  to the system project  302 . 
     In some embodiments, training module  210  can also analyze subsets of aggregated project data  904  that include engineering drawings to learn user-defined associations between drawing elements and automation objects  222 , code modules  508 , or visualizations  510 . For example, some system projects  302  may include engineering drawings (e.g., P&amp;ID drawings, mechanical drawings, flow diagrams, etc.) that include drawing elements representing such industrial assets as tanks, pumps, safety devices, motor drives, power supplies, piping, etc. The system projects  302  may also include user-defined control code modules  508 , visualizations  510  (e.g., HMI objects or screens, dashboards, AR/VR objects, etc.), and/or automation objects  222  having defined associations with elements represented by the drawings. Training module  210  can be configured to recognize, based on training analysis performed on the aggregated project data  904 , that designers frequently associate particular code modules  508 , visualizations  510 , or automation objects  222  with respective drawing elements. Based on these learned associations, training module  210  can train the analytic module  520  to automatically create these associations in new system projects  302  when these drawing elements are discovered in engineering drawings submitted to the IDE system  202  (as described above in connection with  FIG.  5   ). For example, if a developer creates or submits a P&amp;ID drawing comprising drawing elements for which commonly associated code modules  508 , visualizations  510 , or automation objects  222  have been discovered by the training component  210 , project generation component  206  can map the drawing elements with the appropriate project elements in accordance with the trained analytic module  520 . 
     In some embodiments, training component  210  can also analyze aggregated project data  904  to learn correlations between design goals specified by the design input  512  (e.g., a goal that a bottling line must be capable of producing a specified minimum number of bottles per second during normal operation, a material transfer operation, a web tension control requirement, waste water treatment requirements, etc.) and code, visualizations, automation objects, device configurations or parameter settings, drawings, or other project elements that are frequently generated by developers to satisfy these design goals. Based on these learned associations, training component  210  can train the analytic module  520  to recommend or implement these frequently used project elements when subsequent design input  512  specifying the design goal is received. 
     In addition to using the trained analytic module  520  in connection with developing new system projects  302 , some embodiments of IDE system  202  can also use the analytic module  520  to convert legacy control projects that were developed using other development platforms to system projects  302  that accord with the object-based system project format supported by IDE system  202 .  FIG.  11    is a diagram illustrating submission of a legacy control project  1102  to the IDE system  202  for conversion into an object-based system project  302 . In this embodiment, IDE system  202  includes a conversion component  212  configured to receive legacy control project data  1102  submitted by a developer (e.g., a ladder logic program file, a structured text program file, a function block diagram program file, a sequential function chart file, etc.) and convert the legacy control project data  1102  to a system project  302  having one or more of the project features described above. This allows existing control projects to be migrated to the platform supported by IDE system  202 . 
     For example, in response to receipt of legacy control project data  1102 , conversion component  212  can intelligently map existing code routines or other project elements discovered in the legacy control project data  1102  to respective automation objects  222  or code modules  508 . When mapping legacy project elements to automation objects  222 , conversion component  212  may either map an existing automation object  222  from the automation object library  502  to appropriate elements discovered in the legacy project, or may generate a new automation object  222  for inclusion in the new system project  302 . In the former case, conversion component  212  can be configured to recognize segments of control code within the legacy control project data  1102  that correspond to an equivalent automation object  222  available in the automation object library  502 . For example, conversion component  212  may recognize that a code segment within the legacy project is intended to control a certain industrial asset (e.g., a pump, a valve, a stamping press, etc.) for which an automation object  222  is available in the automation object library  502 . Accordingly, conversion component  212  can replace or supplement this code segment in the new system project  302  with the appropriate automation object  222  corresponding to this industrial asset. If the automation object  222  for the asset has an associated recommended visualization for rendering a graphical representation of the asset (e.g., on an HMI or AR/VR application), this visualization will also be included in the new system project  302 . 
     In another example, the conversion component  212  may recognize that a certain segment of control code is used multiple times within the imported legacy control project data  1102 . Based on this recognition, conversion component  212  can create a new automation object  222  representing this code segment and use this new automation object  222  within the new system project  302  in place of the original code segment. 
     Conversion component  212  can leverage analytic module  520  in connection with recognizing code segments having associated automation objects  222 . In this regard, training component  210  can learn to recognize such code segments based on the training analysis performed on aggregated project data  904 , and train the analytic module  520  to recognize these code segments in subsequent legacy projects. The new automation object  222  can include logic corresponding to the code segments, alarm definitions for the code segment, the ability to record historical data for the code segment, or any other automation object properties discussed above in connection with  FIG.  4   . 
     Conversion component  212  can also optimize or standardize segments of control code discovered in the legacy project. For example, conversion component  212  can be configured to infer control functionality of a control code segment discovered within the legacy control project data  1102 , and to determine whether a predefined code module  508  for performing this inferred functionality is available in the IDE system&#39;s industry knowledgebase. If such a code module  508  is available, the conversion component  212  will replace the original control code segment with the appropriate code module  508  in the new system project  302 . This can effectively convert previously written control code to a preferred, standardized format represented by the pre-defined code modules  508 . To assist with mapping of legacy code segments to code modules  508 , training component  210  can train the analytic module  520  to recognize legacy code segments that correspond to certain types of automation functions having corresponding predefined code modules  508 . Conversion component  212  can then leverage analytic module  520  during conversion in connection with performing these mappings. 
     In some embodiments, conversion component  212  can also perform transformations on control code found in the legacy control project data  1102  to optimize the control programming; e.g., by discovering and removing dead code, rewriting code portions to remove complexity, etc. 
     In some embodiments, conversion component  212  can also discern inherent hierarchies within imported legacy code based on recognition of which segments of code pass data to each other, and re-organize the control code in the new system project  302  based on these discovered hierarchies. The discovered hierarchies can also be used to define hierarchical relationships between any automation objects  222  added to the new system project  302 , or can be incorporated into the IDE system&#39;s model of the plant. As in previous examples, analytic module  520  can be trained by the training component  210  to assist the conversion component  212  to recognize these hierarchies. 
     Also, in some embodiments, conversion component  212  can be configured to generate engineering documents from imported legacy code by reverse engineering algorithmic flowcharts or state machines that were used as the basis for writing the original code. These engineering documents can include, but are not limited to, state machine diagrams representing the control algorithm implemented by the control program, I/O drawings generated based on discovery of inputs and outputs defined in the control program, bills of material, or other such documentation. 
     When legacy control project data  1102  is converted to a new system project  302  as described above, at least a portion of the resulting system project  320  can be added to the aggregated project data  904  to enhance the set of training data used by the training component  210  to train the analytic module  520 . In some embodiments, portions of the legacy control project data  1102  can also be stored in association with the system project  302  as part of the aggregated project data  904  to assist the training component  210  in learning to recognize legacy code segments that correspond to automation objects  222 , code modules  508 , visualizations  510 , engineering documents, or other system project elements. Thus, as more legacy control projects  1102  are converted, the base of aggregated project data  904  is increased, and the accuracy of the analytic module  520  is improved. 
     In some embodiments, in addition to maintaining a global analytic module  520  for generating and converting system projects, IDE system  202  can also allow third parties (e.g., OEMs, system integrators, etc.) to create their own analytic module  520  and perform conversions of their customers&#39; projects. For example, a secure portion of the cloud platform  806  can be allotted to the third party and instances of the IDE system&#39;s services can be instantiated on the third party&#39;s secure portion of the cloud. Using these segregated services, third parties can allow their customers to provide their legacy project data to the OEM&#39;s platform, which converts the project data and provides a new version based on a customized analytic module  520  provided by the third party. 
     In some embodiments, IDE system  202  can provide further training to the analytic module  520  based on runtime data collected from automation systems at the industrial enterprises  902  after system projects  302  have been deployed. In some implementations, analysis of runtime or performance data for the purposes of training an analytic module  520  can be performed separately for different third-party users of the IDE system  202 . This can allow a third party with a large customer base to anonymously collect project data from their customers for the purpose of improving their services. For example, an OEM that manufactures turbines may wish to collect performance metrics on all their installed turbines across their customer base to learn performance patterns. In this scenario, IDE system  202  can serve as a trusted proxy that collects this information anonymously (agnostic to turbine owner) and provide this information to the OEM. The data provided to the OEM will be aggregated and abstracted from the asset owners. In such embodiments, the OEMs can provide an application programming interface (API) to the data exchange layer that ensures data from their customers will be output in a format readable by the OEM. 
     In some embodiments, automation objects  222  that make up a system project  302  can also be configured with runtime analytic capabilities that allow the automation objects to learn the runtime behavior of their corresponding industrial assets based on analysis of real-time performance data collected from the automation systems represented by system project  302 . The automation objects  222  can store this learned runtime behavior as part of their identity. This information can be used for a variety of purposes, including but not limited to predictive analysis, further training of the analytic module  520 , automatic reconfiguration of system project elements (e.g., control code, visualizations, etc.) based on learned runtime behaviors of the industrial assets, or other such uses. 
       FIGS.  12 - 14     b  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.  12    illustrates an example methodology  1200  for developing an industrial automation system project using an industrial IDE system with the aid of a trained analytic module. Initially at  1202 , industrial design data for an automation system being developed is received via interaction with an industrial IDE system. The industrial design data can comprise, for example, control programming, visualization development input, specified design goals or specifications for the automation system, or other such design input. At  1204 , design feedback is rendered by the IDE system based on analysis of the design data performed using a trained analytic module. Example design feedback can include, for example, control code syntax highlighting or error highlighting designed to enforce in-house or industry-standard coding practices, suggestions for rewriting or reorganizing control code to conform to defined programming standards, suggested automation objects to be added to the design project based on an inference of the programmer&#39;s intentions, or other such feedback. 
     At  1206 , portions of an industrial automation system project are generated based on analysis of the design data performed using the trained analytic module. This can include, for example, automatically adding selected predefined code modules for performing automation functions inferred from the design input, automatically adding automation objects corresponding to industrial assets represented by the design input, or other such development functions. 
     At  1208 , a determination is made as to whether project development is complete. This determination may be made, for example, in response to an indication from the developer that the automation system project is ready to be parsed and compiled. If development is not complete (NO at step  1208 ) the methodology returns to step  1202  and development continues. Steps  1202 - 1206  are repeated until development is complete (YES at step  1208 ), at which time the methodology proceeds to step  1210 . 
     At  1210 , the industrial automation system project is compiled into one or more executable files that can be deployed and executed on at least one of an industrial control device (e.g., a PLC or another type of industrial control device), a human-machine interface terminal, or another type of industrial device. At  1212 , at least a portion of the system project is added to a store of aggregated project data to be used as training data for training the analytic module. 
       FIG.  13   , illustrates an example methodology  1300  for converting a legacy industrial control program to an upgraded format by an industrial IDE system using a trained analytic module. Initially, at  1302 , an industrial control program file is received at the industrial IDE system. The program file may be, for example, a ladder logic program file, a sequential function chart program file, a function block diagram program file, a structured text program file, or a control program file of another format. At  1304 , a conversion is performed on the industrial control program file received at step  1302  to yield an upgraded system project, wherein the conversion is performed based on an analysis of the industrial control program file using a trained analytic module. The conversion can involve, for example, replacing code segments in the legacy program file with predefined code segments that perform similar or equivalent functionality, replacing code segments with automation objects having corresponding functionality, removing unused code, reorganizing the program code based on discovered relationships or hierarchies within the program, or other such conversion functions. 
     At  1306 , at least a portion of the system project and the original program file are added to a store of aggregated project data to be used as training data for training the analytic model used to perform the conversion. 
       FIG.  14   a    illustrates a first part of an example methodology  1400   a  for converting a legacy industrial control program to an upgraded format by an industrial IDE system using a trained analytic module. Initially, at  1402 , an industrial control programming file (e.g., a ladder logic file, a structured text file, a function block diagram file, etc.) is received at an industrial IDE system. At  1404 , the industrial control program file is analyzed using a trained analytic module. At  1406 , an industrial automation system project is generated based on results of the analysis performed at step  1404 . 
     At  1408 , a determination is made as to whether code segments are discovered in the control program file for which corresponding automation objects supported by the IDE system are available. This determination can be made based on an inference of the functionality of the code segments, as determined based in part on the trained analytic module. If such code segments are discovered (YES at step  1408 ), the methodology proceeds to step  1410 , where the discovered code segments are replaced with the corresponding automation objects in the system project generated at step  1406 . If no such code segments are discovered (NO at step  1408 ), the methodology proceeds without performing step  1410 . 
     The methodology continues with the second part  1400   b  illustrated in  FIG.  14   b   . At  1412 , a determination is made as to whether repeatedly used code segments are present within the program file for which an automation object can be generated. This determination can be made based on an inference of the functionality of the repeated code segments, as inferred based in part on the trained analytic module. If such code segments are discovered (YES at step  1412 ), the methodology proceeds to step  1414 , where an automation object corresponding to the code segments discovered at step  1412  is generated, and the code segments are replaced in the system project with the generated automation object. 
     If no repeatedly used code segments for which an automation object can be generated are discovered at step  1412  (NO at step  1412 ), the methodology proceeds to step  1416  without performing step  1414 . At  1416 , a determination is made as to whether a code segment is discovered in the program file that performs an automation function for which a predefined code module is available. This determination can be made based on an inference of the functionality of the code segment, as inferred based in part on the trained analytic module. If such a code segment is discovered (YES at step  1416 ), the methodology proceeds to step  1418 , where the code segment is replaced in the system project with the predefined code module. 
     If no such code segment is discovered (NO at step  1416 ), the methodology proceeds to step  1420  without performing step  1418 . At  1420 , at least a portion of the system project generated as a result of steps  1406 - 1418  and the industrial control program file received at step  1402  are added to a store of aggregated project data to be used as training data for training the analytic module. 
     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.  15  and  16    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. 
     With reference again to  FIG.  15   , the example environment  1500  for implementing various embodiments of the aspects described herein includes a computer  1502 , the computer  1502  including a processing unit  1504 , a system memory  1506  and a system bus  1508 . The system bus  1508  couples system components including, but not limited to, the system memory  1506  to the processing unit  1504 . The processing unit  1504  can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit  1504 . 
     The system bus  1508  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  1506  includes ROM  1510  and RAM  1512 . A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  1502 , such as during startup. The RAM  1512  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  1502  further includes an internal hard disk drive (HDD)  1514  (e.g., EIDE, SATA), one or more external storage devices  1516  (e.g., a magnetic floppy disk drive (FDD)  1516 , a memory stick or flash drive reader, a memory card reader, etc.) and an optical disk drive  1520  (e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD  1514  is illustrated as located within the computer  1502 , the internal HDD  1514  can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment  1500 , a solid state drive (SSD) could be used in addition to, or in place of, an HDD  1514 . The HDD  1514 , external storage device(s)  1516  and optical disk drive  1520  can be connected to the system bus  1508  by an HDD interface  1524 , an external storage interface  1526  and an optical drive interface  1528 , respectively. The interface  1524  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein. 
     The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  1502 , the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein. 
     A number of program modules can be stored in the drives and RAM  1512 , including an operating system  1530 , one or more application programs  1532 , other program modules  1534  and program data  1536 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  1512 . The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems. 
     Computer  1502  can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system  1530 , and the emulated hardware can optionally be different from the hardware illustrated in  FIG.  15   . In such an embodiment, operating system  1530  can comprise one virtual machine (VM) of multiple VMs hosted at computer  1502 . Furthermore, operating system  1530  can provide runtime environments, such as the Java runtime environment or the .NET framework, for application programs  1532 . Runtime environments are consistent execution environments that allow application programs  1532  to run on any operating system that includes the runtime environment. Similarly, operating system  1530  can support containers, and application programs  1532  can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application. 
     Further, computer  1502  can be enable with a security module, such as a trusted processing module (TPM). For instance with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer  1502 , e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution. 
     A user can enter commands and information into the computer  1502  through one or more wired/wireless input devices, e.g., a keyboard  1538 , a touch screen  1540 , and a pointing device, such as a mouse  1542 . Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit  1504  through an input device interface  1544  that can be coupled to the system bus  1508 , but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc. 
     A monitor  1544  or other type of display device can be also connected to the system bus  1508  via an interface, such as a video adapter  1546 . In addition to the monitor  1544 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  1502  can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  1548 . The remote computer(s)  1548  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  1502 , although, for purposes of brevity, only a memory/storage device  1550  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  1552  and/or larger networks, e.g., a wide area network (WAN)  1554 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet. 
     When used in a LAN networking environment, the computer  1502  can be connected to the local network  1552  through a wired and/or wireless communication network interface or adapter  1556 . The adapter  1556  can facilitate wired or wireless communication to the LAN  1552 , which can also include a wireless access point (AP) disposed thereon for communicating with the adapter  1556  in a wireless mode. 
     When used in a WAN networking environment, the computer  1502  can include a modem  1558  or can be connected to a communications server on the WAN  1554  via other means for establishing communications over the WAN  1554 , such as by way of the Internet. The modem  1558 , which can be internal or external and a wired or wireless device, can be connected to the system bus  1508  via the input device interface  1542 . In a networked environment, program modules depicted relative to the computer  1502  or portions thereof, can be stored in the remote memory/storage device  1550 . It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used. 
     When used in either a LAN or WAN networking environment, the computer  1502  can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices  1516  as described above. Generally, a connection between the computer  1502  and a cloud storage system can be established over a LAN  1552  or WAN  1554  e.g., by the adapter  1556  or modem  1558 , respectively. Upon connecting the computer  1502  to an associated cloud storage system, the external storage interface  1526  can, with the aid of the adapter  1556  and/or modem  1558 , manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface  1526  can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer  1502 . 
     The computer  1502  can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
       FIG.  16    is a schematic block diagram of a sample computing environment  1600  with which the disclosed subject matter can interact. The sample computing environment  1600  includes one or more client(s)  1602 . The client(s)  1602  can be hardware and/or software (e.g., threads, processes, computing devices). The sample computing environment  1600  also includes one or more server(s)  1604 . The server(s)  1604  can also be hardware and/or software (e.g., threads, processes, computing devices). The servers  1604  can house threads to perform transformations by employing one or more embodiments as described herein, for example. One possible communication between a client  1602  and servers  1604  can be in the form of a data packet adapted to be transmitted between two or more computer processes. The sample computing environment  1600  includes a communication framework  1606  that can be employed to facilitate communications between the client(s)  1602  and the server(s)  1604 . The client(s)  1602  are operably connected to one or more client data store(s)  1608  that can be employed to store information local to the client(s)  1602 . Similarly, the server(s)  1604  are operably connected to one or more server data store(s)  1610  that can be employed to store information local to the servers  1604 . 
     What has been described above includes examples of the subject innovation. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the disclosed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the subject innovation are possible. Accordingly, the disclosed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. 
     In particular and in regard to the various functions performed by the above described components, devices, circuits, systems and the like, the terms (including a reference to a “means”) used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., a functional equivalent), even though not structurally equivalent to the disclosed structure, which performs the function in the herein illustrated exemplary aspects of the disclosed subject matter. In this regard, it will also be recognized that the disclosed subject matter includes a system as well as a computer-readable medium having computer-executable instructions for performing the acts and/or events of the various methods of the disclosed subject matter. 
     In addition, while a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes,” and “including” and variants thereof are used in either the detailed description or the claims, these terms are intended to be inclusive in a manner similar to the term “comprising.” 
     In this application, the word “exemplary” is used to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. 
     Various aspects or features described herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips . . . ), optical disks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ], smart cards, and flash memory devices (e.g., card, stick, key drive . . . ).