Method and system for configuring components in industrial control systems

Abstract

The present disclosure relates to a method of generating recommendations for configuring components of industrial control systems. The method comprises monitoring configuration parameters of a component (102) of an industrial control system, during configuration of the component (102). The configuration parameters correspond to configuration requirements of the component (102). Further, the method comprises generating one or more recommendations for configuring the component (102), based on a configuration framework corresponding to the configuration requirements. The configuration framework is selected from a plurality of configuration frameworks using an Artificial Intelligence (Al) model (108). Each of the plurality of configuration frameworks is generated by mapping the configuration requirements with the corresponding configuration parameters of the component (102).

Description

TITLE: “METHOD AND SYSTEM FOR CONFIGURING COMPONENTS IN INDUSTRIAL CONTROL SYSTEMS”TECHNICAL FIELD

[0001] The present disclosure generally relates to industrial automation. More particularly, the present disclosure relates to methods and systems for configuring components in industrial control systems.BACKGROUND

[0002] An industrial control system is an electronic control system for automated control and operation of the industrial processes. Industrial control systems include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), Industrial Automation and Control Systems (IACS), Programmable Logic Controllers (PLCs), Programmable Automation Controllers (PACs), and the like.

[0003] Industrial control systems manage different components such as, field components / equipments, controllers, network components, graphic components, and the like. Various functions of the components such as control logic execution, historian management, alarm management, and the like, are combined into a single integrated control system. Real world objects such as tanks, motors, valves, etc., are digitally represented as objects in the industrial control systems. The industrial control systems monitor and control these objects to enable processes such as asset management, production management or any other processes that requires control and monitoring.

[0004] The components of the industrial control system need to be configured and maintained for proper operation and functioning of the industrial control systems. The components of the industrial control system are configured manually by a user (for example, an operator or an engineer) associated with the industrial control systems. The user needs to understand plant layout and design, create relevant libraries and / or topologies, and configure the components. During each step of configuration process, the user must refer to necessary documentation.

[0005] There are several challenges associated with configuration of the components in the industrial control systems. The manual configuration of the components is prone to human error. These errors can lead to system malfunctions, safety hazards, or operationalinefficiencies. Engineering or configuring the components typically involves repetitive tasks, such as creating and configuring control loops, defining alarm settings, and the like. These tasks can be time-consuming and labour-intensive, which reduces productivity. Further, there may be certain inconsistencies in design, configuration, and documentation of the industrial control system, making it challenging to maintain and troubleshoot the industrial control system. The user may face difficulty in coping with increasing complexity and scale of modem industrial control systems. Also, there are challenges in terms of collaboration and information sharing among users / engineers.

[0006] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.SUMMARY

[0007] In an embodiment, the present disclosure discloses a method of generating recommendations for configuring components of industrial control systems. The method comprises monitoring configuration parameters of a component of an industrial control system, during configuration of the component. The configuration parameters correspond to configuration requirements of the component. Further, the method comprises generating one or more recommendations for configuring the component, based on a configuration framework corresponding to the configuration requirements. The configuration framework is selected from a plurality of configuration frameworks using an Artificial Intelligence (Al) model. Each of the plurality of configuration frameworks is generated by mapping the configuration requirements with the corresponding configuration parameters of the component.

[0008] In an embodiment, the present disclosure discloses a method of generating configuration frameworks for components of industrial control systems. The method comprises obtaining control data associated with a component of an industrial control system. The control data comprises configuration requirements and configuration parameters of the component. Further, the method comprises mapping the configuration requirements of the component with corresponding configuration parameters, using an Al model. Thereafter, the method comprises generating a plurality of configuration frameworks for the component, based on the mapping.

[0009] In an embodiment, the present disclosure discloses a method of generating configuration frameworks for components of industrial control systems. The method comprises obtaining control data associated with a component of an industrial control system. The control data comprises configuration requirements and configuration parameters of the component. Further, the method comprises mapping the configuration requirements of the component with corresponding configuration parameters, using an Al model. Furthermore, the method comprises generating a plurality of configuration frameworks for the component, based on the mapping. Thereafter, the method comprises performing fine-tuning of the Al model, based on an input received from a user for at least one configuration framework among the plurality of configuration frameworks.

[0010] In an embodiment, the present disclosure discloses a computing system for generating recommendations for configuring components of industrial control systems. The computing system comprises a processor and a memory. The processor is configured to monitor configuration parameters of a component of an industrial control system, during configuration of the component. The configuration parameters correspond to configuration requirements of the component. Further, the processor is configured to generate one or more recommendations for configuring the component, based on a configuration framework corresponding to the configuration requirements. The configuration framework is selected from a plurality of configuration frameworks using an Artificial Intelligence (Al) model. Each of the plurality of configuration frameworks is generated by mapping the configuration requirements with the corresponding configuration parameters of the component.

[0011] In an embodiment, the present disclosure discloses a computing system for generating configuration frameworks for components of industrial control systems. The computing system comprises a processor and a memory. The processor is configured to obtain control data associated with a component of an industrial control system. The control data comprises configuration requirements and configuration parameters of the component. Further, the processor is configured to map the configuration requirements of the component with corresponding configuration parameters, using an Al model. Thereafter, the processor is configured to generate a plurality of configuration frameworks for the component, based on the mapping.

[0012] In an embodiment, the present disclosure discloses a computing system for generating configuration frameworks for components of industrial control systems. The computing system comprises a processor and a memory. The processor is configured to obtain control data associated with a component of an industrial control system. The control data comprises configuration requirements and configuration parameters of the component. Further, the processor is configured to map the configuration requirements of the component with corresponding configuration parameters, using an Al model. Furthermore, the processor is configured to generate a plurality of configuration frameworks for the component, based on the mapping. Thereafter, the processor is configured to perform fine-tuning of the Al model, based on an input received from a user for at least one configuration framework among the plurality of configuration frameworks.

[0013] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

[0014] The novel features and characteristics of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying figures. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:

[0015] Figure 1 illustrates an exemplary environment for configuring components of industrial control systems, in accordance with some embodiments of the present disclosure;

[0016] Figure 2 illustrates a detailed diagram of a computing system for configuring components of industrial control systems, in accordance with some embodiments of the present disclosure;

[0017] Figures 3A and 3B show exemplary illustrations for generating configuration frameworks for components of industrial control systems, in accordance with some embodiments of the present disclosure;

[0018] Figures 4A-4C show exemplary illustrations for generating recommendations for configuring components of industrial control systems, in accordance with some embodiments of the present disclosure;

[0019] Figure 5 shows an exemplary flow chart illustrating method steps for generating recommendations for configuring components of industrial control systems, in accordance with some embodiments of the present disclosure

[0020] Figures 6 and 7 show exemplary flow charts illustrating method steps for generating configuration frameworks for components of industrial control systems, in accordance with some embodiments of the present disclosure; and

[0021] Figure 8 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

[0022] It should be appreciated by those skilled in the art that any block diagram herein represents conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.DETAILED DESCRIPTION

[0023] In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit thedisclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0025] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises. . . a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0026] The present disclosure provides methods and systems for configuring components of industrial control systems. In the present disclosure, an Artificial Intelligence (Al) model is trained by providing historical configuration requirements and corresponding historical configuration data used for configuring the components of the industrial control systems. The Al model is trained to map the configuration requirements with specific configuration data to generate configuration frameworks. For instance, the Al model identifies a configuration snippet corresponding to a control narrative defined for configuring a component of the industrial control system. The Al model generates multiple configuration frameworks for configuring various components of the industrial control system. When a user is configuring a component of the industrial control system in real time, the Al model monitors the user configuring the component according to configuration requirements. The Al model selects relevant configuration framework based on the configuration requirements, generates recommendations configuring of the component based on the configuration framework. The recommendations may include, generating configuration data, assisting in completion of configuration programs, identifying errors in configuration, and suggesting relevant actions to correct the errors, and the like.

[0027] In this way, the Al model assists the user in configuring the components of the industrial control system. The present disclosure enables reduction of errors and time required in configuration of the components. This enhances productivity, efficiency, and collaboration in configuration of the components of the industrial control systems. In the present disclosure, the Al model is fine-tuned based on inputs received from the user to update the generated configuration frameworks. Also, the Al model is fine-tuned based on inputs received from theuser for the recommendations. In this way, the present disclosure enables receiving feedback from users and improvement in the performance of the Al model.

[0028] Figure 1 illustrates an exemplary environment 100 for configuring components of industrial control systems, in accordance with some embodiments of the present disclosure. The exemplary environment 100 comprises a component 102, a user 104, and a computing system 106. The component 102 is a part of an industrial control system (not illustrated in Figures). An industrial control system is an electronic control system for automated control and operation of the industrial processes. The industrial control system may include, but are not limited to, Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), Industrial Automation and Control Systems (IACS), Programmable Logic Controllers (PLCs), and Programmable Automation Controllers (PACs). The industrial control system comprises various components such as field components / equipments, controllers, network components, graphic components, and the like. A single component 102 of the industrial control system is shown in Figure 1 for illustrative purposes only, and this should not be considered as limiting. The component 102 may include a field component, a control component or a controller, an interface element or a graphic component, a network component, and the like. The component 102 needs to be configured to enable necessary operation in the industrial control system. The component 102 may be configured by the user 104 and the user 104 may be an operator of the component 102.

[0029] In the present disclosure, the computing system 106 is configured to generate recommendations for configuring the component 102 of the industrial control system. Herein, the computing system 106 monitors configuration parameters of the component 102 when the user 104 is configuring the component 102. The user 104 configures the component 102 based on configuration requirements of the component 102. For instance, the component 102 may be a controller associated with a motor. In the present description, the configuration of the component 102 includes hardware / software configuration of the component 102 and configuration processes associated with the component 102 such as bulk import, testing, commissioning, documentation, and the like.

[0030] The configuration requirements of the component 102 may comprise a control narrative defining control settings of a controller. The user 104 may configure the component 102 based on the control narrative. The computing system 106 may monitor the configuration parameters (for example, the control settings) of the component 102. Further, the computing system 106may generate one or more recommendations for configuring the component 102, based on a configuration framework corresponding to the configuration requirements, using an Artificial Intelligence (Al) model 108 associated with the computing system 106. The configuration framework is selected from a plurality of configuration frameworks based on the configuration requirements. Referring to the above-stated example, the control narrative may specifically define speed settings of the controller. The computing system 106 may select the configuration framework including configurations for setting speed of the controller. The computing system 106 may generate the one or more recommendations based on the selected configuration framework. For instance, the computing system 106 may generate a recommendation including a control logic for setting the speed of the controller. Such recommendations may be transmitted / displayed to the user 104.

[0031] The computing system 106 generates the plurality of configuration frameworks using the Al model 108. Herein, the computing system 106 obtains control data associated with the component 102. The control data comprises configuration requirements and configuration parameters of the component 102. Referring to the above-stated example, the control data associated with the controller of the motor may include the configuration requirements for speed settings, torque settings, current settings, duty cycle values, Input / Output (VO) connections and corresponding configurations for configuring the settings and the connections. The computing system 106 maps the configuration requirements of the component 102 with corresponding configuration parameters, using the Al model 108. For instance, the computing system 106 identifies a code snippet for setting duty cycle values and maps the code snippet with the control requirement for setting the duty cycle values. The computing system 106 generates a plurality of configuration frameworks for the component 102, based on the mapping. For instance, the control requirement associated with setting the duty cycle values along with the code snippet forms the configuration framework. The computing system 106 may receive an input from a user to update the configuration framework. In such case, the Al model 108 may be fine-tuned to update the configuration framework based on the user input.

[0032] The computing system 106 may be a computing device such as, a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, a cloud-based server, router, and the like. In one implementation, the computing system 106 may be a part of the industrial control system. In another implementation, the computing system 106 may be implemented in an edge computingplatform. In yet another implementation, the computing system 106 may be implemented in a cloud computing platform. In an embodiment, a single computing system 106 may be used to generate the one or more recommendations and the plurality of configuration frameworks. In another embodiment, different computing systems may be used to generate the one or more recommendations and the plurality of configuration frameworks.

[0033] Figure 2 illustrates a detailed diagram of the computing system 106 for configuring components of industrial control system, in accordance with some embodiments of the present disclosure. The computing system 106 comprises an Input / Output (I / O) interface 202, a memory 204, and a processor 206. In some embodiments, the memory 204 may be communicatively coupled to the processor 206. The memory 204 stores instructions executable by the processor 206. The processor 206 may comprise at least one data processor for executing program components for executing user or system-generated requests. The memory 204 may be communicatively coupled to the processor 206. The memory 204 stores instructions, executable by the processor 206, which, on execution, may cause the processor 206 to generate the one or more recommendations and the plurality of configuration frameworks for configuring the components of the industrial control system.

[0034] In an embodiment, the memory 204 may include one or more modules 210 and computation data 208. The one or more modules 210 may be configured to perform the steps of the present disclosure using the computation data 208, to generate the one or more recommendations and the plurality of configuration frameworks for configuring the components of the industrial control system. In an embodiment, each of the one or more modules 210 may be a hardware unit which may be outside the memory 204 and coupled with the computing system 106. As used herein, the term modules 310 refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and / or other suitable components that provide described functionality. The one or more modules 210 when configured with the described functionality defined in the present disclosure will result in a novel hardware. Further, the I / O interface 202 is coupled with the processor 206 through which an input signal or / and an output signal is communicated. For example, the computing system 106 may communicate the one or more recommendations to the user 104 via the I / O interface 202.

[0035] In one implementation, the modules 210 may include, for example, an input module 226, a mapping module 228, a framework generation module 230, a fine-tuning module 232, a monitoring module 234, a recommendation generation module 236, and auxiliary modules 238. It will be appreciated that such aforementioned modules 210 may be represented as a single module or a combination of different modules. In one implementation, the computation data 208 may include, for example, input data 212, mapping data 214, framework data 216, fine- tuning data 218, monitoring data 220, recommendation data 222, and auxiliary data 224.

[0036] In an embodiment, the input module 226 may be configured to obtain control data associated with the component 102 of the industrial control system. The control data comprises configuration requirements and configuration parameters of the component 102. The configuration requirements of the component 102 may comprise one or more requirements specified for configuring the component 102. The configuration requirements specify conditions and identify variables for configuring the component 102. For instance, consider the component 102 is a controller. In such case, the control requirement may include a control narrative defining alarm set points and shutdown values. In another example, consider the component 102 is a Human Machine Interface (HMI) element. In such case, the configuration requirements may specify serial ports, network ports, control panel requirements, mounting requirements (rack mount, panel mount, shelf mount, etc.), and the like. In yet another example, consider the component 102 as a network component. In such case, the configuration requirements may specify Internet Protocol (IP) address of each network interface on every machine, host names of each machine of the industrial control system. The configuration parameters of the component 102 may include control program, configuration diagrams or control diagrams, connection configurations, and the like.

[0037] In an embodiment, the input module 226 may obtain the control data associated with the component 102 as one or more files. The one or more files may comprise a control requirement file listing different configuration requirements of the component 102 and configuration files comprising configuration data or the configuration parameters of the component 102. For example, the control data obtained for the motor may include the control requirement file with different configuration requirements corresponding to different parameters of the motor and the configuration files including the configuration data for configuring the parameters of the motor. A person skilled in the art will appreciate that the control data may be obtained in any form other than the above-mentioned form, and this shouldnot be considered as limiting. Referring to Figure 3A, an exemplary control requirement including a control narrative 302 is illustrated. As shown, the control narrative defines specifications of controllers and valves for efficient separator operation. Referring back to Figure 2, the control data of the component 102 may be stored as the input data 212 in the memory 204.

[0038] In an embodiment, the mapping module 228 may be configured to receive the input data 212 from the input module 226. Further, the mapping module 228 may be configured to map the configuration requirements of the component 102 with corresponding configuration parameters, using an Al model 108. Herein, the Al model 108 is pre-trained with historical configuration parameters and historical configuration requirements associated with configuration of the component 102 as training datasets. The historical configuration parameters and the historical configuration requirements may be obtained from a historian or a database associated with the industrial control system. The historian may comprise stored data associated with past / historical configurations of components of the industrial control system. For instance, the historian may comprise control narratives and related manual configurations of control components performed by the operators. Further, the Al model 108 may be pretrained by providing data such as, but not limited to, Piping & Instrumentation Diagrams (P&ID), VO lists, signal lists and tag information.

[0039] In an embodiment, datasets provided to the Al model 108 may be split into training, validation, and test datasets. The Al model 108 may be trained using the training datasets. Then, the Al model 108 may be tuned using the validation dataset using techniques such as hyperparameter tuning. Finally, the performance of the Al model 108 may be evaluated on the test dataset. In an embodiment, the Al model 108 may be trained by providing negative datasets to improve performance of the Al model 108. The Al model 108 may be, but not limited to, a Large Language Model (LLM), a Recurrent Neural Network (RNN), a transformer model, and the like. A person skilled in the art will appreciate that the Al model 108 may be any model other than the above-mentioned models, and this should not be considered as limiting.

[0040] The mapping module 228 may map the configuration requirements of the component 102 with corresponding configuration parameters, using the Al model 108. In an embodiment, the mapping module 228 may parse the control data of the component 102 to identify the configuration requirements. The mapping module 228 may use any known parsing techniques, such as, top-down parsing technique to parse the control data. As the Al model 108 is providedwith the historical configuration requirements and the historical configuration parameters, the mapping module 228 may identify configuration parameters or configuration data corresponding to the identified control requirement, using the Al model 108. For instance, the Al model 108 may learn context of the obtained control data from the historical configuration requirements and the historical configuration parameters and track relationships in the control data like the words in a sentence, to map the configuration requirements of the component 102 with corresponding configuration parameters. A person skilled in the art will appreciate that any other techniques may be used to map the configuration requirements of the component 102 with corresponding configuration parameters, from the obtained data.

[0041] Referring again to Figure 3A, the mapping module 228 may map the control narrative 302 of a level controller with corresponding configuration snippet or tokenized passage or subset. In another example, the mapping module 228 may map the control requirement of an HMI element with an Abstract Syntax Tree representation of the code. Referring back to Figure 2, the configuration requirements mapped with corresponding configuration parameters of the component 102 may be stored as the mapping data 214 in the memory 204.

[0042] In an embodiment, the framework generation module 230 may be configured to receive the mapping data 214 from the mapping module 228. Further, the framework generation module 230 may be configured to generate a plurality of configuration frameworks for the component 102, based on the mapping. The framework generation module 230 may generate a configuration framework for the component 102 by aggregating the configuration parameters or the configuration data of the component 102 for various configuration requirements. For instance, the framework generation module 230 may generate a configuration framework for a flow level controller, comprising configuration data corresponding to various configuration requirements specified for configuring the flow level controller. The configuration framework may be a configuration file comprising control programs, control diagrams, connection configurations, and the like. In an embodiment, the framework generation module 230 may receive a prompt from a user / operator for generating the plurality of configuration frameworks. For instance, the framework generation module 230 may receive a natural language prompt from a user to generate a configuration framework for a specified configuration of a motor and associated controllers. The framework generation module 230 may generate the configuration framework based on the natural language prompt received from the user.

[0043] The framework generation module 230 may generate the plurality of configuration frameworks for different components of the industrial control system. For example, the plurality of configuration frameworks may include, but not be limited to, control code framework, graphics framework, field device configuration framework, and communication network configuration framework. Further, the framework generation module 230 may generate the plurality of configuration frameworks for various tests of the components of industrial control system. For example, the plurality of configuration frameworks may include, but not be limited to, system integration test, functional test, and factory acceptance test.

[0044] In an embodiment, the framework generation module 230 may be configured to generate the plurality of configuration frameworks for both greenfield scenario and brownfield scenario in the industrial control system. The greenfield scenario and brownfield scenario are different approaches for configuring the components in the industrial control system. The greenfield scenario refers to configuring the components from a start state or complete reengineering of the components. The brownfield scenario refers to configuring upgrades of the components such as hardware upgrades, software upgrades, communication interface upgrades, graphics upgrades, and the like. In the greenfield scenario, the framework generation module 230 may generate the plurality of configuration frameworks for the component 102 as stated in above paragraphs of the description. In the brownfield scenario, the framework generation module 230 may generate the plurality of configuration frameworks for the component 102 based on modification in control strategies according to the upgrades. For instance, the framework generation module 230 may receive the configuration requirements mapped with corresponding configuration parameters of the component 102 from the mapping module 228. The configuration requirements may be mapped with the corresponding configuration parameters based on a current configuration of the industrial control system, a target component for the upgrade, and modification in the control strategies. The framework generation module 230 may generate an updated configuration framework for the upgrade based on the modification in the control strategies.

[0045] Referring again to Figure 3A, the framework generation module 230 may generate a configuration framework 306 including a control code diagram. In an example, the configuration framework 306 may be an Extensible Markup Language (XML) file with a Resource Description Framework (RDF) representation of relationships between variables that enables generation of the control code diagram. Referring back to Figure 2, the plurality ofconfiguration frameworks for the component 102 may be stored as the framework data 216 in the memory 204.

[0046] In an embodiment, the fine-tuning module 232 may be configured to receive the framework data 216 from the framework generation module 230. Further, the fine-tuning module 232 may be configured to perform fine-tuning of the Al model 108. Herein, the fine- tuning module 232 may communicate the plurality of configuration frameworks for the component 102 to a user / operator of the component 102, via the VO interface 202. The fine- tuning module 232 may receive an input from the user for at least one configuration framework among the plurality of configuration frameworks. The fine-tuning module 232 may update the at least one configuration framework, based on the user input.

[0047] The fine-tuning of the Al model 108 is required to complete automatically generated configuration frameworks and integrate information from the plurality of configuration frameworks for a working and testable industrial control system. In embodiment, the fine- tuning may be performed by providing a prompt-based virtual assistant. The virtual assistant translates a prompt from the user to a set of tasks, a workflow, and then executes the workflow by discovering and invoking Application Programming Interfaces (APIs) needed for the execution of each task. An access to a list of APIs may be provided to the fine-tuning module 232 such as access to P&ID layout, elements, and relations, access to control libraries, access to graphic libraries, access to device libraries, access to standards, access to other models used in the industrial control system, and the like. Further, code snippets including diagrams or textual representation of the code, information including specific objects or snippets of code tagged to input specifications may be provided to the fine-tuning module 232.

[0048] Reference is made to Figure 3B illustrating the fine-tuning of the Al model 108. A user 308 may review each of the plurality of configuration frameworks generated for the component 102. The user 308 may provide an input to update a configuration framework 306 among the plurality of configuration frameworks. The user 308 may provide the input as “Add power outage handler”. The fine-tuning module 232 may discover relevant API as shown in 310 and invoke API as shown in 312. The fine-tuning module 232 may fetch libraries relevant to the power outage handler. Further, the fine-tuning module 232 may update the configuration framework 306 by adding configurations for the power outage handler. Referring back to Figure 2, the input received from the user and the updated configuration framework may be stored as the fine-tuning data 218 in the memory 204.

[0049] In an embodiment, the monitoring module 234 may be configured to monitor configuration parameters of the component 102 of the industrial control system, during configuration of the component 102. Herein, the monitoring module 234 monitors the configuration parameters of the component 102 when the user 104 is configuring the component 102. The user 104 configures the component 102 based on the configuration requirements of the component 102. The monitoring module 234 reviews the configuration parameters of the component 102 based on a configuration framework corresponding to the configuration requirements of the component 102. The configuration framework is selected from a plurality of configuration frameworks based on the configuration requirements. For example, the component 102 may be an HMI element. The configuration requirements of the component 102 may comprise HMI layout requirements. The user 104 may configure the component 102 based on the specified HMI layout requirements. The monitoring module 234 may review the configuration of the HMI element by the user 104, based on a configuration framework generated corresponding to the control requirement of the HMI element. The configuration requirements, the configuration parameters, and the configuration framework may be stored as the monitoring data 220 in the memory 204.

[0050] In an embodiment, the recommendation generation module 236 may be configured to receive the monitoring data 220 from the monitoring module 234. The recommendation generation module 236 may be configured to generate one or more recommendations for configuring the component 102, using the Al model 108. Herein, the recommendation generation module 236 may generate the one or more recommendations for configuring the component 102 based on monitoring of the configuration with respect to the selected configuration framework. In an embodiment, the Al model 108 for generating the one or more recommendations may be same as the Al model 108 for generating the plurality of configuration frameworks. In another embodiment, the Al model 108 for generating the one or more recommendations may be different than the Al model 108 for generating the plurality of configuration frameworks. In an embodiment, the one or more recommendations may correspond to generation of configuration data of the component 102. For example, consider that the user 104 is configuring a flow level controller. In such case, the recommendation generation module 236 may generate the one or more recommendations including control logic or skeletal code for configuring the flow level controller according to the configuration requirements. Hence, the recommendation generation module 236 assists the user 104 byproviding auto completion of code during configuration of the component 102. The auto completion of code may include generating entire code for configuration of the component 102 or completing remaining code when the user is configuring the component 102 or suggesting alternative code snippets. In another example, consider that the user 104 is creating graphics in graphics editor. In such case, the recommendation generation module 236 may generate the one or more recommendations for graphical designs, plant layout, process parameters that needs to be monitored, and the like. In yet another example, the recommendation generation module 236 may assist in auto creation of trend configuration for important process parameters, plant structures or system topology. In an example, the user 104 may be configuring a network component. The recommendation generation module 236 may assist in configuring security settings of the network component.

[0051] In another embodiment, the one or more recommendations may correspond to an error in the configuration of the component 102. For example, the recommendation generation module 236 may suggest potential fallacies in control code. In another example, the recommendation generation module 236 may assist in auto correcting of signal lists while configuration VO connections. In yet another embodiment, the one or more recommendations may correspond to a missed event during the configuration of the component 102. For example, the recommendation generation module 236 may suggest configuration of important alarms. A person skilled in the art will appreciate that the one or more recommendations may include recommendations other than the above-mentioned recommendations.

[0052] Referring to Figure 4A, the user 104 may be configuring pumps in the industrial control system according to the configuration requirements specified in a control narrative. The user 104 requires content that contains description of working of pump station control, list of set point specifications, and control code for corresponding control logic. The recommendation generation module 236 provides the one or more recommendations as illustrated in Figure 4A. The one or more recommendations include narrative extraction from the control narrative (a), extraction of the set point specifications (b), and the control code (c). Referring to Figure 4B, the user may be generating a set of test cases for configuring valves from a control code written in Structured Text (ST) 408. The recommendation generation module 236 provides the one or more recommendations including the set of test cases, as shown in 410. Referring to Figure 4C, the user may be configuring the pumps in the industrial control system using a control code 412. The recommendation generation module 236 may provide the one or morerecommendations to add alarm for monitoring pump failure and water levels to the control code 412, as shown in 414 . Additionally, the recommendation generation module 236 may assist in completion of the control code for the alarm.

[0053] The auxiliary data 224 may store data, including temporary data and temporary files, generated by the one or more modules 210 for performing the various functions of the computing system 106. The one or more modules 210 may also include the auxiliary modules 238 to perform various miscellaneous functionalities of the computing system 106. The auxiliary data 224 may be stored in the memory 204. It will be appreciated that the one or more modules 210 may be represented as a single module or a combination of different modules.

[0054] Figure 5 shows an exemplary flow chart illustrating method steps for generating the one or more recommendation for configuring the components of the industrial control system, in accordance with some embodiments of the present disclosure. As illustrated in Figure 5, the method 500 may comprise one or more steps. The method 500 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

[0055] The order in which the method 500 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0056] At step 502, the computing system 106 monitors the configuration parameters of the component 102 when the user 104 is configuring the component 102. The user 104 configures the component 102 based on the configuration requirements of the component 102. The computing system 106 reviews the configuration parameters of the component 102 based on a configuration framework corresponding to the configuration requirements of the component 102. The configuration framework is selected from a plurality of configuration frameworks based on the configuration requirements.

[0057] At step 504, the computing system 106 generates the one or more recommendations for configuring the component 102, using the Al model 108. Herein, the computing system 106 may generate the one or more recommendations for configuring the component 102 based on monitoring of the configuration with respect to the selected configuration framework. In an embodiment, the one or more recommendations may correspond to generation of configuration data of the component 102. In another embodiment, the one or more recommendations may correspond to an error in the configuration of the component 102. In yet another embodiment, the one or more recommendations may correspond to a missed event during the configuration of the component 102. A person skilled in the art will appreciate that the one or more recommendations may include recommendations other than the above-mentioned recommendations .

[0058] Figure 6 shows an exemplary flow chart illustrating method steps for generating the plurality of configuration frameworks for configuring the components of the industrial control system, in accordance with some embodiments of the present disclosure. As illustrated in Figure 6, the method 600 may comprise one or more steps. The method 600 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

[0059] The order in which the method 600 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0060] At step 602, the computing system 106 obtains the control data associated with the component 102 of the industrial control system. The control data comprises configuration requirements and configuration parameters of the component 102. The configuration requirements of the component 102 may comprise one or more requirements specified for configuring the component 102. The configuration requirements specify conditions and identify variables for configuring the component 102.

[0061] At step 604, the computing system 106 maps the configuration requirements of the component 102 with corresponding configuration parameters, using the Al model 108. Herein, the Al model 108 is pre-trained with historical configuration parameters and historical configuration requirements associated with configuration of the component 102 as training datasets. The historical configuration parameters and the historical configuration requirements may be obtained from a historian or a database associated with the industrial control system. The historian may comprise stored data associated with past / historical configurations of components of the industrial control system. Further, the Al model 108 may be pre-trained by providing data such as, but not limited to, Piping & Instrumentation Diagrams (P&ID), VO lists, signal lists and tag information. In an embodiment, the computing system 106 may parse the control data of the component 102 to identify the configuration requirements. The computing system 106 may identify configuration parameters or configuration data corresponding to the identified control requirement, using the Al model 108. A person skilled in the art will appreciate that any other techniques may be used to map the configuration requirements of the component 102 with corresponding configuration parameters, from the obtained data.

[0062] At step 606, the computing system 106 generates the plurality of configuration frameworks for the component 102, based on the mapping. The computing system 106 may generate a configuration framework for the component 102 by aggregating the configuration parameters or the configuration data of the component 102 for various configuration requirements. In an embodiment, the computing system 106 may receive a prompt from a user / operator for generating the plurality of configuration frameworks. The computing system 106 may generate the plurality of configuration frameworks for different components of the industrial control system. For example, the plurality of configuration frameworks may include, but not be limited to, control code framework, graphics framework, field device configuration framework, and communication network configuration framework. Further, the framework generation module 230 may generate the plurality of configuration frameworks for various tests of the components of industrial control system. For example, the plurality of configuration frameworks may include, but not be limited to, system integration test, functional test, and factory acceptance test.

[0063] Figure 7 shows an exemplary flow chart illustrating method steps for fine-tuning the plurality of configuration frameworks for configuring the components of the industrial control system, in accordance with some embodiments of the present disclosure. As illustrated in Figure7, the method 700 may comprise one or more steps. The method 700 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

[0064] The order in which the method 700 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0065] The steps 702-706 are same as the steps 602-606, and hence the steps are not detailed again for the sake of brevity.

[0066] At step 708, the computing system 106 performs fine-tuning of the Al model 108. Herein, the computing system 106 may communicate the plurality of configuration frameworks for the component 102 to a user / operator of the component 102. The computing system 106 may receive an input from the user for at least one configuration framework among the plurality of configuration frameworks. The input may be received to update the at least one configuration framework based on a review by the user. The computing system 106 may update the at least one configuration framework, based on the user input.COMPUTER SYSTEM

[0067] Figure 8 illustrates a block diagram of an exemplary computer system 800 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 800 may be used to implement the computing system 106. Thus, the computer system 800 may be used for configuring components of industrial control systems. The computer system 800 may comprise a Central Processing Unit 804 (also referred as “CPU” or “processor”). The processor 804 may comprise at least one data processor. The processor 804 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

[0068] The processor 804 may be disposed in communication with one or more input / output (I / O) devices (not shown) via I / O interface 802. The I / O interface 802 may employ communication protocols / methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE (Institute of Electrical and Electronics Engineers) -1394, serial bus, universal serial bus (USB), infrared, PS / 2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, VGA, IEEE 816.n / b / g / n / x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

[0069] Using the I / O interface 802, the computer system 800 may communicate with one or more I / O devices. For example, the input device 820 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device / source, etc. The output device 822 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.

[0070] The processor 804 may be disposed in communication with the communication network 818 via a network interface 806. The network interface 806 may communicate with the communication network 818. The network interface 806 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / internet protocol (TCP / IP), token ring, IEEE 816.11a / b / g / n / x, etc. The communication network 818 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. The network interface 806 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / internet protocol (TCP / IP), token ring, IEEE 816.11a / b / g / n / x, etc.

[0071] The communication network 818 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol / Intemet Protocol (TCP / IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

[0072] In some embodiments, the processor 804 may be disposed in communication with a memory 810 (e.g., RAM, ROM, etc. not shown in Figure 8) via a storage interface 808. The storage interface 808 may connect to memory 810 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE- 1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

[0073] The memory 810 may store a collection of program or database components, including, without limitation, user interface 812, an operating system 814, web browser 816 etc. In some embodiments, computer system 800 may store user / application data, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle ® or Sybase®.

[0074] The operating system 814 may facilitate resource management and operation of the computer system 800. Examples of operating systems include, without limitation, APPLE MACINTOSH1* OS X, UNIXR, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION™ (BSD), FREEBSD™, NETBSD™, OPENBSD™, etc.), LINUX DISTRIBUTIONS™ (E.G., RED HAT™, UBUNTU™, KUBUNTU™, etc.), IBM™ OS / 2, MICROSOFT™ WINDOWS™ (XP™, VISTA™ / 7 / 8, 10 etc.), APPLERIOS™, GOOGLERANDROID™, BLACKBERRY1* OS, or the like.

[0075] In some embodiments, the computer system 800 may implement the web browser 816 stored program component. The web browser 816 may be a hypertext viewing application, for example MICROSOFT1* INTERNET EXPLORER™, GOOGLE1* CHROME™0, MOZILLA1*FIREFOX™, APPLERSAFARI™, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers 816 may utilize facilities such as AJAX™, DHTML™, ADOBERFLASH™, JAVASCRIPT™, JAVA™, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 800 may implement a mail server (not shown in Figure) stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP™, ACTIVEX™, ANSI™ C++ / C#, MICROSOFT1*, .NET™, CGI SCRIPTS™, JAVA™, JAVASCRIPT™, PERL™, PHP™, PYTHON™, WEBOBJECTS™, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT1* exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 800 may implement a mail client stored program component. The mail client (not shown in Figure) may be a mail viewing application, such as APPLE1* MAIL™, MICROSOFT1* ENTOURAGE™, MICROSOFT1* OUTLOOK™, MOZILLA1* THUNDERBIRD™, etc.

[0076] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc Read-Only Memory (CD ROMs), Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

[0077] Embodiments of the present disclosure provides methods and systems for configuring components of industrial control systems. The present disclosure provides systems that assist the user in configuring the components of the industrial control system. The present disclosure enables reduction of errors and time required in configuration of the components. This enhances productivity, efficiency, and collaboration in configuration of the components of theindustrial control systems. In the present disclosure, the Al model is fine-tuned based on inputs received from the user to update the generated configuration frameworks. Also, the Al model is fine-tuned based on inputs received from the user for the recommendations. In this way, the present disclosure enables receiving feedback from users and improvement in the performance of the Al model.

[0078] The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

[0079] The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

[0080] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

[0081] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

[0082] When a single device or article is described herein, it will be readily apparent that more than one device / article (whether or not they cooperate) may be used in place of a single device / article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device / article may be used in place of the more than one device or article or a different number of devices / articles may be used instead of the shown number of devices or programs. The functionality and / or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality / features. Thus, other embodiments of the invention need not include the device itself.

[0083] The illustrated operations of Figures 5-7 show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order,modified, or removed. Moreover, steps may be added to the above-described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

[0084] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

[0085] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.Referral Numerals:

Claims

We claim:

1. A method of generating recommendations for configuring components of industrial control systems, the method comprising: monitoring, by a processor, configuration parameters of a component (102) of an industrial control system, during configuration of the component (102), wherein the configuration parameters correspond to configuration requirements of the component (102); and generating, by the processor, one or more recommendations for configuring the component (102), based on a configuration framework corresponding to the configuration requirements, wherein the configuration framework is selected from a plurality of configuration frameworks using an Artificial Intelligence (Al) model (108), wherein each of the plurality of configuration frameworks is generated by mapping the configuration requirements with the corresponding configuration parameters of the component (102).

2. The method as claimed in claim 1, wherein the Al model (108) is pre-trained with historical configuration parameters and historical configuration requirements associated with the configuration of the component (102).

3. The method as claimed in claim 1, wherein the component (102) comprises a field component, a control component, an interface element, and a network component.

4. The method as claimed in claim 1, comprising performing fine-tuning of the Al model (108), upon generating the plurality of configuration frameworks, wherein the fine-tuning of the Al model (108) comprising: receiving an input from a user for at least one configuration framework among the plurality of configuration frameworks; and updating the at least one configuration framework, based on the user input.

5. The method as claimed in claim 1, wherein the one or more recommendations correspond to generation of configuration data of the component (102), an error in the configuration of the component (102), and a missed event during the configuration of the component (102).

6. A method of generating configuration frameworks for components of industrial control systems, the method comprising: obtaining control data associated with a component (102) of an industrial control system, the control data comprising configuration requirements and configuration parameters of the component (102); mapping the configuration requirements of the component (102) with corresponding configuration parameters, using an Artificial Intelligence (Al) model (108); and generating a plurality of configuration frameworks for the component (102), based on the mapping.

7. The method as claimed in claim 6, wherein the Al model (108) is pre-trained with historical configuration parameters and historical configuration requirements associated with configuration of the component (102).

8. A method of generating configuration frameworks for components of industrial control systems, the method comprising: obtaining control data associated with a component (102) of an industrial control system, the control data comprising configuration requirements and configuration parameters of the component (102); mapping the configuration requirements of the component (102) with corresponding configuration parameters, using an Artificial Intelligence (Al) model (108); generating a plurality of configuration frameworks for the component (102), based on the mapping; and performing fine-tuning of the Al model (108), based on an input received from a user for at least one configuration framework among the plurality of configuration frameworks.

9. A computing system (106) for generating recommendations for configuring components of industrial control systems, the computing system (106) comprises: a processor; and a memory, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:monitor configuration parameters of a component (102) of an industrial control system, during configuration of the component (102), wherein the configuration parameters correspond to configuration requirements of the component (102); and generate one or more recommendations for configuring the component (102), based on a configuration framework corresponding to the configuration requirements, wherein the configuration framework is selected from a plurality of configuration frameworks using an Artificial Intelligence (Al) model (108), wherein each of the plurality of configuration frameworks is generated by mapping the configuration requirements with the corresponding configuration parameters of the component (102).

10. A computing system (106) for generating configuration frameworks for components of industrial control systems, the computing system (106) comprises: a processor; and a memory, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: obtain control data associated with a component (102) of an industrial control system, the control data comprising configuration requirements and configuration parameters of the component (102); map the configuration requirements of the component (102) with corresponding configuration parameters, using an Artificial Intelligence (Al) model (108); and generate a plurality of configuration frameworks for the component (102), based on the mapping.

11. A computing system (106) for generating configuration frameworks for components of industrial control systems, the computing system (106) comprises: a processor; and a memory, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: obtain control data associated with a component (102) of an industrial control system, the control data comprising configuration requirements and configuration parameters of the component (102);map the configuration requirements of the component (102) with corresponding configuration parameters, using an Artificial Intelligence (Al) model (108); generate a plurality of configuration frameworks for the component (102), based on the mapping; and perform fine-tuning of the Al model (108), based on an input received from a user for at least one configuration framework among the plurality of configuration frameworks.

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