292 results about "Industrial automation systems" patented technology

Technology disclosed herein includes a prompt engineering service that integrates artificial intelligence with the programming systems of an industrial automation environment to design a system of the industrial automation environment. The interface service leverages the capabilities of a large language model (LLM) trained on industrial automation workflows to provide accurate and relevant system design information. For example, the interface service receives system configuration data and generates a first prompt requesting a category associated with the system configuration data. The interface service uses the first prompt to generate a response from the LLM. The interface service generates a second prompt requesting a user interface message for offering assistance to configure the system based on the category. The interface service uses the second prompt to generate the user interface message and displays the message in a user interface.

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

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

The invention relates to the technical field of signal control, and discloses an intelligent signal I / O control system and method for industrial automation. The method comprises the following steps: carrying out acquisition and feature analysis on various signals in the industrial automation system to obtain a signal feature model; designing a two-stage I / O control scheme to obtain a first control strategy; constructing a state-trend matrix, and introducing an evaluation function to quantify a control effect to perform dynamic iteration updating to obtain a target control parameter; performing PLC autonomous fault diagnosis to obtain a fault diagnosis result; performing network cooperative control analysis on the plurality of PLCs based on a fault diagnosis result to obtain a hierarchical central coordination mechanism; and establishing a multi-dimensional performance index system of a hierarchical central coordination mechanism, and performing evaluation and adaptive optimization on the first control strategy to obtain a second control strategy, thereby realizing intelligent perception, accurate control and fault diagnosis of a complex industrial environment, thereby improving the performance and stability of the whole automation system.

The invention discloses an industrial communication protocol conversion system and conversion method. The method comprises the following steps: acquiring data of an industrial communication source protocol; protocol conversion: converting the source protocol data into target protocol data according to a sequence of protocol analysis, standardized modeling and protocol repackaging; according to protocol analysis, original data transmitted from different hardware interfaces are analyzed according to corresponding industrial protocols; the standardized modeling parses the data into a data model in a unified format; protocol repackaging: recoding the data model into a data frame conforming to a communication specification according to a target protocol rule; and data output: pushing the obtained target protocol data to a third-party server through an MQTT protocol or directly storing the target protocol data in a database for a third-party system to access. Through three core technologies of protocol analysis, unified data modeling and protocol packaging, lossless conversion among different industrial protocols is realized, and an efficient and reliable data communication infrastructure is provided for an industrial automation system.

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

The embodiment of the invention provides a cross-platform data communication board card data processing method and device. Uniform interface abstraction is achieved by creating a driving interface object layer and an application program interface layer. A protocol identification module and a protocol template library are innovatively designed, and flexible protocol analysis and conversion are supported. A user state event simulation mechanism and a buffer grading mechanism are introduced, a lock-free concurrent queue is adopted to process real-time data, a disk maps and stores overflow data, and dynamic scaling of a processing thread is realized through a load balancing scheduler. According to the method, the platform limitation and the performance bottleneck of a traditional communication board card are broken through, and an efficient and reliable data processing solution is provided for an industrial automation system.

The invention is suitable for the technical field of redundancy coding, and particularly relates to a data redundancy coding method and system based on deterministic network cooperative transmission, and the method comprises the steps: delimiting an industrial production area, and positioning a relay node which at least comprises edge equipment and a network switch, key indexes of the relay nodes are collected, and the relay nodes are divided into main nodes and common nodes; the industrial production tasks are collected, the master control server and the execution end are extracted, the control command sent by the master control server is received, and the industrial production tasks are coded according to a preset coding sequence. By constructing the hash chain, the control command can be decoded, the traceability of the control command is improved, meanwhile, the fault tolerance of the industrial automation system is further enhanced, it is guaranteed that the control command can be accurately and timely transmitted to the execution end, the response efficiency of the execution end is greatly improved, and normal operation of industrial production is guaranteed.

A method includes transmitting, from a computing device, a request to generate a configuration file for an industrial automation device disposed within an industrial automation system configured to perform an industrial automation process, wherein the industrial automation device is communicatively coupled to an operational technology (OT) network, transmitting, from the computing device, one or more characteristics of the industrial automation device, the industrial automation system, the industrial automation process, the OT network, or any combination thereof, receiving, at the computing device, the configuration file for the industrial automation device, wherein the configuration file for the industrial automation device was generated using one or more large language models (LLMs) based on the one or more characteristics of the industrial automation device, the industrial automation system, the industrial automation process, the OT network, or any combination thereof, and installing the configuration file on the industrial automation device.

A human-interface module (HIM) for an industrial automation system includes processing circuitry, and a memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations including receiving instructions to control one or more industrial automation devices of the industrial automation system via a cloud-based computing system or a mobile computing device communicatively coupled to the HIM. The operations may also include generating one or more control signals based on the instructions, transmitting the one or more control signals to the one or more industrial automation devices via an Ethernet connection, receiving process data associated with the one or more industrial automation devices, and transmitting the process data to the mobile computing device.

There is provided an explainer system for explaining an alarm raised by a machine learned model of an industrial automation system. The explainer system is configured to: receive model output from the machine learned model trained to predict anomalous behaviour in the industrial automation system and to raise the alarm; process the model output using at least one prediction explanation technique to identify at least one influential feature which contributed to the model output; use the identified at least one influential feature to extract contextual information from at least one machine-readable information source pertaining to the industrial automation system; and prepare the extracted contextual information for display to an operator of the industrial automation system, to enable the operator to select an appropriate action to take in response to the alarm for ensuring proper functioning of the industrial automation system.

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

Automation device, method and system for transmitting data within an industrial automation system, wherein at least one first automation device makes available payload data comprising control, measurement and / or status data for reception by control applications executing in second automation devices, where the first automation device sends datagrams comprising the payload data or a selection of payload data to at least one multicast group, the second automation devices register themselves selectively as subscribers at least in a multicast group and each have at least one network adapter that forwards received datagrams associated with the multicast groups, via a socket assigned to a network protocol stack functional unit of an operating system of the respective second automation device, to a filter unit of the respective second automation device.

The present disclosure provides an artificial intelligence-based alarm management based on structured time series data and unstructured contextual information where the conventional methods fails to do. Initially, the system receives a user query associated with an industry automation system and classifies into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) feedback for previous response using a query classifier. Simultaneously, the user query is converted into an associated timeseries query representation. Furthermore, a plurality of database queries is generated based on the timeseries query representation using query generator. Further, data pertaining to the user query is retrieved from an associated database based on database queries using Deep Learning models, thereby identifying a critical alarm, and to take appropriate actions if there is any anomaly. Finally, the retrieved data is displayed to the user in a user readable format.

A system includes processing circuitry and a memory, accessible by the processing circuitry, storing instructions that, when executed by the processing circuitry, cause the processing circuitry to receive data collected during operation of an industrial automation device of an industrial automation system configured to perform an industrial automation process, process the received data, including applying one or more rules to filter the received data, prioritize the received data, or both, and transmit the processed received data to a device twin for the industrial automation device, wherein the device twin includes an interface by which one or more applications may interact with the industrial automation device, wherein the device twin runs in a cloud environment.

A method for providing control applications, wherein each of the control applications is provided by a flow control component, which is loadable onto a flow control environment formed via a server device and that is executed thereon, where control applications that require selected security authorizations are assigned a respective label as security-critical control applications, at least one respective flow condition is ascertained for the selected security authorizations for the control applications that are assigned a label as a security-critical application, the flow control environment checks for the occurrence of the respective flow condition while the flow control components for the control applications are being executed, and where the execution of each of the flow control components is terminated when the respective flow condition occurs.

The embodiment of the invention provides an industrial automation system. The industrial automation system comprises an edge layer and an equipment layer, wherein the equipment layer comprises one or more pieces of industrial equipment deployed on a production line system, and sensors and actuators deployed on the one or more pieces of industrial equipment, the sensors are used for collecting monitoring data of the industrial equipment, and the actuators are used for executing control actions of the industrial equipment; the edge layer comprises a plurality of edge control units, and the edge control units acquire monitoring data of the one or more industrial devices and adjust actuators of the one or more industrial devices according to the monitoring data to execute control actions; wherein the plurality of edge control units are in communication connection through a self-organizing network or a point-to-point network.

For data transmission within an industrial automation system, at least one first automation device (100) makes payload data, comprising control, measurement, or status data, available for reception by control applications (213-215) running in second automation devices (200, 300). The first automation device sends the payload data or a selection of payload data (110) to one or more multicast groups. The second automation devices selectively register as participants in at least one multicast group and each has at least one network adapter (210) that forwards received datagrams assigned to the multicast groups via a socket (230) associated with a network protocol stack functional unit (220) of an operating system (211) of the respective second automation device to a filter unit (240) of the respective second automation device.

In a virtual commissioning of an industrial automation system, an industrial system or process is simulated by a simulation engine (118) utilizing a digital twin (112) of the industrial system or process. A virtual data acquisition system (120) utilizes a simulation snapshot (128) to generate data communicated as a model input (140) to a virtual inference system (124) having an AI model (138) deployed on it. The virtual inference system (124) processes the model input (140) using the AI model (138) to generate a model output (142) communicated to a virtual automation controller (126). The virtual automation controller (126) processes the model output (142) using automation logic (116) running on it to generate control data (144) communicated to the simulation engine (118). A closed-loop behavior of the automation logic (116) responsive to interaction with the AI model (138) is thereby validated. Communications between the virtual system components (118, 120, 124, 126) are based on data models and / or runtime interfaces that match communications between corresponding physical system components (102, 104, 106, 108) of the industrial automation system.

Described systems and methods may enable a cloud-computing system to use data model-based analysis to manage maintenance of an industrial automation system based on life stage predictions and real-time evaluations. Based on the data model-based analyses, the cloud-computing system may instruct an industrial control system of the industrial automation system to implement recommended adjustments and / or to confirm a part replacement occurred in situ. The recommended adjustment may include a part replacement. The cloud-computing system may generate instructions to cause shipment of the part to be installed as part of the part replacement to the industrial automation system.

An industrial integrated development environment (IDE) provides a development framework for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Projects creating using embodiments of the IDE system can be built on an object-based model rather than, or in addition to, a tag-based architecture. To this end, the IDE system can support the use of automation objects that serve as building blocks for this object-based development structure. Project data models defining collections of automation objects and their functional relationships can be stored in a model library for selective inclusion in system projects.

The invention discloses an industrial signal enhancement method based on dynamic adaptive filtering and self-supervised learning, and the method comprises the steps: firstly, purifying an original signal through preprocessing steps including DC drift removal, abnormal point elimination and amplitude normalization, and extracting a time-frequency feature matrix of the signal through short-time Fourier transform; and secondly, dynamic adaptive filtering kernel parameters are generated in real time by adopting a lightweight 1D convolutional neural network, the preprocessed signals are filtered, and optimized signal output is generated. Then, a signal reconstruction network under a self-supervised learning mechanism is designed, the network takes filtered signals as input, statistical consistency between input signals and reconstructed signals is optimized through a composite loss function, and retention of useful signal components and noise suppression are achieved. Finally, the method is deployed in an industrial edge computing unit or an embedded industrial personal computer, signal enhancement and denoising operation can be executed in real time, and the efficiency and accuracy of an industrial automation system are improved.

A system includes processing circuitry and a memory, accessible by the processing circuitry, storing instructions that, when executed by the processing circuitry, cause the processing circuitry to receive data from a device twin representative of an industrial automation device of an industrial automation system configured to perform an industrial automation process, wherein the industrial automation device is communicatively coupled to an operational technology (OT) network, wherein the device twin includes an interface by which one or more applications may interact with the industrial automation device, determine that the industrial automation device is accessible via a plurality of edge devices, apply one or more policies to select a particular edge device of the plurality of edge devices, and transmit the data to the particular edge device for transmission to the industrial automation device.

Systems and methods described herein may involve an industrial network device that performs an operation based on symbolic data received via an input terminal. The systems and methods may involve processing circuitry coupled via an output terminal to the input terminal. The processing circuitry may provide a virtualized control system communicatively coupled to the industrial network device via the output terminal, receive an event notification from a first container provided by one or more computing devices external to the industrial automation system, operate the virtualized control system to expose the event notification via a symbolic common industrial protocol (CIP) namespace to provide the symbolic data to the input terminal based on the event notification, and transmit the symbolic data corresponding to the virtualized control system via the one or more input terminals to the industrial network device.

An industrial integrated development environment (IDE) provides a development framework for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Projects creating using embodiments of the IDE system can be built on an object-based model rather than, or in addition to, a tag-based architecture. To this end, the IDE system can support the use of automation objects that serve as building blocks for this object-based development structure. To ensure consistency within and between projects, as well as to ensure that a given industrial project is dynamically updated to reflect changes to an industrial asset's attributes (e.g., control code, visualization definitions, testing scripts, analytic code, etc.), embodiments of the IDE system can use automation object inheritance features to propagate changes made to an automation object definition to all instances of the automation object used throughout a control project.

A system for providing causal graph relationships for industrial automation systems. The system receives a first input indicative of a selection of a design artifact associated with an industrial automation system having one or more industrial automation devices, where the design artifact defines an industrial automation project configured to control operations of the one or more industrial automation devices. In response to the first input, the system parses the design artifact to identify causal relationships between the one or more industrial automation devices, stores data indicative of the identified causal relationships in a database, and generates causal graphs based on the stored data. The system receives a second input indicative of a request to generate a custom view of the industrial automation system, and generates a custom view of the industrial automation system in response to the second input, where the custom view includes the causal graphs.

Systems and methods described herein may relate to a system including a plurality of industrial devices that each perform one or more operations within an industrial automation system having a plurality of hierarchical levels. Each of the plurality industrial devices may include a compute surface able to perform software tasks. The system may include a system of processors that receives trigger event data from a device corresponding to a first hierarchical level of the plurality of hierarchical levels. In addition, the system of processors is configured to identify a target device associated with additional data associated with the trigger event data, deploy a container to the target device, generate a data pattern based on the trigger event data and the acquired additional data, determine a remedial action associated with the trigger event data based on the data pattern, and instruct a device to implement the remedial action.

A system includes processing circuitry and a memory, accessible by the processing circuitry, storing instructions that, when executed by the processing circuitry, cause the processing circuitry to receive discovery data including one or more characteristics of an industrial automation device of an industrial automation system configured to perform an industrial automation process, wherein the industrial automation device is communicatively coupled to an operational technology (OT) network, request, from a database, catalog data for the industrial automation device, and generate, based on the discovery data and the catalog data, a device twin for the industrial automation device, wherein the device twin comprises an interface by which one or more applications may interact with the industrial automation device.

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

The present invention generally relates to a computer-implemented method of inserting a component (116, 118, 120) into a process graphics (120) of an industrial automation system, the method comprising: receiving (S102) a data signal indicative of a drawing input (112) in a work area (114) of a graphics platform shown on a computer display; identifying (S104) a workspace (114) of the drawing input; identifying (S106) at least one characteristic of the drawing input, predicting (S108) at least one candidate component that best matches the at least one characteristic of the drawing input from the list of available components (116, 118, 120) stored in the data storage (108), and providing (S110) the list of at least one candidate component on the computer display.