PLC PROGRAM GENERATOR/COPILOT USING GENERATIVE AI

    公开(公告)号:US20250147736A1

    公开(公告)日:2025-05-08

    申请号:US18437941

    申请日:2024-02-09

    Abstract: An integrated development environment (IDE) for uses a generative artificial intelligence (AI) model to generate industrial control code in accordance with functional requirements provided to the industrial IDE system as natural language prompts. The system's generative AI model leverages both a code repository storing sample control code and a document repository that stores device or software manuals, program instruction manuals, functional specification documents, or other technical documents. These repositories are synchronized by digitizing selected portions of document text from the document repository into control code for storage in the code repository, as well as contextualizing control code from the code repository into text-based documentation for storage in the document repository.

    EMPLOYING A BATCH MODEL IN ROOT CAUSE ANALYSIS OF INDUSTRIAL BATCH PERFORMANCE ANALYTICS

    公开(公告)号:US20250147501A1

    公开(公告)日:2025-05-08

    申请号:US18503866

    申请日:2023-11-07

    Abstract: A method may include receiving, via a processing system, a selection of a first dataset associated with one or more operations of one or more industrial automation components of an industrial system that may perform a batch operation. The method may involve generating an optimized dataset based on the dataset, receiving a second dataset associated with one or more additional operations of one or more additional industrial automation components of an additional industrial system that may perform an additional batch operation, and determining one or more deviations between the optimized dataset and the second dataset. The method may also involve determining a contribution of each of a set of parameters to the one or more deviations and generating a visualization representative of the contribution of each of a set of parameters to the deviation.

    Industrial Batch Dataset Generation for Operations Modeling

    公开(公告)号:US20240402697A1

    公开(公告)日:2024-12-05

    申请号:US18679061

    申请日:2024-05-30

    Abstract: A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving data associated with industrial equipment, pre-processing the data using pre-processing files associated with a modeling technique to generate pre-processed data, generating training dataset files based on the pre-processed data, and generating a model representative of expected operations of the industrial equipment based on the training dataset files. The instructions cause the processing circuitry to perform operations including storing an association between the training dataset files with the modeling technique, the industrial equipment, or both in a database, receiving a request to generate an additional model representative of additional expected operations of additional industrial equipment, receiving additional data associated with the additional industrial equipment, retrieving the training dataset files based on the additional data, and generating the additional model based on the training dataset files and the additional data.

    Industrial Batch Processing Operation Control for Use with Artificial Intelligence (AI) Models

    公开(公告)号:US20240402691A1

    公开(公告)日:2024-12-05

    申请号:US18679130

    申请日:2024-05-30

    Abstract: A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving a set of data associated with industrial devices of an industrial system, and retrieving pre-processing files and training datasets files associated with the industrial devices from a database, wherein the pre-processing files are configured to transform the data for generating a model representative of the industrial devices, and wherein the training dataset files are representative of operational characteristics of the industrial devices over time. The instructions cause the processing circuitry to perform operations including generating a set of prediction data representative of expected operations of the industrial devices based on the set of data and the model, determining commands for adjusting operational settings of the industrial devices based on the set of prediction data, and sending the commands to the industrial devices.

    Industrial Batch Processing Dynamic User Interface

    公开(公告)号:US20240402690A1

    公开(公告)日:2024-12-05

    申请号:US18679113

    申请日:2024-05-30

    Abstract: A non-transitory tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations including receiving data associated with one or more industrial devices of an industrial system, and retrieving one or more pre-processing files and one or more training datasets files associated with a model from a database, wherein the one or more pre-processing files are configured to transform the data, and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time. The instructions cause the processing circuitry to perform operations including receiving one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display, and generating the model based on the training dataset files and the one or more inputs.

    SYSTEMS AND METHODS FOR BATCH SYNCHRONIZATION IN INDUSTRIAL BATCH ANALYTICS

    公开(公告)号:US20240370007A1

    公开(公告)日:2024-11-07

    申请号:US18457478

    申请日:2023-08-29

    Abstract: An illustrative method includes a batch analytic system receiving batch data of a batch generated in an industrial process, wherein the batch data includes K samples collected during the batch and each sample includes J values corresponding to J process variables of the industrial process, applying, for each process variable among the J process variables of the industrial process, a first function to K values of the process variable in the K samples of the batch to determine a first feature value of the process variable for the batch, aggregating first feature values corresponding to the J process variables that are determined for the batch using the first function to form a batch representation of the batch, and performing an operation using the batch representation of the batch.

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