619 results about "Industrial systems" patented technology

The invention discloses an industrial system automatic fault diagnosis method based on a large language model. According to the method, a three-layer mapping system of industrial data, natural language description and knowledge reasoning is constructed, field multi-source sensor data are subjected to semantic conversion, and a quantitative calculation model based on a large language model is constructed based on historical data and logs. And a fault case is matched in real time with the help of a retrieval-enhancement generation technology to serve as a reference, the fault case and abnormal information are input into a knowledge reasoning model based on a large language model together, a structured logical reasoning chain is generated, and a diagnosis conclusion containing candidate faults, cause analysis and disposal suggestions is further output. Meanwhile, through user feedback and a reinforcement learning mechanism, the model and the knowledge base are adaptively updated, the defects of traditional static rules and expert experience are effectively overcome, the accuracy, interpretability and robustness of fault detection are remarkably improved, and the method adapts to complex and changeable working condition requirements.

The invention relates to a digital twin workshop management and control method and system based on data multilayer fusion, and belongs to the field of service-oriented industrial system data science, and the method comprises the steps: obtaining the global multi-source heterogeneous data of a digital twin workshop, and carrying out the data classification, data processing and data storage of the global multi-source heterogeneous data, carrying out data-level fusion to obtain digital twin workshop standard data; extracting standard data of the digital twin workshop, performing semantic association, feature extraction and feature analysis on the standard data of the digital twin workshop, and performing feature level fusion to obtain key state features of the digital twin workshop; the method comprises the following steps: constructing a decision-making model oriented to a digital twin workshop, inputting key operation features of the digital twin workshop, performing decision-making level fusion based on the decision-making model, decision-making optimization and decision-making method solution, and outputting a workshop production management and control decision. The method can provide support for digital twin workshop operation data governance analysis and production management and control accurate decision.

An AI-powered system for optimizing industrial workflows and predictive maintenance, which includes the following: a network of sensors configured to collect real-time data from industrial plants, including temperature, pressure, vibration, and energy consumption metrics; a central control unit configured to process and analyze the collected data using machine learning algorithms that continuously improve by learning from both historical and real-time data to increase prediction accuracy; a predictive maintenance module configured to analyze data to predict potential equipment failures, schedule proactive maintenance tasks, and prioritize maintenance activities based on the predicted impact of the failure and the criticality of the equipment; a workflow optimization module configured to dynamically adjust operating parameters to optimize the process flow, including the detection of inefficiencies and the suggestion of improvements in real time; an integration module configured to connect and interact with existing industrial control systems and ERP (Enterprise Resource Planning) software to ensure a seamless flow of data between the platforms; a user interface configured to visualize data in real time, provide alerts and customizable dashboards, and is accessible via desktop and mobile devices for remote monitoring and management; an alarm and notification module configured to deliver critical alarms and maintenance notifications via email, SMS and in-app notifications to ensure a timely response; a cloud-based architecture configured to facilitate scalable data storage, remote access, and management across multiple industrial sites; a reporting module configured to generate comprehensive reports on plant performance, maintenance history, and workflow efficiency for analysis and decision-making; A security protocol to protect data integrity and ensure secure communication between all system components.

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 discloses a simulation and optimization method for a natural gas industrial system in a rough environment, and relates to the technical field of intelligent optimization, and the method comprises the steps: collecting environment data, constructing a physical constraint diffusion model, and generating an extreme environment data set through embedding a hydrodynamic equation and a thermodynamic law; in combination with real sensor data of a plurality of gas field nodes, pre-training a federated transfer learning framework, eliminating cross-environment data distribution difference through resistance feature alignment, and constructing a global environment threat prediction model; based on the simulation result and the real-time sensor data flow, a multi-target fitness function is constructed, and control variables are generated through population evolution and dynamic strategy adjustment; and inputting the control variable into a pre-trained digital twinborn verification model, comparing a simulation result with a residual error of a real-time sensor data stream, triggering an incremental learning mechanism, and updating a global environmental threat prediction model. According to the method, the high-fidelity extreme environment data set is generated through the physical constraint diffusion model, and the physical consistency of multi-field coupling simulation is improved.

The present invention provides systems and methods for cellular network performance monitoring and optimization, enabling SIM-based devices to dynamically adapt to changing network conditions for improved connectivity. The invention introduces a process that includes determining baseline path performance through detailed probing of network metrics, continuously assessing current path performance via real-time monitoring, and instructing the SIM to switch from its current connected mobile network carrier to an alternate carrier when predefined performance thresholds are not met. Switching instructions are securely delivered Over-The-Air (OTA) to the SIM, ensuring seamless transitions to the most efficient and reliable network path. The system leverages both active and passive application layer observations to optimize latency, throughput, and reliability while supporting diverse applications, including IoT devices, industrial systems, and consumer devices.

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) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention discloses an intelligent platform system for modern industrial system construction, and the system specifically comprises a heterogeneous data fusion center which is used for collecting and standardizing the multi-source heterogeneous data of enterprise equipment and environment in an industrial chain in real time; the knowledge graph construction module is used for generating a real-time operation situation graph according to the multi-source heterogeneous data; the AI decision engine is used for executing risk prediction and resource optimization path planning according to the real-time operation situation map; the intelligent dispatching center is used for dynamically reconfiguring idle equipment, talents and funds of an industrial chain according to resource optimization path planning; and the ecological value contract module is used for calling a block chain to convert the data assets into on-chain verifiable collaborative benefits according to the reconfiguration result of the intelligent dispatching center. According to the invention, industrial chain data fusion, situation awareness, intelligent decision, resource dynamic configuration and data asset collaborative income conversion are realized, and the overall operation efficiency and collaborative value of an industrial system are improved.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

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 discloses an industrial AI assistant cross-modal interaction method based on a dynamic knowledge graph, and the method comprises the following steps: collecting texts, voices, images and multi-source sensor data in an industrial system, carrying out the modal recognition, feature extraction and time alignment, and generating an event feature set and a state feature set with timestamps. Modeling a time dependency relationship between event types by constructing a multivariable Hawkes model, and outputting an event trigger sequence and trigger strength; and in combination with a neural controlled differential equation model, guiding the state to evolve along with time and jump at a specific moment to form a state evolution trajectory. Performing fusion coding on the event and the state, constructing a dynamic knowledge graph with a causal structure and semantic continuity, and generating interactive output based on context reasoning; and after system feedback is received, the triggering strength and the state track are updated, and continuous evolution of the knowledge graph and reverse optimization of model parameters are achieved.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention relates to the technical field of digital twinning, discloses a digital twinning-driven transformer substation state artificial intelligence evaluation system, and aims to solve the problems that the diagnosis precision is reduced and the model adaptability is insufficient when a traditional digital twinning model is inconsistent with a physical entity state. The system constructs a closed-loop architecture comprising a state perception and deviation quantification module, a systematic risk and cognitive focus distribution module, a focused causal diagnosis and introspection verification module and a cognitive twin adaptive evolution module. The method can actively sense the state deviation of a physical entity, intelligently distributes diagnosis resources based on the evaluation of systematic risks, and achieves the efficient diagnosis of a fault source. The system determines the real state of a physical entity through introspection verification, and performs closed-loop updating on a reference twin model by using the result, so that the reference twin model can be continuously evolved, and the accuracy, the automation level and the full life cycle adaptability of state monitoring and fault diagnosis of a complex industrial system are remarkably improved.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention discloses an unsupervised anomaly detection method for an energy storage battery sensor system, and the method comprises the steps: carrying out the time sequence view and sensor view processing of sensor data, and obtaining a multivariable time sequence and a state value; feature vectors in the time sequence view are obtained through GRU, feature vectors in the sensor view are obtained through GCN, and then the feature vectors are spliced together to obtain combined features; encoding the combined features through an encoder to obtain latent variables; the decoder decodes the latent variables to reconstruct the whole data set, and a reconstruction error of the decoder is obtained through calculation; calculating to obtain a physical model error based on priori knowledge of the energy storage battery sensor system; calculating a final abnormal score based on the reconstruction error and the physical model error; and when the abnormal score is greater than a set abnormal threshold value, judging that abnormity exists and marking. According to the invention, the anomaly detection capability of the multi-sensor system is enhanced, and a novel solution is provided for intelligent operation and maintenance of a complex industrial system.

Systems and methods of controlling graphic displays within a multi-display human machine interface (“HMI”) environment for industrial systems. One system includes an electronic processor configured to determine a display parameter for a header portion of a graphic display of the plurality of graphic displays. The electronic processor may be configured to determine, based on the display parameter, an arrangement for the graphic display, where the arrangement maintains the header portion of the graphic display within a display frame for the graphic display. The electronic processor may be configured to generate the graphic display based on the arrangement. The electronic processor may be configured to display the HMI-client application across the plurality of display devices and display the graphic display within the display frame for the graphic display on a first display device of the plurality of display devices.

The invention discloses an industrial network threat state monitoring method and system, belongs to the technical field of industrial system security, solves the limitation of single flow detection through collaborative analysis of fused flow characteristics, equipment logs, threat intelligence and vulnerability scanning data, establishes a real-time association mechanism of log events and flow anomalies, and improves the safety of the system. Threat confirmation and response time is shortened from a traditional hour level to a minute level, and active strategy adjustment is realized based on linkage of a configuration baseline deviation degree and a vulnerability scanning result.

The invention relates to a multi-physics field coupling simulation method and equipment for industrial digital twinning. The method comprises the following steps: automatically dividing a plurality of heterogeneous physical sub-domains based on actual physical field data of industrial equipment, and defining a solving region and a boundary condition for each sub-domain; and then, a variable-driven region division mechanism is utilized to construct a parallel computing map of multiple physical domains, and efficient synchronization of inter-region coupling state variables and real-time interaction of boundary data are realized. In the parallel simulation process, calculation tasks and resources are dynamically and optimally distributed through a self-adaptive scheduling algorithm, bottleneck areas are effectively recognized and eliminated, and the overall simulation efficiency and stability are improved. According to the invention, co-evolution of a plurality of physical processes such as structure, heat, flow, magnetism, electricity and the like can be realized, and unified, flexible and extensible simulation support is provided for digital twinning, state monitoring, performance prediction, control optimization and other applications of a complex industrial 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 invention discloses an automatic testing method oriented to a large industrial system RPC API (Remote Procedure Call Application Program Interface). According to the method, API documents and historical calling data of tenants are used as input, and automatic API testing is achieved. The method mainly comprises three stages of API dependency model construction, API test sequence generation and API sequence parameter rendering. The construction of the API dependency model is realized based on an API document and historical calling data of tenants, and comprises three steps: static analysis of the API document, parameter clustering based on a large model, and dependency relationship filtering based on the historical calling data of the tenants. The generation of the API test sequence is based on an API dependency graph generated in the previous stage, a to-be-tested API is taken as a starting point, a front dependency API is searched for continuously in an iteration mode to generate the sequence, and finally filtering is carried out through CRUD semantics. The rendering of the API sequence parameters is realized by constructing a dynamic dictionary and recommending large model parameters aiming at the API sequence generated in the previous stage.

The invention belongs to the technical field of industrial system integration, and provides a design method for rapid adaptive integration of a cross-domain system. Through the method, developers extract business logic rules of the industry, standardize characteristic mapping and select, develop and integrate functional modules, logic processes and scene components of the cross-domain system, so that the cross-domain system which conforms to industry characteristics and meets business circulation is formed, and meanwhile, the development efficiency of the cross-domain system is improved. The newly added industry logic component can be classified into each industry characteristic model based on the knowledge base, and a multi-dimensional characteristic vector is generated in the platform, so that cross-industry integrated component multiplexing is realized, and the cross-field applicability and flexibility of the platform are further improved. Modularized construction and configuration type development are supported, mapping and matching functions are carried out on various industrial manufacturing industry characteristics such as machining, electronic and electrical appliances and equipment sets, the requirements for subdivision production in the industrial field and rapid adaptation of business processes are met, the system integration cost is reduced, and the application development efficiency is greatly improved.

The invention discloses a network attack detection method fusing momentum contrast learning and Transform, and the method comprises the steps: collecting a large amount of untagged data of an industrial system network, carrying out the unsupervised learning through employing the momentum contrast learning and fusing the Transform, extracting the high-dimensional and long-sequence features in the data, constructing a feature embedding space with high discrimination, and carrying out the recognition of a network attack through employing the momentum contrast learning and the Transform. The method comprises the following steps of: performing dimension reduction on high-dimensional features by using a principal component analysis method, taking the features subjected to dimension reduction as input of a BDSCAN density clustering algorithm, performing clustering classification, optimizing parameters in the DBSCAN algorithm through a grey wolf optimization algorithm, outputting optimal algorithm parameters through iteration, and ending a program until a detection performance requirement is met. The method can be better suitable for a scene with high-dimensional and complex data, is suitable for a condition that the data has no label, improves the overall network detection accuracy and robustness, and enhances the network detection practicability and adaptability.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention discloses a pollution source tracing method based on a three-dimensional fluorescence spectrum, and relates to the technical field of water quality monitoring, a water sample of an upstream specific pollution discharge unit is collected as a pollution source, a mixed water sample formed by mixing different pollution source water samples according to different volumes or proportions is prepared, and a pollution source database adapted to parallel factor analysis is established; according to the pollution source tracing method, a good linear model between a pollution source and a three-dimensional fluorescence spectrum characteristic value is established, a shortest path algorithm is proposed to quantitatively solve the volume or proportion of the pollution source in a monitored water sample, and the pollution source is traced to a specific pollution discharge unit. According to the invention, the sewage three-dimensional fluorescence spectrum database established by mixing upstream pollution source drainage improves the application range of parallel factor analysis, so that the three-dimensional fluorescence spectrums of sewage of different industrial systems can be subjected to effective parallel factor analysis together; compared with a parallel factor analysis-Tucker congruence coefficient method, the method has the advantage that the pollution source can be traced to a more specific level.

A method includes receiving an indication of an inspection of an industrial automation device being triggered, displaying, via a display of a mobile computing device, instructions for performing the inspection of the industrial automation device, receiving, via one or more sensors of the mobile computing device, one or more sensors communicatively coupled to the mobile computing device, or both, inspection data associated with the industrial automation device, processing the inspection data associated with the industrial automation device to generate results of the inspection of the industrial automation device, and displaying, via the display of the mobile computing device, the results of the inspection of the industrial automation device.

A method may include receiving, via a computing system, a request to generate one or more visualizations based on one or more datasets provided by an industrial device within an industrial system. The computing system may be communicatively coupled to the industrial device via a data backplane. The method may also involve accessing a visualization platform system in response to receiving the request, sending design data associated with the one or more visualizations to the visualization platform system, and receiving a visualization project file for execution by the computing system from the visualization platform system. The visualization project file may be generated based on the design data. The method may also include generating the one or more visualizations based on the visualization project file and presenting the one or more visualizations via an electronic display device.

The invention relates to the technical field of industrial production control, and discloses an industrial parameter prediction method, device and equipment based on deep learning, and the prediction method comprises the steps: obtaining a first numerical sequence of a target industrial parameter in a first time period; the target industrial parameters comprise a first industrial parameter and a second industrial parameter in the first industrial system; the first numerical value sequence of the target industrial parameter comprises numerical values of the target industrial parameter at a plurality of different moments; at the first moment, the numerical value change of the first industrial parameter can influence the numerical value of the second industrial parameter at the second moment; the second moment is the next moment of the first moment; inputting the first numerical value sequence into an industrial parameter prediction model, and predicting to obtain a numerical value of a second industrial parameter at a third moment; the industrial parameter prediction model is a deep learning model constructed based on an Informer model and / or an LSTM model. The method can improve the prediction precision and efficiency of the related parameters in the industrial generation system.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The utility model relates to the technical field of industrial system air purification filters, and particularly discloses a V-shaped filter which comprises a filter element, a rubber line and an outer frame. And the filter element is positioned on an air circulation path in the filter and is formed by folding a whole piece of filter paper. The glue line is a continuous glue line and is adhered to the surface of the filter paper, and two ends of the glue line coincide with two ends of the filter paper. The outer frame is located on the peripheral side of the filter element, abuts against the filter element and is bonded with the glue line. The air filter has the effects of improving the purification air volume and meeting the industrial waste gas purification requirement.