506 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.

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) 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.

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.

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 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 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.

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.

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 invention provides a network security intrusion detection method and device, and belongs to the field of network security, and the method comprises the steps: constructing a multi-modal directed graph of the industrial Internet of Things based on the state data, log data and physical signal data of each device in the industrial Internet of Things; performing cross-modal embedding learning on the multi-modal directed graph by adopting the trained hierarchical graph attention network to obtain a joint embedding vector of the industrial Internet of Things; and performing causal dependency relationship derivation on the joint embedded vector based on a trained time sequence causal convolutional network, generating an attack path and attack confidence, and taking the attack path and the attack confidence as a network security intrusion detection result of the industrial Internet of Things. According to the network security intrusion detection method and device provided by the invention, malicious behaviors violating the operation logic of an industrial system can be effectively captured, and the network security intrusion detection effect is improved.

The embodiment of the invention discloses a data processing method and an industrial system, and relates to the technical field of industrial computing. The method is applied to an industrial system, and the industrial system comprises a computing device. The method comprises the following steps: acquiring equipment information of first industrial equipment; generating a first protocol mapping rule based on the type of the first industrial protocol and the interface description information; wherein the first protocol mapping rule is different from a second protocol mapping rule corresponding to the type of the second industrial protocol; and obtaining source data, and converting the source data into a target format based on the first protocol mapping rule to obtain corresponding target data. In the application, the first protocol mapping rule is generated based on the type of the first industrial protocol and the interface description information, and the new protocol mapping rule is dynamically generated in response to the newly accessed first industrial protocol in the industrial system, so that dynamic interoperation of multiple protocol devices is realized, the protocol adaptation efficiency is improved, and the user experience is improved. And the overall flexibility of the system is improved.

The present invention discloses a method for detecting abnormalities in mechanical rotating equipment under variable working conditions based on feature alignment residual GAN. The method comprises: constructing a time-frequency graph from rotating machinery data, reconstructing the time-frequency graph using an encoder GE(x) and a decoder GD(x) respectively combined with an autoencoder network of a residual module ResNet, and using a discriminator D to distinguish the authenticity of the reconstructed data and the source data to determine abnormal data. The loss function of the network contains an adversarial loss L. adv , context loss L of the generator reconstruction data error con , encoder loss L enc , and the loss L of Wasserstein distance for constructing ranking parity data classification for eigenvalue alignment w‑gan The present invention has a good effect on detecting abnormalities of rotating machinery under variable working conditions, can detect abnormalities of rotating machinery equipment in advance, ensure the long-term stable operation of industrial systems, and avoid the occurrence of serious accidents, which has important social significance.

The invention provides an encryption access control method and platform for industrial security data, and relates to the technical field of data encryption access, and the method comprises the steps: carrying out the attribute classification and code identification of an industrial system data set; performing security level division on the industrial system data set according to the data security quantitative index system; performing matching analysis on the multi-level industrial security data set based on the data encryption algorithm list; encrypting and storing the industrial system data set based on the industrial data identification code set by adopting a multi-stage data encryption algorithm to generate an industrial encrypted data warehouse; and establishing a fine-grained access mechanism and a decryption interaction mechanism according to the user role library, and performing access authority distribution and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. According to the invention, the technical problem that the comprehensive requirements of high safety, high precision and high flexibility in a modern industrial system are difficult to meet in the prior art can be solved.

The invention provides a soft measurement method and device for industrial multi-rate acquisition and a medium, and the method comprises the steps: collecting historical data in an industrial process, and dividing the historical data into a plurality of pieces of sampling rate data; feature extraction is carried out on different sampling rate data, and dimension regularization is carried out to obtain each scale feature; according to each scale feature and the query source, obtaining each bidirectional cross attention matrix, and performing fusion to obtain a bidirectional cross attention feature; dynamically calibrating the bidirectional cross attention features based on historical features to obtain a soft measurement model; and inputting to-be-predicted data in the industrial process into the soft measurement model to obtain a quality variable prediction result in the industrial process. According to the method, the device and the medium, the problem of low industrial process quality variable prediction accuracy caused by insufficient feature extraction, insufficient cross-scale association mining and limited utilization of historical target trend information when a soft measurement method of an existing industrial system processes multi-sampling-rate data can be solved.

Methods and systems for controlling an industrial system are disclosed. The industrial system includes a plurality of sub-components. The method includes receiving time-series data representative of operations of at least one of the plurality of sub-components, applying a machine learning algorithm (MLA) configured to process the time-series data to generate: a root cause of an anomaly in operation of the at least one of the plurality of sub-components; and a remedial action parameter to bring operation of the at least one of the plurality of sub- components to specification. The method also includes transmitting instructions for triggering the remedial action.

The invention discloses an intelligent industrial system real-time defect detection and visual quality evaluation system and method based on a self-supervised visual Transform, and relates to the technical field of automatic visual detection and defect recognition in industrial manufacturing. The characterization capability of the visual Transform and the labeling efficiency of self-supervised learning are combined, and effective visual detection independent of labeling training data is realized. A hybrid Transform-convolutional backbone network, a multi-target SSL strategy and a hardware optimization deployment mechanism are integrated, so that the method is simultaneously suitable for high-precision defect positioning and real-time processing of an edge manufacturing system. And the abnormal scoring and positioning module is used for identifying and spatially positioning defects of different scales and types by utilizing the learned potential features. By providing a solution which is extensible, does not need to be labeled and has real-time capability, long-term challenges in the field of industrial visual inspection are solved, and the requirements of industrial 4.0 and 5.0 ecosystems are met.

The invention relates to a superconducting power knowledge system construction method and system based on a lightweight large model. According to the method, multi-source data is utilized for unified representation, deep semantic information is obtained through a lightweight large model, knowledge extraction is achieved, and a basic knowledge graph is constructed; further forming a knowledge graph which can be dynamically updated through knowledge fusion; and in combination with real-time data, adaptive multi-hop reasoning is executed, and a diagnosis and decision result with a causal chain is generated. According to the method, the semantic understanding and reasoning ability of a lightweight large model is taken as a core, the structural expression advantage of a knowledge graph is combined, the limitation of updating lagging and fixed rule reasoning rigidness of a traditional static knowledge base is broken through, and precise cognition and causal chain inference of a complex system state are achieved. The method has universality and mobility, and is suitable for knowledge modeling and intelligent diagnosis of various electric power scenes and other complex industrial systems. The method is verified by taking a superconducting power system as an example, and the feasibility and effectiveness of the construction path are proved.

The invention provides a total element-oriented multi-agent collaborative resource optimization method in an industrial internet, and aims to solve the key problems of difficulty in cross-layer coordination, low scheduling efficiency and the like caused by sensing, decision-making and execution of network work at remarkably different time elements (second level, minute level and hour level) in a multi-networked industrial system. According to the method, an MTPHN (full time element prediction hierarchical network) framework is constructed for a multi-networked industrial system environment with time isomerism, three core modules of feature pyramid prediction, cross-time element synchronization and cooperative scheduling are provided, and effective modeling and vertical-horizontal two-dimensional resource coordination of heterogeneous time streams are realized. According to the method, the production efficiency, the equipment utilization rate and the production value in different operation scenes can be effectively improved, and the method is suitable for dynamic resource scheduling and cross-level collaborative optimization application in a multi-networked industrial system.

The invention discloses an industrial system data resource aggregation utilization platform system and method, and belongs to the field of intelligent platforms. The industrial system data resource aggregation utilization platform system comprises a multi-source heterogeneous data fusion center module, enterprise production data, supply chain data, market dynamic data and policy and regulation data are converted into a mapping data model of a unified structure, and dynamic weight reconstruction is carried out on the correlation degree between nodes through a graph neural network; by setting a multi-source heterogeneous data fusion center module, on the basis of a vector space mapping mechanism and a graph modeling framework, enterprise production data, supply chain data, market dynamic data and policy and regulation data are converted into a graph data model of a unified structure, and a graph neural network is introduced to carry out dynamic weight learning on the correlation degree between graph nodes, so that the dynamic weight learning is realized. Therefore, the problem of data islands existing in a traditional system is effectively solved, and the aggregation utilization efficiency of industrial data resources is improved.

The invention relates to a perception-transmission-control fusion-oriented industrial semantic communication method, system and device and a medium, and the system comprises a to-be-observed system which builds a cloud side-end collaborative industrial Internet of Things system and is used for collecting multi-modal data in an industrial scene; the industrial semantic communication sending end and the industrial semantic communication receiving end are used for realizing semantic feature extraction, identification and efficient transmission of industrial data, namely multi-modal data in an industrial scene; and the feedback controller is used for generating a control signal according to the optimized feedback control logic and acting on the industrial semantic communication system. The invention provides a new mode to ensure efficient communication between machines and improve the semantic interoperation capability of an industrial heterogeneous system, so as to support intelligent man-machine-object interaction of M2M, H2M, H2H and the like.