353 results about "Industrial systems" patented technology

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.

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.

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 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.

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 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 belongs to the field of information processing, discloses an industrial multi-modal data semantic alignment method based on vector space and topological constraints, and aims to solve the problem that multi-modal data semantic segmentation in an industrial scene is difficult to unify and associate. The method comprises the following steps: performing feature extraction and structured analysis on modal data, and mapping the modal data to a unified semantic vector space through a projection layer; on the basis of vector similarity matching, topological structure constraints derived from process drawings and the like are introduced, neighbor relation verification is carried out on candidate entities, context logic verification is carried out in combination with a large model, and therefore the accuracy of cross-source entity alignment is remarkably improved; mixed retrieval is supported by establishing an efficient vector database index, and incremental updating of the knowledge graph is achieved through large-model-assisted reasoning and rule base verification. Vector semantics and specific structural dependency of an industrial system are effectively fused, and high-precision and evolvable cross-modal semantic alignment and intelligent association analysis are achieved.

The invention discloses an OLTC fault diagnosis method based on self-adaptive chaotic disturbance optimization of multi-feature fusion. The method is used for solving the problem that a joint optimal solution is difficult to find in a high-dimensional and non-linear coupled parameter space in an existing algorithm. According to the method, multi-source local features of vibration, voiceprint and current are deeply learned through a double-residual fusion feature extractor, and channel attention fusion and physical experience feature splicing are carried out. And feature redundancy is eliminated through discriminant weighted principal component analysis, and discriminant information is amplified. The hierarchical optimization algorithm of hybrid chaotic mapping performs global search on a hyper-parameter set of the whole diagnosis framework, and intelligent balance of model exploratory performance and convergence is realized by integrating continuous hybrid chaotic disturbance and a self-adaptive objective function. And the classification robustness is ensured through the adaptive covariance matrix constraint. According to the method, the optimal configuration of the diagnosis framework can be adaptively obtained, and a solution with high generalization capability is provided for fault diagnosis of a complex industrial system.

The invention discloses a man-machine interaction language model output post-processing system supporting field-level score comparison and a rule engine and an optimization method. And the post-processing system cooperatively works through an information recording module, a score comparison module, a modification module, a manual check confirmation module and a data output module. According to the post-processing optimization method, a framework and a prototype software product are built according to the post-processing logic of the test stage and the post-processing logic of the working stage. According to the post-processing optimization method, firstly, a generation result and a structured demand are collected and marked, then the text quality is evaluated through BLEU and ROUGE, optimization and modification are carried out according to a scoring result, finally, the text is output according to a standard format and is in butt joint with an industrial system, and it is ensured that the text is accurate and available. According to the method, traceable, intervening and integratable high-quality post-processing of the output result of the language model is realized, and the requirements of complex task scenes in industrial application on the content, structure and quality control of the intelligently generated text are met.

The invention relates to the technical field of steel production management, and particularly discloses an intelligent safety management system for steel production, which comprises a multi-source data acquisition module, a data fusion processing module, a digital twin modeling module, a production risk identification module, a dynamic risk assessment module and an intelligent early warning decision module. The method comprises the following steps: acquiring multi-source data of steel production, carrying out fusion processing on the multi-source data, constructing a digital twinborn model of a steel production line, updating the digital twinborn model of steel production based on security situation awareness data, generating a corresponding production risk coefficient and an environment risk coefficient, and further establishing a steel production dynamic risk assessment model. Performing dynamic risk assessment on steel production; by introducing multi-source heterogeneous data fusion, digital twin model dynamic evolution and quantitative risk assessment technologies, the real-time performance, accuracy and decision-making efficiency of steel production safety management are improved, and an innovative solution is provided for safety management and control of a complex industrial system.

The invention provides a root fault positioning scheme applied to an industrial system. In the scheme of the invention, confusion factors corresponding to initialized unobservable equipment in a preset historical time period and causal relationship diagrams corresponding to all the equipment are configured as a model of learnable parameters, and the model is subjected to multi-round iterative training until convergence so as to obtain the causal relationship diagrams of all the equipment; and then obtaining a root fault positioning result of the fault equipment in the industrial system when the fault occurs based on the causal relationship graph corresponding to all the equipment in the industrial system. According to the technical scheme of the invention, the more accurate causal relationship among the devices in the industrial system can be obtained, so that the reliability of the root fault positioning result of the industrial system is improved, and the problem of false alarm or missing report in root fault positioning is avoided.

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 cloud and edge integrated industrial agent distributed task execution method and system. The method comprises the following steps: constructing a global agent at a cloud end, and deploying a local agent at an edge device; the global agent receives the production task and decomposes the production task into sub-tasks; sub-tasks are dynamically allocated based on the real-time state and capability of each edge device; the local agent executes the distributed subtasks and keeps state synchronization with the global agent; and establishing a cloud and edge collaborative learning mechanism. The system comprises a task analysis module, a resource sensing module, a dynamic allocation module, a collaborative execution module and an incremental learning module. According to the method, the problems of insufficient collaboration of cloud computing and edge computing and rigid task allocation in a traditional industrial system are solved, and intelligent collaboration of cloud edge resources and efficient execution of tasks are realized.

The invention discloses a time sequence root cause analysis method and system based on dynamic causal inference, and belongs to the technical field of industrial system fault diagnosis and intelligent operation and maintenance. The method comprises the following steps: collecting and preprocessing multivariable time series data in an industrial system; performing dynamic causal topology discovery by using an improved DAG-GNN algorithm, and generating a dynamic causal topology reflecting system state transition; performing spatio-temporal feature aggregation of causal guidance through GCN and LSTM, and extracting joint representation fusing node local abnormal information and a system-level global fault trend; node-level and system-level collaborative analysis is carried out through a multi-granularity root cause inference module, and a root cause candidate set with confidence ranking is generated; according to the method, the defects in the aspects of dynamic causal modeling, feature coupling and multi-granularity reasoning in the prior art are overcome, accurate tracing of a cross-level fault chain can be achieved, and reliable and explainable decision support is provided for intelligent operation and maintenance of an industrial system.

The invention discloses a multi-source operation exception association analysis and grading disposal method and system. The method comprises the steps of telemetry data collection and caching, non-blocking sending and self-adaptive readaptation, sliding time window and matrix construction, exception association clustering and comprehensive scoring, exception type judgment and grading alarm and self-healing disposal. The method comprises the following steps: continuously sensing a program execution state and an abnormal behavior, realizing data peak clipping storage by local cache, introducing a self-adaptive backoff retry mechanism, decentralizing transmission of abnormal data on the premise of not blocking a main service, and identifying and distinguishing anomalies in combination with abnormal propagation characteristics and an influence coupling relationship. And after quantitative judgment, automatically outputting a matched grading treatment strategy and an operation recovery measure. The closed-loop operation treatment mechanism of coverage sensing, transmission, aggregation, judgment and disposal constructed by the invention can realize rapid sensing, accurate attribution and adaptive response of the abnormity in a complex industrial load environment, and the stability and reliability of the operation of an industrial system are remarkably improved.

The invention discloses an industrial CT system geometric parameter sphere calibration method for multi-objective table switching, and belongs to the technical field of industrial CT systems. The method comprises the following steps: establishing a mathematical mapping relationship between direction vectors of X and Y directions of an objective table and a virtual rotation axis and key geometric parameters of a system, and incorporating the relationship into a nonlinear optimization model; a residual function between a predicted value and a measured value of a projection position is constructed by collecting projection data of a multi-ball motif in different objective table states, a direction vector and geometric parameters are optimized at the same time by using a least square method, and finally dynamic compensation and accurate correction of mechanical deviation caused by replacement of an objective table are realized. According to the method, the problem of adaptation of multiple objective tables is effectively solved, the imaging quality, geometric consistency and measurement precision of a CT system under different objective table configurations are remarkably improved, and meanwhile, the method has good robustness and engineering application value.

The invention provides a dynamic model verification method based on incremental updating and multi-source constraint fusion, which comprises the following steps of: identifying a change node of a quality prediction model of an industrial system, and generating a local verification sub-graph based on a topological structure of a computational graph of the model; reversely tracing the model parameters influenced by the change along the local verification sub-graph, and training the model parameters to realize dynamic updating of the local verification sub-graph; generating candidate paths according to the updated local verification subgraph, dynamically adjusting the weight of the multi-source constraint, generating fusion constraint, performing constraint conflict detection on the candidate paths according to the fusion constraint, eliminating candidate paths inconsistent with the multi-source fusion constraint, constructing an optimized objective function, and combining Bayesian search and a local disturbance strategy to obtain a multi-source fusion path. Selecting an optimal verification path; and running the model on the optimal verification path, positioning source parameters of nodes with abnormal verification, adjusting the source parameters, regenerating the local verification sub-graph, and gradually realizing verification of the quality prediction model.

The invention belongs to the technical field of industrial internet, safety early warning and artificial intelligence crossing, and particularly relates to an accident advanced early warning and active intervention method and system for complex industrial systems (such as energy, chemical engineering and traffic), which integrate digital twinning, a physical information neural network and deep reinforcement learning. Comprising the following steps: multi-physics field coupling sensing and data assimilation; constructing and initializing a physical information risk field; performing dynamic inversion and deduction on a risk field; identifying a risk fission point and generating an intervention path; and through advanced early warning and strategy simulation feedback, interpretable insight of risk generation, conduction and evolution paths is realized, an active intervention strategy is finally generated, and an intelligent early warning and risk management and control closed loop is formed.

The invention discloses an improved active-disturbance-rejection control method based on error and input delay compensation, relates to the technical field of industrial control, and aims to solve the problem of insufficient input delay and control precision of a high-order inertial system. The method comprises the following steps of: modeling a controlled object into a dynamic system with inertia characteristics, constructing an input delay compensation link and an error-based active-disturbance-rejection link, inputting a control error and a compensation value into an extended state observer (ESO), estimating an error related state quantity and a disturbance tracking value in real time, calculating a control quantity through a state error feedback control law, and compensating the control quantity; and closed-loop accurate adjustment is realized. The invention further comprises an improved second-order active-disturbance-rejection control scheme to adapt to higher precision requirements. The control structure is simplified, control signals and system output are synchronously controlled, the anti-interference capability and the tracking performance are considered, and the method can be widely applied to high-order inertial industrial system control in the fields of chemical engineering, thermal power generation and the like.

A method includes, using a mobile device connected to an industrial network of an industrial system, receiving a chainable compute service assignment from a first intelligent industrial device of the industrial system that assigns a chainable compute service to the mobile device or assignment from a cloud-based device or system connected to the industrial network that assigns the chainable compute service to the mobile device; and using the mobile device, performing the chainable compute service.

The application relates to a fault detection method and device. Multidimensional monitoring data of a target industrial system is acquired, which contains multi-source sensor monitoring data and target monitoring index data at different time points. A training data set is constructed using the multidimensional monitoring data, the multi-source sensor monitoring data is used as a training sample, the corresponding target monitoring index data is used as a training label, a preset model is trained to obtain a fault recognition model. In detection, the multi-source sensor monitoring data to be detected is input into the fault recognition model to obtain target monitoring index prediction data. Whether the data is abnormal is determined by comparing the prediction data and the actual second target monitoring index data, and the fault detection result of the target industrial system is determined, so that more comprehensive and accurate detection of system faults is realized, and the probability of misjudgment and missed judgment is effectively reduced.

The invention discloses a method and a system for identifying a dynamic flow pattern of a solid-liquid two-phase flow, and relates to the technical field of solid-liquid two-phase flow detection. Through a dynamic mode decomposition method, dimension reduction processing is carried out on a snapshot matrix formed by flow pattern images at different moments, and the defects that the efficiency is low and real-time prediction cannot be carried out due to the fact that a traditional computational fluid mechanics method is purely used for iteration solving are overcome. Meanwhile, no auxiliary sensor is needed, and compared with a current complex identification system, the flow pattern of the solid-liquid two-phase flow in the dynamic change process can be rapidly and accurately identified only by combining a high-speed flow camera. The problems of poor real-time performance, high economic cost, complex operation system, narrow application range and the like generally existing in the current identification method are solved. The method has important engineering value for guaranteeing the flowing safety of solid-liquid two-phase flow related to industrial systems such as deep-sea hydrate exploitation and conveying, circulating fluidized bed reactors and dynamic ice storage ice slurry storage.

A method may include querying, via a processing system, a database comprising a list of cybersecurity threats associated with operational technology (OT) devices within an industrial system. The method may also include identifying OT devices associated with the list of cybersecurity threats, generating scripts configured to confirm that the OT devices are associated with at least one cybersecurity threat of the list of cybersecurity threats, and sending the scripts to the one or more OT devices. The method may then involve determining that the OT devices are associated with the at least one cybersecurity threat based on responses from the OT devices generated based on the scripts, generating instructions for resolving the at least one cybersecurity threat based on a generative artificial intelligence (AI) system, and sending the instructions to one or more devices.

According to the industrial message transmission method and receiving method of the heterogeneous industrial system and the related devices, the heterogeneous industrial system comprises a plurality of industrial devices and at least one intermediate language translation device, the transmission method is applied to the intermediate language translation device, and the method comprises the steps that firstly, heterogeneous industrial messages sent by the industrial devices are received; secondly, based on target operation information corresponding to the heterogeneous industrial message, performing translation mapping on the heterogeneous industrial message to obtain a standard industrial message; then, a control code corresponding to the heterogeneous industrial message is generated, and a sending industrial message is generated based on the control code and the standard industrial message; and finally, the sending industrial message is sent to another industrial device, so that the problems of difficult intercommunication, complex management, low efficiency and the like caused by protocol heterogeneity in the prior art are effectively solved.

The invention discloses a device and method for synthesizing a fourth-generation refrigerant R1235ze through photocatalysis of VDF, and belongs to the technical field of green chemical engineering. The core of the method is that a g-C3N4 / BiVO4 heterojunction photocatalyst is adopted to catalyze VDF to react with hydrogen chloride to generate R1234ze under the mild condition of 70-90 DEG C under the irradiation of simulated sunlight (AM1.5 G). The catalyst is prepared by taking melamine, bismuth nitrate and ammonium metavanadate as precursors and carrying out hydrothermal reaction and calcination treatment under specific conditions. The matched industrial system realizes the whole process integration from the continuous preparation of the catalyst to the online separation of the product. According to the method, the reaction temperature is successfully reduced to about 80 DEG C from traditional 400 DEG C or above, energy consumption is reduced by about 75%, CO2 emission is reduced by about 60%, meanwhile, the product selectivity is not lower than 93%, the quantum efficiency is not lower than 12%, the purity reaches up to 99.5% or above, and a subversive solution is provided for green and low-cost industrial production of R1234ze.