278 results about "Industrial network" patented technology

The invention discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

The embodiment of the invention provides an industrial network data anomaly detection method and related equipment, and the method comprises the steps: firstly, obtaining industrial network data, and generating a physical model corresponding to the industrial network data; next, inputting the industrial network data into a time sequence feature extraction network for feature prediction to obtain a residual prediction value, and obtaining a mixed physical model based on the physical model and the residual prediction value; then, obtaining a mixed predicted value of the industrial network data based on the mixed physical model, and obtaining a residual sequence based on a difference value between the mixed predicted value and an actual observation value of the industrial network data; and finally, on the basis of the residual sequence, calculating to obtain a cumulative statistical value of the industrial network data, and on the basis of the cumulative statistical value, obtaining an anomaly detection result of the industrial network data, thereby remarkably improving the accuracy and robustness of the anomaly detection of the industrial network.

The invention relates to the technical field of industrial network control, in particular to a side-cloud collaborative software-defined industrial control system, which comprises a side control action configuration module, a multi-source signal collection module, a state mutation recognition module, a cloud scheduling module and a collaborative control generation module. In the invention, through decoupling control action configuration, multi-channel signal acquisition, data label construction, sudden change state identification and a remote scheduling execution process and realizing standardized processing, an edge control basic instruction structure body can be quickly constructed based on an equipment state and a unified time sequence data label mechanism is established; in combination with the dynamic sensing ability of a sudden change state and a feedback point identification mode, efficient discrimination and rapid classification of key control points can be realized, task chain execution can be rapidly mapped to a key path and real-time regulation and control are implemented, rapid response to industrial field multi-source change and accurate issuing of a control instruction are realized, and the task chain execution efficiency is improved. And the real-time performance and the collaboration of the control process are greatly improved.

The invention discloses a laser operation process control system based on multi-element fusion, and particularly relates to the field of automatic control, which comprises a multi-element perception fusion module, an intelligent decision module, a multi-actuator cooperative control module, a system general control and man-machine interaction module and a self-learning and optimization module, geometric morphology, a temperature field, plasma features and environmental data of an operation area are collected in real time through integration of multiple sensors, a fusion situation information graph is generated through feature extraction and fusion, forward prediction is carried out in combination with a high-fidelity digital twin model, morphology evolution, thermodynamic behaviors and potential defects of a machining area are predicted, and the machining precision is improved. Based on the prediction result, a laser, a movement mechanism and an auxiliary gas unit are synchronously controlled through a high-speed industrial network, collaborative operation, coordination task scheduling, state monitoring and man-machine interaction of multiple actuators are achieved, and model parameters and decision strategies are continuously optimized by comparing actual data with the prediction result.

The invention discloses an industrial control equipment vulnerability identification method and system based on a double-BERT model, and the method comprises the steps: an asset analysis stage: analyzing the flow to generate structured asset data; in the semantic matching stage, a multilingual Sension-BERT model is used for extracting vectors and matching a vulnerability library; in the version identification stage, the BERT is finely adjusted to judge the influence condition of the version; and in the vulnerability confirmation stage, an identification result is manually rechecked. The system comprises an asset processing subsystem and a vulnerability intelligent identification subsystem, and the asset processing subsystem comprises a flow analysis module, an equipment fingerprint identification module, an asset information management module, a scanning task scheduling module and a vulnerability display and screening module. And the vulnerability intelligent identification subsystem comprises a semantic vector matching module, a version matching judgment module and an identification result output module. According to the method, the problems of high asset isomerism, weak semantic matching capability and low version comparison accuracy in the current industrial network can be solved, and the vulnerability identification quality and efficiency are improved.

The invention provides a multi-ring state sensing communication network optimization method, relates to the field of industrial network communication, and solves the technical problems of communication uncertainty and insufficient system reliability caused by message conflicts in the prior art. The method comprises the following steps of: when a communication system is initialized, logically dividing a plurality of nodes into a plurality of data transmission rings by a centralized management node on a single physical bus, and initializing an annular frame for controlling a sending permission for each transmission ring; each node transmits a token based on the ring frame, and after the nodes in the ring obtain the token of the ring, the nodes obtain the bus sending authority, construct a data frame containing a target node address and send the data frame to the bus to perform data communication between the nodes; each node continuously detects communication states of adjacent nodes and a transmission ring through a heartbeat mechanism and a ring frame timeout mechanism; and automatically triggering a data ring reconstruction process and updating a token transmission path when detecting that the node fails, the node is added or the centralized management node is disconnected.

The invention relates to an industrial quality inspection data cooperative transmission method driven by image recognition, in particular to the field of artificial intelligence, which analyzes video frames in real time through a lightweight neural network, accurately locates and extracts key areas and features in industrial product images, constructs a dynamic semantic scene graph to understand contents and divide priorities, and improves the quality inspection efficiency. The core of the method is that non-uniform intelligent coding is carried out on video streams according to semantic importance of contents, high-fidelity transmission of key information such as defects is ensured, non-key areas are greatly compressed to save bandwidth, scheduling is carried out in the transmission process according to priorities, confidence feedback based on a cloud recognition result is introduced, closed-loop control is formed, and high-fidelity transmission is realized. According to the method, the front-end analysis model, the coding strategy and the network path are adaptively optimized, so that low-delay and high-reliability visual quality inspection data transmission and accurate identification can be continuously and stably realized in a complex industrial network environment, and the overall efficiency and the intelligent level of online quality inspection are remarkably improved.

The invention discloses an industrial network fault intelligent diagnosis method and system based on multi-source data, and the method comprises the steps: synchronously collecting data from a plurality of data sources of an industrial network, and extracting a time sequence statistical feature, a flow entropy feature and a protocol conformity feature to form a multi-dimensional feature vector; establishing a dynamic baseline model by adopting a sliding window online learning method, and calculating a comprehensive anomaly score for anomaly detection; the fault suspicion degree is calculated based on the equipment incidence matrix and the fault propagation model to realize fault source positioning; carrying out fault type identification and root cause analysis by adopting Bayesian reasoning and a knowledge rule base; and outputting a structured diagnosis report containing the fault source, the type, the root cause and the disposal suggestion. According to the invention, early warning, accurate positioning and intelligent diagnosis of industrial network faults are realized, and the operation and maintenance efficiency, safety and reliability of the industrial control network are significantly improved.

The invention discloses a 5G industrial gateway rapid networking method and system, and the method comprises the steps: building a routing protocol neighbor relation between an industrial gateway and a server module and a core network module, and synchronizing a link state database; and respectively executing a shortest path priority algorithm on the three nodes, and calculating respective shortest paths to other nodes according to the link state database so as to generate respective optimal IP routing tables. When the industrial gateway receives a first data packet, the industrial gateway forwards the first data packet to a core network module through a base station and a private network bearer network according to an internal optimal IP routing table and a target IP address in the first data packet; and the core network module forwards the first data packet to the data network module according to the optimal IP routing table and the target IP address in the core network module. According to the invention, link state databases of the industrial gateway, the server module and the core network module are synchronized through a routing protocol, so that each device masters the topological structure of the whole network, and dynamic addressing of the industrial network is effectively supported.

The invention discloses a method and system for adapting an industrial intelligent gateway to multiple industrial protocols, and relates to the technical field of industrial Internet of Things edge computing, and the method comprises the steps: monitoring industrial network traffic self-learning, and constructing a protocol fingerprint database; dynamically constructing a virtual equipment twinborn body based on the fingerprint database to form a simulation environment; automatically verifying protocol configuration in a simulation environment; the application verification configuration is connected with the entity device to collect data; performing in-stream calculation processing on the data stream in real time to generate an advanced event and feature data; according to the method, automatic identification and compatibility of unknown protocols are realized, production accidents are avoided through virtual simulation verification, data value extraction is synchronously completed in the data acquisition process, and the security, reliability and data processing efficiency of protocol adaptation are effectively improved.

The invention provides an industrial edge network micro-service optimization method based on dynamic arrangement and migration expansion, which comprises the following steps: abstracting an industrial edge network into an intelligent industrial network model formed by heterogeneous edge nodes and links, modeling an industrial application request into a request chain formed by combining a plurality of micro-services with a data dependency relationship in sequence, calculating a micro-service instance deployment scheme and a request routing scheme; counting a time-varying request stream, analyzing the minimum core number meeting the time delay constraint in a statistical result by using a multivariate performance analysis model based on an m / m / c queue network and a binary search algorithm, and carrying out dynamic expansion and contraction adjustment on core resources of deployed micro-service instances; and solving a mixed integer nonlinear programming migration decision problem according to the initial dynamic scaling adjustment result and the migration cost so as to optimize the optimal collaborative deployment and request routing of the micro-service instance between the edge nodes. According to the invention, the service continuity and the overall energy efficiency of the industrial edge network system are improved.

The invention relates to the technical field of hydropower station intelligent operation and maintenance, in particular to a hydropower station operation and maintenance system based on the Internet of Things, which comprises an intelligent sensing layer, an edge computing layer, a cloud analysis layer and a decision execution layer, and data interaction is carried out among the layers through a special industrial network protocol; the intelligent sensing layer comprises a multi-mode sensing array and an environment self-adaptive acquisition unit; the edge calculation layer comprises a multi-source asynchronous data alignment unit and a fault feature extraction unit; the cloud analysis layer comprises a digital twinborn updating unit and a collaborative decision-making unit; the decision execution layer comprises a self-adaptive control execution unit; cavitation erosion and mechanical vibration signals are monitored at the same time through the multi-mode sensing array, and the system can diagnose faults more accurately and reduce the misjudgment rate in combination with the compensation of the environment self-adaptive acquisition unit for external factors.

The invention particularly relates to a password security management method applied to industrial security, which relates to the technical field of industrial control system security, and comprises the following steps of: deploying password strategies in batches in an arranged controlled simulation environment, triggering test case execution according to priorities, and monitoring system response data to obtain a test result; wherein in the test case execution process, whether the obtained execution success rate is correct or not needs to be judged through the verification error coefficient obtained through analysis. According to the invention, an isolation simulation test platform highly consistent with a real production environment is established, an HMI / operator station, a PLC / DCS controller and an industrial network structure are accurately duplicated in combination with a digital twinning technology, and a test process and the production environment are completely isolated; serious consequences, such as automatic process interruption and production line stop, possibly caused by password strategy change testing are avoided from the source.

The invention relates to the technical field of industrial control system network security, and discloses an industrial network attack strategy prediction and defense decision-making system, which comprises a semantic modeling module used for analyzing a configuration and communication file construction function and variable coupling hypergraph, and describing causal mapping from a network instruction to a physical variable; the dynamic evolution module is used for reconstructing a phase space based on the physical time sequence data, identifying an implicit coupling relationship and updating a hypergraph structure; the strategy prediction module is used for deducing a physical trajectory after instruction execution by combining the hypergraph and the phase space model, and judging a destructive attack strategy according to the maximum Lyapunov index; and the defense decision module triggers a virtual-real state bifurcation mechanism, generates an inertia compensation signal to maintain physical stability, and generates and constructs feedback data to cheat an attacker. According to the method, through cross-domain coupling modeling and dynamic deduction, physical destructiveness is accurately predicted, production maintenance and attack spoofing are considered through virtual-real bifurcation, and the reliability of defense decision making is effectively improved.

Apparatuses, systems, and methods for providing enhanced visualization of lifecycle information and management for a network, such as an industrial network, enable the creation / design of a virtualized network. The virtualized network eases the physical deployment / installation of a real network by using augmented reality (AR) to enable configuration / monitoring of the network using the virtualized network and real-time and / or historical information. Performance issues may be identified and visually conveyed to a user using AR overlay. The virtualized network may be created by a user via a network layout creation tool that combines both physical and logical mappings of devices and elements. One or more AR tools may be used to add, edit, remove, and / or manipulate elements of the network model using gestures. The virtualized network may be implemented as a digital twin of the location and used to assist in the physical deployment, maintenance, and monitoring of a network at the location.

The invention provides a cooperative control system for dual-network heterogeneous equipment, and the system comprises a master station which communicates with a relay station through a first industrial network; at least one substation communicating with the relay station over a second industrial network; wherein the master station and the sub-station carry out data synchronization through a data synchronization method. According to the invention, through the collaborative architecture of the master station, the substations and the relay stations, in combination with the dynamic protocol conversion and dual-network data mirroring technology, the problem of data synchronization among heterogeneous network equipment is effectively solved, meanwhile, the continuous operation of the system is ensured by using a hardware accelerated fault isolation mechanism, and the method has the effects of improving the reliability and flexibility of the system and reducing the downtime.

The invention relates to the technical field of industrial network identification traffic scheduling, and discloses an industrial identification traffic intelligent scheduling method and system, and the method comprises the steps: S1, receiving an identification analysis request sent by an industrial network terminal, extracting a service emergency degree feature of an application layer and a transmission reliability feature of a transmission layer in the identification analysis request; s2, analyzing a dynamic coupling influence relationship between the service emergency degree feature and the transmission reliability feature, and generating a priority feature tag corresponding to the request based on the dynamic coupling influence relationship in combination with a preset balance strategy; s3, distributing the identification analysis request to a preset high-throughput processing path or a low-delay processing path based on the priority feature tag; according to the invention, the problems that the conflict between the service timeliness and the transmission stability is difficult to balance and the dynamic scheduling requirement of diversified services in an industrial scene is difficult to meet in the prior art can be solved.

The invention relates to the field of attack monitoring, and discloses a cellular-based network attack monitoring method and device, equipment and a storage medium. The method comprises the following steps: acquiring to-be-monitored flow data in real time; obtaining a to-be-monitored element spore sequence based on the to-be-monitored flow data; acquiring a feature vector corresponding to the to-be-monitored element spore sequence; inputting the feature vector into a preset reconstruction model to obtain a reconstruction feature vector corresponding to the to-be-monitored element spore sequence; the reconstruction model is obtained through unsupervised training; and determining whether an attack behavior exists according to the feature vector and the reconstructed feature vector. Therefore, the method does not need to depend on the comprehensiveness of attack samples, enables the reconstruction model to find unknown threats under the condition of no labeled data, provides reliable guarantee for industrial network security, and improves the monitoring effect of attack behaviors.

The invention discloses an industrial network traffic anomaly detection and source tracing method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: converting a traffic data package of an industrial network into a process characteristic analysis software package, extracting a plurality of dimension statistical features from the process characteristic analysis software package, and storing the extracted statistical features in a database; converting the statistical characteristics of each dimension into table data; performing classification coding processing on connection behavior features in the table data to obtain a target coding result, normalizing statistical features to obtain a normalization result, converting time features to obtain a conversion result, obtaining a mixed feature vector, constructing a DAGMM model comprising a self-codec and a Gaussian mixture model by using an unsupervised mode, and obtaining a mixed feature vector; performing anomaly detection on the mixed feature vector to obtain an anomaly detection result; and if the traffic is abnormal, performing reverse decoding on the mixed feature vector by using a tracing algorithm to obtain a tracing result, thereby improving the efficiency of performing anomaly detection and tracing on the industrial network traffic.

The invention discloses a network data transmission deterministic time delay guarantee method of a spatial characteristic enhanced Informer model, which comprises the following steps of: 1, predicting a time sequence of key service traffic based on the Informer model, and extracting a long-period time characteristic and a future traffic trend through a Prob sparse self-attention mechanism; 2, a graph neural network is introduced into the model to perform modeling on an underground switch topological structure, the defect that Informer only depends on time characteristics and neglects network topological information is improved, and a spatial state matrix is generated and is combined with a future flow trend to realize enhancement of spatial characteristics; 3, an asynchronous scheduling mechanism is designed, prediction calculation and short-period scheduling execution are decoupled, and real-time performance is guaranteed; and 4, generating an optimal step traffic shaper parameter by using the spatio-temporal characteristics to smooth burst traffic, allocate bandwidth and reduce delay jitter, and meanwhile, combining burst detection and redundancy scheduling protection to guarantee deterministic delay. The method is suitable for industrial networks with high real-time requirements such as coal mines and rail transit.

The invention relates to the technical field of industrial network communication, in particular to an SDN (Software Defined Network) industrial network routing method based on deep traffic classification and dynamic pheromone optimization. According to the method, an SMOTE algorithm is improved, a target sub-region is selected through an adaptive boundary to perform oversampling to balance sample data, the problem of flow imbalance of the SDN multi-access interconnection architecture is solved, meanwhile, a CNN-LSTM model fused with an attention mechanism is established, and depth feature extraction is performed on samples after the samples are balanced so as to meet dynamic features of the flow in a short sequence.

The invention provides an industrial network security situation prediction method and system based on a generative large model, and relates to the technical field of industrial network security, and the method comprises the steps: obtaining time sequence network behavior data of a plurality of monitoring nodes in an industrial network, and extracting multi-dimensional features to obtain a security feature vector; decomposing the feature vector into a periodic baseline component and a transient disturbance component through frequency domain transformation, and obtaining a decomposition situation feature through sparsity constraint screening; performing semantic space mapping by utilizing a generative large model to obtain semantic enhanced situation representation, calculating security anomaly correlation between nodes based on decomposed situation characteristics, and constructing a dynamic incidence matrix; and constructing a propagation operator based on the dynamic incidence matrix, carrying out multi-step iterative propagation on the semantic enhancement situation representation, introducing an attenuation factor to simulate abnormal influence diffusion, and obtaining a security situation prediction result in a future time window.

The invention discloses an automatic route construction method based on topology information carried by a registration message and a controller. The method comprises the following steps: an edge gateway obtains topological information of a service device mounted under the edge gateway, packages the topological information, a gateway identifier and a northbound interface IP address of the edge gateway into a UDP registration message, and sends the UDP registration message to a controller; and the controller receives and analyzes the UDP registration message, establishes a mapping relation from the MAC address of the service device to the northbound interface IP address of the edge gateway to which the service device belongs in a global forwarding table, and determines a target edge gateway according to the global forwarding table when an original data frame sent to a target service device is received, thereby realizing accurate forwarding of the data frame. According to the application, the plug and play of the southbound service device and the automatic construction of the route can be realized, the complexity and cost of network operation and maintenance are reduced, the network storm is avoided through accurate forwarding, and the stability of a large-scale industrial network is improved.

The invention relates to a method and a system for capturing and analyzing industrial network protocol data packets, which solve the technical problem that the existing industrial protocol analysis technology is poor in adaptability to unknown, private or non-standard protocols, and the method comprises the following steps: capturing and screening network data packets for protocol identification. If the protocol is the known protocol, calling a preset analyzer for analysis; if the protocol is an unknown protocol, extracting a protocol feature vector of the protocol, obtaining a candidate protocol and confidence through reasoning of a neural mapping model, and dynamically judging whether to start interactive reasoning to obtain user feedback, calibrating a parameterized protocol template and instantiating to generate an executable analysis rule according to the structural matching degree of an existing analyzer; and finally, registering and using the new analyzer to realize adaptive learning and analysis. The method has the following effects: intelligent identification of an unknown protocol, dynamic construction of an analyzer and system self-expansion are autonomously realized through an adaptive learning mechanism, and normal form upgrading from manual reverse analysis to system autonomous evolution is completed.

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.

This invention belongs to the field of industrial network performance optimization technology, specifically providing a DDS-5G network collaborative optimization method based on fuzzy logic. By setting static mapping rules, the QoS Policy parameters of the DDS are mapped to the QoS flow identifiers of the QoS flows in the 5G network, thereby achieving initial DDS-5G collaboration. Simultaneously, by establishing a fuzzy logic inference system, using the QoS Policy parameters and real-time network state parameters as input variables and 5QI as the output variable, and by optimizing and adjusting the QoS Policy parameters of the DDS through DQN and updating the fuzzy logic parameters of the fuzzy logic inference system, further DDS-5G network collaborative optimization is achieved. Through a cross-layer adaptation mechanism, efficient collaboration between the application layer and the 5G network is realized, making it suitable for scenarios with high requirements for low latency and high reliability, such as industrial automation and intelligent manufacturing.

The invention is suitable for the technical field of industrial network security protection and intelligent risk assessment, and provides a network security reinforcement strategy generation method and system based on AI analysis, and the method comprises the steps: obtaining a current to-be-assessed target instruction, and an initial security risk score value generated for the target instruction, meanwhile, historical control instruction data of equipment corresponding to the target instruction is obtained; the method can identify recessive intrusion behaviors which conform to the issuing rule but have operation sequence abnormity, parameter floating abnormity or response time sequence abnormity, is especially suitable for early discovery and trend perception of long-term latent attacks, breaks through the limitation that protocol legal instruction behavior abnormity is difficult to identify in the prior art, and improves the detection accuracy. The problem of misjudgment of a scoring system in a critical region due to context deficiency is further relieved, the stability, interpretability and dynamic adaptability of network risk assessment are remarkably improved, and the method has good engineering practicability and popularization value.

The application relates to the technical field of industrial network identification flow scheduling, and discloses an intelligent industrial identification flow scheduling method and system.The method comprises the following steps: S1, receiving an identification analysis request sent by an industrial network terminal, and extracting a service urgency feature of an application layer and a transmission reliability feature of a transmission layer in the identification analysis request; S2, analyzing a dynamic coupling influence relationship between the service urgency feature and the transmission reliability feature, and generating a priority feature label corresponding to the request based on the dynamic coupling influence relationship and a preset balancing strategy; and S3, distributing the identification analysis request to a preset high-throughput processing path or a low-latency processing path based on the priority feature label.The application can solve the problems that the prior art is difficult to balance the conflict between service timeliness and transmission stability and difficult to meet the dynamic scheduling demand of diversified services in an industrial scene.

The invention relates to the technical field of network security, and discloses a primary equipment network security reinforcement method, computer equipment and a storage medium, by setting a dynamic threshold judgment and hierarchical response mechanism in an instruction receiving link, abnormal high-frequency operation for primary equipment can be effectively identified and blocked, and the security of the primary equipment is enhanced. The equipment operation risk caused by a malicious instruction is obviously reduced; meanwhile, in combination with double judgment of an upper limit of operation times and a preset multiple, accurate grading alarm from suspected attacks to confidential attacks is realized, the accuracy of threat identification is greatly improved, and false alarms are reduced; by intelligently agenting the confidential attack traffic to the electric power honeypot, the security of real equipment is protected, an effective environment is provided for analysis and tracking of attack behaviors, and the active defense capability of the system in an industrial network environment in which a feature library cannot be updated in real time is enhanced.