375 results about "Industrial network" patented technology

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

The invention discloses an industrial network risk perception and collaborative early warning method based on a dynamic risk map, and relates to the technical field of industrial internet security, and the method comprises the steps: S1, multi-source perception deployment; s2, heterogeneous data fusion acquisition; s3, constructing a knowledge graph engine; s4, analyzing depth data; s5, performing dynamic risk assessment; and S6, intelligent early warning decision making. According to the industrial network risk perception and collaborative early warning method based on the dynamic risk map, through fusion perception of OT layer data such as equipment states and process parameters, the problems of single perception dimension, evaluation lagging and disjunction in the prior art are solved, the false alarm rate is extremely low, and the method is suitable for popularization and application. Particularly, a dynamic adjustment mechanism of a time-varying risk weight matrix is improved, novel attacks can be dynamically responded, meanwhile, cross-domain risk conduction analysis is achieved, the accuracy and response speed of industrial network security early warning are improved, and meanwhile a closed-loop mechanism of attack path prediction and disposal suggestions is constructed.

An industrial computer multi-protocol adaptive control system based on edge computing relates to the technical field of industrial automation control, and performs three-level progressive protocol identification on new industrial equipment accessed to an industrial network diagram to obtain a communication protocol of the new industrial equipment or mark the new industrial equipment as a protocol unknown state; constructing a reverse representation tree of each communication protocol, performing protocol analysis on the new industrial equipment marked as a protocol unknown state based on the reverse representation tree, and performing protocol analysis on the new industrial equipment with a known communication protocol based on a Docker container mirror image; performing dynamic protocol conversion on the analysis result of the new industrial equipment according to the real-time state data of the new industrial equipment and a preset protocol conversion rule; setting a self-adaptive buffer area and a dynamic priority of the target equipment according to a communication protocol of the new industrial equipment; temporary dynamic feedback detection is carried out on the new industrial equipment provided with the temporary dynamic feedback detection mechanism, and the accuracy and efficiency of the multi-protocol identification process are remarkably improved.

The invention relates to the crossing field of industrial control system (ICS) safety and artificial intelligence, and particularly discloses a PLC high-interaction honeypot system based on multi-agent task splitting and RAG enhancement, which adopts a localized multi-agent collaborative architecture based on edge computing and is composed of a high-simulation equipment layer and an intelligent decision-making layer. The high-simulation equipment layer comprises a PLC dynamic mirror image, an HMI interface and a sensor data generator, and an active trapping environment is constructed through protocol fingerprint confusion and virtual and real data fusion technologies. The decision-making layer deploys a multi-agent task scheduling engine, integrates four kinds of agents including protocol analysis, behavior analysis, threat assessment and response generation, and realizes attack context perception and strategy dynamic generation based on a local RAG knowledge base. The load balancing agent dynamically allocates tasks according to equipment resources, and cooperates with offline knowledge update (USB flash disk encryption synchronization threat features) to form a closed-loop defense system, thereby ensuring physical isolation of an industrial network and realizing high-fidelity active defense.

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 network analysis, and discloses an industrial network topology structure safety robustness simulation analysis method and system, and the method comprises the steps: carrying out the modeling of each equipment node in an industrial network as a node, carrying out the modeling of a communication link between equipment as an edge, and constructing an industrial network topology model; setting a disturbance scene model; performing disturbance simulation on the industrial network topology model in a simulation environment, and recording a topological structure change process and connectivity evolution information thereof after disturbance; performing feature learning and embedding on the network topological graph before and after disturbance by adopting a graph neural network model, and outputting a robustness score R-score in a corresponding disturbance scene; calculating a robustness evaluation index based on the simulation data; and outputting a weakness identification result and an optimization suggestion of the topological structure. According to the invention, the structure robustness, safety controllability and adaptive adjustment capability of an industrial network in the face of a complex operation environment and potential threats are improved.

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 invention relates to the technical field of industrial switches, and discloses a multi-layer protection method and system for a high-performance industrial switch. The method comprises the following steps: carrying out topology analysis on an industrial network to obtain a security topology matrix and an industrial data flow vector; executing triple encapsulation to obtain a security encapsulation data packet; constructing a cross-layer associated three-layer security policy group; based on the three-layer security policy group, deploying a multi-head security monitor to implement parallel threat detection and SM3 algorithm integrity verification, and generating security decision information and an integrity verification result; and inputting the security decision information and the integrity verification result into the variational inference model to execute joint iterative optimization of transmission efficiency and security intensity to obtain an optimal protection parameter, so that cooperative work of different network layer protection strategies is realized, the overall protection effect is improved, continuously changing security challenges in an industrial network can be effectively dealt with, and the safety of the industrial network is improved. And the limitation of a traditional static protection method is broken through.

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 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 discloses a network security protection management system and method based on big data, and relates to the technical field of network security management, and the method comprises the steps: collecting industrial protocol layer data, physical sensor data and process parameters, extracting network behavior features, physical features and process correlation features, and constructing an industrial security feature vector; according to the security weight coefficient and the industrial security feature vector of the current process stage, calculating a threat score of the equipment, and dynamically adjusting a threat level through a three-level verification mechanism; constructing an equipment dependency graph, performing risk diffusion calculation based on the adjacent matrix and the attenuation coefficient, and generating a risk diffusion priority list to adjust an isolation strategy; an improved multi-target genetic algorithm is adopted to solve a Pareto optimal solution set, an equipment recovery sequence and a process parameter adjustment scheme are determined, and a recovery instruction is issued to realize safe recovery, so that the dynamic perception and accurate defense capability for industrial network threats is improved, and the safety and stability of industrial production are guaranteed.

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 relates to the technical field of industrial networks, in particular to a time-sensitive service flow resource adaptation method for large-bandwidth multicast flow awareness, which comprises the following steps: S1, a controller collects registered service flow information and real-time network state information; s2, after service flow priorities are divided according to the information, a mapping relation between service flows and multiple queues is established, and a forwarding rule is established according to the mapping relation; s3, establishing an optimization target and a resource constraint according to the mapping relation and the forwarding rule; and S4, designing a Markov decision according to the optimization target and the resource constraint, training the neural network model according to the Markov decision and the double-delay depth deterministic strategy gradient so as to output a scheduling decision, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. According to the invention, a basic queue model support is provided for end-to-end deterministic transmission of the unicast stream and the multicast stream through the mapping relation and the forwarding rule, the utilization rate of network resources and the number of service streams capable of being subjected to incremental scheduling are improved, and the worst end-to-end delay of the large-bandwidth multicast stream is reduced.

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 belongs to the technical field of intrusion detection, and discloses an industrial Internet of Things intrusion detection method based on time sequence clustering. An intrusion detection model based on TCN-GRU-Temporal Attention is provided, and the model combines the advantages of long and short term memory processing, dynamic feature capture and key information focusing, and is especially suitable for processing complex time sequence tasks. A multi-dimensional time sequence clustering algorithm based on an evaluation index is provided, k-medoids clustering edge nodes based on DTW are used, and the intrusion detection accuracy and the system response speed in a federated learning environment are improved. The industrial network intrusion detection method provided by the invention also shows good performance under non-independent identically distributed data, and effectively improves the recognition capability for complex attack modes.

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 discloses an industrial Ethernet switch based on an FPGA. The industrial Ethernet switch comprises an input network interface, an FPGA core processing unit, an output network interface, a monitoring management interface, a clock generation module, a power module and a data storage module. According to the switch, the FPGA assembly line architecture is applied, analysis, switching and forwarding of industrial network frames are achieved, the end-to-end transmission delay of the industrial network frames is greatly reduced by means of the parallel processing characteristic of hardware, and the strict requirement of real-time control frames for low delay can be met. The advantages of parallel processing of FPGA hardware are fully exerted, transmission delay is remarkably reduced, the reliability in an industrial environment is enhanced through a redundant power supply and hybrid storage design, real-time monitoring of network data is supported, the state of an industrial network is conveniently mastered in time, and the method is suitable for an industrial control network with extremely high requirements for real-time performance and stability.

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 protocol integration platform realized based on JavaWeb. Comprising a protocol adaptation layer used for carrying out adaptive connection with each hardware interface, a service management layer used for carrying out configuration management and task scheduling optimization, an edge computing layer used for realizing data filtering and event triggering, and a cloud collaboration layer used for realizing big data storage and AI model reasoning prediction. The signal output end of the protocol adaptation layer is connected with the signal input end of the service management layer, the signal output end of the service management layer is connected with the signal input end of the edge computing layer, and the signal output end of the edge computing layer is connected with the signal input end of the cloud collaboration layer. Equipment instructions of different protocols are uniformly converted into a standard data format and packaged into a TCP / IP protocol for transmission, meanwhile, conversion of cross-platform protocols such as OPC UA is supported, and the multi-protocol compatibility problem of the industrial Internet of Things is solved.

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 an Internet of Things IP address state real-time detection method based on AI, particularly relates to the technical field of industrial network operation and maintenance, and is used for solving the problems of equipment health state monitoring distortion and insufficient pre-diagnosis capability caused by frequent switching between a dynamic communication environment and a network topology in the prior art. The method comprises the following steps: extracting high-dimensional time series data based on equipment identifiers, dynamic transmission path fingerprints and protocol features, and generating a multi-dimensional feature vector representing an equipment health co-evolution rule through adaptive feature selection; generating an interaction feature graph by combining probability evaluation of a Gaussian mixture model and a hidden Markov model, and describing relevance between communication behaviors and health risks between the devices; utilizing a dynamic graph neural network to model linkage change of equipment behaviors and network topology in real time, and outputting a health state dynamic evolution path; and finally, optimizing a maintenance strategy weight through reinforcement learning, and triggering a hierarchical maintenance action. And the accuracy and the real-time response capability of predictive maintenance in an industrial Internet of Things scene are remarkably improved.

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 embodiment of the invention is suitable for the technical field of industrial network management, and provides a network topological graph generation method and device and terminal equipment, and the method comprises the steps: determining a topological model corresponding to a to-be-generated network topological graph; obtaining a plurality of target device models from a preset knowledge base according to the topological information of the topological model; the topological information comprises the topological hierarchy of the topological model and the total number of each type of target equipment model; the knowledge base comprises a plurality of network equipment models, a topology level of each network equipment model and a connection rule between the network equipment models; generating a hierarchical layout of all the target equipment models based on the topology hierarchy and the total number of each target equipment model; and connecting all the target device models according to the connection rule and the hierarchical layout, and generating a network topological graph. By adopting the method, the manpower and time cost consumed when the network topology structure is drawn can be reduced.

The invention relates to the technical field of industrial networks, in particular to a clustering network flow scheduling method based on star flash, which comprises the following steps: S1, collecting service flow information and cluster node state information according to a management G node and a terminal T node; s2, establishing a clustering network flow scheduling model according to the service flow information and the cluster node state information; s3, establishing target optimization and constraint conditions according to the clustering network flow scheduling model; and S4, designing a Markov decision according to target optimization and constraint conditions, training a neural network model according to the Markov decision and a multi-agent near-end strategy, outputting a scheduling decision according to the trained neural network, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. And the service flow is transmitted according to the multi-dimensional resource adaptation result and a multi-queue unicast / multicast flow shaping mechanism. According to the invention, the load balancing capability of the star flash clustering network system can be improved, and the end-to-end transmission delay of the service flow can be reduced at the same time.

The invention discloses an intelligent early warning system for an industrial safety production situation, particularly relates to the field of industrial network safety, and is used for solving the problems of real-time splicing of hidden abnormal sequences and quantification of cross-subnet dangerous potential energy in an industrial network in which variable topology and hidden channels coexist. Tracking the whole network dynamic state; the transient waveform is decomposed by a multi-scale wavelet packet to form a micro-impact fingerprint index, and abnormity is accurately detected; calculating a time slot compression potential and an energy jump amplitude in combination with gap virtual node interpolation and energy vector interpolation, and identifying hidden threats in a time domain and an energy domain respectively; extracting an intersection criticality through double logarithmic spiral mapping, driving a multi-body energy diffusion model, and drawing a danger potential value propagation gradient in real time; through comparison of the risk matrix and the dynamic inflection point criterion, the early warning advance and the coverage subnet range are significantly improved, the linkage trigger chain outbreak is avoided, the anomaly recognition sensitivity is improved, the link repair period is shortened, and the false alarm rate is reduced.

The invention provides a dynamic redundancy transmission method and system based on EtherCAT to Profinet, and a storage medium, and the method comprises the steps: analyzing a data frame through an EtherCAT protocol controller, and determining an operation priority according to an operation instruction; according to the operation priority, storing the operation instruction in a corresponding interrupt storage area or polling storage area; extracting a second instruction based on the priority according to a preset instruction extraction mechanism, and mapping and converting the second instruction into a corresponding Profinet protocol data frame; based on the link state of the feedback instruction of the Profinet protocol data frame, if it is judged that the link is abnormal, starting a redundant transmission port; according to the method, key instructions are processed preferentially through instruction double-area storage, and the instruction real-time requirement is met; and double bottlenecks of industrial network bandwidth and reliability are solved through a protocol mapping table.

The invention relates to the field of industrial anti-interference, in particular to an industrial network working condition coupling frequency spectrum avoiding method and system. The method comprises the following steps: constructing a main / sub-network spectrum interference coupling matrix, fusing multi-node spectrum data to generate a global map, and establishing a conflict probability model to screen a high-risk frequency band; and generating an optimal frequency band matching pair and a reconfiguration instruction set based on the cost matrix and a Hungary algorithm, and finally optimizing an avoidance strategy by taking throughput improvement and interference suppression as rewards by using a Q learning model. According to the invention, the problem that the network coupling relationship is not considered in the traditional spectrum management is solved, the dynamic refined management of spectrum resources in industrial scenes such as iron and steel plants is realized, the interference influence is obviously reduced, and the network communication quality and reliability are improved.